CN114114029A - SOC and SOH collaborative estimation system and method for electric vehicle - Google Patents

Abstract

The invention relates to a SOC and SOH collaborative estimation system and a method for an electric vehicle. A system for performing a power estimation process of an electric vehicle using a controller is provided. The controller estimates an internal state of an energy storage power source of the electric vehicle. The internal state represents the state of charge SOC and/or state of health SOH of the energy storage power source. The controller estimates a SOC value and/or a SOH value of the energy storage power source based on at least one of: current level, voltage level, temperature, and time based information. The controller also estimates a bounded SOC value based on the SOC value, the first upper bound, the first lower bound, and/or estimates a bounded SOH value based on the SOH value, the second upper bound, and the second lower bound. The controller then controls an electrification process of the electric vehicle based on the bounded SOC value and/or the SOH value.

Description

SOC and SOH collaborative estimation system and method for electric vehicle

Cross Reference to Related Applications

This application claims the benefit of U.S. provisional application No.63/036,198 filed on 8/6/2020.

Technical Field

The present disclosure relates generally to methods and systems for diagnosing power management systems used in electric vehicles, and more particularly, to estimating an internal state of an energy storage power source of the power management system.

Background

The power management system may be used for an electric only vehicle (EV) and/or a Hybrid Electric Vehicle (HEV) having an electric motor and an Internal Combustion Engine (ICE). As used herein, the term "electric vehicle" refers to hybrid electric vehicles and/or pure electric vehicles that provide an alternative to conventional fuel engine systems to supplement or completely replace engine systems such as the ICE. In one example, the electric vehicle is an Extended Range Electric Vehicle (EREV). In an EREV, the primary electric drive is implemented using a battery or associated Rechargeable Energy Storage System (RESS) that acts as a Direct Current (DC) voltage source to an electric motor, generator or transmission, which in turn may be used to provide the energy required to rotate one or more of the wheels of the vehicle. When the charge of the RESS is depleted, backup power may come from the ICE to provide auxiliary on-board power generation.

During operation, the power management system estimates the internal state of an energy storage power source (such as a battery) in the electric vehicle for maintaining the electric vehicle at an appropriate level within its operating range. Typically, the internal state of the storage power source is a state-of-charge (SOC) and/or a state-of-health (SOH) state of the storage power source. For example, SOC information may be used as a fuel gauge for the battery, and SOH information may be used as an indication of the present total capacity and/or internal resistance of the battery. In another example, the SOC information represents available energy or power left in the energy storage power source, and the SOH information represents a degree of deterioration of the energy storage power source.

Since SOC and SOH information cannot be directly measured, an estimation algorithm is used to estimate SOC and SOH information of the energy storage power source. Existing estimation algorithms known as Kalman (Kalman) filters may be used to estimate SOC and SOH information. An exemplary kalman filter includes: a Dual Nonlinear Kalman Filter (DNKF), an extended kalman filter, an unscented kalman filter, a volumetric kalman filter, and the like. The Kalman filter estimates the SOC and SOH information of the energy storage power supply by calculating estimated SOC and SOH values and corresponding error bounds (error bound).

However, this dual estimation method of kalman filter is prone to bias after a predetermined period of time. For example, for a newly assembled and validated battery pack, SOC and SOH estimates may initially be accurate, but deviations in SOC and SOH estimates may occur after a certain period of time due to increased sensor bias and noise, hardware and/or software failures of the power management system, or battery degradation due to component aging of the electric vehicle. In addition, large deviations in SOC and SOH estimates may cause unnecessary damage to the power management system and other components of the electric vehicle.

As such, it is desirable to reduce or eliminate SOC and SOH estimation bias and limit the corresponding error bound. Accordingly, there is an opportunity to develop enhanced power management systems and methods that can more efficiently estimate SOC and SOH information of an energy storage power source.

Disclosure of Invention

In one embodiment of the present disclosure, a controller that performs a power estimation process of an electric vehicle is provided. The controller includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the controller to: the power estimation process is performed by estimating an internal state of an energy storage power source of the electric vehicle using a processor. The internal state represents at least one of: state of charge (SOC) and state of health (SOH) of the stored energy power source. The processor further causes the controller to estimate at least one of a SOC value and a SOH value of the stored energy power source based on at least one of: a present current level, a present voltage level, and a present temperature associated with the stored energy power source, and time-based information. The processor also causes the controller to at least one of: calculating a first upper bound and a first lower bound associated with the SOC value, and estimating a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; and calculating a second upper bound and a second lower bound associated with the SOH value, and estimating a bounded SOH value of the energy storage source based on the SOH value, the second upper bound, and the second lower bound. The controller then controls an electrification (electrical) process of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value.

In one aspect, the processor causes the controller to calculate an amp-hour (amp-hour) SOC and a voltage SOC. The amp-hour SOC is based on a present current level and a present temperature associated with the energy storage power source; and the voltage SOC is based on a present voltage level and a present temperature associated with the stored energy power source. The processor then further causes the controller to calculate a first upper bound and a first lower bound associated with the SOC value based on the amp-hour SOC and the voltage SOC. The maximum value of the ampere-hour SOC and the voltage SOC may be used for the first upper bound, and the minimum value of the ampere-hour SOC and the voltage SOC may be used for the first lower bound. The processor also causes the controller to filter the voltage SOC to remove noise.

In another aspect, the processor causes the controller to calculate a full-cycle SOH and a partial-cycle SOH. The full cycle SOH is based on a start time and an end time associated with a full charge cycle of the stored energy power source, and the partial cycle SOH is based on a start time and an end time associated with a partial charge cycle of the stored energy power source. The processor then further causes the controller to calculate a second upper bound and a second lower bound associated with the SOH value based on the full-cycle SOH and the partial-cycle SOH. The maximum of the full-cycle SOH and the partial-cycle SOH may be used for a second upper bound, and the minimum of the full-cycle SOH and the partial-cycle SOH may be used for a second lower bound.

In yet another aspect, the time-based information includes one or more historical estimates of SOC and SOH values. In yet another aspect, the processor further causes the controller to estimate the bounded SOC value and the bounded SOH value based on whether a predetermined period of time has elapsed. In another aspect, the controller controls the electrification process by at least one of: modifying cooling of the stored energy power source, modifying a charge/discharge limit of the stored energy power source, reducing a number of charge/discharge cycles of the stored energy power source, and modifying a minimum SOC threshold. The controller may include a dual non-linear kalman filter.

In another embodiment of the present disclosure, a method of performing a power estimation process of an electric vehicle using a controller is provided. The method comprises the following steps: the electric power estimation process is performed by estimating an internal state of an energy storage power source of the electric vehicle, and the internal state represents at least one of: state of charge (SOC) and state of health (SOH) of the stored energy power source. The method further comprises the steps of: estimating at least one of a SOC value and a SOH value of the energy storage power source based on at least one of: a present current level, a present voltage level, and a present temperature associated with the stored energy power source, and time-based information. The method further comprises the steps of: calculating a first upper bound and a first lower bound associated with the SOC value; estimating a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; calculating a second upper bound and a second lower bound associated with the SOH value; and estimating a bounded SOH value of the stored energy power source based on the SOH value, the second upper bound, and the second lower bound. Furthermore, the method comprises the steps of: the electrification process of the electric vehicle is controlled based on at least one of the bounded SOC value and the bounded SOH value.

In one aspect, the method comprises the steps of: the ampere hour SOC and the voltage SOC are calculated. The amp-hour SOC is based on a present current level and a present temperature associated with the energy storage power source; and the voltage SOC is based on a present voltage level and a present temperature associated with the stored energy power source. The method further comprises the steps of: a first upper bound and a first lower bound associated with the SOC value are calculated based on the amp-hour SOC and the voltage SOC. The maximum value of the ampere-hour SOC and the voltage SOC may be used for the first upper bound, and the minimum value of the ampere-hour SOC and the voltage SOC may be used for the first lower bound. The method further comprises the steps of: the voltage SOC is filtered to remove noise.

In another aspect, the method comprises the steps of: the full cycle SOH and the partial cycle SOH are calculated. The full cycle SOH is based on a start time and an end time associated with a full charge cycle of the stored energy power source, and the partial cycle SOH is based on a start time and an end time associated with a partial charge cycle of the stored energy power source. The method further comprises the steps of: a second upper bound and a second lower bound associated with the SOH value are calculated based on the full-cycle SOH and the partial-cycle SOH. The maximum of the full-cycle SOH and the partial-cycle SOH may be used for a second upper bound, and the minimum of the full-cycle SOH and the partial-cycle SOH may be used for a second lower bound.

In yet another aspect, the time-based information includes one or more historical estimates of SOC and SOH values. In yet another aspect, the method comprises the steps of: the bounded SOC value and the bounded SOH value are estimated based on whether a predetermined period of time has elapsed. In another aspect, the method comprises the steps of: controlling the electrification process by at least one of: modifying cooling of the stored energy power source, modifying a charge/discharge limit of the stored energy power source, reducing a number of charge/discharge cycles of the stored energy power source, and modifying a minimum SOC threshold. The method of performing the power estimation process may be performed by a dual non-linear kalman filter.

While multiple embodiments are disclosed, still other embodiments of the presently disclosed subject matter will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the disclosed subject matter. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

Drawings

The above-mentioned and other features and objects of this disclosure and the manner of attaining them will become more apparent and the disclosure itself will be better understood by reference to the following description of embodiments of the disclosure taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of an engine and motor system featuring a power estimator for an electric vehicle according to an embodiment of the present disclosure;

fig. 2A and 2B illustrate an exemplary configuration of an energy storage power source for use in an electric vehicle according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the power estimator of FIG. 1, according to an embodiment of the present disclosure;

FIG. 4 is a flow diagram illustrating an exemplary SOC bounding (bounding) process using the power estimator of FIG. 1, according to an embodiment of the present disclosure;

FIG. 5 is a flow diagram illustrating another exemplary SOC delimitation process using the power estimator of FIG. 1, according to an embodiment of the present disclosure;

FIG. 6 is a flow diagram illustrating an exemplary SOH delimitation process using the power estimator of FIG. 1 according to an embodiment of the present disclosure;

FIG. 7 is a flow diagram illustrating another exemplary SOH delimitation process using the power estimator of FIG. 1 according to an embodiment of the present disclosure;

FIG. 8 is another schematic diagram of the power estimator of FIG. 1, according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of an SOC delimiting unit of the power estimator of FIG. 1, according to an embodiment of the present disclosure;

FIG. 10A is a flow diagram illustrating an exemplary method for estimating SOC using upper and lower bounds of a battery according to an embodiment of the present disclosure;

fig. 10B is a flow chart illustrating an exemplary method of estimating SOC using upper and lower bounds of a battery pack having a plurality of battery cells or groups of battery cores, according to an embodiment of the present disclosure;

fig. 11A is a flow diagram illustrating an example method of generating a bounded SOC estimate for a battery (e.g., battery cell) in accordance with an embodiment of the present disclosure;

fig. 11B is a flow chart illustrating an exemplary method of generating a bounded SOC estimate for a battery pack having a plurality of battery cells or groups of battery cells in accordance with an embodiment of the present disclosure;

fig. 12A is a schematic diagram for use in a battery management device/chip according to an embodiment of the present disclosure;

fig. 12B is another schematic diagram for use in a battery management device/chip according to an embodiment of the present disclosure;

fig. 13 is a schematic diagram of a battery management system/device/chip according to an embodiment of the present disclosure;

FIG. 14 illustrates an example of an SOC-OCV model and/or function according to an embodiment of the present disclosure; and

15A and 15B illustrate examples of bounded SOC estimates according to embodiments of the present disclosure.

Corresponding reference characters indicate corresponding parts throughout the several views. Although the drawings represent embodiments of the present disclosure, the drawings are not necessarily to scale and certain features may be exaggerated in order to better illustrate and explain the present disclosure. The exemplifications set out herein illustrate embodiments of the disclosure, in one form, and such exemplifications are not to be construed as limiting the scope of the disclosure in any manner.

Detailed Description

The embodiments disclosed below are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may utilize the teachings of the embodiments. One of ordinary skill in the art will recognize that the implementations provided may be implemented in hardware, software, firmware, and/or combinations thereof. The programming code according to embodiments may be implemented in any feasible programming language, such as C, C + +, HTML, XTML, JAVA, or any other feasible high-level programming language, or a combination of high-level and low-level programming languages.

Referring now to FIG. 1, a hybrid powertrain 100 of an electric vehicle 102 is illustrated. The electric vehicle 102 may be plugged into an electrical outlet to be connected to a power grid system (not shown) for performing an electrification process of the electric vehicle 102. In various embodiments, the electrification process may refer to various operations related to power generation and power distribution and management associated with the electric vehicle 102. Exemplary electrification processes include: modifying battery cooling, modifying charge and/or discharge limits, reducing the number of charge and/or discharge cycles, modifying minimum state of charge thresholds, and the like. The electric vehicle 102 may be a commercial vehicle, such as a bus connectable to a power grid system.

In one embodiment, the grid system may be a grid system implemented in a particular commercial facility, such as a bus stop. In another embodiment, the grid system may be a grid system implemented in a power grid that incorporates multiple power stations (such as power plants and other power generation facilities). In fig. 1, although electric vehicle 102 is described as a parallel hybrid system, the present disclosure may also be applied to a range extended vehicle or a series hybrid vehicle to suit different applications. As such, the electric vehicle 102 may be any electric vehicle having an electric propulsion system (e.g., a hybrid vehicle, a pure electric vehicle, and/or an extended range vehicle).

Although an electric vehicle 102 having an Internal Combustion Engine (ICE)104 is shown, the present disclosure may be applied to a purely electric vehicle powered only by a battery without the ICE 104. The ICE 104 may be powered by any type of fuel, such as gasoline, diesel, natural gas, liquefied petroleum gas, biofuel, or the like. In this embodiment, the hybrid system 100 may include an ICE 104 having a crankshaft 106 and a crankshaft sprocket (not shown) coupled to the crankshaft. The ICE 104 is not particularly limited and may be onboard (e.g., an extended range vehicle) or offboard (e.g., a genset located at a bus stop).

The hybrid system 100 may also include an electric motor 108 in mechanical communication with the crankshaft sprocket. For example, the motor 108 may be a traction motor used to propel the electric vehicle 102. In various implementations, the electric motor 108 may be coupled to a speed sensor 110, a torque sensor 112, the ICE 104, a clutch or torque converter 114, and a transmission 116 via the crankshaft 106. In various implementations, the speed sensor 110 and the motor 108 are in mechanical communication with the crankshaft 106. Also, the motor 108 is not particularly limited, and may be, for example, a motor/generator, a synchronous motor, or an induction motor.

In an embodiment, hybrid system 100 further includes a controller 118 in electrical communication with speed sensor 110 and torque sensor 112. The controller 118 may include a non-transitory memory 120 having instructions that, in response to execution by the processor 122, cause the processor 122 to determine a speed or torque value of the motor 108. The electric motor 108 receives power from a rechargeable energy storage power source 124, such as a battery pack or battery pack, and the energy storage power source 124 may provide data representing state of charge (SOC) and/or state of health (SOH) information to the controller 118. The processor 122, the non-transitory memory 120, and the controller 118 are not particularly limited and may be, for example, physically separate. Additionally, the vehicle monitoring unit 128 may be included in the controller 118 or may be a separate unit from the controller 118 to suit different applications.

In some implementations, the controller 118 may form part of a processing subsystem that includes one or more computing devices having memory, processing, and communication hardware. The controller 118 may be a single device or a distributed device, and the functions of the controller 118 may be performed by hardware and/or as computer instructions on a non-transitory computer-readable storage medium, such as the non-transitory memory 120.

In certain embodiments, the controller 118 includes one or more interpreters, determiners, evaluators, regulators, and/or processors 122 that functionally execute the operations of the controller 118. The description herein including interpreters, determiners, evaluators, regulators, and/or processors emphasizes the structural independence of certain aspects of the controller 118 and illustrates a set of operations and responsibilities of the controller 118. Other groupings that perform similar overall operations are understood to be within the scope of the present disclosure. The interpreter, determiner, evaluator, adjuster, and processor may be implemented in hardware and/or as computer instructions on a non-transitory computer-readable storage medium, and may be distributed across various hardware or computer-based components.

Example and non-limiting implementation elements that functionally execute the operations of the controller 118 include: sensors providing any value determined herein (such as speed sensor 110 and torque sensor 112), sensors providing any value that is a precursor to a value determined herein, data link and/or networking hardware (including communication chips, oscillating crystals, communication links, cables, twisted pair wires, coaxial wires, shielded wires, transmitters, receivers and/or transceivers), logic circuitry, hardwired logic circuitry, reconfigurable logic circuitry in a particular non-transitory state configured according to module specifications, any actuator (including at least an electric actuator, a hydraulic actuator, or a pneumatic actuator), solenoids, operational amplifiers, analog control elements (springs, filters, integrators, adders, dividers, gain elements), and/or digital control elements.

Certain operations described herein include operations for interpreting and/or determining one or more parameters or data structures. Interpreting or determining as utilized herein includes receiving a value by any method known in the art, including at least: receiving a value from a data link or network communication, receiving an electronic signal (e.g., a voltage, frequency, current, or PWM signal) indicative of the value, receiving a computer-generated parameter indicative of the value, reading the value from a memory location on a non-transitory computer-readable storage medium, receiving the value as a runtime parameter by any means known in the art, and/or by receiving a default value from which the value of an interpretation parameter can be calculated, and/or interpreted by reference as a parameter value.

In the illustrated embodiment, the controller 118 includes a power estimator 126 configured to estimate an internal state of the energy storage power source 124 of the electric vehicle 102. The internal state of the stored energy power source 124 represents the SOC and/or SOH of the stored energy power source 124. The power estimator 126 may be configured to set at least one of an upper bound and a lower bound for estimating the SOC and/or SOH of the stored energy power source 124. During the power estimation process of the SOC and/or SOH of the storage power source 124, the power estimator 126 automatically applies at least one of the upper and lower bounds to filter out or cut off high or low values associated with the SOC and/or SOH of the storage power source 124 to prevent any potentially large estimation bias that may cause unnecessary damage to the electric vehicle 102. The power estimator 126 may use an independent and separate bounding algorithm to perform power estimation processing on the SOC and SOH information of the energy storage power source 124. In the following, in the paragraphs related to fig. 3 to 9, a detailed description of the delimitation algorithm is provided.

In one embodiment, the power estimator 126 is configured to measure a present current level and/or a present voltage level of the stored power source 124 through the use of the vehicle monitoring unit 128. For example, the power estimator 126 is configured to automatically communicate with the vehicle monitoring unit 128 to determine a present current level and a present voltage level of the stored power source 124 of the electric vehicle 102. In one embodiment, the vehicle monitoring unit 128 may be a telematics system associated with the electric vehicle 102. In an embodiment, the vehicle monitoring unit 128 is configured to monitor one or more vehicle characteristics associated with the electric vehicle 10.

For example, the vehicle characteristics may include: information of one or more components of the electric vehicle 102, such as the ICE 104 or the electric motor 108, navigation information based on a navigation system (e.g., Global Positioning System (GPS)), thermal information (e.g., temperature) of one or more components of the electric vehicle 102, such as a present temperature of the electric motor 108, environmental information related to a particular route of a task of the electric vehicle 102 (e.g., time, weather, road or load conditions, etc.). Other exemplary components of the electric vehicle 102 may include electrification, a powertrain, and various vehicle components, such as an energy storage power source 124 (e.g., a battery), an electric motor 108, an ICE 104, a charging system, a cooling system, a separate generator (not shown), a drivetrain or powertrain (e.g., a crankshaft), a driveshaft assembly (not shown), and so forth.

In an embodiment, the power estimator 126 automatically communicates with the vehicle monitoring unit 128 to obtain thermal information of at least one electrical device of the electric vehicle 102 (such as the stored energy power source 124) that is provided to the vehicle monitoring unit 128 by the temperature sensor 132. For example, the power estimator 126 communicates with a vehicle monitoring unit 128 to detect the temperature of the battery pack. In another example, the power estimator 126 communicates with the vehicle monitoring unit 128 to detect the temperature of the electric motor 108. Other suitable uses of the temperature sensor 132 are also contemplated as being suitable for this application.

In one embodiment, the power estimator 126 interfaces with a network 130, such as a wireless communication facility (e.g., a Wi-Fi access point). In another embodiment, the network 130 may be a controller area network (e.g., CAN bus) onboard the electric vehicle 102. In yet another embodiment, the network 130 may be a cloud computing network off-board the electric vehicle 102. Other similar networks known in the art are also contemplated. For example, the network 130 may be a cloud network or a vehicle-to-grid (V2G) network between the electric vehicles 102 and a grid system, or a vehicle-to-vehicle (V2V) network between the electric vehicles 102. In embodiments, any type of computer network having a collection of computers, servers, and other hardware interconnected by communication channels is contemplated, such as the internet, an intranet, an ethernet, a LAN, a cloud network, and so forth.

Referring now to fig. 2A and 2B, an exemplary configuration of the stored energy power source 124 is shown. In fig. 2A, the stored energy power source 124 comprises a single battery. In one embodiment, the vehicle monitoring unit 128 may measure the present voltage level V and the present current level I of the stored power source 124 and send the present voltage level V and the present current level I to the power estimator 126 for subsequent processing as needed. In fig. 2B, the stored energy power source 124 includes a battery pack having a plurality of battery cells 124a, 124B, …, 124 n. In this example, the battery pack includes: a first array having battery cells 124a (e.g., i-1), a second array having battery cells 124b (e.g., i-2), and an nth array having battery cells 124n (e.g., i-n). In one embodiment, the vehicle monitoring unit 128 may measure the present voltage level V of each array of the stored power source 124₁，V₂，...V_nAnd a current level I, and a current voltage level V₁，V₂，...V_nAnd the present current level I are sent to the power estimator 126 for subsequent processing as needed. Other suitable arrangements are also envisaged to suit different applications.

Various embodiments of the power estimator and SOC delimitation process are described with reference to fig. 3-9. Additional embodiments illustrating methods of estimating SOC boundaries are described with reference to fig. 10A-15B. The foregoing embodiments may be implemented in a vehicle as described with reference to fig. 1, 2A, and 2B, as follows.

Referring now to FIG. 3, an exemplary schematic diagram of the power estimator 126 is shown. In the illustrated embodiment, the power estimator 126 includes a SOC/SOH estimator 200 and a boundary estimator 202. The SOC/SOH estimator 200 is configured to estimate a SOC value SOC based on a present current level I and/or a present voltage level V of the energy storage power source 124_Est(e.g., 60%). For example, SOC/SOH estimator 200 may be a DNKF. Further, the SOC/SOH estimator 200 is configured to estimate the SOH value SOH based on the present current level I and/or the present voltage level V of the stored energy power source 124_Est(e.g., 80%).

Although power estimator 126 is shown in fig. 3 as integrating SOC/SOH estimator 200 and boundary estimator 202, in some embodiments, SOC/SOH estimator 200 and boundary estimator 202 may be separately or independently installed in any suitable system associated with electric vehicle 102. Returning to fig. 1, in one embodiment, a Battery Management System (BMS)134 may be installed separately from the controller 118. The BMS 134 may include another non-transitory memory 136 and a processor 138. In this example, the BMS 134 may include the boundary estimator 202 in the processor 138 along with other control algorithms. In another example, the BMS 134 may include a SOC/SOH estimator 200 in the processor 138 to adapt to different applications. In various implementations, the BMS 134 may perform power estimation processing on the SOC and SOH information of the energy storage power source 124. Also, the BMS 134 may provide an estimate of the available power of the stored energy power source 124.

Returning to FIG. 3, in one embodiment, SOC/SOH estimator 200 includes: SOC estimator 204, SOC regulator 206, SOH estimator 208, and SOH regulator 210. The SOC estimator 204 is configured to estimate SOC based on the present current level I of the energy storage power source 124, the universal embedded battery model, and/or time-based information_Est. For example, it may be based on having a measured SOC for the electric vehicle 102_EstOne or more ofEstimating SOC from historically inputted time-based information_Est. SOC regulator 206 is configured to receive SOC from SOC estimator 204_EstAnd adjusts the SOC based on the present voltage level V of the stored energy power source 124_Est. For example, the SOC may be corrected or tuned based on the present voltage level V of the stored energy power source 124 currently measured by the vehicle monitoring unit 128_Est。

SOH estimator 208 is configured to estimate SOH based on a generic embedded battery model and time-based information_Est. For example, the SOH may be based on a measurement of the SOH for the electric vehicle 102_EstTo estimate SOH_Est. SOH adjuster 210 is configured to receive SOH from SOH estimator 208_EstAnd adjusts the SOH based on the present voltage level V of the stored energy power supply 124_Est. For example, the SOH may be corrected or tuned based on the present voltage level V of the stored energy power source 124 as currently measured by the vehicle monitoring unit 128_Est. In some embodiments, other suitable parameters (e.g., battery resistance, impedance, or conductance) that change as the stored energy power source 124 ages may also be used to estimate SOH_Est。

In one embodiment, boundary estimator 202 is configured to estimate a bounded SOC value SOC_BoundedAnd/or a bounded SOH value SOH_Bounded. For example, boundary estimator 202 calculates SOC_BoundedSo that the SOC is reduced_BoundedSet at SOC_EstUpper bound of and SOC_EstBetween the lower bound of (c). In another example, the boundary estimator 202 calculates the SOH_BoundedSo that SOH is converted to_BoundedSet at SOH_EstUpper bound of (3) and SOH_EstBetween the lower bound of (c).

In the illustrated embodiment, boundary estimator 202 includes an SOC bounding unit 212 and an SOH bounding unit 214. In one embodiment, SOC delimitation unit 212 is configured to determine SOC values SOC based on Ampere hours (based on Ah)_AhAnd a voltage-based SOC value SOC_VOn the basis of which the SOC is calculated_BoundedUpper and lower bounds. In one embodiment, the SOH delimiting unit 214 is configured to base the SOH value SOH of a full cycle on_FAnd based on the moietyCyclic SOH value SOH_POn the basis of which the SOH is calculated_BoundedUpper and lower bounds. For example, when a full charge cycle is available for the stored energy power source 124, the SOH may be calculated_FAnd when a partial charge cycle is available for the stored energy power source 124, the SOH may be calculated_P。

In some implementations, boundary estimator 202 may determine a self-updated SOC at block 216_BoundedWhether a predetermined period of time (e.g., a macroscopic time of about 1 month to 2 months) has elapsed. The predetermined period of time may be adjusted as desired. When the boundary estimator 202 determines that it is time to update the SOC based on a predetermined period of time_BoundedTime of (3), SOC delimitation unit 212 outputs SOC_BoundedFor subsequent processing by other systems of the electric vehicle 102.

For example, the SOC may be_BoundedTo SOH conditioner 210 or to a display device for viewing by a technician. In another example, when boundary estimator 202 determines that it is not time to update SOC based on a predetermined period of time_BoundedCan be compared with the SOC_BoundedAs a feedback value to SOC estimator 204. Albeit for SOC_BoundedBlock 216 is shown, but may be for SOH_BoundedBlock 216 is implemented to suit the application. Furthermore, SOH can be converted to SOH_BoundedAs a feedback value to at least one of SOC estimator 204 and SOH estimator 208 or to a display device for subsequent viewing.

Referring now to FIG. 4, an illustrative SOC delimitation process is shown, according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid system 100 is not particularly limited and may perform any of the methods described within the scope of the present disclosure. In fig. 4, a method 400 of performing SOC delimitation processing using SOC delimitation unit 212 is shown. More specifically, when a single battery is used for the energy storage power source 124 (e.g., fig. 2A) for a predetermined period of time (e.g., microscopic time of about 1 to 2 seconds), the SOC delimiting unit 212 performs one or more steps shown in fig. 4.

At block 402, SOC estimator 204 estimates SOC based on the present current level I of the energy storage power source 124 and the time-based information_Est. For example, a general purpose SOC estimator (such as DNKF) may be used to estimate SOC_Est。

At block 404, the SOC delimiting unit 212 calculates SOC based on the present current level I and the present temperature T of the energy storage power source 124_Ah. In one example, SOC may be calculated using coulomb counting techniques_Ah. An exemplary SOC may be defined as shown in expression (1) below_Ah。

Where I (t) is the input current during time t, SOC₀Is shown at an initial time t₀The initial SOC, and Capacity, represents the present total Capacity generated by the stored energy power source 124.

At block 406, the SOC delimiting unit 212 calculates SOC based on the present voltage level V and the present temperature T of the energy storage power source 124_V. An exemplary SOC may be defined as shown in expression (2) below_V. In one embodiment, the SOC may be calibrated_VFiltering is performed to remove noise due to dynamic voltage response.

SOC_V(t)＝[OCV^-1(V(t)+I(t)·R₀(T))]_filtered (2)

Where OCV is the battery open circuit voltage, which is a function of SOC; r₀Indicating the internal resistance of the cell, which depends on the temperature T. In one example, the filtering may be performed using a single-pole low-pass filter. In one embodiment, a single pole low pass filter may be used to remove the SOC_VMay be caused by measurement and imperfect fidelity of the OCV approximation. Other suitable filters are also envisaged to suit different applications. In another example, a linear phase low pass filter may also be used to remove amplification noise.

At block 408, the SOC delimitation unit 212 bases on the SOC calculated in blocks 404 and 406, respectively_AhAnd SOC_VTo calculate SIC_EstUpper and lower bounds. Can be as shown in a tableExemplary upper bounds are defined as shown by expressions (3) and (5), and exemplary lower bounds may be defined as shown by expressions (4) and (6).

SOC_u，bnd(t)＝max{SOC_Ah(t)，SOC_V(t)}+|SOC_Ah(t)-SOC_V(t)|+E_desg (3)

SOC_l，bnd(t)＝min{SOC_Ah(t)，SOC_V(t)}-|SOC_Ah(t)-SOC_V(t)|-E_desg (4)

SOC_u，bnd(t)∈[0，1] (5)

SOC_l，bnd(t)∈[0，1] (6)

Wherein E is_desgRepresenting a predetermined or designed error margin selected by the SOC delimiting unit 212.

As shown in expressions (3) and (4) above, the SOC may be expressed_AhAnd SOC_VIs used as a baseline for the upper bound, but additional tolerances may be applied. Furthermore, the SOC can be adjusted_AhAnd SOC_VIs used as a baseline for the lower bound, and then additional margin may be applied. For example, SOC is modeled by considering errors/noise in the current sensor and/or voltage sensor and other errors according to the measured voltage shown in expression (2) (e.g., OCV is calculated as a function of SOC)_AhAnd SOC_VThe abstraction value in between is used as an extra margin. Thus, the abstract value | SOC_Ah-SOC_V| may represent an uncertainty in the measurement and/or model used for SOC bounding processing. In some embodiments, additional accuracy margins (such as E) may be applied_desg) (e.g., applying an accuracy of ± 3%).

At block 410, SOC delimitation unit 212 generates an SOC set at an upper bound_u，bnd(t) and lower bound SOC_l，bndSOC between (t)_Bounded. An exemplary SOC may be defined as shown in expression (7) below_Bounded。

SOC_l，bnd(t)≤SOC_Bounded(t)≤SOC_u，bnd(t) (7)

At block 412, the controller 118 bases the SOC_BoundedControl electric vehicleElectrification of the vehicle 102. For example, the controller 118 may modify the battery cooling or charge and/or discharge limits, reduce the number of charge and/or discharge cycles, or be based on the SOC_BoundedTo modify the minimum state of charge threshold.

Referring now to fig. 5, another illustrative SOC bounding process is shown, according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid system 100 is not particularly limited and may perform any of the methods described within the scope of the present disclosure. In fig. 5, a method 500 of performing SOC delimitation processing using SOC delimitation unit 212 is shown. More specifically, when the battery pack is used for the energy storage power source 124 (e.g., fig. 2B) for a predetermined period of time (e.g., microscopic time of about 1 to 2 seconds), the SOC delimiting unit 212 performs one or more steps shown in fig. 5.

At block 502, SOC estimator 204 estimates SOC of all cells 124a, 124b, …, 124n in each array I based on the present current level I of the energy storage power source 124 and the time-based information_Est，i. For example, a general purpose SOC estimator (such as DNKF) may be used to estimate SOC_Est，i. In one embodiment, n, n represents a plurality of cells or groups of cells connected in series for i ═ 1,2, ….

At block 504, the SOC delimiting unit 212 calculates SOC based on the present current level I and the present temperature T of the energy storage power source 124_Ah. In one example, SOC may be calculated using coulomb counting techniques_Ah. An exemplary SOC may be defined as shown in expression (8) below_Ah。

Where I (t) is the input current during time t, SOC₀Is shown at an initial time t₀The initial SOC, and Capacity, represents the present total Capacity generated by the stored energy power source 124.

At block 506, the SOC delimiting unit 212 calculates based on the present voltage level V and the present temperature T of the stored energy power source 124SOC of all cells 124a, 124b, …, 124n in the battery pack_V，i. An exemplary SOC may be defined as shown in expression (9) below_V，i. In one embodiment, the SOC may be calibrated_V，iFiltering is performed to remove noise due to dynamic voltage response.

SOC_V，i(t)＝[OCV^-1(V_i(t)+I(t)R_0，i]_filtered (9)

Where OCV is the battery open circuit voltage, which is a function of SOC; r_0，iIndicating the internal cell resistance of each array i, which depends on the temperature T. In one example, the filtering may be performed using a single-pole low-pass filter. In one embodiment, a single pole low pass filter may be used to remove the SOC_V，iMay be due to measurement and imperfect fidelity of the OCV approximation. Other suitable filters are also envisaged to suit different applications. In another example, a linear phase low pass filter may also be used to remove amplification noise.

At block 508, the SOC delimitation unit 212 bases on the SOC calculated in blocks 504 and 506, respectively_AhAnd SOC_V，iTo calculate the SOC_EstUpper and lower bounds. An exemplary upper bound may be defined as shown in expressions (10) and (12), and an exemplary lower bound may be defined as shown in expressions (11) and (13).

SOC_u，bnd(t)＝max(SOC_Ah(t)，SOC_V，i(t)}+max{|SOC_Ah(t)-SOC_V，i(t)|}+E_desg (10)

SOC_l，bnd(t)＝min{SOC_Ah(t)SOC_V，i(t)}-max{|SOC_Ah(t)-SOC_V，i(t)|}-E_desg (11)

SOC_u，bnd(t)∈[0，1] (12)

SOC_l，bnd(t)∈[0，1] (13)

Wherein E is_desgRepresenting a predetermined or designed error margin selected by the SOC delimiting unit 212.

Expressions (10) and (11) as aboveAs shown, the SOC can be_AhAnd SOC_V，iIs used as a baseline for the upper bound, but additional tolerances may be applied. Furthermore, the SOC can be adjusted_AhAnd SOC_V，iIs used as a baseline for the lower bound, and then additional margin may be applied. For example, SOC is modeled by considering errors/noise in the current sensor and/or voltage sensor and other errors according to the measured voltage shown in expression (9) (e.g., OCV is calculated as a function of SOC)_AhAnd SOC_V，iThe abstraction value in between is used as an extra margin. Thus, the maximum value max { | SOC_Ah-SOC_V，i| may represent an uncertainty in the measurement and/or model used for the SOC bounding process. In some embodiments, additional accuracy margins (such as E) may be applied_desg) (e.g., applying an accuracy of ± 3%).

At block 510, SOC delimitation unit 212 generates an SOC set to an upper bound_u，bnd(t) and lower bound SOC_l，bndSOC between (t)_Bounded. The SOC can be defined as shown in the following expression (14)_Bounded。

SOC_l，bnd(t)≤SOC_Bounded，i(t)≤SOC_u，bnd(t) (14)

At block 512, the controller 118 bases the SOC_Bounded，iControls the electrification process of the electric vehicle 102. For example, the controller 118 may modify the battery cooling or charge and/or discharge limits, reduce the number of charge and/or discharge cycles, or be based on the SOC_Bounded，iTo modify the minimum state of charge threshold.

Referring now to FIG. 6, an illustrative SOH bounding process is shown, according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid system 100 is not particularly limited and may perform any of the methods described within the scope of the present disclosure. In fig. 6, a method 600 of performing SOH bounding processing using the SOH bounding unit 214 is shown. More specifically, the SOH delimiting unit 214 performs one or more steps shown in fig. 6 when a single battery is used for the stored energy power supply 124 (e.g., fig. 2A) for a predetermined period of time (e.g., a macroscopic time of about 1 month to 2 months).

At block 602, the SOH estimator 208 estimates the SOH based on the time-based information_Est. For example, a general purpose SOC estimator (such as DNKF) may be used to estimate SOH_Est。

At block 604, the SOH delimiting unit 214 calculates the SOH based on the start time and the end time associated with the stored energy power source 124_F. Can convert SOH_FStored in memory 120 for subsequent retrieval and processing. For example, the full cycle SOH value represents the SOH value SOH estimated at the last capacity check of the storage power source 124_F(L). Every n charge cycles or m months, it is generally recommended to fully charge and discharge the stored energy power source 124. When such full charge and discharge operations are possible, an exemplary SOH at the time of performing the last capacity check of the full charge cycle may be defined as shown in the following expression (15)_F(L)。

Where I (t) is the input current during time t,

indicating the start time of the upper voltage limit of the stored energy power source 124 during full discharge,

representing the end time when the voltage reaches the lower limit of the cutoff voltage of the stored power source 124 during full discharge, and the Nominal Capacity (Nominal Capacity) representing the total Capacity generated by the stored power source 124 when the stored power source 124 is newly installed at 100% SOH.

However, when such full charge and discharge operations are not available or allowed for some applications, another exemplary full cycle SOH value SOH may be defined as shown in expression (16) below_F(L)。

Where I (t) is the input current during time t,

representing a starting time before full charge when the SOC value of the stored energy power source 124 is less than about 20%,

representing the end time when the voltage reaches the upper voltage limit during full charge, and a Nominal Capacity (Nominal Capacity) representing the total Capacity generated by the stored energy power source 124 when the stored energy power source 124 is newly installed at 100% SOH. In the embodiment, the exemplary full-cycle SOH value SOH shown in expression (16) may be calculated in the following cases (1) to (3)_F(L): (1) after the last run of the electric vehicle 102, the SOC value of the stored energy power source 124 is less than 20%; (2) the subsequent charging event fully charges the stored energy power source 124 to an upper voltage limit (e.g., SOC-100%); and (3) the temperature of the stored energy power source 124 is approximately between 25 degrees celsius and 35 degrees celsius (25 degrees celsius to 35 degrees celsius).

At block 606, the SOH delimiting unit 214 calculates a partial-cycle SOH value SOH based on the start time and end time associated with the stored energy power source 124_P. SOH can also be_PStored in memory 120 for subsequent retrieval and processing. For example, the partial cycle SOH value represents the SOH value SOH estimated at the last capacity check of the storage power source 124_P(L). An exemplary SOH at the time of performing the last capacity check of the partial charge cycle may be defined as shown in the following expression (17)_P(L)。

Wherein, t₁Represents the start time of a partial cycle, and t₂Indicating the end time of the partial cycle.

At block 608, SOH is determinedThe boundary unit 214 is based on the SOH calculated in blocks 604 and 606, respectively_F(L) and SOH_P(L) to calculate SOH_EstUpper and lower bounds. An exemplary upper bound may be defined as shown by expressions (18) and (20), and an exemplary lower bound may be defined as shown by expressions (19) and (21).

SOH_u，bnd(L)＝max{SOH_F(L)，SOH_P(L)}+|SOH_F(L)-SOH_P(L)|+E_desg (18)

SOH_l，bnd(L)＝min{SOH_F(L)，SOH_P(L)}-|SOH_F(L)-SOH_P(L)|-E_desg (19)

SOH_u，bnd(L)∈[0，1] (20)

SOH_l，bnd(L)∈[0，1] (21)

Wherein E is_desgRepresenting a predetermined or design error margin selected by SOH delimiting unit 214.

SOH can be expressed as shown in expressions (18) and (19) above_FAnd SOH_PIs used as a baseline for the upper bound, but additional tolerances may be applied. Furthermore, SOH can be converted to SOH_FAnd SOH_PIs used as a baseline for the lower bound, and then additional margin may be applied. For example, by considering SOH_FAnd SOH_PUncertainty of estimate, SOH can be_FAnd SOH_PThe abstraction value in between is used as an extra margin. In some cases, SOH_FMay not be accurate enough due to undesirable variations during the performance of a volume check every few months. As another example, SOH_PMay be less accurate due to undesirable sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., charge loss due to time lapse). Thus, the abstract value | SOH_F(L)-SOH_P(L) | may represent an uncertainty in the measurements and/or models used for SOH bounding processing. In some embodiments, additional accuracy margins (such as E) may be applied_desg) (e.g., applying an accuracy of ± 3%).

At block 610, SOH bounding unit 214 generates an SOH that is set at an upper bound_u，bnd(L) and lower boundarySOH_l，bndSOH between (L)_Bounded. An exemplary SOH may be defined as shown in expression (22) below_Bounded。

SOH_l，bnd(L)≤SOH_Bounded(L)≤SOH_u，bnd(L) (22)

At block 612, the controller 118 bases the SOH_BoundedControls the electrification process of the electric vehicle 102. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge and/or discharge cycles, or be based on SOH_BoundedTo modify the minimum state of charge threshold.

Referring now to FIG. 7, another illustrative SOH bounding process is shown, according to an embodiment of the subject matter disclosed herein. As disclosed herein, the hybrid system 100 is not particularly limited and may perform any of the methods described within the scope of the present disclosure. In fig. 7, a method 700 of performing SOH bounding processing using the SOH bounding unit 214 is shown. More specifically, when the battery pack is used for the energy storage power supply 124 (e.g., fig. 2B) for a predetermined period of time (e.g., a macroscopic time of about 1 month to 2 months), the SOH delimiting unit 214 performs one or more steps shown in fig. 7.

At block 702, the SOH estimator 208 estimates the SOH based on the time-based information_Est，i. For example, a general purpose SOC/SOH estimator (such as DNKF) may be used to estimate SOH_Est，i。

At block 704, the SOH delimiting unit 214 calculates a full cycle SOH value SOH of the battery pack based on the start time and the end time associated with the stored energy power source 124_F. Can convert SOH_FStored in memory 120 for subsequent retrieval and processing. For example, the full cycle SOH value represents the SOH value SOH estimated at the last capacity check of the storage power source 124_F(L). Every n charge cycles or m months, it is generally recommended to fully charge and discharge the stored energy power source 124. When such full charge and discharge operations are possible, an exemplary SOH at the time of performing the last capacity check of the full charge cycle may be defined as shown in the following expression (23)_F(L)。

Where I (t) is the input current during time t,

indicating the start time of the upper voltage limit of the stored energy power source 124 during full discharge,

representing the end time when the voltage reaches the lower limit of the cutoff voltage of the stored power source 124 during full discharge, and the Nominal Capacity (Nominal Capacity) representing the total Capacity generated by the stored power source 124 when the stored power source 124 is newly installed at 100% SOH.

However, when such full charge and discharge operations are not available or allowed for some applications, another exemplary full cycle SOH value SOH may be defined as shown in expression (24) below_F(L)。

Where I (t) is the input current during time t,

representing a starting time when the SOC value of the stored energy power source 124 is less than 20% during full charge,

representing the end time when the voltage reaches the upper voltage limit during full charge, and a Nominal Capacity (Nominal Capacity) representing the total Capacity generated by the stored energy power source 124 when the stored energy power source 124 is newly installed at 100% SOH. In the embodiment, the exemplary SOH shown in expression (24) can be calculated in the following cases (1) to (3)_F(L): (1) at the end of the electric vehicle 102After the primary operation, the SOC value of the energy storage power supply 124 is less than 20%; (2) the subsequent charging event fully charges the stored energy power source 124 to an upper voltage limit (e.g., SOC-100%); and (3) the temperature of the stored energy power source 124 is approximately between 25 degrees celsius and 35 degrees celsius (25 degrees celsius to 35 degrees celsius).

At block 706, the SOH delimiting unit 214 calculates a partial-cycle SOH value SOH based on the start time and the end time associated with the stored energy power source 124_P，i. SOH can also be_P，iStored in memory 120 for subsequent retrieval and processing. For example, the partial cycle SOH value represents the SOH value SOH estimated at the last capacity check of the storage power source 124_P，i(L). An exemplary SOH at the time of performing the last capacity check of the partial charge cycle may be defined as shown in the following expression (25)_P，i(L)。

Wherein, t₁Represents the start time of a partial cycle, and t₂Indicating the end time of the partial cycle.

At block 708, the SOH bounding unit 214 bases on the SOH calculated in blocks 704 and 706, respectively_F(L) and SOH_P，i(L) to calculate SOH_EstUpper and lower bounds. An exemplary upper bound may be defined as shown by expressions (26) and (28), and an exemplary lower bound may be defined as shown by expressions (27) and (29).

SOH_u，bnd(L)＝max{SOH_F(L)，SOH_P，i(L)}+max{|SOH_F(L)-SOH_P，i(L)|}+E_desg (26)

SOH_l，bnd(L)＝min{SOH_F(L)，SOH_P，i(L)}-max{|SOH_F(L)-SOH_P，i(L)|}-E_desg (27)

SOH_u，bnd(L)∈[0，1] (28)

SOH_l，bnd(L)∈[0，1] (29)

Wherein E is_desgThe representation is delimited by an SOH bounding cell 214 selected predetermined or design error tolerance.

SOH can be expressed as shown in the above expressions (26) and (27)_FAnd SOH_P，iIs used as a baseline for the upper bound, but additional tolerances may be applied. Furthermore, SOH can be converted to SOH_FAnd SOH_P，iIs used as a baseline for the lower bound, and then additional margin may be applied. For example, by considering SOH_FAnd SOH_P，iUncertainty of estimate, SOH can be_FAnd SOH_P，iThe abstraction value in between is used as an extra margin. In some cases, SOH_FMay not be accurate enough due to undesirable variations during the performance of a volume check every few months. As another example, SOH_P，iMay be less accurate due to undesirable sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., charge loss due to time lapse). Thus, the maximum max { | SOH_F(L)-SOH_P，i(L) | } may represent uncertainty in the measurements and/or models used for SOH bounding processing. In some embodiments, additional accuracy margins (such as E) may be applied_desg) (e.g., applying an accuracy of ± 3%).

At block 710, SOH bounding unit 214 generates an SOH set at upper bound_u，bnd(L) and lower bound SOH_l，bndSOH between (L)_Bounded，i. An exemplary SOH may be defined as shown in expression (30) below_Bounded，i。

SOH_l，bnd(L)≤SOH_Bounded，i(L)≤SOH_u，bnd(L) (30)

At block 712, the controller 118 bases the SOH_Bounded，iControls the electrification process of the electric vehicle 102. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge and/or discharge cycles, or be based on SOH_Bounded，iTo modify the minimum state of charge threshold.

Referring now to FIG. 8, another exemplary schematic diagram of the power estimator 126 is shown. In the illustrated embodiment, power estimator 126 includes an SOC/SOH estimator 200 and an SOC delimiting unit 212.The illustrated embodiments may be used for both individual battery cells and battery packs to suit different applications. In fig. 8, the SOC/SOH estimator 200 is configured to receive the present current level I and the present voltage level V of the stored power source 124 from the vehicle monitoring unit 128. Further, the SOC/SOH estimator 200 is configured to receive the present temperature T of the stored energy power source 124 from the vehicle monitoring unit 128. The SOC/SOH estimator 200 is configured to estimate SOC based on a present current level I, a present voltage level V, and a present temperature T of the stored energy power source 124_Est(e.g., 60%). Will SOC_EstTo the SOC delimiting unit 212.

SOC delimitation unit 212 is configured to receive SOC from SOC/SOH estimator 200_EstAnd also receives the present current level I, the present voltage level V, and the present temperature T of the stored power source 124 from the vehicle monitoring unit 128. SOC delimitation unit 212 is configured to be based on a present current level I, a present voltage level V, and a present temperature T and SOC_EstTo calculate the SOC_EstUpper and lower bounds that can be applied. SOC delimitation unit 212 is configured to generate an SOC set between an upper bound and a lower bound_Bounded. Configuring SOC delimiting unit 212 to output SOC_BoundedUpper and lower bounds for subsequent processing as needed. For example, the controller 118 may be based on SOC_BoundedTo control the electrification process of the electric vehicle 102.

Referring now to FIG. 9, an exemplary schematic diagram of SOC bounding unit 212 shown in FIG. 8 is shown. In the illustrated embodiment, the SOC delimiting unit 212 includes an Ah-based SOC calculating unit 900 and a voltage-based SOC calculating unit 902. Configuring the Ah-based SOC calculation unit 900 to calculate SOC based on the present current level I of the energy storage power source 124_Ah. The voltage-based SOC calculation unit 902 is configured to calculate SOC based on a present voltage level V, a present temperature T, and a present current level I of the energy storage power source 124_V. A filter pair SOC such as a single pole low pass filter may be used_VFiltering is performed to remove noise.

An exemplary calculation of the upper bound using the MinMax cell, Add1 cell, Abs cell, constant, and Add cell is shown in expression (31) below:

SOC_u，bnd＝max{SOC_Ah，SOC_V}+|SOC_Ah-SOC_V|+E_desg (31)

an exemplary calculation of the lower bound using the MinMax1 cell, Add3 cell, Abs1 cell, Constant1, and Add2 cell is shown in expression (32) below:

SOC_l，bnd＝min{SOC_Ah，SOC_V}-|SOC_Ah-SOC_V|-E_desg (32)

the SOC delimitation unit 212 further comprises a filtering unit 904 configured to receive the SOC_EstAn upper bound and a lower bound. The filtering unit 904 is configured to use the upper and lower bounds for SOC_EstFiltering is performed and an SOC is generated_BoundedSo that SOC is_BoundedIs set between an upper bound and a lower bound. SOC delimitation unit 212 may output SOC_BoundedUpper and lower bounds for subsequent processing as desired.

Although SOC/SOH estimator 200 and SOC delimiting unit 212 are shown in fig. 8 and 9, other suitable arrangements (such as SOC/SOH estimator 200 and SOH delimiting unit 214) are also contemplated as appropriate for different applications.

As indicated above, in an embodiment of the present disclosure, a controller is provided that performs a power estimation process for an electric vehicle (such as electric vehicle 102) that includes a controller 118 having a non-transitory memory 120 and a processor 122. The non-transitory memory 120 includes instructions that, in response to execution by the processor 122, cause the various methods described below to be performed, including estimating an internal state of an energy storage power source (e.g., a battery). Example variables for internal states include SOC and SOH. In some embodiments of the present disclosure, SOC estimation is performed for a battery including battery cells and a battery pack. Generally, a battery cell and a battery pack are used in an electric vehicle or a hybrid vehicle. Thus, SOC is an indicator of the remaining charge of the battery system. The SOC quantifies the available energy at the present time and reflects the remaining power range. Knowledge of the SOC allows for high performance to be provided as needed while ensuring that the battery operates within safe limits. SOC is typically not a direct measurement, but rather incorporates an estimate in one way or another.

The SOC indicates the equivalent of the fuel gauge of the battery. SOC estimation of a battery system is an important input for balance calculation, energy calculation, and power calculation. Accurate SOC estimation provides benefits such as lifetime, performance, reliability, etc. The estimation is usually with some error due to sensing errors and/or model errors. For an SOC estimator, if the calibration is bad and/or bad data is fed to it, the estimated SOC is misinterpreted as a true SOC. Therefore, it is critical to define reliable estimation error bounds (e.g., upper bounds for estimated SOC and lower bounds for estimated SOC) in real-time so that the estimated SOC will be reliable for its intended use. The BMS based on such reliable SOC estimation ensures safe operation of the battery while actively utilizing the entire battery pack capacity. Existing methods tend to estimate battery SOC without providing estimation error bounds. Without real-time SOC error boundaries, the use of battery systems may be overly conservative, particularly in very high and low SOC ranges, as overcharging and overdischarging typically occurs with inaccurate SOC information in such cases.

Some embodiments of the present disclosure are directed to SOC estimates that each have an upper and lower bound that, in some cases, are relatively narrow but reliable. Furthermore, certain embodiments of the present disclosure are directed to systems and methods for independently determining upper and lower bound estimates in real time by combining current, voltage, and temperature measurements. In some cases, by limiting the estimated SOC by upper and lower bounds, the BMS reduces the possible difference in SOC estimation, ensuring that the battery can operate safely and aggressively, such that full battery capacity is fully utilized.

Additionally, the upper and lower SOC bounds in the present disclosure may be used for on-board diagnostics of sensors and conventional embedded SOC estimators. Once the upper and/or lower bounds are increasingly expanded, the BMS may determine that uncertainty in the measurements is increasing and that it is time to recalibrate the sensors and/or SOC estimator. Some embodiments of the present disclosure provide robust battery SOC estimation under various operating conditions throughout the battery life by including upper and lower SOC bounds.

At least some embodiments of the present disclosure are directed to a method of estimating SOC of a battery. The method comprises the following steps: receiving a series of current data indicative of measurements of current flowing through the battery; receiving a series of voltage data indicative of measurements of a voltage of the battery; calculating an ampere-hour based SOC estimate (Ah-SOC) using the series of current data; calculating a voltage-based SOC estimate (V-SOC) using the series of voltage data and the series of current data; and generating a bounded SOC estimate comprising an upper SOC bound and a lower SOC bound. The upper SOC bound is determined based on the larger of the Ah-SOC and the V-SOC. The lower SOC bound is determined based on the smaller of the Ah-SOC and the V-SOC.

At least some embodiments of the present disclosure are directed to a method of estimating SOC of a battery pack having a plurality of battery cells. The method comprises the following steps: receiving a series of current data for each of the plurality of battery cells, the series of current data indicative of measurements of current flowing through the battery cell; receiving a series of voltage data for each of the plurality of battery cells, the series of voltage data indicative of measurements of a voltage of the battery cell; calculating an ampere-hour based SOC estimation (Ah-SOC) for each of the plurality of battery cells using the series of current data for the respective battery cell; calculating a voltage-based SOC estimate (V-SOC) for each of the plurality of battery cells using the series of voltage data and the series of current data for the respective battery cell; and generating a bounded SOC estimate comprising an upper SOC bound and a lower SOC bound. The upper SOC boundary is determined based on values of Ah-SOC and V-SOC of the plurality of battery cells. The lower SOC bound is determined based on values of Ah-SOC and V-SOC of the plurality of battery cells.

Referring now to fig. 10A, an illustrative method 1000A for estimating SOC using upper and lower bounds of a battery (e.g., battery cells) is shown in accordance with an embodiment of the subject matter disclosed herein. Aspects of embodiments of method 1000A may be performed, for example, by a battery management system, a battery management device, controller 118, and/or an integrated circuit chip. As used herein, a BMS refers to a system, device, and/or integrated circuit chip for measuring, estimating, and/or managing the usage of a battery (e.g., battery cells, battery packs, pack groups, etc.). One or more steps of method 1000A are optional and/or may be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to the method 1000A. In some embodiments, the BMS may measure or receive SOC measurements (1050A). The BMS receives a series of current data (1100A) and a series of voltage data (1150A) of the battery. In an embodiment, the series of current data and voltage data is collected in real time. In an embodiment, the series of current data and voltage data is collected while the battery is running. In some cases, the series of current data and voltage data is associated with time information (e.g., a timestamp).

Next, the BMS calculates an ampere-hour based SOC (Ah-SOC) based on the current data (1200A). In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data. In one embodiment, the Ah-SOC is calculated based on an integration of at least a portion of the series of current data. In some cases, the Ah-SOC is determined based on a capacity of the battery. As used herein, the capacity of a battery refers to a measure of the charge stored in the battery (e.g., in amp-hours).

Further, the BMS may calculate a voltage-based SOC (V-SOC) (1250A). In some embodiments, the V-SOC is determined based on at least a portion of the series of voltage data. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery has a known SOC-OCV relationship, where OCV refers to the open circuit voltage of the battery. In an embodiment, the BMS estimates the OCV and determines the V-SOC based on the inverse of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement at the time and the voltage measurement at the time. In some cases, the OCV is estimated using an internal resistance of the battery, which changes with temperature, as a parameter. In an embodiment, the V-SOC is further determined based on an inverse open circuit voltage function. In an embodiment, the V-SOC is further determined by applying a filter. In some cases, the filter is a low pass filter.

In an embodiment, the BMS determines an upper SOC bound based on the greater of the Ah-SOC and the V-SOC (1300A). In some cases, the upper SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC. The difference between the Ah-SOC and the V-SOC may indicate a sensing error and/or a model error. In some cases, an upper SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the SOC upper bound is determined based at least in part on an absolute value of a difference between the Ah-SOC and the V-SOC plus a greater of the Ah-SOC and the V-SOC.

In some embodiments, the BMS determines a lower SOC bound based on the lesser of the Ah-SOC and the V-SOC (1350A). In some cases, a lower SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC. In some cases, a lower SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the lower SOC bound is determined based at least in part on the smaller of the Ah-SOC and the V-SOC minus an absolute value of a difference between the Ah-SOC and the V-SOC.

In some embodiments, the BMS may generate a bounded SOC estimate having an upper SOC bound and a lower SOC bound (1400A). As used herein, a bounded SOC estimate includes an upper SOC estimate bound and/or a lower SOC estimate bound. In some cases, the BMS may compare the SOC measurement to an upper SOC bound and a lower SOC bound (1450A), for example, to determine a bounded SOC value. In one example, the BMS sets the bounded SOC value to an upper SOC bound if the SOC measurement is greater than the upper SOC estimate. In one example, the BMS sets the bounded SOC value to the lower SOC limit if the SOC measurement is less than the lower SOC limit. In some cases, the BMS sets the bounded SOC value to the SOC measurement if the SOC measurement is less than or equal to an upper SOC limit and the SOC measurement is greater than or equal to a lower SOC limit. In some cases, the bounded SOC estimate further includes a bounded SOC value. In some designs, the SOC measurement is received from a conventional SOC measurement device.

Referring now to fig. 10B, an illustrative method 1000B for estimating SOC using upper and lower bounds of a battery pack having a plurality of battery cells or groups of battery cells is shown in accordance with an embodiment of the subject matter disclosed herein. Aspects of embodiments of method 1000B may be performed, for example, by a battery management system, a battery management device, and/or an integrated circuit chip. One or more steps of method 1000B are optional and/or may be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to the method 1000B. As used herein, a battery cell refers to a single battery cell or a group of battery cells whose electrodes (including anodes and cathodes) are connected together. In some embodiments, the BMS may measure or receive SOC measurements for individual ones of the plurality of battery cells (1050B). The BMS receives a series of current data (1100B) of the respective battery cells and a series of voltage data (1150B) of the respective battery cells. In an embodiment, the series of current data and voltage data is collected in real time. In an embodiment, the series of current data and voltage data is collected while the battery pack is operating. In some cases, the series of current data and voltage data is associated with time information (e.g., a timestamp).

Next, the BMS calculates an ampere-hour-based SOC estimation (Ah-SOC) of each battery cell based on the current data of the corresponding battery cell (1200B). In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data for the respective battery cell. In one embodiment, the Ah-SOC of the battery cell is calculated based on an integration of at least a portion of the series of current data for the respective battery cell. In some cases, the Ah-SOC of a respective battery cell is determined based on the capacity of the battery cell. In some cases, the capacity of the battery cell is represented by the integration of the current from when the SOC is 0 to when the SOC is 1.

In addition, the BMS may calculate a voltage-based SOC (V-SOC) of each battery cell (1250B). In some embodiments, the V-SOC of the battery cell is determined based on at least a portion of the series of voltage data for the respective battery cell. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery cells have a known SOC-OCV relationship, where OCV refers to the open circuit voltage of the battery. In an embodiment, the BMS estimates the OCV of the battery cell and determines the V-SOC based on an inverse of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement at the time and the voltage measurement at the time. In some cases, the OCV is estimated using an internal resistance of the battery, which changes with temperature, as a parameter. In some cases, the temperature data is received by the BMS and used to calculate the internal resistance of the battery. In an embodiment, the V-SOC is further determined based on an inverse open circuit voltage function. In an embodiment, the V-SOC is further determined by applying a filter. In some cases, the filter is a low pass filter.

In an embodiment, the BMS determines an upper SOC bound based on a value of Ah-SOC and a value of V-SOC of battery cells in the battery pack (1300B). In some cases, the upper SOC bound is determined based on a maximum of the Ah-SOC and the V-SOC of the battery cells in the battery pack. In some cases, the upper SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC of the battery cell. In one embodiment, the upper SOC limit is determined based at least in part on a maximum difference between the Ah-SOC and the V-SOC among absolute differences between the Ah-SOC and the V-SOC of all the battery cells, respectively. In some cases, an upper SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the upper SOC bound is determined based at least in part on a maximum of the Ah-SOC and the V-SOC of the battery cells in the battery pack plus a maximum absolute value of a difference between the Ah-SOC and the V-SOC of the battery cells, respectively.

In certain embodiments, the BMS determines a lower SOC bound based on the values of the Ah-SOC and the V-SOC of the battery cells in the battery pack (1350B). In some cases, the lower SOC bound is determined based on a minimum of the Ah-SOC and the V-SOC of the battery cells in the battery pack. In some cases, the lower SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC of the battery cell. In some cases, a lower SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the lower SOC bound is determined based on a minimum value of the Ah-SOC and the V-SOC of the battery cells in the battery pack minus a maximum absolute value of a difference between the Ah-SOC and the V-SOC of the battery cells, respectively.

In some embodiments, the BMS may generate a bounded SOC estimate having an upper SOC bound and a lower SOC bound (1400B). In some cases, the BMS may compare (1450B) the SOC measurement of the battery cell to an upper SOC limit and a lower SOC limit, for example, to determine a bounded SOC value. In one example, the BMS sets the bounded SOC value to an upper SOC bound if the corresponding SOC measurement is greater than the upper SOC estimate. In one example, the BMS sets the bounded SOC value to the lower SOC limit if the corresponding SOC measurement is less than the lower SOC limit. In some cases, the BMS sets the bounded SOC value to the corresponding SOC measurement if the corresponding SOC measurement is less than or equal to the upper SOC limit and the corresponding SOC measurement is greater than or equal to the lower SOC limit. In some cases, the bounded SOC estimate further includes a bounded SOC value. In some designs, the SOC measurement is received from a conventional SOC measurement device.

Referring now to fig. 11A, an illustrative method 2000A of generating a bounded SOC estimate for a battery (e.g., battery cell) is shown in accordance with an embodiment of the subject matter disclosed herein. Aspects of embodiments of method 2000A may be performed by a BMS. One or more steps of method 2000A are optional and/or may be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to the method 2000A. In some embodiments, the BMS may measure or receive temperature data (2050A). The BMS receives or measures a series of current data (2100A) and a series of voltage data (2150A) of the battery. In an embodiment, the series of current data and voltage data is collected in real time. In an embodiment, the series of current data and voltage data is collected while the battery is running. In some cases, the series of current data and voltage data is associated with time information (e.g., a timestamp).

Next, the BMS determines the capacity of the battery (2170A). In one embodiment, the capacity is determined by integrating the current (I) over time (t). In one embodiment, the capacity of the battery and/or battery cell is calculated using the following expression (33).

Where Capacity is the integral of the current (I) from time SOC-0 to time SOC-1.

In an embodiment, the BMS calculates an ampere hour based SOC (Ah-SOC) (2200A). In some cases, the Ah-SOC is determined based on the current data. In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data. In one embodiment, the Ah-SOC is calculated based on an integration of at least a portion of the series of current data. In some cases, the Ah-SOC is determined based on a capacity of the battery. In one case, the Ah-SOC of the battery and/or battery cells at time t may be estimated using the following expression (34).

Therein, SOC_Ah(t) is Ah-SOC, SOC at time t₀Is at t₀SOC estimate of time, i (t) is current data measured over time, and Capacity is the Capacity of the battery and/or battery cells.

In addition, the BMS may determine the resistance of the battery (2220A). The resistance of the battery is typically affected by temperature. In some embodiments, the BMS may retrieve an SOC-OCV model (2240A), where OCV is the open circuit voltage. In an embodiment, the SOC-OCV model represents a non-linear relationship. FIG. 14 illustrates an example of an SOC-OCV model and/or function. In this example, the SOC and OCV have a nonlinear relationship. In some cases, the SOC-OCV model is affected by temperature, so that the BMS retrieves or selects the SOC-OCV model according to the temperature at which the SOC estimation is made.

In some embodiments, the BMS may calculate a voltage-based SOC (V-SOC) (2250A), for example, based on many of the parameters listed above. In some embodiments, the V-SOC is determined based on at least a portion of the series of voltage data. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In an embodiment, the BMS estimates the OCV and determines the V-SOC based on the inverse of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement at the time and the voltage measurement at the time. In some cases, the OCV is estimated using an internal resistance of the battery, which changes with temperature, as a parameter. In an embodiment, the V-SOC is further determined based on an inverse open circuit voltage function. In an embodiment, the V-SOC is further determined by applying a filter.

In one case, the V-SOC of the battery and/or battery cells may be estimated using the following expression (35).

SOC_V(t)＝f(OCV^-1(V(t)+I(t)R₀(T)) (35)

Therein, SOC_V(t) is V-SOC at time t, f () is the filter function, OCV^-1() Is an inverse function of SOC-OCV, V (t) is voltage data measured at time t, I (t) is current data measured at time t, and R₀(T) is the resistance of the battery estimated based on the measured temperature T. In one embodiment, the filter is a low pass filter.

In an embodiment, the BMS determines an upper SOC bound based on the greater of the Ah-SOC and the V-SOC (2300A). In some cases, the upper SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC. The difference between the Ah-SOC and the V-SOC may indicate a sensing error and/or a model error. In some cases, an upper SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the SOC upper bound is determined based at least in part on the greater of the Ah-SOC and the V-SOC plus an absolute value of a difference between the Ah-SOC and the V-SOC.

In one case, the upper bound of the SOC estimate for the battery and/or battery cell at time t may be determined using expression (36) below.

SOC_Ubnd(t)＝max{SOC_Ah(t)，SOC_V(t)}+|SOC_Ah(t)-SOC_V(t)|+E (36)

Therein, SOC_Ubnd(t) is an upper bound on the SOC estimation at time t, SOC_Ah(t) is Ah-SOC, SOC at time t_V(t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, | SOC_Ah(t)-SOC_V(t) | may be an indication of sensing error and/or model error.

In certain embodiments, the BMS determines a lower SOC bound based on the lesser of the Ah-SOC and the V-SOC (2350A). In some cases, a lower SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC. In some cases, a lower SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the lower SOC bound is determined based at least in part on the smaller of the Ah-SOC and the V-SOC minus an absolute value of a difference between the Ah-SOC and the V-SOC.

In one case, the lower bound of the SOC estimation of the battery and/or battery cell at time t may be determined using the following expression (37).

SOC_Lbnd(t)＝min{SOC_Ah(t)，SOC_V(t)}-|SOC_Ah(t)-SOC_V(t)|-E (37)

Therein, SOC_Lbnd(t) is the lower bound of the SOC estimate at time t, SOC_Ah(t) is Ah-SOC, SOC at time t_V(t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, | SOC_Ah(t)-SOC_V(t)|May be an indication of sensing errors and/or model errors.

In some implementations, the BMS may generate a bounded SOC estimate having an upper SOC bound and a lower SOC bound (2400A). In some cases, the BMS may compare SOC measurements to upper and lower SOC bounds to determine bounded SOC values, for example, on the fly. In one example, the BMS sets the bounded SOC value to an upper SOC bound if the SOC measurement is greater than the upper SOC estimate. In one example, the BMS sets the bounded SOC value to the lower SOC limit if the SOC measurement is less than the lower SOC limit. In some cases, the BMS sets the bounded SOC value to the SOC measurement if the SOC measurement is less than or equal to an upper SOC limit and the SOC measurement is greater than or equal to a lower SOC limit. In some cases, the bounded SOC estimate further includes a bounded SOC value. In some designs, the SOC measurement is received from a conventional SOC measurement device.

Referring now to fig. 11B, an illustrative method 2000B of generating a bounded SOC estimate for a battery pack having a plurality of battery cells or groups of battery cells is shown, in accordance with an embodiment of the subject matter disclosed herein. Aspects of embodiments of the method 2000B may be performed, for example, by a battery management system, a battery management device, and/or an integrated circuit chip. One or more steps of method 2000B are optional and/or may be modified by one or more steps of other embodiments described herein. Additionally, one or more steps of other embodiments described herein may be added to method 2000B. In some embodiments, the BMS may measure or receive temperature data of the battery pack (2050B). The BMS receives a series of current data (2100B) for each battery cell and a series of voltage data (2150B) for each battery cell. In an embodiment, the series of current data and voltage data is collected in real time. In an embodiment, the series of current data and voltage data is collected while the battery pack is operating. In some cases, the series of current data and voltage data is associated with time information (e.g., a timestamp).

Next, the BMS determines the capacity of each battery cell (2170B). In one embodiment, the capacity is determined by integrating the current (I) of the respective battery cell over time (t). In one embodiment, the capacity of the battery cell may be determined using expression (33) above. The BMS calculates an ampere-hour-based SOC (Ah-SOC) of each battery cell based on the current data of the corresponding battery cell (2200B). In some embodiments, the Ah-SOC is determined based on at least a portion of the series of current data for the respective battery cell. In one embodiment, the Ah-SOC of the battery cell is calculated based on an integration of at least a portion of the series of current data for the respective battery cell. In some cases, the Ah-SOC of a respective battery cell is determined based on the capacity of the battery cell. In some cases, the capacity of the battery cell is represented by the current integration over the time from when the SOC is 0 to when the SOC is 1.

In one case, the Ah-SOC of the battery cell i in the battery pack at time t may be estimated using the following expression (38).

Therein, SOC_Ah，i(t) is Ah-SOC, SOC of the battery cell i at time t₀Is at t₀SOC estimation of time, I_i(t) is the current data of the battery cell i measured at time t, and Capacity_iIs the capacity of battery cell i. In one embodiment, the measurement of current may be the current of the battery pack.

In addition, the BMS may determine the resistance of each battery cell (2220B). The resistance of the battery cell is generally influenced by temperature. In some embodiments, the BMS may retrieve an SOC-OCV model (2240B), where OCV is the open circuit voltage of the battery cells. In some embodiments, the SOC-OCV model represents a non-linear relationship. FIG. 14 illustrates an example of an SOC-OCV model and/or function. In the illustrated example, the SOC and OCV have a non-linear relationship. In some cases, the SOC-OCV model is affected by temperature, so that the BMS retrieves the SOC-OCV model according to the current temperature at which the SOC estimation is made.

Further, the BMS may calculate a voltage-based SOC (V-SOC) of each battery cell (2250B). In some embodiments, the battery cell V-SOC is determined based on at least a portion of the series of voltage data for the respective battery cell. In some embodiments, the V-SOC is determined based on at least a portion of the series of current data. In some cases, the battery cells have a known SOC-OCV relationship, where OCV refers to the open circuit voltage of the battery. In an embodiment, the BMS estimates the OCV of the battery cell and determines the V-SOC based on an inverse of the SOC-VOC relationship. In some cases, OCV is estimated using the current measurement at the time and the voltage measurement at the time. In some cases, the OCV is estimated using an internal resistance of the battery, which changes with temperature, as a parameter. In an embodiment, the V-SOC is further determined based on an inverse open circuit voltage function. In an embodiment, the V-SOC is further determined by applying a filter.

In one case, the V-SOC of the battery cell i at time t may be estimated using the following expression (39):

SOC_V，i(t)＝f(OCV^-1(V_i(t)+I_i(t)R_0，i(T)) (39)

therein, SOC_V(t) is V-SOC at time t, f () is the filter function, OCV^-1() Is the inverse function/model of SOC-OCV, V_i(t) is the current data of the battery cell I measured at time t, I_i(t) is the current data of the battery cell i measured at time t, and R_0，i(T) is the resistance of the battery cell i estimated on the basis of the measured temperature T. In some cases, the filter is a low pass filter.

In certain embodiments, the BMS determines an upper SOC bound based on the values of Ah-SOC and V-SOC of the battery cells in the battery pack (2300B). In some cases, the upper SOC bound is determined based on a maximum of the Ah-SOC and the V-SOC of the battery cells in the battery pack. In some cases, the upper SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC of the battery cell. In one embodiment, the upper SOC limit is determined based at least in part on a maximum difference between the Ah-SOC and the V-SOC among absolute differences between the Ah-SOC and the V-SOC of all the battery cells, respectively. In some cases, an upper SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the upper SOC bound is determined based at least in part on a maximum of the Ah-SOC and the V-SOC of the battery cells in the battery pack plus a maximum of a difference between the Ah-SOC and the V-SOC of the battery cells, respectively.

In one case, the upper bound of the SOC estimation of the battery pack at time t may be determined using the following expression (40).

SOC_Ubnd(t)＝max{SOC_Ah，i(t)，SOC_V，i(t)}+max{|SOC_Ah，i(t)-SOC_V，i(t)|}+E (40)

Therein, SOC_Ubnd(t) is an upper bound on the SOC estimation at time t, SOC_Ah，i(t) is Ah-SOC, SOC at time t_V，i(t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, | SOC_Ah，i(t)-SOC_V，i(t) | may be an indication of sensing error and/or model error. In this embodiment, the maximum value of Ah-SOC and V-SOC is used. In other embodiments, different selection criteria for Ah-SOC and V-SOC may be used.

In certain embodiments, the BMS determines a lower SOC bound based on the values of Ah-SOC and V-SOC of the battery cells in the battery pack (2350B). In some cases, the lower SOC bound is determined based on a minimum of the Ah-SOC and the V-SOC of the battery cells in the battery pack. In some cases, the lower SOC bound is determined based at least in part on a difference between the Ah-SOC and the V-SOC of the battery cell. In some cases, a lower SOC bound is determined, for example, based at least in part on the design accuracy of the battery, to fine tune the estimation. In one embodiment, the lower SOC bound is determined based on a minimum value of the Ah-SOC and the V-SOC of the battery cells in the battery pack minus a maximum value of a difference between the Ah-SOC and the V-SOC of the battery cells, respectively.

In one case, the lower bound of the SOC estimation of the battery and/or battery cell at time t may be determined using the following expression (41).

SOC_Lbnd(t)＝min{SOC_Ah，i(t)，SOC_V，i(t)}-max{|SOC_{Ah i}(t)-SOC_{V i}(t)|}-E （41）

Therein, SOC_Lbnd(t) is the lower bound of the SOC estimate at time t, SOC_Ah，i(t) is Ah-SOC, SOC at time t_V，i(t) is the V-SOC at time t, and E is the estimated design accuracy (e.g., 3%). In some cases, | SOC_Ah，i(t)-SOC_V，i(t) | may be an indication of sensing error and/or model error. In this embodiment, the minimum value of Ah-SOC and V-SOC is used. In other embodiments, different selection criteria for Ah-SOC and V-SOC may be used.

In some implementations, the BMS may generate a bounded SOC estimate having an upper SOC bound and a lower SOC bound (2400B). In some cases, the BMS may, for example, compare SOC measurements of the battery cells to upper and lower SOC boundaries during operation to determine bounded SOC values. In one example, the BMS sets the bounded SOC value to an upper SOC bound if the corresponding SOC measurement is greater than the upper SOC estimate. In one example, the BMS sets the bounded SOC value to the lower SOC limit if the corresponding SOC measurement is less than the lower SOC limit. In some cases, the BMS sets the bounded SOC value to the corresponding SOC measurement if the corresponding SOC measurement is less than or equal to the upper SOC limit and the corresponding SOC measurement is greater than or equal to the lower SOC limit. In some cases, the bounded SOC estimate further includes a bounded SOC value. In some designs, the SOC measurement is received from a conventional SOC measurement device.

Referring now to fig. 12A, a schematic diagram for use in BMS3000A is shown. In some cases, BMS3000A is configured to implement any of the embodiments described herein. In this example, BMS3000A includes a plurality of input terminals 3100A, including, for example: a current data terminal 3120A configured to receive current data 3002A, a temperature data terminal 3130A configured to receive temperature data 3003A, a voltage data terminal 3140A configured to receive voltage data 3004A, and/or an SOC measurement terminal 3160A. In this example, BMS3000A is configured to be coupled to SOC measurement device/chip 3300A. SOC measurement device/chip 3300A is configured to receive current data 3002A at terminal 3320A, temperature data 3003A at terminal 3330A, and voltage data 3004A at terminal 3340A, and output the SOC measurement via 3420A. BMS3000A receives the SOC measurement via terminal 3160A. BMS3000A is configured to generate a plurality of output data at the plurality of output terminals 3200A based on input data received from input terminal 3100A. In one embodiment, the output terminal 3200A includes: a bounded SOC terminal 3220A configured to output a bounded SOC estimate 3520A, an upper bound terminal 3240A configured to output an upper SOC bound 3540A, and/or a lower bound terminal 3260A configured to output a lower SOC bound 3560A.

Referring now to fig. 12B, a schematic diagram for use in BMS3000B is shown. In some cases, BMS3000B is configured to implement any of the embodiments described herein. In this example, BMS3000B has a plurality of input terminals 3100B including, for example: a current terminal 3120B configured to receive current data, a temperature terminal 3130B configured to receive temperature data, a voltage terminal 3140B configured to receive voltage data, and an SOC terminal 3160B configured to receive SOC measurements. Current terminal 3120B is coupled to Ah-based SOC estimator 3200B and V-based SOC estimator 3250B. Temperature terminal 3130B and voltage terminal 3140B are coupled to V-based SOC estimator 3250B. Ah-based SOC estimator 3200B is configured to output Ah-SOC estimate 3220B based on the current data. V-based SOC estimator 3250B is configured to output V-SOC estimate 3270B based on the current data, the temperature data, and the voltage data.

Ah-SOC estimator 3200B and V-SOC estimator 3250B are configured to provide Ah-SOC estimates 3220B and V-SOC estimates 3270B, either directly or indirectly, to a number of operators for various computations, including, for example: 3300B, 3320B, 3340B, 3360B, 3420B, 3440B, 3520B, 3550B and 3580B. In one embodiment, the operator 3300B is a maximum operator configured to select a maximum value based on the input; operator 3360B is a minimum operator configured to select a minimum value based on the input; and operators 3320B and 3340B are differential operators configured to compute differences between inputs. In one embodiment, the operators 3420B and 3440B are absolute value operators configured to generate the absolute value of the input. In one embodiment, the operators 3520B and 3550B are arithmetic operators configured to apply arithmetic calculations to the input. In this example, BMS3000B includes design accuracy parameters for a battery (e.g., battery cells, battery packs, and/or battery packs) stored or received at 3460B. Operator 3520B is configured to generate an SOC upper bound 3540B. Operator 3550B is configured to generate a SOC lower bound 3560B.

The operator 3580B is configured to receive SOC measurements via the terminal 3160B, SOC upper bound 3540B and SOC lower bound 3560B and determine a bounded SOC value 3590B, e.g., to determine a bounded SOC value. In one embodiment, the operator 3580B performs a saturation function. In one example, the operator 3580B sets the bounded SOC value to an upper SOC bound if the corresponding SOC measurement is greater than the upper SOC estimate. In one example, the operator 3580B sets the bounded SOC value to the lower SOC bound if the corresponding SOC measurement is less than the lower SOC bound. In some cases, operator 3580B sets the bounded SOC value to the corresponding SOC measurement if the corresponding SOC measurement is less than or equal to the upper SOC limit and the corresponding SOC measurement is greater than or equal to the lower SOC limit. In some cases, the bounded SOC estimation includes: there are bounded SOC values, an upper SOC bound, and a lower SOC bound. In this example, BMS3000B includes a plurality of output terminals 3600B for outputting a bounded SOC estimate, including, for example: bounded SOC terminal 3620B outputting bounded SOC value 3590B, SOC upper bound terminal 3640B outputting SOC upper bound 3540B, and SOC lower bound terminal 3660B outputting SOC lower bound 3560B.

Referring now to fig. 13, a schematic diagram of BMS 4000 is shown. BMS 4000 is configured to implement any of method 1000A, method 1000B, method 2000A, and/or method 2000B as described herein. In this example, the BMS 4000 includes: a sensor group 4005, an SOC estimator 4300, and a memory 4400. In some embodiments, the sensor group 4005 comprises a temperature sensor 4100 and/or a current/voltage sensor 4200. In one embodiment, SOC estimator 4300 is implemented on a computing device (e.g., a processor or processing unit). In one embodiment, SOC estimator 4300 is implemented using the schematic shown in fig. 12A and/or fig. 12B. In some cases, memory 4400 includes, for example, a data repository 4500 that stores current data, voltage data, temperature data, SOC measurements, bounded SOC estimates, and the like.

In some embodiments, a computing device (e.g., SOC estimator 4300) includes a bus that directly and/or indirectly couples the following devices: a processor, a memory, an input/output (I/O) port, an I/O component, and a power supply. Any number of additional components, different components, and/or combinations of components may also be included in the computing device. The bus representation may be one or more buses (such as an address bus, a data bus, or a combination thereof, for example). Similarly, in some embodiments, the computing device may include: a plurality of processors, a plurality of memory components, a plurality of I/O ports, a plurality of I/O components, and/or a plurality of power supplies. In addition, any number or combination of these components may be distributed and/or replicated across multiple computing devices.

In one implementation, the memory 4400 includes computer-readable media in the form of volatile and/or nonvolatile memory, transitory and/or non-transitory storage media, and may be removable, non-removable, or a combination thereof. Examples of media include: random Access Memory (RAM); read Only Memory (ROM); an Electrically Erasable Programmable Read Only Memory (EEPROM); a flash memory; an optical or holographic medium; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; data transmission; and/or any other medium that can be used to store information and that can be accessed by a computing device (e.g., such as quantum state memory, etc.). In some embodiments, memory 4400 stores computer-executable instructions for causing a processor (e.g., SOC estimator 4300) to implement aspects of embodiments of system components discussed herein and/or to perform aspects of embodiments of the methods and processes described herein.

The computer-executable instructions may include, for example: computer code, machine-useable instructions, etc., such as program components capable of being executed by one or more processors associated with a computing device, for example. The program components can be programmed using any number of different programming environments, including various languages, development suites, frameworks, and the like. Some or all of the functionality contemplated herein may also or alternatively be implemented in hardware and/or firmware.

Data repository 4500 may be implemented using any of the configurations described below. The data repository may include: random access memory, flat files, XML files, and/or one or more database management systems (DBMS) executing on one or more database servers or data centers. The database management system may be a Relational (RDBMS), Hierarchical (HDBMS), multidimensional (MDBMS), object oriented (ODBMS or OODBMS) or Object Relational (ORDBMS) database management system, or the like. The data repository may be, for example, a single relational database. In some cases, a data repository may include multiple databases that may exchange and aggregate data through a data integration process or software application. In an exemplary embodiment, at least a portion of data repository 4500 may be hosted in a cloud data center. In some cases, the data repository may be hosted on a single computer, server, storage device, cloud server, or the like. In some other cases, the data repository may be hosted on a series of networked computers, servers, or devices. In some cases, the data repository may be hosted on a layer of data storage, including local, regional, and central.

The various components of BMS 4000 may communicate via or be coupled to a communication interface (e.g., a wired or wireless interface). The communication interface includes, but is not limited to, any wired or wireless short and long range communication interfaces. The wired interface may use cables, wires, etc. The short-range communication interface may be, for example, a Local Area Network (LAN), conforming to a known communication standard, such as

Standard, IEEE 802 standard (e.g., IEEE 802.11),

Or interfaces to similar specifications, such as those based on the IEEE 802.15.4 standard, or other public or proprietary wireless protocols. The long-range communication interface may be, for example, a Wide Area Network (WAN), a cellular network interface, a satellite communication interface, or the like. The communication interface may be within a private computer network, such as an intranet, or on a public computer network, such as the internet.

Referring now to fig. 15A and 15B, fig. 15A and 15B illustrate examples of bounded SOC estimation. FIG. 15A shows one example of a bounded SOC estimation having relatively close upper and lower bounds, such that the difference between the upper and lower bounds is relatively small. FIG. 15B illustrates one example of a bounded SOC estimation with relatively distinct upper and lower bounds, such that the difference between the upper and lower bounds is relatively large. In one example, the BMS may be configured to recalibrate the sensor and/or the SOC estimator if a difference between the upper SOC bound and the lower SOC bound is greater than a predetermined threshold.

Additional exemplary embodiments of the foregoing aspects of the disclosure are described below.

There is provided a method of estimating a state of charge (SOC) of a battery, the method comprising the steps of: receiving a series of current data indicative of measurements of current flowing through the battery; receiving a series of voltage data indicative of measurements of a voltage of the battery; calculating an ampere-hour based SOC estimate (Ah-SOC) using the series of current data; calculating a voltage-based SOC estimate (V-SOC) using the series of voltage data and the series of current data; and generating a bounded SOC estimate comprising an upper SOC bound and a lower SOC bound. The upper SOC bound is determined based on the larger of the Ah-SOC and the V-SOC; and the lower SOC bound is determined based on the smaller of the Ah-SOC and the V-SOC.

Further, the Ah-SOC is determined based on an integration of at least a portion of the series of current data, and the Ah-SOC is determined based on a capacity of the battery. In addition, a V-SOC is determined based on at least one voltage data of the series of voltage data.

The method further comprises the following steps: receiving temperature data; and determining an internal resistance of the battery based at least in part on the temperature data. The V-SOC is determined based on at least one of the series of current data and the internal resistance of the battery.

Further, the V-SOC is determined based on an inverse function of the open circuit voltage. In addition, the V-SOC is further determined by applying a filter, wherein the filter is a low pass filter.

Further, the SOC upper bound is determined based on a difference between the Ah-SOC and the V-SOC. Additionally, an upper SOC bound is determined based at least in part on the greater of the Ah-SOC and the V-SOC plus an absolute value of a difference between the Ah-SOC and the V-SOC. Moreover, an upper SOC bound is determined based at least in part on a design accuracy of the battery.

Additionally, a lower SOC bound is determined based at least in part on an absolute value of a greater of the Ah-SOC and the V-SOC minus a difference between the Ah-SOC and the V-SOC. Additionally, a lower SOC bound is determined based at least in part on a design accuracy of the battery.

The method further comprises the following steps: receiving a SOC measurement; and comparing the SOC measurement to an upper SOC bound and a lower SOC bound to determine a bounded SOC value. If the SOC measurement is greater than the upper SOC bound, the bounded SOC value is set to the upper SOC bound. If the SOC measurement is less than the lower SOC bound, the bounded SOC value is set to the lower SOC bound. If the SOC measurement is less than or equal to the upper SOC bound and the SOC measurement is greater than or equal to the lower SOC bound, the bounded SOC value is set to the SOC measurement. The bounded SOC estimate also includes a bounded SOC value.

A method of estimating a state of charge (SOC) of a battery pack having a plurality of battery cells is provided, the method comprising: receiving a series of current data for each of the plurality of battery cells, the series of current data indicative of measurements of current flowing through the battery cell; receiving a series of voltage data for each of the plurality of battery cells, the series of voltage data indicative of measurements of a voltage of the battery cell; calculating an ampere-hour based SOC estimation (Ah-SOC) for each of the plurality of battery cells using the series of current data for the respective battery cell; calculating a voltage-based SOC estimate (V-SOC) for each of the plurality of battery cells using the series of voltage data and the series of current data for the respective battery cell; and generating a bounded SOC estimate comprising an upper SOC bound and a lower SOC bound. The upper SOC bound is determined based on values of Ah-SOCs and V-SOCs of the plurality of battery cells, and the lower SOC bound is determined based on values of Ah-SOCs and V-SOCs of the plurality of battery cells.

Furthermore, the Ah-SOC of the battery cell is determined based on an integration of at least a portion of the series of current data of the battery cell. In addition, the Ah-SOC of the battery cell is determined based on the capacity of the battery cell. Also, the V-SOC of the battery cell is determined based on at least a portion of the series of voltage data for the battery cell.

The method further comprises the following steps: receiving temperature data; and determining an internal resistance of the battery cell based at least in part on the temperature data. The V-SOC of the battery cell is determined based on at least a portion of the internal resistance and the series of current data for the battery cell.

In addition, the V-SOC of the battery cell is also determined based on the inverse open-circuit voltage function. In addition, the V-SOC of the battery cell is also determined by applying a filter, wherein the filter is a low pass filter.

Further, an upper SOC bound is determined based at least in part on a maximum of the Ah-SOC and the V-SOC of the plurality of battery cells. In addition, the SOC upper bound is determined based on a difference between the Ah-SOC and the V-SOC of the plurality of battery cells.

Further, an upper SOC bound is determined based at least in part on an absolute value of a greater of the Ah-SOC and the V-SOC of the plurality of battery cells plus a maximum difference in differences between the Ah-SOC and the V-SOC of the plurality of battery cells. Additionally, an upper SOC bound is determined based at least in part on a design accuracy of the battery.

Further, a lower SOC bound is determined based at least in part on a maximum of the Ah-SOC and the V-SOC of the plurality of battery cells. In addition, the lower SOC bound is also determined based on a difference between the Ah-SOC and the V-SOC of the plurality of battery cells.

Further, a lower SOC bound is determined based at least in part on an absolute value of a minimum of the Ah-SOC and the V-SOC of the plurality of battery cells minus a maximum of differences between the Ah-SOC and the V-SOC of the plurality of battery cells. Additionally, a lower SOC bound is determined based at least in part on a design accuracy of the battery.

The method further comprises the following steps: receiving one or more SOC measurements for the plurality of battery cells; and comparing the SOC measurement to an upper SOC bound and a lower SOC bound to determine a bounded SOC value. If the SOC measurement is greater than the upper SOC bound, the bounded SOC value is set to the upper SOC bound. If the SOC measurement is less than the lower SOC bound, the bounded SOC value is set to the lower SOC bound. If the SOC measurement is less than or equal to the upper SOC bound and the SOC measurement is greater than or equal to the lower SOC bound, the bounded SOC value is set to the SOC measurement. The bounded SOC estimate also includes a bounded SOC value.

There is provided a method of performing a power estimation process of an electric vehicle using a controller, the method including the steps of: performing a power estimation process by estimating an internal state of an energy storage power source of the electric vehicle; estimating at least one of a state of charge (SOC) value and a state of health (SOH) value of the energy storage power source based on at least one of: a present current level, a present voltage level, and a present temperature associated with the stored energy power source, and time-based information; calculating a first upper bound and a first lower bound associated with the SOC value; estimating a bounded SOC value of the energy storage power source based on the SOC value, the first upper bound, and the first lower bound; calculating a second upper bound and a second lower bound associated with the SOH value; estimating a bounded SOH value of the energy storage source based on the SOH value, the second upper bound, and the second lower bound; and controlling the electrification process of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value. The internal state represents at least one of: SOC and SOH of the stored energy power source.

Any of the steps of the above-mentioned method are used to control the electrification process of the electric vehicle.

There is provided a vehicle comprising a controller comprising a processor and a memory, the memory having processing instructions operable, when executed by the processor, to implement any of the above mentioned methods.

There is provided a battery management system comprising a controller comprising a processor and a memory, the memory having processing instructions operable, when executed by the processor, to implement any of the above mentioned methods. A vehicle is provided that includes the battery management system.

It should be understood that the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in a practical system. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as a critical, required, or essential feature or element of any or all the claims. Accordingly, the scope is not to be limited by anything else than the appended claims, in which reference to an element in the singular is not intended to mean "one and only one" unless explicitly so stated, but rather "one or more. Furthermore, where a phrase similar to "A, B or at least one of C" is used in the claims, it is intended that the phrase be interpreted to mean that there may be a single a in an embodiment, a single B in an embodiment, a single C in an embodiment, or any combination of elements A, B or C in a single embodiment; for example, a and B, A and C, B and C, or a and B and C.

In the detailed description herein, references to "one embodiment," "an example embodiment," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading this description, it will become apparent to a person skilled in the relevant art how to implement the disclosure in alternative embodiments.

Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein should be construed in accordance with the definition of 35u.s.c.112(f), unless the element is explicitly recited using the phrase "means for …". As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Various modifications and additions may be made to the exemplary embodiments discussed without departing from the scope of the presently disclosed subject matter. For example, although the embodiments described above refer to particular features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the subject matter disclosed herein is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the appended claims along with all equivalents thereof.

Claims (20)

1. A controller that performs a power estimation process of an electric vehicle, the controller comprising:

a processor; and

a memory comprising instructions that, when executed by the processor, cause the controller to:

performing the power estimation process by estimating an internal state of an energy storage power source of the electric vehicle, the internal state representing at least one of: the state of charge (SOC) and the state of health (SOH) of the energy storage power supply;

estimating at least one of a SOC value and a SOH value of the stored energy power source based on at least one of: a present current level, a present voltage level, and a present temperature associated with the stored energy power source, and time-based information; and

at least one of the following 1) and 2): 1) calculating a first upper bound and a first lower bound associated with the SOC value, and estimating a bounded SOC value of the energy-storage power source based on the SOC value, the first upper bound, and the first lower bound; and 2) calculating a second upper bound and a second lower bound associated with the SOH value, and estimating a bounded SOH value of the stored energy power source based on the SOH value, the second upper bound, and the second lower bound;

wherein the controller controls the electrification process of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value.

2. The controller of claim 1, wherein the instructions, when executed by the processor, further cause the controller to:

calculating an amp-hour SOC based on the present current level and the present temperature associated with the stored energy power source;

calculating a voltage SOC based on the present voltage level and the present temperature associated with the stored energy power source; and

calculating the first upper bound and the first lower bound associated with the SOC value based on the Ampere-hour SOC and the voltage SOC.

3. The controller of claim 2, wherein a maximum of the amp-hour SOC and the voltage SOC is used for the first upper bound and a minimum of the amp-hour SOC and the voltage SOC is used for the first lower bound.

4. The controller of claim 2, wherein the instructions, when executed by the processor, further cause the controller to filter the voltage SOC to remove noise.

5. The controller of claim 1, wherein the instructions, when executed by the processor, further cause the controller to:

calculating a full cycle SOH based on a start time and an end time associated with a full charge cycle of the stored energy power source;

calculating a partial cycle SOH based on a start time and an end time associated with a partial charge cycle of the stored energy power source; and

calculating the second upper bound and the second lower bound associated with the SOH value based on the full-cycle SOH and the partial-cycle SOH.

6. The controller of claim 5, wherein a maximum of the full-cycle SOH and the partial-cycle SOH is used for the second upper bound and a minimum of the full-cycle SOH and the partial-cycle SOH is used for the second lower bound.

7. The controller of claim 1, wherein the time-based information comprises one or more historical estimates of the SOC value and the SOH value.

8. The controller of claim 1, wherein the instructions, when executed by the processor, further cause the controller to estimate the bounded SOC value and the bounded SOH value based on whether a predetermined period of time has elapsed.

9. The controller of claim 1, wherein the controller controls the electrification process by at least one of: modifying cooling of the stored energy power source, modifying a charge/discharge limit of the stored energy power source, reducing a number of charge/discharge cycles of the stored energy power source, and modifying a minimum SOC threshold.

10. The controller of claim 1, wherein the controller comprises a dual non-linear kalman filter.

11. The controller of claim 1, wherein the energy storage power source is a battery, and the instructions, when executed by the processor, further cause the controller to:

receiving a series of current data indicative of a measurement of current flowing through the battery;

receiving a series of voltage data indicative of measurements of a voltage of the battery;

calculating an amp-hour SOC estimate Ah-SOC using the series of current data;

calculating a voltage SOC estimate V-SOC using the series of voltage data and the series of current data; and

generating the bounded SOC value for the battery, the bounded SOC value including the first upper bound and the first lower bound;

wherein the first upper bound is determined based on a larger value of the Ah-SOC and the V-SOC, and the first lower bound is determined based on a smaller value of the Ah-SOC and the V-SOC.

12. The controller of claim 1, wherein the energy storage power source is a battery pack having a plurality of battery cells, and the instructions, when executed by the processor, further cause the controller to:

receiving a series of current data for each of the plurality of battery cells, the series of current data indicating a measurement of current flowing through the battery cell;

receiving a series of voltage data for each of the plurality of battery cells, the series of voltage data indicating a measurement of a voltage of the battery cell;

calculating an ampere-hour SOC estimate, Ah-SOC, for each of the plurality of battery cells using the series of current data for the respective battery cell;

calculating a voltage SOC estimate V-SOC for each of the plurality of battery cells using the series of voltage data and the series of current data for the respective battery cell; and

generating the bounded SOC value for the battery pack, the bounded SOC value including the first upper bound and the first lower bound;

wherein the first upper bound is determined based on values of the Ah-SOC and the V-SOC of the plurality of battery cells, and the first lower bound is determined based on values of the Ah-SOC and the V-SOC of the plurality of battery cells.

13. A method of performing a power estimation process of an electric vehicle using a controller, the method comprising:

performing the power estimation process by estimating an internal state of an energy storage power source of the electric vehicle, the internal state representing at least one of: the state of charge (SOC) and the state of health (SOH) of the energy storage power supply;

estimating at least one of a SOC value and a SOH value of the stored energy power source based on at least one of: a present current level, a present voltage level and a present temperature associated with the stored energy power source, and time-based information;

calculating a first upper bound and a first lower bound associated with the SOC value;

estimating a bounded SOC value of the energy-storage power source based on the SOC value, the first upper bound, and the first lower bound;

calculating a second upper bound and a second lower bound associated with the SOH value;

estimating a bounded SOH value of the stored energy power source based on the SOH value, the second upper bound, and the second lower bound; and

controlling an electrification process of the electric vehicle based on at least one of the bounded SOC value and the bounded SOH value.

14. The method of claim 13, wherein estimating the first upper bound and the first lower bound further comprises:

calculating an amp-hour SOC based on the present current level and the present temperature associated with the stored energy power source;

calculating a voltage SOC based on the present voltage level and the present temperature associated with the stored energy power source; and

calculating the first upper bound and the first lower bound associated with the SOC value based on the amp-hour SOC and the voltage SOC;

wherein a maximum of the amp-hour SOC and the voltage SOC is used for the first upper bound and a minimum of the amp-hour SOC and the voltage SOC is used for the first lower bound.

15. The method of claim 14, further comprising the steps of: filtering the voltage SOC to remove noise.

16. The method of claim 13, wherein estimating the second upper bound and the second lower bound further comprises:

calculating a full cycle SOH based on a start time and an end time associated with a full charge cycle of the stored energy power source;

calculating a partial cycle SOH based on a start time and an end time associated with a partial charge cycle of the stored energy power source; and

calculating the second upper bound and the second lower bound associated with the SOH value based on the full-cycle SOH and the partial-cycle SOH;

wherein a maximum of the full cycle SOH and the partial cycle SOH is used for the second upper bound and a minimum of the full cycle SOH and the partial cycle SOH is used for the second lower bound.

17. The method of claim 13, wherein the time-based information comprises one or more historical estimates of the SOC value and the SOH value.

18. The method of claim 13, wherein estimating the bounded SOC value and the bounded SOH value is further based on whether a predetermined period of time has elapsed.

19. The method of claim 13, wherein controlling the electrification process comprises at least one of: modifying cooling of the stored energy power source, modifying a charge/discharge limit of the stored energy power source, reducing a number of charge/discharge cycles of the stored energy power source, and modifying a minimum SOC threshold.

20. The method of claim 13, wherein the power estimation process is performed by a dual non-linear kalman filter.

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