WO2020131450A1

WO2020131450A1 - Soc and soh co-estimation systems and methods for electric vehicles

Info

Publication number: WO2020131450A1
Application number: PCT/US2019/065171
Authority: WO
Inventors: Guodong FAN; Ruigang Zhang; Jordan LOOS; Aleksey Yezerets
Original assignee: Cummins Inc.
Priority date: 2018-12-21
Filing date: 2019-12-09
Publication date: 2020-06-25
Also published as: CN113287242A; CN113287242B

Abstract

A system is provided for performing a power estimation process for an electric vehicle using a controller. The controller estimates an inner state of an energy storage supply of the electric vehicle. The inner state represents a state-of-charge (SOC) and/or a state-of-health (SOH) of the energy storage supply. The controller also estimates an SOC value and/or an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, a present temperature, and time-based information. The controller further estimates a bounded SOC value based on the SOC value, a first upper bound, a the first lower bound, and/or estimates a bounded SOH value based on the SOH value, a second upper bound, and a second lower bound. The controller then controls an electrification process of the electric vehicle based on the bounded SOC and/or SOH values.

Description

SOC AND SOH CO-ESTIMATION SYSTEMS AND METHODS

FOR ELECTRIC VEHICLES

TECHNICAL FIELD

[0001] This disclosure relates generally to methods and systems for diagnosing a power management system used in electric vehicles, and more particularly to estimating an inner state of an energy storage supply of the power management system.

BACKGROUND

[0002] A power management system can be used for both a pure electric vehicle (EV) and/or a hybrid electric vehicle (HEV) having an electric motor and an internal combustion engine (ICE). The term“electric vehicles,” as used herein, refers to hybrid and/or pure electric vehicles which provide an alternative to conventional fuel engine systems for either supplementing or completely replacing the engine systems, such as ICEs. In one example, an electric vehicle is an extended range electric vehicle (EREV). In the EREV, primary electric drive is achieved with a battery or related rechargeable energy storage system (RESS) that acts as a direct current (DC) voltage source to a motor, generator or transmission that in turn can be used to provide the energy needed to rotate one or more of the vehicle's wheels. When the electrical charge from the RESS has been depleted, backup power may come from the ICE to provide auxiliary onboard electrical energy generation.

[0003] During operation, the power management system estimates an inner state of an energy storage supply, such as a battery, in the electric vehicle for maintaining a proper level of electric power within its operational range. Typically, the inner state of the energy storage supply is a state-of-charge (SOC) and/or a state-of-health (SOH) of the energy storage supply. For example, the SOC information can be used as a fuel gauge for the battery, and the SOH information can be used as an indication of a present total capacity and/or internal resistance of the battery. In another example, the SOC information represents an available energy or power left in the energy storage supply, and the SOH information represents a degree of degradation of the energy storage supply.

[0004] Since the SOC and SOH information cannot be directly measured, an estimation algorithm is used to estimate the SOC and SOH information of the energy storage supply. An existing estimation algorithm, known as a Kalman filter, can be used to estimate the SOC and SOH information. Exemplary Kalman filters include a dual nonlinear Kalman filter (DNKF), an extended Kalman filter, an unscented Kalman filter, a cubature Kalman filter, and the like. The Kalman filter estimates the SOC and SOH information of the energy storage supply by calculating estimated SOC and SOH values, and corresponding error bounds.

[0005] However, such dual estimation methods of the Kalman filter are prone to diverge after a predetermined period. For example, the SOC and SOH estimation can be initially accurate for a newly assembled and verified battery pack, but the divergence of the SOC and SOH estimation can occur after a certain time period due to increasing sensor bias and noise, hardware and/or software malfunctions of the power

management system, or battery degradation caused by aging components of the electric vehicle. Further, a large SOC and SOH estimation divergence can cause unwanted damage to the power management system and other components of the electric vehicle.

[0006] As such, it is desirable to reduce or eliminate the SOC and SOH estimation divergence and limit the corresponding error bounds. Accordingly, there are

opportunities to develop enhanced power management systems and methods that can more efficiently estimate the SOC and SOH information of the energy storage supply.

SUMMARY

[0007] In one embodiment of the present disclosure, a controller is provided for performing a power estimation process for an electric vehicle. The controller includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the controller to perform the power estimation process by estimating an inner state of an energy storage supply of the electric vehicle using the processor. The inner state represents at least one of: a state-of-charge (SOC) and a state-of-health (SOH) of the energy storage supply. The processor also causes the controller to estimate at least one of: an SOC value and an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, and a present temperature associated with the energy storage supply, and time-based information. The processor further causes the controller to at least one of: calculate a first upper bound and a first lower bound that are associated with the SOC value and estimate a bounded SOC value of the energy storage supply based on the SOC value, the first upper bound, and the first lower bound, and calculate a second upper bound and a second lower bound that are associated with the SOH value and estimate a bounded SOH value of the energy storage supply 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 at least one of: the bounded SOC value and the bounded SOH value.

[0008] In one aspect, the processor causes the controller to calculate an amp-hour SOC and a voltage SOC. The amp-hour SOC is based on the present current level and the present temperature associated with the energy storage supply, and the voltage SOC is based on the present voltage level and the present temperature associated with the energy storage supply. The processor then causes the controller to calculate the first upper bound and the first lower bound associated with the SOC value based on the amp-hour SOC and the voltage SOC. A maximum value of the amp-hour SOC and the voltage SOC can be used for the first upper bound, while a minimum value of the amp- hour SOC and the voltage SOC can be used for the first lower bound. The processor further causes the controller to filter the voltage SOC to remove noise.

[0009] 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 starting time and an ending time associated with a full charge cycle of the energy storage supply, and the partial-cycle SOH is based on a starting time and an ending time associated with a partial charge cycle of the energy storage supply. The processor then causes the controller to calculate 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. A maximum value of the full-cycle SOH and the partial-cycle SOH can be used for the second upper bound, while a minimum value of the full-cycle SOH and the partial-cycle SOH can be used for the second lower bound.

[0010] In yet another aspect, the time-based information includes one or more historically estimated values of the SOC value and SOH value. In still another aspect, the processor causes the controller to estimate the bounded SOC value and bounded SOH value based on whether a predetermined period has passed. In a further aspect, the controller controls the electrification process by at least one of: modifying a cooling of the energy storage supply, modifying charge/discharge limits of the energy storage supply, reducing a number of charging/discharging cycles of the energy storage supply, and modifying a minimum SOC threshold. The controller may include a dual nonlinear Kalman filter.

[0011] In another embodiment of the present disclosure, a method is provided for performing a power estimation process for an electric vehicle using a controller. The method includes performing the power estimation process by estimating an inner state of an energy storage supply of the electric vehicle, with the inner state representing at least one of: a state-of-charge (SOC) and a state-of-health (SOH) of the energy storage supply. The method also includes estimating at least one of: an SOC value and an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, and a present temperature associated with the energy storage supply, and time-based information. The method further includes calculating a first upper bound and a first lower bound that are associated with the SOC value, estimating a bounded SOC value of the energy storage supply based on the SOC value, the first upper bound, and the first lower bound, calculating a second upper bound and a second lower bound that are associated with the SOH value, and estimating a bounded SOH value of the energy storage supply based on the SOH value, the second upper bound, and the second lower bound. Further, the method includes controlling an electrification process of the electric vehicle based on at least one of: the bounded SOC value and the bounded SOH value.

[0012] In one aspect, the method includes calculating an amp-hour SOC and a voltage SOC. The amp-hour SOC is based on the present current level and the present temperature associated with the energy storage supply, and the voltage SOC is based on the present voltage level and the present temperature associated with the energy storage supply. The method also includes 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. A maximum value of the amp-hour SOC and the voltage SOC can be used for the first upper bound, while a minimum value of the amp-hour SOC and the voltage SOC can be used for the first lower bound. The method further includes filtering the voltage SOC to remove noise. [0013] In another aspect, the method includes calculating a full-cycle SOH and a partial-cycle SOH. The full-cycle SOH is based on a starting time and an ending time associated with a full charge cycle of the energy storage supply, and the partial-cycle SOH is based on a starting time and an ending time associated with a partial charge cycle of the energy storage supply. The method also includes 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. A maximum value of the full-cycle SOH and the partial-cycle SOH can be used for the second upper bound, while a minimum value of the full-cycle SOH and the partial-cycle SOH can be used for the second lower bound.

[0014] In yet another aspect, the time-based information includes one or more historically estimated values of the SOC value and SOH value. In still another aspect, the method includes estimating the bounded SOC value and bounded SOH value based on whether a predetermined period has passed. In a further aspect, the method includes controlling the electrification process by at least one of: modifying a cooling of the energy storage supply, modifying charge/discharge limits of the energy storage supply, reducing a number of charging/discharging cycles of the energy storage supply, and modifying a minimum SOC threshold. The method for performing power estimation process may be carried out by a dual nonlinear Kalman filter.

[0015] 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 restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 an embodiment of the disclosure taken in conjunction with the accompanying drawings, wherein: [0017] FIG. 1 is a schematic diagram of an engine and electric motor system featuring a power estimator for electric vehicles in accordance with embodiments of the present disclosure;

[0018] FIGS. 2A and 2B illustrate exemplary configurations of an energy storage supply used in the electric vehicles in accordance with embodiments of the present disclosure;

[0019] FIG. 3 is a schematic diagram of the power estimator of FIG. 1 in accordance with embodiments of the present disclosure;

[0020] FIG. 4 is a flow chart depicting an exemplary SOC bounding process using the power estimator of FIG. 1 in accordance with embodiments of the present disclosure;

[0021] FIG. 5 is a flow chart depicting another exemplary SOC bounding process using the power estimator of FIG. 1 in accordance with embodiments of the present disclosure;

[0022] FIG. 6 is a flow chart depicting an exemplary SOH bounding process using the power estimator of FIG. 1 in accordance with embodiments of the present disclosure;

[0023] FIG. 7 is a flow chart depicting another exemplary SOH bounding process using the power estimator of FIG. 1 in accordance with embodiments of the present disclosure;

[0024] FIG. 8 is another schematic diagram of the power estimator of FIG. 1 in accordance with embodiments of the present disclosure; and

[0025] FIG. 9 is a schematic diagram of an SOC bounding unit of the power estimator of FIG. 1 in accordance with embodiments of the present disclosure.

[0026] 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 exemplification set out herein illustrates an embodiment 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

[0027] The embodiment disclosed below is not intended to be exhaustive or limit the disclosure to the precise form disclosed in the following detailed description. Rather, the embodiment is chosen and described so that others skilled in the art may utilize its teachings. One of ordinary skill in the art will realize that the embodiments provided can be implemented in hardware, software, firmware, and/or a combination thereof.

Programming code according to the embodiments can be implemented in any viable programming language such as C, C++, HTML, XTML, JAVA or any other viable high- level programming language, or a combination of a high-level programming language and a lower level programming language.

[0028] Referring now to FIG. 1 , a hybrid system 100 for an electric vehicle 102 is illustrated. Electric vehicle 102 can be plugged into an electrical outlet to be connected to a power grid system (not shown) for performing an electrification process of electric vehicle 102. In various embodiments, the electrification process may refer to various operations related to electricity generation and electric power distribution and management associated with electric vehicle 102. Exemplary electrification processes include modification of the battery cooling, modification of the charge and/or discharge limits, reducing the number of charging and/or discharging cycles, modification of the minimum state-of-charge threshold, and the like. Electric vehicle 102 may be a commercial vehicle, such as a transit bus, that is connectable to the power grid system.

[0029] In one embodiment, the power grid system can be a grid system

implemented in a specific commercial facility, such as a bus depot. In another embodiment, the power grid system can be a grid system implemented in a grid network incorporating a plurality of power stations, such as power plants and other power generating facilities. In FIG. 1 , although electric vehicle 102 is depicted as a parallel hybrid system, the present disclosure can also be applied to a range-extended vehicle or a series hybrid vehicle to suit different applications. As such, electric vehicle 102 may be any electric vehicle having an electric propulsion system (e.g., hybrid, pure electric, and/or range-extended vehicles).

[0030] Although electric vehicle 102 with an internal combustion engine (ICE) 104 is shown, the present disclosure can be applied to a pure electric vehicle powered by only batteries without ICE 104. ICE 104 can be powered by any type of fuel, such as gasoline, diesel, natural gas, liquefied petroleum gases, biofuels, and the like. In this embodiment, hybrid system 100 can include ICE 104 having a crankshaft 106 and a crankshaft sprocket (not shown) coupled to the crankshaft. ICE 104 is not particularly limited and can be on-board (e.g., a range-extended vehicle) or off-board (e.g., a genset located at the bus depot).

[0031] Hybrid system 100 can also include an electric motor 108 in mechanical communication with the crankshaft sprocket. For example, electric motor 108 can be a traction motor used for propulsion of electric vehicle 102. In various embodiments, electric motor 108 can be coupled to a speed sensor 1 10, a torque sensor 1 12, ICE 104, a clutch or torque converter 1 14, and a transmission 1 16 via crankshaft 106. In various embodiments, speed sensor 1 10 and electric motor 108 are in mechanical communication with crankshaft 106. Also, electric motor 108 is not particularly limited and, for example, can be a motor/generator, synchronous motor, or an induction motor.

[0032] In embodiments, hybrid system 100 also includes a controller 1 18 in electrical communication with speed sensor 1 10 and torque sensor 1 12. Controller 1 18 can include a non-transitory memory 120 having instructions that, in response to execution by a processor 122, cause processor 122 to determine a speed or torque value of electric motor 108. Electric motor 108 receives electric power from a rechargeable energy storage supply 124, such as a battery pack or assembly, and energy storage supply 124 can provide data representative of state-of-charge (SOC) and/or state-of-health (SOH) information to controller 1 18. Processor 122, non- transitory memory 120, and controller 1 18 are not particularly limited and can, for example, be physically separate. Additionally, a vehicle monitoring unit 128 can be included in controller 1 18 or can be an independent unit separate from controller 1 18 to suit different applications.

[0033] In certain embodiments, controller 1 18 can form a portion of a processing subsystem including one or more computing devices having memory, processing, and communication hardware. Controller 1 18 can be a single device or a distributed device, and functions of controller 1 18 can be performed by hardware and/or as computer instructions on a non-transient computer readable storage medium, such as non- transitory memory 120. [0034] In certain embodiments, controller 1 18 includes one or more interpreters, determiners, evaluators, regulators, and/or processors 122 that functionally execute the operations of controller 1 18. The description herein including interpreters, determiners, evaluators, regulators, and/or processor emphasizes the structural independence of certain aspects of controller 1 18 and illustrates one grouping of operations and responsibilities of controller 1 18. Other groupings that execute similar overall operations are understood within the scope of the present disclosure. Interpreters, determiners, evaluators, regulators, and processors can be implemented in hardware and/or as computer instructions on a non-transient computer readable storage medium, and can be distributed across various hardware or computer-based components.

[0035] Example and non-limiting implementation elements that functionally execute the operations of controller 1 18 include sensors, such as speed sensor 1 10 and torque sensor 1 12, providing any value determined herein, sensors providing any value that is a precursor to a value determined herein, datalink and/or network hardware including communication chips, oscillating crystals, communication links, cables, twisted pair wiring, coaxial wiring, shielded wiring, transmitters, receivers, and/or transceivers, logic circuits, hard-wired logic circuits, reconfigurable logic circuits in a particular non transient state configured according to the module specification, any actuator including at least an electrical, hydraulic, or pneumatic actuator, a solenoid, an op-amp, analog control elements (springs, filters, integrators, adders, dividers, gain elements), and/or digital control elements.

[0036] Certain operations described herein include operations to interpret and/or to determine one or more parameters or data structures. Interpreting or determining, as utilized herein, includes receiving values by any method known in the art, including at least receiving values from a datalink 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-transient computer readable storage medium, receiving the value as a run-time parameter by any means known in the art, and/or by receiving a value by which the interpreted parameter can be calculated, and/or by referencing a default value that is interpreted to be the parameter value. 10037] In the illustrated embodiment, controller 1 18 includes a power estimator 126 configured to estimate an inner state of energy storage supply 124 of electric vehicle 102. The inner state of energy storage supply 124 represents the SOC and/or SOH of energy storage supply 124. 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 energy storage supply 124. During a power estimation process of the SOC and/or SOH of energy storage supply 124, power estimator 126 automatically applies at least one of the upper bound and the lower bound to filter out or cut off high or low values associated with the SOC and/or SOH of energy storage supply 124, thereby preventing any potential large estimation divergence that can cause unwanted damage to electric vehicle 102. Power estimator 126 may perform the power estimation process for the SOC and SOH information of energy storage supply 124 using independent and separate bounding algorithms. Detailed descriptions of the bounding algorithms are provided below in paragraphs relating to FIGS. 3-9.

[0038] In one embodiment, power estimator 126 is configured to measure a present current level and/or a present voltage level of energy storage supply 124 by using a vehicle monitoring unit 128. For example, power estimator 126 is configured to automatically communicate with vehicle monitoring unit 128 to determine the present current and voltage levels of energy storage supply 124 of electric vehicle 102. In one embodiment, vehicle monitoring unit 128 can be a telematics system associated with electric vehicle 102. In embodiments, vehicle monitoring unit 128 is configured to monitor one or more vehicle characteristics related to electric vehicle 102.

[0039] For example, vehicle characteristics can include information of one or more components of electric vehicle 102, such as ICE 104 or electric motor 108, navigational information based on a navigation system (e.g., a global positioning system (GPS)), thermal information (e.g., a temperature) of one or more components of electric vehicle 102, such as a current temperature of electric motor 108, environment information related to a specific route for a mission of electric vehicle 102 (e.g., time of day, weather, road or load conditions, etc.). Other exemplary components of electric vehicle 102 can include electrification, powertrain, and various vehicle components, such as energy storage supply 124 (e.g., a battery), electric motor 108, ICE 104, a charging

- I Q - system, a cooling system, a separate generator (not shown), a drivetrain or powertrain (e.g., a crankshaft), a drive axle assembly (not shown), and the like.

[0040] In embodiments, power estimator 126 automatically communicates with vehicle monitoring unit 128 to obtain thermal information of at least one electric device of electric vehicle 102, such as energy storage supply 124, provided to vehicle monitoring unit 128 by a temperature sensor 132. For example, power estimator 126 communicates with vehicle monitoring unit 128 to detect a temperature of a battery pack. In another example, power estimator 126 communicates with vehicle monitoring unit 128 to detect a temperature of electric motor 108. Other suitable uses of temperature sensor 132 are also contemplated to suit the application.

[0041] In one embodiment, power estimator 126 interfaces with a network 130, such as a wireless communication facility (e.g., a Wi-Fi access point). In another

embodiment, network 130 can be a controller area network (e.g., CAN bus) on-board electric vehicle 102. In yet another embodiment, network 130 can be a cloud computing network off-board electric vehicle 102. Other similar networks known in the art are also contemplated. For example, network 130 can be a cloud network or a vehicle-to-grid (V2G) network between electric vehicle 102 and the power grid system, or a vehicle-to- vehicle (V2V) network between 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, Intranet, Ethernet, LAN, cloud network, etc.

[0042] Referring now to FIGS. 2A and 2B, exemplary configurations of energy storage supply 124 are shown. In FIG. 2A, energy storage supply 124 includes a single battery. In one embodiment, vehicle monitoring unit 128 can measure a present voltage level V and a present current level I of energy storage supply 124 and transmit the present voltage level V and the present current level I to power estimator 126 for subsequent processing as desired. In FIG. 2B, storage supply 124 includes a battery pack having a plurality of battery cells 124a, 124b, . . . 124n. In this example, the battery pack includes a first array (e.g., i = 1 ) having battery cells 124a, a second array (e.g., i = 2) having battery cells 124b, and an n-th array (e.g., i = n) having battery cells 124n. In one embodiment, vehicle monitoring unit 128 can measure a present voltage level Vi, V2, . . . V_n of each array and a present current level I of energy storage supply 124 and transmit the present voltage level Vi , V2, . . . V_n and the present current level I to power estimator 126 for subsequent processing as desired. Other suitable arrangements are also contemplated to suit different applications.

[0043] Referring now to FIG. 3, an exemplary schematic diagram of power estimator 126 is shown. In the illustrated embodiment, power estimator 126 includes an

SOC/SOH estimator 200 and a bound estimator 202. SOC/SOH estimator 200 is configured to estimate an SOC value SOC_Est (e.g., 60%) based on a present current level I and/or a present voltage level V of energy storage supply 124. For example, SOC/SOH estimator 200 can be the DNKF. Further, SOC/SOH estimator 200 is configured to estimate an SOH value SOH_Est (e.g., 80%) based on the present current level I and/or the present voltage level V of energy storage supply 124.

[0044] Although power estimator 126 is shown in FIG. 3 as integrating SOC/SOH estimator 200 and bound estimator 202, in some embodiments, SOC/SOH estimator 200 and bound estimator 202 can be installed separately or independently in any suitable systems associated with electric vehicle 102. Returning to FIG. 1 , in one embodiment, a battery management system (BMS) 134 can be installed separately from controller 1 18. BMS 134 can include another non-transitory memory 136 and processor 138. In this example, BMS 134 can include bound estimator 202 in processor 138 together with other control algorithms. In another example, BMS 134 can include SOC/SOH estimator 200 in processor 138 to suit different applications. In various embodiments, BMS 134 may perform the power estimation process for the SOC and SOH information of energy storage supply 124. Also, BMS 134 can provide an estimation of available power of energy storage supply 124.

[0045] Returning to FIG. 3, in one embodiment, SOC/SOH estimator 200 includes an SOC estimator 204, an SOC adjuster 206, an SOH estimator 208, and an SOH adjuster 210. SOC estimator 204 is configured to estimate SOC_Est based on the present current level I of energy storage supply 124, a generic embedded battery model, and/or time-based information. For example, SOC_Est can be estimated based on the time-based information having one or more historical inputs of SOC_Est measured for electric vehicle 102. SOC adjuster 206 is configured to receive SOC_Est from SOC estimator 204 and adjust SOC_Est based on the present voltage level V of energy storage supply 124. For example, SOC_Est can be corrected or tuned based on the present voltage level V of energy storage supply 124 that is currently measured by vehicle monitoring unit 128.

[0046] SOH estimator 208 is configured to estimate SOH_Est based on a generic embedded battery model and the time-based information. For example, SOH_Est can be estimated based on the historical inputs of SOH_Est measured for electric vehicle 102. SOH adjuster 210 is configured to receive SOH_Est from SOH estimator 208 and adjust SOH_Est based on the present voltage level V of energy storage supply 124. For example, SOH_Est can be corrected or tuned based on the present voltage level V of energy storage supply 124 that is currently measured by vehicle monitoring unit 128. In some embodiments, other suitable parameters that change with an aging process of energy storage supply 124, such as cell resistance, impedance, or conductance, can also be used to estimate SOH_Est.

[0047] In one embodiment, bound estimator 202 is configured to estimate a bounded SOC value SOC_Bounded and/or a bounded SOH value SOH_Bounded. For example, bound estimator 202 calculates SOC_Bounded such that SOC_Bounded is set between an upper bound of SOC_Est and a lower bound of SOC_Est. In another example, bound estimator 202 calculates SOH_Bounded such that SOH_Bounded is set between an upper bound of SOH_Est and a lower bound of SOH_Est.

[0048] In the illustrated embodiment, bound estimator 202 includes an SOC bounding unit 212 and an SOH bounding unit 214. In one embodiment, SOC bounding unit 212 is configured to calculate the upper and lower bounds for SOC_Bounded based on an amp-hour-based (Ah-based) SOC value SOC_AH and a voltage-based SOC value SOCv. In one embodiment, SOH bounding unit 214 is configured to calculate the upper and lower bounds for SOH_Bounded based on a full-cycle-based SOH value SOH_F and a partial-cycle-based SOH value SOHp. For example, SOH_F can be calculated when a full charge cycle is available for energy storage supply 124, and SOH_P can be calculated when a partial charge cycle is available for energy storage supply 124.

[0049] In some embodiments, bound estimator 202 can determine at block 216 whether a predetermined period (e.g., macro time of approximately 1-2 months) has passed since SOC_Bounded has been updated. The predetermined period can be adjusted as desired. When bound estimator 202 determines that it is time to update SOC_Bounded based on the predetermined period, SOC bounding unit 212 outputs SOC_Bounded for subsequent processing by other systems of electric vehicle 102.

[0050] For example, SOC_Bounded can be transmitted to SOH adjuster 210 or to a display device to be viewed by a technician. In another example, when bound estimator 202 determines that it is not the time to update SOC_Bounded based on the predetermined period, SOC_Bounded can be transmitted to SOC estimator 204 as a feedback value.

Although block 216 is shown for SOC_Bounded, block 216 can be implemented for

SOH_Bounded to suit the application. Also, SOH_Bounded can be transmitted to at least one of: SOC estimator 204 and SOH estimator 208 as feedback values, or to the display device for subsequent viewing.

[0051] Referring now to FIG. 4, an illustrative SOC bounding process is shown in accordance with embodiments of the subject matter disclosed herein. As disclosed herein, hybrid system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. In FIG. 4, a method 400 of performing the SOC bounding process is shown using SOC bounding unit 212. More specifically, when a single battery is used for energy storage supply 124 (e.g., FIG. 2A), for a predetermined period (e.g., micro time of approximately 1-2 seconds), SOC bounding unit 212 performs one or more steps shown in FIG. 4.

[0052] At block 402, SOC estimator 204 estimates SOC_Est based on the present current level I of energy storage supply 124 and the time-based information. For example, a generic SOC estimator, such as the DNKF, can be used to estimate SOC_Est.

[0053] At block 404, SOC bounding unit 212 calculates SOC_Ah based on the present current level I and a present temperature T of energy storage supply 124. In one example, SOC_Ah can be calculated using a coulomb counting technique. An exemplary SOC_A can be defined as shown in expression (1 ) below.

where /(t) is an input current during time t, SOC₀ denotes an initial SOC at an initial time of t₀, and Capacity denotes a present total capacity generated by energy storage supply 124.

[0054] At block 406, SOC bounding unit 212 calculates SOCv based on the present voltage level V and the present temperature T of energy storage supply 124. An exemplary SOCv can be defined as shown in expression (2) below. In one

embodiment, SOCv can be filtered to remove noises caused by a dynamic voltage response.

where OCV is a battery open circuit voltage, which is a function of SOC, R₀ denotes a battery internal resistance, which is dependent on the temperature T. In one example, filtered can be performed using a single-pole low-pass filter. In one embodiment, the single-pole low-pass filter can be used to remove the amplified noise in SOCv that may have been caused by the measurement and the imperfect fidelity of the OCV approximation. Other suitable filters are also contemplated to suit different applications. In another example, a linear-phase low-pass filter can be also used to remove the amplification noise.

[0055] At block 408, SOC bounding unit 212 calculates an upper bound and a lower bound of SOC_Est based on SOC_Ah and SOC_v calculated in blocks 404 and 406, respectively. An exemplary upper bound can be defined as shown in expressions (3) and (5), and an exemplary lower bound can be defined as shown in expressions (4) and (6).

SOC_{U bnd}(t) E [0,1] (5)

SOC _bnd(t) E [0,1] (6)

where E_desg denotes a predetermined or designed error margin selected by SOC bounding unit 212.

[0056] As shown in expressions (3) and (4) above, a maximum value of SOC_Ah and SOC_v can be used as a baseline for the upper bound, but an extra margin can be applied. Also, a minimum value of SOC_Ah and SOC_v can be used as a baseline for the lower bound, then the extra margin can be applied. For example, an abstract value between SOC_Ah and SOC_v can be used as the extra margin by considering the errors/noises in a current sensor and/or a voltage sensor and modeling other errors from the measured voltage shown in expression (2) (e.g., OCV calculated as a function of SOC). As such, the abstract value of \SOC_Ah - SOC_v\ can represent a degree of uncertainty in measurements and/or the model used for the SOC bounding process. In some embodiments, an additional accuracy margin, such as E_desg, can be applied (e.g., to apply ±3% accuracy).

[0057] At block 410, SOC bounding unit 212 generates SOCBounded that is set between the upper bound SOC_{u bnd}(t ) and the lower bound SOC_{l bnd}(t). An exemplary SOC_Bounded can be defined as shown in expression (7) below.

SOCi _bnd(t ) < SOC_Bounded(t) < SOC_{u bnd}(t ) (7)

[0058] At block 412, controller 1 18 controls the electrification process of electric vehicle 102 based on SOC_Bounded. For example, controller 1 18 can modify the battery cooling or the charge and/or discharge limits, reduce the number of charging and/or discharging cycles, or modify the minimum state-of-charge threshold based on

SOCBounded-

[0059] Referring now to FIG. 5, another illustrative SOC bounding process is shown in accordance with embodiments of the subject matter disclosed herein. As disclosed herein, hybrid system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. In FIG. 5, a method 500 of performing the SOC bounding process is shown using SOC bounding unit 212. More specifically, when the battery pack is used for energy storage supply 124 (e.g., FIG. 2B), for a predetermined period (e.g., micro time of approximately 1-2 seconds), SOC bounding unit 212 performs one or more steps shown in FIG. 5.

[0060] At block 502, SOC estimator 204 estimates SOCEst_, i for all cells 124a, 124b, . . . 124n in each array / based on the present current level I of energy storage supply 124 and the time-based information. For example, a generic SOC estimator, such as the DNKF, can be used to estimate SOCEst_, i. In one embodiment, for i = 1 , 2, . . .n, n denotes a number of cells or cell groups in series.

[0061] At block 504, SOC bounding unit 212 calculates SOCAh based on the present current level I and a present temperature T of energy storage supply 124. In one example, SOC_Ah can be calculated using a coulomb counting technique. An exemplary SOC_A can be defined as shown in expression (8) below.

where 7(t) is an input current during time t, SOC₀ denotes an initial SOC at an initial time of t₀, and Capacity denotes a present total capacity generated by energy storage supply 124.

[0062] At block 506, SOC bounding unit 212 calculates SOCv_{, i} for all cells 124a, 124b, . . . 124n in the battery pack based on the present voltage level V and the present temperature T of energy storage supply 124. An exemplary SOCv_{, i} can be defined as shown in expression (9) below. In one embodiment, SOCv_{, i} can be filtered to remove noises caused by a dynamic voltage response.

where OCV is a battery open circuit voltage, which is a function of SOC, R_0>i denotes a battery internal resistance for each array /, which is dependent on the temperature T. In one example, filtered can be performed using a single-pole low-pass filter. In one embodiment, the single-pole low-pass filter can be used to remove the amplified noise in SOCv that may have been caused by the measurement and the imperfect fidelity of the OCV approximation. Other suitable filters are also contemplated to suit different applications. In another example, a linear-phase low-pass filter can be also used to remove the amplification noise.

[0063] At block 508, SOC bounding unit 212 calculates an upper bound and a lower bound for SOC_Est based on SOC_Ah and SOC_{v i} calculated in blocks 504 and 506, respectively. An exemplary upper bound can be defined as shown in expressions (10) and (12), and an exemplary lower bound can be defined as shown in expressions (1 1 )

SOC_ufind(t) e [0,1] (12)

SOC _bnd(t) e [0,1] (13)

[0064] As shown in expressions (10) and (1 1 ) above, a maximum value of SOC_Ah and SOC_{v i} can be used as a baseline for the upper bound, but an extra margin can be applied. Also, a minimum value of SOC_Ah and SOC_{v i} can be used as a baseline for the lower bound, then the extra margin can be applied. For example, an abstract value between SOC_Ah and SOC_{v i} can be used as the extra margin by considering the errors/noises in a current sensor and/or a voltage sensor and modeling other errors from the measured voltage shown in expression (9) (e.g., OCV calculated as a function of SOC). As such, the maximum value of maxflsoc^ - SOC_{v i} \) can represent a degree of uncertainty in measurements and/or the model used for the SOC bounding process. In some embodiments, an additional accuracy margin, such as E_desg, can be applied (e.g., to apply ±3% accuracy).

[0065] At block 510, SOC bounding unit 212 generates SOC_Bounded that is set between the upper bound SOC_{u bnd}(t ) and the lower bound SOCi _bnd(t). An SOC_Bounded can be defined as shown in expression (14) below.

SOCi _bnd(t) < SOC_{Bounded i}(t) < SOC_{u bnd}(t) (14)

[0066] At block 512, controller 1 18 controls the electrification process of electric vehicle 102 based on SOC_{Bounded, i}. For example, controller 1 18 can modify the battery cooling or the charge and/or discharge limits, reduce the number of charging and/or discharging cycles, or modify the minimum state-of-charge threshold based on

SOCBounded, i-

[0067] Referring now to FIG. 6, an illustrative SOH bounding process is shown in accordance with embodiments of the subject matter disclosed herein. As disclosed herein, hybrid system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. In FIG. 6, a method 600 of performing the SOH bounding process is shown using SOH bounding unit 214. More specifically, when the single battery is used for energy storage supply 124 (e.g., FIG. 2A), for a predetermined period (e.g., macro time of approximately 1-2 months), SOH bounding unit 214 performs one or more steps shown in FIG. 6.

[0068] At block 602, SOH estimator 208 estimates SOH_Est based on the time-based information. For example, a generic SOC estimator, such as the DNKF, can be used to estimate SOHEst.

[0069] At block 604, SOH bounding unit 214 calculates SOH_F based on a starting time and an ending time associated with energy storage supply 124. SOH_F can be stored in memory 120 for subsequent retrieval and processing. For example, a full- cycle SOH value represents the SOH value SOHF(L) estimated at the last capacity check for energy storage supply 124. For every n charge cycles or m months, a full charge and discharge is typically recommended for energy storage supply 124. When such full charge and discharge operation is available, an exemplary SOH_F(L ) at the last capacity check performing a full charge cycle can be defined as shown in expression (15) below.

v^{i llm} dt

SOH_F(L ) = ^vu,Um _

Nominal Capacity (15)

where /(t) is an input current during time t, t_{Vu lim} denotes a starting time at an upper voltage limit of energy storage supply 124 during a full discharge, t_{Vl lim} denotes an ending time when the voltage reaches a lower cut-off voltage limit for energy storage supply 124 during the full discharge, and Nominal Capacity denotes a total capacity generated by energy storage supply 124 when energy storage supply 124 is newly installed at 100% SOH.

[0070] However, when such full charge and discharge operation is unavailable or not allowed for certain applications, another exemplary full-cycle SOH value SOH_F(L ) can be defined as shown in expression (16) below.

where 7(t) is an input current during time t, t_Vlow denotes a starting time when an SOC value of energy storage supply 124 is less than approximately 20% before a full charge, t_{Vu lim} denotes an ending time when the voltage reaches an upper voltage limit during the full charge, and Nominal Capacity denotes a total capacity generated by energy storage supply 124 when energy storage supply 124 is newly installed at 100% SOH. In embodiments, the exemplary full-cycle SOH value SOH_F(L ) shown in expression (16) can be calculated when (1 ) an SOC value of energy storage supply 124 is less than 20% after the last operation of electric vehicle 102, (2) a following charge event fully charges energy storage supply 124 to an upper voltage limit (e.g., SOC = 100%), and (3) a temperature of energy storage supply 124 is

approximately between 25 and 35 degree Celsius (25-35 °C). [0071] At block 606, SOH bounding unit 214 calculates a partial-cycle SOH value SOH_P based on a starting time and an ending time associated with energy storage supply 124. SOH_P can also be stored in memory 120 for subsequent retrieval and processing. For example, the partial-cycle SOH value represents the SOH value SOHp(L) estimated at the last capacity check for energy storage supply 124. An exemplary SOH_P(L ) at the last capacity check performing a partial charge cycle can be defined as shown in expression (17) below.

where t_x denotes a starting time of the partial cycle and t₂ denotes an ending time of the partial cycle.

[0072] At block 608, SOH bounding unit 214 calculates an upper bound and a lower bound for SOH_Est based on SOH_F(L ) and SOH_P(L ) calculated in blocks 604 and 606, respectively. An exemplary upper bound can be defined as shown in expressions (18) and (20), and an exemplary lower bound can be defined as shown in expressions (19) and (21 ).

SOH_{u bnd}(L) = ma x{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 ) e [0,1] (20)

SOH_{l bnd}(L) E [0,1] (21 )

where E_desg denotes a predetermined or designed error margin selected by SOH bounding unit 214.

[0073] As shown in expressions (18) and (19) above, a maximum value of SOH_F and SOHp can be used as a baseline for the upper bound, but an extra margin can be applied. Also, a minimum value of SOH_F and SOH_P can be used as a baseline for the lower bound, then the extra margin can be applied. For example, an abstract value between SOH_F and SOH_P can be used as the extra margin by considering the uncertainties in the SOH_F and SOH_P estimation. In certain situations, SOH_F may not be accurate due to unwanted changes during the capacity checks performed every few months. As another example, SOH_P may not be accurate due to unwanted sensor errors, battery hysteresis, and unknown coulombic efficiencies (e.g., a loss of charge due to a passage of time). As such, the abstract value of \SOH_F(L) - SOH_P L) \ can represent a degree of uncertainty in measurements and/or the model used for the SOH bounding process. In some embodiments, an additional accuracy margin, such as E_desg , can be applied (e.g., to apply ±3% accuracy).

[0074] At block 610, SOH bounding unit 214 generates SOH_Bounded that is set between the upper bound SOH_{u bnd}(L ) and the lower bound SOH_{l bnd}(L). An exemplary SOH_Bounded can be defined as shown in expression (22) below.

SOHi _bnd(L ) < SOH_Bounded(L) < SOH_{u bnd}(L ) (22)

[0075] At block 612, controller 1 18 controls the electrification process of electric vehicle 102 based on SOH_Bounded. For example, controller 1 18 can modify the battery cooling or the charge and/or discharge limits, reduce the number of charging and/or discharging cycles, or modify the minimum state-of-charge threshold based on

SOHBounded-

[0076] Referring now to FIG. 7, another illustrative SOH bounding process is shown in accordance with embodiments of the subject matter disclosed herein. As disclosed herein, hybrid system 100 is not particularly limited and can perform any of the methods described within the scope of this disclosure. In FIG. 7, a method 700 of performing the SOH bounding process is shown using SOH bounding unit 214. More specifically, when the battery pack is used for energy storage supply 124 (e.g., FIG. 2B), for a predetermined period (e.g., macro time of approximately 1 -2 months), SOH bounding unit 214 performs one or more steps shown in FIG. 7.

[0077] At block 702, SOH estimator 208 estimates SOH_{Est, i} based on the time- based information. For example, a generic SOC/SOH estimator, such as the DNKF, can be used to estimate SOH_{Est, i}.

[0078] At block 704, SOH bounding unit 214 calculates a full-cycle SOH value SOH_F for the battery pack based on a starting time and an ending time associated with energy storage supply 124. SOH_F can be stored in memory 120 for subsequent retrieval and processing. For example, the full-cycle SOH value represents the SOH value SOH_F(L) estimated at the last capacity check for energy storage supply 124. For every n charge cycles or m months, a full charge and discharge is typically recommended for energy storage supply 124. When such full charge and discharge operation is available, an exemplary SOH_F(L ) at the last capacity check performing a full charge cycle can be defined as shown in expression (23) below.

[0079] However, when such full charge and discharge operation is unavailable or not allowed for certain applications, another exemplary full-cycle SOH value SOH_F(L ) can be defined as shown in expression (24) below.

where /(t) is an input current during time t, t_Vlow denotes a starting time when an SOC value of energy storage supply 124 is less than 20% during a full charge, t_{Vu lim} denotes an ending time when the voltage reaches an upper voltage limit during the full charge, and Nominal Capacity denotes a total capacity generated by energy storage supply 124 when energy storage supply 124 is newly installed at 100% SOH.

In embodiments, the exemplary SOH_F(L ) shown in expression (24) can be calculated when (1 ) an SOC value of energy storage supply 124 is less than 20% after the last operation of electric vehicle 102, (2) a following charge event fully charges energy storage supply 124 to an upper voltage limit (e.g., SOC = 100%), and (3) a temperature of energy storage supply 124 is approximately between 25 and 35 degree Celsius (25- 35 °C).

[0080] At block 706, SOH bounding unit 214 calculates a partial-cycle SOH value SOHP, i based on a starting time and an ending time associated with energy storage supply 124. SOHP, , can also be stored in memory 120 for subsequent retrieval and processing. For example, the partial-cycle SOH value represents the SOH value SOHP, i(L) estimated at the last capacity check for energy storage supply 124. An exemplary SOH_{P i}(L ) at the last capacity check performing a partial charge cycle can be defined as shown in expression (25) below.

where t_t denotes a starting time of the partial cycle and t₂ denotes an ending time of the partial cycle.

[0081] At block 708, SOH bounding unit 214 calculates an upper bound and a lower bound for SOH_Est based on SOH_F(L ) and SOH_{P i}(L ) calculated in blocks 704 and 706, respectively. An exemplary upper bound can be defined as shown in expressions (26) and (28), and an exemplary lower bound can be defined as shown in expressions (27)

SOH_{u bnd} ( ) e [0,1] (28)

SOH_{l bnd}(L) E [0,1] (29)

[0082] As shown in expressions (26) and (27) above, a maximum value of SOH_F and SOH_{p i} can be used as a baseline for the upper bound, but an extra margin can be applied. Also, a minimum value of SOH_F and SOH_{P i} can be used as a baseline for the lower bound, then the extra margin can be applied. For example, an abstract value between SOH_F and SOH_{P i} can be used as the extra margin by considering the uncertainties in the SOH_F and SOH_{P i} estimation. In certain situations, SOH_F may not be accurate due to unwanted changes during the capacity checks performed every few months. As another example, SOH_{P i} may not be accurate due to unwanted sensor errors, battery hysteresis, and unknown coulombic efficiencies (e.g., a loss of charge due to a passage of time). As such, the maximum value of max

can represent a degree of uncertainty in measurements and/or the model used for the SOH bounding process. In some embodiments, an additional accuracy margin, such as E_desg, ^can be applied (e.g., to apply ±3% accuracy).

[0083] At block 710, SOH bounding unit 214 generates SOH_{Bounded, i} that is set between the upper bound SOH_{u bnd}(L ) and the lower bound SOH_{l bnd}(L). An exemplary SOH_{Bounded, i} can be defined as shown in expression (30) below. SOHi _bnd(L ) < SOH_{Bounded i} L) < SOH_{u bnd}(L ) (30)

[0084] At block 712, controller 1 18 controls the electrification process of electric vehicle 102 based on SOH_{Bounded, i}. For example, controller 1 18 can modify the battery cooling or the charge and/or discharge limits, reduce the number of charging and/or discharging cycles, or modify the minimum state-of-charge threshold based on

SOHBounded, i-

[0085] Referring now to FIG. 8, another exemplary schematic diagram of power estimator 126 is shown. In the illustrated embodiment, power estimator 126 includes SOC/SOH estimator 200 and SOC bounding unit 212. The illustrated embodiment can be used for both individual battery cells and battery packs to suit different applications.

In FIG. 8, SOC/SOH estimator 200 is configured to receive the present current level I and the present voltage level V of energy storage supply 124 from vehicle monitoring unit 128. Further, SOC/SOH estimator 200 is configured to receive the present temperature T of energy storage supply 124 from vehicle monitoring unit 128.

SOC/SOH estimator 200 is configured to estimate SOC_Est (e.g., 60%) based on the present current level I, the present voltage level V, and the present temperature T of energy storage supply 124. SOC_Est is transmitted to SOC bounding unit 212.

[0086] SOC bounding unit 212 is configured to receive SOC_Est from SOC/SOH estimator 200 and also receive the present current level I, the present voltage level V, and the present temperature T of energy storage supply 124 from vehicle monitoring unit 128. SOC bounding unit 212 is configured to calculate the upper bound and the lower bound that can be applied SOC_Est based on the present current level I, the present voltage level V, and the present temperature T, and the SOC_Est. SOC bounding unit 212 is configured to generate SOC_Bounded that is set between the upper bound and the lower bound. SOC bounding unit 212 is configured to output SOC_Bounded, the upper bound and the lower bound for subsequent processing as desired. For example, controller 1 18 can control the electrification process of electric vehicle based on SOCBounded-

[0087] Referring now to FIG. 9, an exemplary schematic diagram of SOC bounding unit 212 shown in FIG. 8. In the illustrated embodiment, SOC bounding unit 212 includes an Ah-based SOC calculation unit 900 and a voltage-based SOC calculating unit 902. Ah-based SOC calculation unit 900 is configured to calculate SOC_AH based on the present current level I of energy storage supply 124. Voltage-based SOC calculating unit 902 is configured to calculate SOCv based on the present voltage level V, the present temperature T, and the present current level I of energy storage supply 124. SOCv can be filtered to remove noises using a filter, such as the single-pole low- pass filter.

[0088] An exemplary calculation for the upper bound using a MinMax unit, an Add1 unit, an Abs unit, a Constant, and an Add unit is shown below in expression (31 ) below

SOC_{u bnd} = ma x{SOC_Ah , SOC_v } + \SOC_Ah - SOC_v\ + E_desg (31 ).

[0089] An exemplary calculation for the lower bound using a MinMaxl unit, an Add3 unit, an Abs1 unit, a Constant , and an Add2 unit is shown below in expression (32) below.

[0090] SOC bounding unit 212 further includes a filtering unit 904 configured to receive SOC_Est, the upper bound and the lower bound. Filtering unit 904 is configured to filter SOC_Est using the upper bound and the lower bound, and generate SOC_Bounded such that SOC_Bounded that is set between the upper bound and the lower bound. SOC bounding unit 212 can output SOC_Bounded, the upper bound, and the lower bound for subsequent processing as desire.

[0091] Although SOC/SOH estimator 200 and SOC bounding unit 212 are shown in FIGS. 8 and 9, other suitable arrangements, such as SOC/SOH estimator 200 and SOH bounding unit 214, are also contemplated to suit different applications.

[0092] 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. However, the benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements. The scope is accordingly to be limited by nothing other 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.” Moreover, where a phrase similar to“at least one of A, B, or C” is used in the claims, it is intended that the phrase be interpreted to mean that A alone may be present in an embodiment, B alone may be present in an embodiment, C alone may be present in an embodiment, or that any combination of the elements A, B or C may be present in a single embodiment; for example, A and B, A and C, B and C, or A and B and C.

[0093] In the detailed description herein, references to“one embodiment,”“an 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 with the benefit of the present disclosure to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading the description, it will be apparent to one skilled in the relevant art(s) how to implement the disclosure in alternative embodiments.

[0094] 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 is to be construed under the provisions of 35 U.S.C. 1 12(f), unless the element is expressly 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.

[0095] Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the presently disclosed subject matter. For example, while the embodiments described above refer to particular features, the scope of this 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 claims, together with all equivalents thereof.

Claims

CLAIMS What is claimed is:

1. A controller for performing a power estimation process for an electric vehicle , the controller comprising:

a processor; and

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

perform the power estimation process by estimating an inner state of an energy storage supply of the electric vehicle, the inner state representing at least one of: a state-of-charge (SOC) and a state-of-health (SOH) of the energy storage supply, estimate at least one of: an SOC value and an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, and a present temperature associated with the energy storage supply, and time-based information; and

at least one of: 1 ) calculate a first upper bound and a first lower bound that are associated with the SOC value and estimate a bounded SOC value of the energy storage supply based on the SOC value, the first upper bound, and the first lower bound, and 2) calculate a second upper bound and a second lower bound that are associated with the SOH value and estimate a bounded SOH value of the energy storage supply based on the SOH value, the second upper bound, and the second lower bound,

wherein the controller controls an 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:

calculate an amp-hour SOC based on the present current level and the present temperature associated with the energy storage supply;

calculate a voltage SOC based on the present voltage level and the present temperature associated with the energy storage supply; and calculate the first upper bound and the first lower bound associated with the SOC value based on the amp-hour SOC and the voltage SOC.

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

calculate a full-cycle SOH based on a starting time and an ending time associated with a full charge cycle of the energy storage supply;

calculate a partial-cycle SOH based on a starting time and an ending time associated with a partial charge cycle of the energy storage supply; and

calculate 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 value of the full-cycle SOH and the partial-cycle SOH is used for the second upper bound, and a minimum value 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 includes one or more historically estimated values of the SOC value and 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 bounded SOH value based on whether a predetermined period has passed.

9. The controller of claim 1 , wherein the controller controls the electrification process by at least one of: modifying a cooling of the energy storage supply, modifying re ch a rg e/discharge limits of the energy storage supply, reducing a number of

charging/discharging cycles of the energy storage supply, and modifying a minimum SOC threshold.

10. The controller of claim 1 , wherein the controller includes a dual nonlinear

Kalman filter.

1 1 A method of performing a power estimation process for an electric vehicle using a controller, the method comprising:

performing the power estimation process by estimating an inner state of an energy storage supply of the electric vehicle, the inner state representing at least one of: a state-of-charge (SOC) and a state-of-health (SOH) of the energy storage supply; estimating at least one of: an SOC value and an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, and a present temperature associated with the energy storage supply, and time-based information;

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

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

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

estimating a bounded SOH value of the energy storage supply 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.

12. The method of claim 1 1 , 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 energy storage supply; calculating a voltage SOC based on the present voltage level and the present temperature associated with the energy storage supply; 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.

13. The method of claim 12, wherein a maximum value of the amp-hour SOC and the voltage SOC is used for the first upper bound, and a minimum value of the amp-hour SOC and the voltage SOC is used for the first lower bound.

14. The method of claim 12, further comprising filtering the voltage SOC to remove noise.

15. The method of claim 1 1 , wherein estimating the second upper bound and the second lower bound further comprises:

calculating a full-cycle SOH based on a starting time and an ending time associated with a full charge cycle of the energy storage supply;

calculating a partial-cycle SOH based on a starting time and an ending time associated with a partial charge cycle of the energy storage supply; 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.

16. The method of claim 15, wherein a maximum value of the full-cycle SOH and the partial-cycle SOH is used for the second upper bound, and a minimum value of the full-cycle SOH and the partial-cycle SOH is used for the second lower bound.

17. The method of claim 1 1 , wherein the time-based information includes one or more historically estimated values of the SOC value and SOH value.

18. The method of claim 1 1 , wherein estimating the bounded SOC value and bounded SOH value is further based on whether a predetermined period has passed.

19. The method of claim 1 1 , wherein controlling the electrification process includes at least one of: modifying a cooling of the energy storage supply, modifying charge/discharge limits of the energy storage supply, reducing a number of charging/discharging cycles of the energy storage supply, and modifying a minimum SOC threshold.

20. The method of claim 1 1 , wherein the power estimation process is performed by a dual nonlinear Kalman filter.