CN113287242B - System and method for collaborative estimation of SOC and SOH of electric vehicle - Google Patents

System and method for collaborative estimation of SOC and SOH of electric vehicle Download PDF

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Publication number
CN113287242B
CN113287242B CN201980068460.9A CN201980068460A CN113287242B CN 113287242 B CN113287242 B CN 113287242B CN 201980068460 A CN201980068460 A CN 201980068460A CN 113287242 B CN113287242 B CN 113287242B
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soc
soh
controller
power source
energy power
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CN113287242A (en
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范国栋
张瑞刚
J·路斯
A·叶泽列茨
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Cummins Inc
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Cummins Inc
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048Detection of remaining charge capacity or state of charge [SOC]
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B60L58/13Maintaining the SoC within a determined range
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385Arrangements for measuring battery or accumulator variables
    • G01R31/387Determining ampere-hour charge capacity or SoC
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392Determining battery ageing or deterioration, e.g. state of health
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/005Detection of state of health [SOH]
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00Control parameters of input or output; Target parameters
    • B60L2240/40Drive Train control parameters
    • B60L2240/54Drive Train control parameters related to batteries
    • B60L2240/545Temperature
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00Control parameters of input or output; Target parameters
    • B60L2240/40Drive Train control parameters
    • B60L2240/54Drive Train control parameters related to batteries
    • B60L2240/547Voltage
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00Control parameters of input or output; Target parameters
    • B60L2240/40Drive Train control parameters
    • B60L2240/54Drive Train control parameters related to batteries
    • B60L2240/549Current
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Sustainable Energy (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

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 supply of the electric vehicle. The internal state represents a state of charge (SOC) and/or a state of health (SOH) of the energy storage power source. The controller estimates an SOC value and/or an SOH value of the stored energy power source based on at least one of: current level, current voltage level, current 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 electrified process of the electric vehicle based on the bounded SOC value and/or the SOH value.

Description

System and method for collaborative estimation of SOC and SOH of electric vehicle
Technical Field
The present disclosure relates generally to methods and systems for diagnosing a power management system used in an electric vehicle, 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 in 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 a hybrid electric vehicle and/or a pure electric vehicle that provides an alternative to conventional fuel engine systems to supplement or replace entirely an engine system such as an ICE. In one example, the electric vehicle is an Extended Range Electric Vehicle (EREV). In EREV, primary electric drive is achieved using a battery or an associated Rechargeable Energy Storage System (RESS), which acts as a Direct Current (DC) voltage source for 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, the backup power may come from the ICE to provide auxiliary on-board power generation.
During operation, the power management system estimates an 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 energy storage power supply is the state-of-charge (SOC) and/or state-of-health (SOH) of the energy storage power supply. For example, the SOC information may be used as a fuel gauge for the battery, and the SOH information may be used as an indication of the current total capacity and/or internal resistance of the battery. In another example, the SOC information represents available energy or power left in the stored energy power source, and the SOH information represents a degree of degradation of the stored energy power source.
Since SOC and SOH information cannot be measured directly, an estimation algorithm is used to estimate SOC and SOH information for the stored energy power supply. An existing estimation algorithm called a Kalman (Kalman) filter 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 endless (unscented) kalman filter, a bulk kalman filter, and the like. The Kalman filter estimates the SOC and SOH information of the stored energy power supply by calculating the estimated SOC and SOH values and corresponding error bound (error bound).
However, this dual estimation method of the kalman filter is prone to bias after a predetermined period of time. For example, for a newly assembled and verified battery pack, the SOC and SOH estimation may be accurate initially, but after a certain period of time, deviations in the SOC and SOH estimation may occur 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. Further, large SOC and SOH estimation deviations may cause unnecessary damage to the power management system and other components of the electric vehicle.
Thus, 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 for 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 supply. The processor also causes the controller to estimate at least one of an SOC value and an SOH value of the stored energy power source based on at least one of: current level, current voltage level and current temperature, and time-based information associated with the stored energy power source. 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 stored-energy 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 stored energy power source based on the SOH value, the second upper bound, and the second lower bound. The controller then controls an electrified (electrification) 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 the amp-hour SOC and the voltage SOC. The amp-hour SOC is based on a present current level and a present temperature associated with the stored energy 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 also 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 of the amp-hour SOC and the voltage SOC may be used for the first upper bound, and the minimum of the amp-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 also 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 value of the full cycle SOH and the partial cycle SOH may be used for the second upper bound, and the minimum value of the full cycle SOH and the partial cycle SOH may be used for the second lower bound.
In yet another aspect, the time-based information includes one or more historical estimates of SOC values 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 energy storage power supply, modifying a charge limit/discharge limit of the energy storage power supply, reducing a number of charge cycles/discharge cycles of the energy storage power supply, and modifying the minimum SOC threshold. The controller may include a dual nonlinear 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 power estimation process is performed by estimating an internal state of an energy storage power source of the electric vehicle, wherein the internal state represents at least one of: state of charge (SOC) and state of health (SOH) of the stored energy power supply. The method further comprises the steps of: estimating at least one of an SOC value and an SOH value of the stored energy power source based on at least one of: current level, current voltage level and current temperature, and time-based information associated with the stored energy power source. 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 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 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: ampere-hour SOC and voltage SOC were calculated. The amp-hour SOC is based on a present current level and a present temperature associated with the stored energy 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 of the amp-hour SOC and the voltage SOC may be used for the first upper bound, and the minimum of the amp-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: full cycle SOH and 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 value of the full cycle SOH and the partial cycle SOH may be used for the second upper bound, and the minimum value of the full cycle SOH and the partial cycle SOH may be used for the second lower bound.
In yet another aspect, the time-based information includes one or more historical estimates of SOC values and SOH values. In yet another aspect, the method includes 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: the electrification process is controlled by at least one of: modifying cooling of the energy storage power supply, modifying a charge limit/discharge limit of the energy storage power supply, reducing a number of charge cycles/discharge cycles of the energy storage power supply, and modifying the minimum SOC threshold. The method of performing the power estimation process may be performed by a dual nonlinear kalman filter.
While various embodiments are disclosed, other embodiments of the presently disclosed subject matter will become apparent to those skilled in the art from the following detailed description, which illustrates 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 illustration of an engine and motor system featuring an electric 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 supply used 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 flowchart illustrating an exemplary SOC demarcation (bounding) process using the power estimator of FIG. 1, according to an embodiment of the disclosure;
FIG. 5 is a flowchart illustrating another exemplary SOC delimitation process using the power estimator of FIG. 1, according to an embodiment of the disclosure;
FIG. 6 is a flowchart illustrating an exemplary SOH delimitation process using the power estimator of FIG. 1, according to an embodiment of the disclosure;
FIG. 7 is a flow chart 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; and
Fig. 9 is a schematic diagram of an SOC delimitation unit of the power estimator of fig. 1 according to an embodiment of the 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. Those of ordinary skill in the art will recognize that the embodiments provided may be implemented in hardware, software, firmware, and/or combinations thereof. The programming code according to an embodiment may 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 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 the electrified 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 a minimum state of charge threshold, etc. 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 station. In another embodiment, the grid system may be a grid system implemented in a grid that incorporates multiple power stations (such as power plants and other power generation facilities). In fig. 1, although the electric vehicle 102 is described as a parallel hybrid system, the present disclosure may also be applied to an extended range vehicle or a series hybrid vehicle to suit different applications. As such, electric vehicle 102 may be any electric vehicle (e.g., a hybrid vehicle, a pure electric vehicle, and/or a hybrid Cheng Cheliang) having an electric propulsion system.
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 by a battery alone, without ICE 104. The ICE 104 may be powered by any type of fuel, such as gasoline, diesel, natural gas, liquefied petroleum gas, biofuel, and 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. ICE 104 is not particularly limited and may be on-board (e.g., cheng Cheliang up) or off-board (e.g., a generator set 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 embodiments, 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 embodiments, the speed sensor 110 and the motor 108 are in mechanical communication with the crankshaft 106. Moreover, 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, the hybrid system 100 further includes a controller 118 in electrical communication with the speed sensor 110 and the 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 motor 108 receives power from a rechargeable energy storage source 124, such as a battery pack or battery assembly, and the energy storage 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 physically separate, for example. In addition, the vehicle monitoring unit 128 may be included in the controller 118 or may be a stand-alone unit separate from the controller 118 to suit different applications.
In some implementations, the controller 118 may form part of a processing subsystem including one or more computing devices with 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, determinants, evaluators, regulators, and/or processors 122 that functionally execute the operations of the controller 118. The description herein, including an interpreter, determiner, evaluator, regulator and/or processor, 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, regulator, and processor can be implemented in hardware and/or as computer instructions on a non-transitory computer-readable storage medium, and can be distributed across various hardware or computer-based components.
Example and non-limiting implementation components that functionally execute the operations of the controller 118 include: sensors providing any of the values determined herein (such as speed sensor 110 and torque sensor 112), sensors providing any of the values as precursors to the values determined herein, data links and/or network hardware (including communication chips, oscillating crystals, communication links, cables, twisted pair wires, coaxial wires, shielded wires, transmitters, receivers and/or transceivers), logic circuits, hardwired logic circuits, reconfigurable logic circuits in a particular non-transitory state configured according to module specifications, any actuators (including at least electric, hydraulic, or pneumatic actuators), solenoids, operational amplifiers, analog control components (springs, filters, integrators, adders, dividers, gain components), and/or digital control components.
Certain operations described herein include operations for interpreting and/or determining one or more parameters or data structures. As used herein, interpreting or determining includes receiving values by any method known in the art, including at least: receiving values from a data link or network communication, receiving electronic signals (e.g., voltage, frequency, current, PWM signals) indicative of the values, receiving computer-generated parameters indicative of the values, reading the values from memory locations on a non-transitory computer-readable storage medium, receiving the values as runtime parameters by any means known in the art, and/or receiving values from which interpreted parameters can be calculated, and/or by reference to default values that are interpreted as parameter values.
In the illustrated embodiment, the controller 118 includes a power estimator 126 configured to estimate an internal state of the stored energy power source 124 of the electric vehicle 102. The internal state of the energy storage power source 124 represents the SOC and/or SOH of the energy storage 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 SOC and/or SOH of the stored energy power source 124. During the power estimation process of the SOC and/or SOH of the stored energy 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 stored energy power source 124, thereby preventing any potentially large estimated deviations 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 stored energy 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 the present current level and/or the present voltage level of the stored energy 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 energy power source 124 of the electric vehicle 102. In one embodiment, the vehicle monitoring unit 128 may be a telematics (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 the present temperature of the electric motor 108, environmental information related to a particular route of a mission of the electric vehicle 102 (e.g., time, weather, road or load conditions, etc.). Other exemplary components of the electric vehicle 102 can include electrification, a powertrain (powertrain), and various vehicle components, such as an energy storage 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 driveline 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 a vehicle monitoring unit 128 to detect a temperature of the motor 108. Other suitable uses of the temperature sensor 132 are also contemplated as appropriate for the application.
In one embodiment, the power estimator 126 interfaces with a network 130, such as a wireless communication facility (e.g., wi-Fi access point). In another embodiment, the network 130 may be a controller area network (e.g., a CAN bus) onboard the electric vehicle 102. In yet another embodiment, the network 130 may be a cloud computing network external to 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 vehicle 102 and a grid system, or a vehicle-to-vehicle (V2V) network between electric vehicles. In an embodiment, any type of computer network having a collection of computers, servers, and other hardware interconnected by communication channels, such as the Internet, intranets, ethernet, LANs, cloud networks, etc., is contemplated.
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 energy 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 with battery cells 124a (e.g., i=1), a second array with battery cells 124b (e.g., i=2), and an nth array with battery cells 124n (e.g., i=n). In one embodiment, the vehicle monitoring unit 128 may measure the present voltage level V 1、V2、…、Vn and the present current level I for each array of stored-energy power sources 124 and send the present voltage level V 1、V2、…、Vn and the present current level I to the power estimator 126 for subsequent processing as needed. Other suitable arrangements are also contemplated to suit different applications.
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 an SOC/SOH estimator 200 and a boundary estimator 202. The SOC/SOH estimator 200 is configured to estimate the SOC value SOC Est (e.g., 60%) based on the present current level I and/or the present voltage level V of the stored-energy power supply 124. For example, the SOC/SOH estimator 200 may be DNKF. Further, the SOC/SOH estimator 200 is configured to estimate the SOH value SOH Est (e.g., 80%) based on the present current level I and/or the present voltage level V of the stored-energy power supply 124.
Although the power estimator 126 is shown in fig. 3 as an integrated SOC/SOH estimator 200 and boundary estimator 202, in some implementations, the SOC/SOH estimator 200 and boundary estimator 202 may be installed separately or independently in any suitable system associated with the 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 BMS134 may include another non-transitory memory 136 and a processor 138. In this example, the BMS134 may include the boundary estimator 202 in the processor 138 along with other control algorithms. In another example, the BMS134 may include the SOC/SOH estimator 200 in the processor 138 to suit different applications. In various embodiments, the BMS134 may perform power estimation processing on SOC and SOH information of the energy storage power source 124. Moreover, the BMS134 may provide an estimate of the available power of the stored energy power source 124.
Returning to fig. 3, in one embodiment, the SOC/SOH estimator 200 includes: SOC estimator 204, SOC adjuster 206, SOH estimator 208, and SOH adjuster 210. The SOC estimator 204 is configured to estimate the SOC Est based on the present current level I of the stored-energy power supply 124, the general embedded battery model, and/or time-based information. For example, the SOC Est may be estimated based on time-based information having one or more historical inputs of SOCs Est measured for the electric vehicle 102. The SOC regulator 206 is configured to receive the SOC Est from the SOC estimator 204 and to regulate the SOC Est based on the present voltage level V of the stored-energy power supply 124. For example, the SOC Est 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.
The SOH estimator 208 is configured to estimate SOH Est based on the generic embedded battery model and the time-based information. For example, SOH Est may be estimated based on historical inputs of SOH Est measured for electric vehicle 102. The SOH regulator 210 is configured to receive the SOH Est from the SOH estimator 208 and to regulate the SOH Est based on the present voltage level V of the stored-energy power supply 124. For example, SOH Est may be corrected or tuned based on the present voltage level V of the stored-energy power supply 124 currently measured by the vehicle monitoring unit 128. 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, the boundary estimator 202 is configured to estimate the bounded SOC value SOC Bounded and/or the bounded SOH value SOH Bounded. For example, boundary estimator 202 calculates SOC Bounded such that SOC Bounded is set between the upper bound of SOC Est and the lower bound of SOC Est. In another example, boundary estimator 202 calculates SOH Bounded such that SOH Bounded is set between the upper bound of SOH Est and the lower bound of SOH Est.
In the illustrated embodiment, the boundary estimator 202 includes an SOC delimiter unit 212 and an SOH delimiter unit 214. In one embodiment, the SOC delimiting unit 212 is configured to calculate the upper and lower bounds of the SOC Bounded based on the ampere-hour based (Ah based) SOC value SOC Ah and the voltage based SOC value SOC V. In one embodiment, the SOH bounding unit 214 is configured to calculate the upper and lower bounds of SOH Bounded based on the full cycle based SOH value SOH F and the partial cycle based SOH value SOH P. For example, SOH F may be calculated when a full charge cycle is available to the stored-energy power supply 124, and SOH P may be calculated when a partial charge cycle is available to the stored-energy power supply 124.
In some implementations, the boundary estimator 202 may determine whether a predetermined period of time (e.g., a macroscopic time of about 1 month to 2 months) has elapsed since the SOC Bounded has been updated at block 216. The predetermined period of time may be adjusted as desired. When the boundary estimator 202 determines that it is time to update the SOC Bounded based on the predetermined period, the SOC delimiting unit 212 outputs the SOC Bounded for subsequent processing by other systems of the electric vehicle 102.
For example, the SOC Bounded may be sent to the SOH regulator 210 or to a display device for viewing by a technician. In another example, when the boundary estimator 202 determines that it is not time to update the SOC Bounded based on a predetermined period of time, the SOC Bounded may be sent to the SOC estimator 204 as a feedback value. Although block 216 is shown for SOC Bounded, block 216 may be implemented for SOH Bounded to suit the application. Also, SOH Bounded may be sent as a feedback value to at least one of SOC estimator 204 and SOH estimator 208 or to a display device for later viewing.
Referring now to FIG. 4, an exemplary 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 stored energy power source 124 (e.g., fig. 2A) for a predetermined period of time (e.g., a microscopic time of about 1 second to 2 seconds), the SOC delimiter unit 212 performs one or more steps shown in fig. 4.
At block 402, the SOC estimator 204 estimates the SOC Est based on the present current level I of the stored-energy power supply 124 and the time-based information. For example, a generic SOC estimator (such as DNKF) may be used to estimate SOC Est.
At block 404, the SOC delimiter unit 212 calculates the SOC Ah based on the present current level I and the present temperature T of the stored-energy power source 124. In one example, the SOC Ah may be calculated using coulomb counting techniques. An exemplary SOC Ah may be defined as shown in expression (1) below.
Where I (t) is the input current during time t, SOC 0 represents the initial SOC at initial time t 0, and Capacity represents the present total Capacity generated by the stored-energy power supply 124.
At block 406, the SOC delimiter unit 212 calculates the SOC V based on the present voltage level V and the present temperature T of the stored-energy power supply 124. An exemplary SOC V may be defined as shown in expression (2) below. In one embodiment, the SOC V may be filtered to remove noise due to dynamic voltage response.
SOCV(t)=[OCV-1(V(t)+I(t)·R0(T))]filtered (2)
Wherein OCV is the battery open circuit voltage, which is a function of SOC; r 0 represents the internal resistance of the battery, which depends on the temperature T. In one example, filtering (filtered) may be performed using a single-pole low-pass filter. In one embodiment, a single pole low pass filter may be used to remove amplified noise in the SOC V that may be caused by measurements of OCV approximations and imperfect fidelity. Other suitable filters are also conceivable to be suitable for different applications. In another example, a linear phase low pass filter may also be used to remove amplification noise.
At block 408, the SOC delimiting unit 212 calculates the upper and lower bounds of the SOC Est based on the SOCs Ah and v calculated in blocks 404 and 406, respectively. An exemplary upper bound may be defined as shown in expressions (3) and (5), and an exemplary lower bound may be defined as shown in expressions (4) and (6).
SOCu,bnd(t)=max{SOCAh(t),SOCV(t)}+|SOCAh(t)-SOCV(t)|+Edesg (3)
SOCl,bnd(t)=min{SOCAh(t),SOCV(t)}-|SOCAh(t)-SOCV(t)|-Edesg (4)
SOCu,bnd(t)E[0,1] (5)
SOCl,bnd(t)E[0,1) (6)
Where E desg represents a predetermined or designed error margin selected by SOC delimiting unit 212.
As shown in expressions (3) and (4) above, the maximum value of SOC Ah and SOC v may be used as the baseline for the upper bound, but additional margin may be applied. Moreover, the minimum values of SOC Ah and SOC v may be used as a baseline for the lower bound, and then additional tolerances may be applied. For example, the abstract value between SOC Ah and SOC v is used as an additional margin by taking into account errors/noise in the current sensor and/or voltage sensor and modeling other errors according to the measured voltage as shown in expression (2) (e.g., OCV is calculated as a function of SOC). In this way, the abstract value \SOC Ah-SOCv \may represent the uncertainty in the measurements and/or models used for the SOC delimitation process. In some implementations, additional accuracy tolerances (such as E desg) may be applied (e.g., applying ± 3% accuracy).
At block 410, the SOC delimiting unit 212 generates an SOC Bounded set between the upper bound SOC u,bnd (t) and the lower bound SOC l,bnd (t). An exemplary SOC Bounded may be defined as shown in expression (7) below.
SOCi,bnd(t)≤SOCBounded(t)≤SOCu,bnd(t) (7)
At block 412, the controller 118 controls the electrified process of the electric vehicle 102 based on the SOC Bounded. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge cycles and/or discharge cycles, or modify the minimum state of charge threshold based on the SOC Bounded.
Referring now to FIG. 5, another exemplary 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. 5, a method 500 of performing SOC delimitation processing using SOC delimitation unit 212 is shown. More specifically, when a battery pack is used for the stored energy power source 124 (e.g., fig. 2B) for a predetermined period of time (e.g., a microscopic time of about 1 second to 2 seconds), the SOC delimiter unit 212 performs one or more steps shown in fig. 5.
At block 502, the SOC estimator 204 estimates the SOCs Est,i of all the cells 124a, 124b, & gt, 124n in each array I based on the present current level I of the stored-energy power supply 124 and the time-based information. For example, a generic SOC estimator (such as DNKF) may be used to estimate SOC Est,i. In one embodiment, for i=1, 2,..n, n represents a plurality of cells or groups of cells in series.
At block 504, the SOC delimiter unit 212 calculates the SOC Ah based on the present current level I and the present temperature T of the stored-energy power source 124. In one example, the SOC Ah may be calculated using coulomb counting techniques. An exemplary SOC Ah may be defined as shown in expression (8) below.
Where I (t) is the input current during time t, SOC 0 represents the initial SOC at initial time t 0, and Capacity represents the present total Capacity generated by the stored-energy power supply 124.
At block 506, the SOC delimiting unit 212 calculates the SOC V,i of all the cells 124a, 124b, & gt, 124n in the battery pack based on the present voltage level V and the present temperature T of the stored-energy power source 124. An exemplary SOC V,i may be defined as shown in expression (9) below. In one embodiment, the SOC V,i may be filtered to remove noise due to dynamic voltage response.
SOCV,i(t)=[OCV-1(Vi(t)+I(t)R0,i]filtered (9)
Wherein OCV is the battery open circuit voltage, which is a function of SOC; r 0>i represents the internal resistance of the battery of each array i, which depends on the temperature T. In one example, filtering (filtered) may be performed using a single-pole low-pass filter. In one embodiment, a single pole low pass filter may be used to remove amplified noise in the SOC V,i, which may be due to measurements of OCV approximations and imperfect fidelity. Other suitable filters are also conceivable to be suitable for different applications. In another example, a linear phase low pass filter may also be used to remove amplification noise.
At block 508, the SOC delimiting unit 212 calculates the upper and lower bounds of the SOC Est based on the SOCs Ah and v,i calculated in blocks 504 and 506, respectively. 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).
SOCu,bnd(t)=max{SOCAh(t),SOCV,i(t)}+max{|SOCAh(t)-SOCV,i(t)|}+Edesg (10)
SOCl,bnd(t)=min{SOCAh(t),SOCV,i(t)}-max{|SOCAh(t)-SOCV,i(t)|}-Edesg (11)
SOOufind(t)e[0,1] (12)
SOCl,bnd(t)e[0,1] (13)
Where E desg represents a predetermined or design error margin selected by SOC delimiting unit 212.
As shown in expressions (10) and (11) above, the maximum values of SOC Ah and SOC v,i may be used as the base lines of the upper bound, but additional tolerances may be applied. Moreover, the minimum values of SOC Ah and SOC v,i may be used as a baseline for the lower bound, and then additional tolerances may be applied. For example, the abstract value between SOC Ah and SOC v,i is used as an additional margin by taking into account errors/noise in the current sensor and/or voltage sensor and modeling other errors according to the measured voltage as shown in expression (9) (e.g., OCV is calculated as a function of SOC). Thus, the maximum max { |soc Ah-SOCv,i } may represent the uncertainty in the measurements and/or models used for the SOC delimitation process. In some implementations, additional accuracy tolerances (such as E desg) may be applied (e.g., applying ± 3% accuracy).
At block 510, the SOC delimiting unit 212 generates an SOC Bounded set between the upper bound SOC u,bnd (t) and the lower bound SOCi ,bnd (t). SOC Bounded may be defined as shown in expression (14) below.
SOCi,bnd(t)≤SOCBounded,j(t)≤SOCu,bnd(t) (14)
At block 512, the controller 118 controls the electrified process of the electric vehicle 102 based on the SOC Bounded,i. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge cycles and/or discharge cycles, or modify the minimum state of charge threshold based on the SOC Bounded,i.
Referring now to fig. 6, an exemplary SOH delimitation process is shown in accordance with 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 delimitation processing using the SOH delimitation unit 214 is shown. More specifically, when a single battery is used for the stored energy power source 124 (e.g., fig. 2A) 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. 6.
At block 602, SOH estimator 208 estimates SOH Est based on the time-based information. For example, a generic SOC estimator (such as DNKF) may be used to estimate SOH Est.
At block 604, the SOH delimiting unit 214 calculates SOH F based on the start time and end time associated with the stored-energy power supply 124. SOH F may be stored in memory 120 for later retrieval and processing. For example, the full cycle SOH value represents the SOH value SOH F (L) estimated at the last capacity check of the stored-energy power supply 124. For every n charge cycles or m months, it is generally recommended that the stored energy power source 124 be fully charged and discharged. When such full charge and discharge operations can be performed, an exemplary SOH F (L) at the time of performing the last capacity check of the full charge cycle can be defined as shown in the following expression (15).
Where I (t) is the input current during time t, t Vulim represents the start time of the upper voltage limit of the energy storage power supply 124 during full discharge, t VI,lim represents the end time when the voltage reaches the lower voltage limit of the energy storage power supply 124 during full discharge, and the nominal capacity (Nominal Capacity) represents the total capacity generated by the energy storage power supply 124 when the energy storage power supply 124 is newly installed at 100% SOH.
However, when some applications cannot use or allow such full charge and discharge operations, another exemplary full cycle SOH value SOH F (L) may be defined as shown in expression (16) below.
Where I (t) is the input current during time t, t Vlow represents the start time when the SOC value of the stored-energy power supply 124 is less than about 20% prior to full charge, t Vulim represents the end time when the voltage reaches the upper voltage limit during full charge, and nominal capacity (Nominal Capacity) represents the total capacity generated by the stored-energy power supply 124 when the stored-energy power supply 124 was most recently installed at 100% SOH. In an embodiment, an exemplary full cycle SOH value SOH F (L) shown in expression (16) can be calculated in the following cases (1) to (3): (1) After the last run of the electric vehicle 102, the SOC value of the stored energy power supply 124 is less than 20%; (2) Subsequent charging events fully charge the stored energy power supply 124 to an upper voltage limit (e.g., soc=i00%); and (3) the temperature of the stored energy power source 124 is approximately between 25 degrees celsius and 35 degrees celsius (25 degrees celsius and 35 degrees celsius).
At block 606, the SOH delimiting unit 214 calculates a partial cycle SOH value SOH P based on the start time and the end time associated with the stored-energy power supply 124. SOH P may also be stored in memory 120 for later retrieval and processing. For example, the partial cycle SOH value represents the SOH value SOH P (L) estimated at the last capacity check of the stored-energy power supply 124. An exemplary SOH P (L) at the time of performing the last capacity check of the partial charge cycle can be defined as shown in expression (17) below.
Where t 1 denotes the start time of the partial cycle and t 2 denotes the end time of the partial cycle.
At block 608, the SOH bounding unit 214 calculates the upper and lower bounds of SOH Est based on SOH F (L) and SOH P (L) calculated in blocks 604 and 606, respectively. An exemplary upper bound may be defined as shown in expressions (18) and (20), and an exemplary lower bound may be defined as shown in expressions (19) and (21).
SOHu,bnd(L)=max{SOHF(L),SOHP(L)}+\SOHF(L)-SOHP(L)\+Edesg (18)
SOHl,bnd(L)=min{SOHF(L),SOHP(L)}-\SOHF(L)-SOHP(L)\-Edesg (19)
SOHU,bnd(L)e[0,1] (20)
SOHl,bnd(L)E[0,1] (21)
Where E desg represents a predetermined or design error margin selected by SOH delimiting unit 214.
As shown in expressions (18) and (19) above, the maximum value of SOH F and SOHp may be used as the baseline for the upper bound, but additional margin may be applied. Moreover, the minimum values of SOH F and SOH P may be used as a baseline for the lower bound, and then additional tolerances may be applied. For example, by taking into account the uncertainty of SOHF and SOH P estimates, the abstract value between SOH F and SOH P can be used as an additional margin. In some cases, SOH F may not be accurate enough due to unnecessary changes during performance of the capacity check every few months. As another example, SOH P may be inaccurate due to unnecessary sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., loss of charge due to time lapse). Thus, the abstract value SOH F(L)-SOHP (L) may represent the uncertainty in the measurements and/or models used for the SOH bounding process. In some implementations, additional accuracy tolerances (such as E desg) may be applied (e.g., applying ± 3% accuracy).
At block 610, the SOH bounding unit 214 generates SOH Bounded set between the upper bound SOH u,bnd (L) and the lower bound SOH l,bnd (L). An exemplary SOH Bounded may be defined as shown in expression (22) below.
SOHi,bnd(L)≤SOHBounded(L)≤SOHu,bnd(L) (22)
At block 612, the controller 118 controls the electrified process of the electric vehicle 102 based on the SOH Bounded. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge cycles and/or discharge cycles, or modify the minimum state of charge threshold based on SOH Bounded.
Referring now to fig. 7, another exemplary SOH delimitation process is shown in accordance with 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 delimitation processing using the SOH delimitation unit 214 is shown. More specifically, when a battery pack is used for the stored energy power source 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 SOH Est,i based on the time-based information. For example, a generic 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 F of the battery pack based on the start time and the end time associated with the stored-energy power supply 124. SOH F may be stored in memory 120 for later retrieval and processing. For example, the full cycle SOH value represents the SOH value SOH F (L) estimated at the last capacity check of the stored-energy power supply 124. For every n charge cycles or m months, it is generally recommended that the stored energy power source 124 be fully charged and discharged. When such full charge and discharge operations can be performed, an exemplary SOH F (L) at the time of performing the last capacity check of the full charge cycle can be defined as shown in the following expression (23).
Where I (t) is the input current during time t, t Vulim represents the start time of the upper voltage limit of the energy storage power supply 124 during full discharge, t Vl,lim represents the end time when the voltage reaches the lower voltage limit of the energy storage power supply 124 during full discharge, and the nominal capacity (Nominal Capacity) represents the total capacity generated by the energy storage power supply 124 when the energy storage power supply 124 is newly installed at 100% SOH.
However, when some applications are not able to use or allow such full charge and discharge operations, another exemplary full cycle SOH value SOH F (L) may be defined as shown in expression (24) below.
Where I (t) is the input current during time t, t Vlow represents the start time during full charge when the SOC value of the stored-energy power supply 124 is less than 20%, t Vu,lim represents the end time when the voltage reaches the upper voltage limit during full charge, and nominal capacity (Nominal Capacity) represents the total capacity generated by the stored-energy power supply 124 when the stored-energy power supply 124 was most recently installed at 100% SOH. In an embodiment, an exemplary SOH F (L) shown in expression (24) may be calculated in the following cases (1) to (3): (1) After the last run of the electric vehicle 102, the SOC value of the stored energy power supply 124 is less than 20%; (2) A subsequent charging event fully charges the stored energy power supply 124 to an upper voltage limit (e.g., soc=i00%); and (3) the temperature of the stored energy power source 124 is approximately between 25 degrees celsius and 35 degrees celsius (25 degrees celsius and 35 degrees celsius).
At block 706, the SOH delimiting unit 214 calculates a partial cycle SOH value SOH P,i based on the start time and the end time associated with the stored-energy power supply 124. SOH P,i may also be stored in memory 120 for later retrieval and processing. For example, the partial cycle SOH value represents the SOH value SOH P,i (L) estimated at the last capacity check of the stored-energy power supply 124. An exemplary SOH P,i (L) at the time of performing the last capacity check of the partial charge cycle can be defined as shown in the following expression (25).
Where t 1 denotes the start time of the partial cycle and t 2 denotes the end time of the partial cycle.
At block 708, the SOH bounding unit 214 calculates the upper and lower bounds of SOH Est based on SOH F (L) and SOH P,i (L) calculated in blocks 704 and 706, respectively. An exemplary upper bound may be defined as shown in expressions (26) and (28), and an exemplary lower bound may be defined as shown in expressions (27) and (29).
SOHu,bnd(L)=max{SOHF(L),SOHP,i(L)}+max{|SOHF(L)-SOHP,i(L)|}+Edesg (26)
SOHi,bnd(L)=min{SOHF(L),SOHP,i(L)}-max{|SOHF(L)-SOHP,i(L)|}-Edesg (27)
SOHu,bnd(L)e[0,1] (28)
SOHl,bnd(L)E[0,1] (29)
Where E desg represents a predetermined or design error margin selected by SOH delimiting unit 214.
As shown in expressions (26) and (27) above, the maximum values of SOH F and SOH P,i may be used as the base lines of the upper bound, but additional tolerances may be applied. Moreover, the minimum values of SOH F and SOH P,i may be used as a baseline for the lower bound, and then additional tolerances may be applied. For example, by taking into account the uncertainties of SOH F and SOH P,i estimates, the abstract values between SOH F and SOH P,i can be used as additional tolerances. In some cases, SOH F may not be accurate enough due to unnecessary changes during performance of the capacity check every few months. As another example, SOH P,i may be inaccurate due to unnecessary sensor errors, battery hysteresis, and unknown coulombic efficiency (e.g., loss of charge due to time lapse). Thus, the maximum max { |soh F(L)-SOHP,i (L) | } may represent the uncertainty in the measurements and/or models used for the SOH bounding process. In some implementations, additional accuracy tolerances (such as E desg) may be applied (e.g., applying ± 3% accuracy).
At block 710, the SOH bounding unit 214 generates SOH Bounded,i set between an upper bound SOH ubnd (L) and a lower bound SOH l,bnd (L). An exemplary SOH Bounded,i may be defined as shown in expression (30) below.
SOHi,bnd(L)≤SOHBounded,j(L)≤SOHu,bnd(L) (30)
At block 712, the controller 118 controls the electrified process of the electric vehicle 102 based on the SOH Bounded,i. For example, the controller 118 may modify battery cooling or charge and/or discharge limits, reduce the number of charge cycles and/or discharge cycles, or modify the minimum state of charge threshold based on SOH Bounded,i.
Referring now to fig. 8, another exemplary schematic of the power estimator 126 is shown. In the illustrated embodiment, the power estimator 126 includes an SOC/SOH estimator 200 and an SOC delimiter 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 energy 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 the SOC Est (e.g., 60%) based on the present current level I, the present voltage level V, and the present temperature T of the stored-energy power supply 124. The SOC Est is sent to the SOC delimiting unit 212.
The SOC delimiter unit 212 is configured to receive the SOC Est from the SOC/SOH estimator 200 and also to receive the present current level I, the present voltage level V, and the present temperature T of the stored-energy power source 124 from the vehicle monitoring unit 128. The SOC delimiter 212 is configured to calculate the upper and lower bounds that the SOC Est can apply based on the present current level I, the present voltage level V, and the present temperature T and SOC Est. The SOC delimiting unit 212 is configured to generate the SOC Bounded set between the upper and lower bounds. The SOC delimiter 212 is configured to output the SOC Bounded, the upper bound, and the lower bound for subsequent processing as needed. For example, the controller 118 may control the electrification process of the electric vehicle 102 based on the SOC Bounded.
Referring now to FIG. 9, an exemplary schematic diagram of the SOC delimiting 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. The Ah-based SOC calculation unit 900 is configured to calculate the SOC Ah based on the present current level I of the stored-energy power source 124. The voltage-based SOC calculation unit 902 is configured to calculate the SOC V based on the present voltage level V, the present temperature T, and the present current level I of the stored-energy power source 124. The SOC V may be filtered using a filter, such as a single pole low pass filter, to remove noise.
Exemplary calculations of the upper bound using the MinMax unit, add1 unit, abs unit, constants, and Add unit are shown in expression (31) below:
SOCu,bnd=max{SOCAh,SOCv}+\SOCAh-SOCv\+Edesg (31)。
Exemplary calculations for the lower bound using the MinMax1 unit, add3 unit, abs1 unit, constants, and Add2 unit are shown in expression (32) below:
SOCl,bnd=min{SOCAh,SOCV}-|SOCAh-SOCV|-Edesg (32)。
SOC delimiter 212 also includes a filter unit 904 configured to receive SOC Est, an upper bound, and a lower bound. The filtering unit 904 is configured to filter the SOC Est using the upper and lower bounds and generate the SOC Bounded such that the SOC Bounded is set between the upper and lower bounds. The SOC delimiter 212 may output the SOC Bounded, the upper bound, and the lower bound for subsequent processing as needed.
Although an SOC/SOH estimator 200 and an SOC delimiter unit 212 are shown in fig. 8 and 9, other suitable arrangements (such as an SOC/SOH estimator 200 and an SOH delimiter unit 214) are also contemplated to suit different applications.
It should be appreciated that the connecting lines shown in the various figures contained herein are intended to represent example 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, any benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element. Accordingly, the scope is not to be limited by nothing other than the appended claims, wherein 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 "at least one of A, B or C" is used in the claims, the phrase is intended to be construed 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. Furthermore, 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, one of ordinary skill in the relevant art will understand how to implement the present disclosure in alternative embodiments.
Moreover, 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 specification 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 example embodiments discussed without departing from the scope of the presently disclosed subject matter. For example, although the above embodiments 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 claims and all equivalents thereof.

Claims (12)

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 electric power estimation process by estimating an internal state of an energy storage power source of the electric vehicle, the internal state representing a state of charge SOC of the energy storage power source;
Estimating an SOC 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
Calculating upper and lower bounds associated with the SOC value, and estimating a bounded SOC value of the stored-energy power source based on the SOC value, the upper bound, and the lower bound,
Wherein the stored energy power source is a battery and the instructions, when executed by the processor, further cause the controller to:
generating the bounded SOC value of the battery, the bounded SOC value including the upper bound and the lower bound;
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 upper and lower bounds associated with the SOC value based on the amp-hour SOC and the voltage SOC,
Wherein a maximum value of the amp-hour SOC and the voltage SOC is used for the upper bound and a minimum value of the amp-hour SOC and the voltage SOC is used for the lower bound, and
Wherein the controller controls an electrification process of the electric vehicle based on the bounded SOC value.
2. The controller of claim 1, wherein the instructions, when executed by the processor, further cause the controller to filter the voltage SOC to remove noise.
3. The controller of claim 1, wherein the time-based information comprises one or more historical estimates of the SOC value.
4. The controller of claim 1, wherein the instructions, when executed by the processor, further cause the controller to estimate the bounded SOC value based on whether a predetermined period of time has elapsed.
5. The controller of claim 1, wherein the controller controls the electrified process by at least one of: modifying cooling of the stored energy power supply, modifying a charge limit/discharge limit of the stored energy power supply, reducing a number of charge cycles/discharge cycles of the stored energy power supply, and modifying a minimum SOC threshold.
6. The controller of claim 1, wherein the controller comprises a dual nonlinear kalman filter.
7. A method of performing a power estimation process of an electric vehicle using a controller, the method comprising the steps of:
Performing the electric power estimation process by estimating an internal state of an energy storage power source of the electric vehicle, the internal state representing a state of charge SOC of the energy storage power source;
Estimating an SOC 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 an upper bound and a lower bound associated with the SOC value;
estimating a bounded SOC value of the energy storage power supply based on the SOC value, the upper bound, and the lower bound;
wherein the stored energy power source is a battery and the method further comprises:
generating the bounded SOC value of the battery, the bounded SOC value including the upper bound and the lower bound;
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;
Calculating the upper and lower bounds associated with the SOC values 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 upper bound and a minimum of the amp-hour SOC and the voltage SOC is used for the lower bound; and
And controlling an electrification process of the electric vehicle based on the bounded SOC value.
8. The method of claim 7, further comprising the step of: the voltage SOC is filtered to remove noise.
9. The method of claim 7, wherein the time-based information includes one or more historical estimates of the SOC value.
10. The method of claim 7, wherein estimating the bounded SOC value is further based on whether a predetermined period of time has elapsed.
11. The method of claim 7, wherein controlling the electrification process comprises at least one of: modifying cooling of the stored energy power supply, modifying a charge limit/discharge limit of the stored energy power supply, reducing a number of charge cycles/discharge cycles of the stored energy power supply, and modifying a minimum SOC threshold.
12. The method of claim 7, wherein the power estimation process is performed by a dual nonlinear kalman filter.
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