WO2024067388A1 - 一种电池健康状态估算方法、系统、设备和介质 - Google Patents

一种电池健康状态估算方法、系统、设备和介质 Download PDF

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Publication number
WO2024067388A1
WO2024067388A1 PCT/CN2023/120636 CN2023120636W WO2024067388A1 WO 2024067388 A1 WO2024067388 A1 WO 2024067388A1 CN 2023120636 W CN2023120636 W CN 2023120636W WO 2024067388 A1 WO2024067388 A1 WO 2024067388A1
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Prior art keywords
charging
battery
charge
state
battery pack
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PCT/CN2023/120636
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English (en)
French (fr)
Inventor
吴正国
李东江
伊炳希
郑昌文
江振文
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深蓝汽车科技有限公司
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Publication of WO2024067388A1 publication Critical patent/WO2024067388A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the present application relates to the field of power batteries, and in particular to a battery health status estimation method, system, device and medium.
  • the state of health reflects the degree of attenuation of the current capacity of the power battery. Its main characterization parameters are capacity and internal resistance. At present, the battery management system generally uses the attenuation of capacity as a characterization indicator of the SOH of the power battery of new energy vehicles. Accurate estimation of SOH not only helps the safe use and maintenance of the power battery, but also can further improve the accuracy of SOC estimation, and is conducive to the evaluation of the value of used vehicles. Usually the on-board BMS will calculate the current SOH value of the vehicle, but due to hardware limitations, the vehicle cannot store a large amount of data. At the same time, SOH is closely related to the battery management system strategy.
  • the present application proposes a battery health state estimation method, system, device and medium, which mainly solves the problem of poor accuracy of battery health state estimation in the existing methods.
  • the present application provides a battery health status estimation method, comprising:
  • the current health state of the battery pack is determined according to the optimal chargeable quantity of each charging segment, wherein the battery pack is composed of a plurality of battery cells.
  • the battery pack is determined according to the optimal chargeable power of each charging segment. After the current health status, it also includes:
  • the current state of charge and the current state of health are output to the vehicle-side battery management system.
  • determining the current state of charge of the corresponding battery pack according to the charging data of the most recent charging segment and the current health state includes:
  • the current state of charge is calculated according to the current chargeable amount of electricity and the accumulated charged amount of electricity.
  • the method before obtaining charging data of each battery cell in a plurality of charging segments within a preset time period, the method further includes:
  • test data of each battery under standard working conditions including charging capacity and voltage of the battery cell at different temperatures and currents, and determine the state of charge of the battery cell at a corresponding time node according to the charging capacity;
  • the internal resistance of the battery cell at different temperatures, currents and charge states is determined according to the difference between the electromotive force curve and the voltage curve.
  • determining the electromotive force curve of the corresponding battery cell at different states of charge according to the voltage curve includes:
  • the voltage curve is interpolated to obtain curves with zero current at different temperatures as electromotive force curves at different charge states.
  • determining a first relationship between the state of charge and the electromotive force in the corresponding charging segment according to the charging data includes:
  • the electromotive force value at the corresponding time point is determined according to the internal resistance, current and corresponding voltage value of the battery cell at the corresponding time point, and a first relationship between the state of charge and the electromotive force in the corresponding charging segment is obtained, wherein the initial state of charge, current, voltage value and temperature are included in the charging data of the corresponding charging segment.
  • determining the current health state of the battery pack according to the optimal chargeable power of each charging segment includes:
  • Filtering is performed based on the optimal charging power of each charging segment to obtain a filtered charging power of the battery pack;
  • the current health state of the battery pack is determined according to the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack.
  • the method before determining the current health state of the battery pack according to the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack, the method further includes:
  • the rated capacity of the battery pack is updated according to the entire pack capacity to complete capacity calibration.
  • a target loss function is constructed according to the first relationship and a preset second relationship between the state of charge and the electromotive force, and optimization is performed according to the target loss function, including:
  • the gradient descent method is used to obtain the optimal initial state of charge and the optimal chargeable capacity of the corresponding charging segment to minimize the value of the target loss function.
  • the present application also provides a battery health status estimation system, comprising:
  • a charging data acquisition module used to acquire charging data of each battery cell in a plurality of charging segments within a preset time period, each charging segment corresponding to a continuous charging time period;
  • the electromotive force determination module is used to determine the state of charge and the voltage in the corresponding charging segment according to the charging data.
  • an optimization module configured to construct a target loss function according to the first relationship and a preset second relationship between the state of charge and the electromotive force, and to optimize according to the target loss function to obtain an optimal chargeable power corresponding to the charging segment when the target loss function is minimized;
  • the state evaluation module is used to determine the current health state of the battery pack according to the optimal chargeable power of each charging segment, wherein the battery pack is composed of a plurality of battery cells.
  • the present application also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the battery health status estimation method when executing the computer program.
  • the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the battery health status estimation method are implemented.
  • the present application provides a battery health status estimation method, system, device and medium, which have the following beneficial effects.
  • the present application obtains the charging data of each battery cell in multiple charging segments within a preset time period, and each charging segment corresponds to a continuous charging time period; determines a first relationship between the state of charge and the electromotive force in the corresponding charging segment based on the charging data; constructs a target loss function based on the first relationship and a preset second relationship between the state of charge and the electromotive force, and optimizes according to the target loss function to obtain the optimal chargeable amount of the corresponding charging segment when the target loss function is minimized; determines the current health state of the battery pack based on the optimal chargeable amount of each charging segment.
  • the present application uses the battery's historical charging data to evaluate the health state, which can effectively improve the accuracy of health state estimation, is not restricted by conditions such as temperature and voltage, and is conducive to updating the initial state of charge on the vehicle side based on the estimated health state, so that the battery management system can provide a more reasonable charging and discharging strategy.
  • FIG1 is a schematic diagram of an application scenario of a battery health status estimation system in an embodiment of the present application.
  • FIG. 2 is a schematic diagram of the structure of a terminal according to an embodiment of the present application.
  • FIG3 is a flow chart of a method for estimating a battery health status in an embodiment of the present application.
  • FIG4 is a schematic diagram of the overall process of estimating the battery health status in another embodiment of the present application.
  • FIG. 5 is a schematic diagram of different charging curves and electromotive force calculation of a battery at a certain temperature in an embodiment of the present application.
  • FIG. 6 is a schematic diagram of calculating the capacity of a battery cell in an embodiment of the present application.
  • FIG. 7 is a module diagram of a battery health status estimation system in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a device in an embodiment of the present application.
  • the health status estimation method in the prior art usually collects data during DC charging of the power battery and calculates the real-time capacity that meets the preferred conditions through ampere-hour integration.
  • the ratio of the average capacity to the rated capacity of each month is calculated to obtain the relative SOH, and then the fitting curve of SOH is obtained through polynomial fitting and continuously updated.
  • There are errors in using the current data in the cloud for ampere-hour integration and there are estimation errors in the vehicle-side SOC estimation itself. Using the vehicle-side SOC value to reverse the SOH value will lead to error accumulation, making the estimated SOH value inaccurate.
  • the present application provides a battery health status estimation method, system equipment and medium.
  • the technical solution of the present application is elaborated in detail below in conjunction with specific embodiments.
  • FIG. 1 is a schematic diagram of the application scenario of the battery health status estimation system in one embodiment of the present application.
  • the vehicle-side system corresponding to the vehicle can collect data for each charging of the electric vehicle, such as the charging start time, end time, battery temperature, battery voltage, charging current, and charged power during the charging process.
  • the initial state of charge of the vehicle can be retrieved from the cloud server 200 as the initial state of charge reference data for the current charging start stage.
  • the vehicle uploads the collected charging data to the battery data management platform in the cloud through the network 100.
  • the battery data management platform is set at the server 200 and is used to organize the charging data uploaded by the vehicle, such as dividing each historical charging data into multiple charging segments according to each continuous charging time period, so that each charging segment corresponds to a continuous charging time period. Data cleaning can also be performed to remove invalid data or other interfering data.
  • the server 200 can save the battery pack uploaded by the battery supplier. Rated capacity, and test data before the battery leaves the factory, etc. Based on the test data, the voltage curve of the corresponding battery pack relative to the capacity curve and the electromotive force curve relative to the capacity curve are generated.
  • the test data can be obtained by statistically analyzing the charge and discharge data of the battery under standard working conditions at different temperatures and current conditions.
  • server 200 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, as well as big data and artificial intelligence platforms.
  • cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, as well as big data and artificial intelligence platforms.
  • the vehicle side can also calculate the cumulative charging power of the corresponding charging segment based on the ampere-hour points, and upload the cumulative charging power to the server 200 through the network 100 to avoid errors in the cumulative charging power calculation performed by the cloud server 200.
  • the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart voice interaction device, a smart home appliance, and a vehicle terminal, but is not limited thereto.
  • FIG. 2 is a schematic diagram of the structure of a terminal 400 provided in an embodiment of the present application.
  • the terminal 400 shown in FIG. 2 includes: at least one processor 410, a memory 450, at least one network interface 420, and a user interface 430.
  • the various components in the terminal 400 are coupled together via a bus system 440.
  • the bus system 440 is used to realize the connection and communication between these components.
  • the bus system 440 also includes a power bus, a control bus, and a status signal bus.
  • various buses are labeled as bus system 440 in FIG. 2 .
  • Processor 410 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.
  • DSP digital signal processor
  • the user interface 430 includes one or more output devices 431 that enable presentation of media content, including one or more speakers and/or one or more visual display screens.
  • the user interface 430 also includes one or more input devices 432, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.
  • the memory 450 may be removable, non-removable, or a combination thereof.
  • Exemplary hardware devices include solid-state memory, hard drives, optical drives, etc.
  • the memory 250 may optionally include one or more storage devices that are physically remote from the processor 410.
  • the memory 450 includes a volatile memory or a nonvolatile memory, and may also include a volatile and a nonvolatile memory.
  • the non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM).
  • ROM read-only memory
  • RAM random access memory
  • the memory 450 described in the embodiment of the present application is intended to include any suitable type of memory.
  • memory 450 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.
  • Operating system 451 including system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks;
  • a network communication module 452 for reaching other computing devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 include: Bluetooth, Wireless Compatibility Authentication (WiFi), and Universal Serial Bus (USB), etc.;
  • a presentation module 453 for enabling presentation of information via one or more output devices 431 (e.g., display screen, speaker, etc.) associated with the user interface 430 (e.g., a user interface for operating peripherals and displaying content and information);
  • output devices 431 e.g., display screen, speaker, etc.
  • the user interface 430 e.g., a user interface for operating peripherals and displaying content and information
  • the input processing module 454 is used to detect one or more user inputs or interactions from one of the one or more input devices 432 and translate the detected inputs or interactions.
  • the device provided in the embodiments of the present application can be implemented in software.
  • Figure 2 shows a battery health status estimation system 455 stored in the memory 450, which can be software in the form of programs and plug-ins, including the following software modules: a charging data acquisition module 4551, an electromotive force determination module 4552, an optimization module 4553 and a status evaluation module 4554. These modules are logical, and therefore can be arbitrarily combined or further split according to the functions implemented.
  • the system provided in the embodiments of the present application can be implemented in hardware.
  • the system provided in the embodiments of the present application can be a processor in the form of a hardware decoding processor, which is programmed to execute the battery health status estimation method provided in the embodiments of the present application.
  • the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.
  • ASICs application-specific integrated circuits
  • DSPs digital signal processor
  • PLDs programmable logic devices
  • CPLDs complex programmable logic devices
  • FPGAs field programmable gate arrays
  • the terminal or server can implement the battery health status estimation method provided in the embodiments of the present application by running a computer program.
  • the computer program can be a native program or software module in the operating system; it can be a native application (APP, Application), that is, a program that needs to be installed in the operating system to run, such as a social application APP or a message sharing APP; it can also be a small program, that is, a program that can be run only by downloading it to a browser environment; it can also be a small program or a web client program that can be embedded in any APP.
  • the above-mentioned computer program can be any form of application, module or plug-in.
  • the battery health status estimation method provided in the embodiment of the present application will be explained below in combination with the exemplary application and implementation of the device provided in the embodiment of the present application.
  • the present application provides a method for estimating a battery health state, which includes the following steps.
  • Step S300 acquiring charging data of each battery cell in a plurality of charging segments within a preset time period, each of the charging segments corresponding to a continuous charging time period.
  • the charging data uploaded by the vehicle may be stored via a cloud platform, and the charging data may include: charging time, charging current, battery voltage, battery temperature, accumulated charged power, and other data.
  • Step S301 obtaining test data of each battery under standard working conditions, wherein the test data includes the charging capacity and voltage of the battery cell at different temperatures and currents, and determining the charge state of the battery cell at the corresponding time node according to the charging capacity.
  • the capacity of the battery under standard operating conditions can be tested, and the constant current charging curve of the battery under different temperatures and current conditions can be tested, and its temperature, charging current, charging capacity and voltage can be recorded.
  • the temperature and charging current range should be larger than the temperature range used by the battery, and the charging current setting in the small current range should be denser to ensure the accuracy of the subsequent calculation of the electromotive force curve.
  • Step S302 determining the electromotive force curve of the corresponding battery cell at different states of charge according to the voltage curve.
  • the electromotive force-capacity curve (EMF curve) of the battery is estimated by an outward interpolation estimation method, and the value R(SOC,T,I) of the battery internal resistance at different SOCs, different temperatures, and different currents is calculated based on the charging curve and the EMF curve.
  • Step S303 determining the different temperatures, voltages and The internal resistance of the battery cell under current and charge state.
  • determining the electromotive force curve of the corresponding battery cell at different states of charge according to the voltage curve includes:
  • the voltage curve is interpolated to obtain curves with zero current at different temperatures as electromotive force curves at different charge states.
  • the voltage curve of the battery at different charging capacities when the current is 0 is estimated by using an external interpolation estimation method.
  • This curve is the electromotive force-capacity curve (EMF curve) of the battery, and the value R(SOC, T, I) of the internal resistance of the battery under different SOCs, different temperatures, and different currents is calculated based on the charging curve and the EMF curve.
  • U represents the voltage
  • EMF represents the electromotive force of the battery.
  • the voltage when the current is 0 can be preliminarily estimated by extrapolation as the electromotive force EMF value.
  • the external interpolation method can use linear interpolation or other interpolation methods.
  • the reason for using this method to calculate the EMF curve instead of statically calculating the voltage is that it takes a long test time to measure the open circuit voltage of the battery after static, and the number of measurement points is limited. Using the charging curve extrapolation method to calculate the EMF curve can greatly save test time.
  • Step S304 determining the internal resistance of the battery at different temperatures, currents and charge states according to the difference between the electromotive force curve and the voltage curve.
  • R(SOC,I,T) represents the internal resistance of the battery at different SOCs, different currents, and different temperatures.
  • the product of the internal resistance and the current is the difference between the voltage curve and the EMF curve. This value is less affected by the current and the influence of the current can be ignored during the calculation process.
  • the charging data uploaded by the vehicle to the Internet of Vehicles cloud platform can be extracted.
  • the extracted fields include current, voltage, time, battery SOC, cumulative charged power, etc.
  • the cumulative charged power is not a field required to be uploaded according to the national standard (GBT32960), and the vehicle side can be required to upload this field.
  • the frequency of calculating the cumulative charging capacity at the vehicle end is higher and the accuracy is higher.
  • the data of the Internet of Vehicles cloud platform can also be used to calculate the cumulative charging capacity.
  • the ampere-hour integration method is used to calculate the charged capacity.
  • the discrete data integration method is used. Assume that there are n rows of charging data, and the charging time is recorded as t 1 , t 2 , ..., t n , and the current is recorded as I 1 , I 2 , ..., In , then the charged capacity is quantity However, due to the bandwidth, the cloud data has a long cycle and the calculation accuracy is average.
  • the selection of the specific cumulative charge calculation location can be set according to the actual application requirements and is not limited here.
  • Step S310 determining a first relationship between the state of charge and the electromotive force in a corresponding charging segment according to the charging data.
  • determining a first relationship between the state of charge and the electromotive force in a corresponding charging segment according to the charging data includes:
  • the electromotive force value at the corresponding time point is determined according to the internal resistance, current and corresponding voltage value of the battery cell at the corresponding time point, and a first relationship between the state of charge and the electromotive force in the corresponding charging segment is obtained, wherein the initial state of charge, current, voltage value and temperature are included in the charging data of the corresponding charging segment.
  • each cell is calculated separately.
  • a point is selected at the beginning of charging as the initial point of calculation.
  • the actual SOC of the battery at this moment is recorded as x 0 .
  • the point at time i after the initial point is selected and recorded as x i .
  • Qm represents the current chargeable amount of the monomer
  • ⁇ Idt represents the charge from x0 to xi, which is calculated by the cumulative charge or the cloud-side current-time ampere-hour integration method.
  • the estimated internal resistance at time i is shown, and its value is calculated by interpolation based on the R(SOC,I,T) table of the previous performance test.
  • the electromotive force value at time i can be obtained as EMF( xi ) based on the EMF curve obtained in the test.
  • Step S320 constructing a target loss function according to the first relationship and a preset second relationship between the state of charge and the electromotive force, optimizing according to the target loss function, and obtaining the optimal chargeable power corresponding to the charging segment when the target loss function is minimized.
  • a target loss function is constructed according to the first relationship and a preset second relationship between the state of charge and the electromotive force, and optimization is performed according to the target loss function, including:
  • the gradient descent method is used to obtain the optimal initial state of charge and the optimal chargeable capacity of the corresponding charging segment to minimize the value of the target loss function.
  • a plurality of charging time points are selected respectively, and x 0 and Q m are calculated by solving the optimal solution.
  • the loss function is in Taking the battery SOC and the rated capacity of the whole pack uploaded at the initial moment as the starting point of calculation, the gradient descent method is used to calculate x 0 and Q m so that the loss function L is minimized.
  • Step S330 determining the current health state of the battery pack according to the optimal chargeable power of each charging segment, wherein the battery pack is composed of a plurality of battery cells.
  • determining the current health state of the battery pack according to the optimal chargeable power of each charging segment includes:
  • Filtering is performed based on the optimal charging power of each charging segment to obtain a filtered charging power of the battery pack;
  • the current health state of the battery pack is determined according to the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack.
  • Calculate the charging time of each charging segment Calculate the current chargeable capacity of the entire pack of batteries based on a certain filtering method, and calculate the current health status of the power battery based on the definition of SOH in It represents the battery pack capacity after filtering.
  • the filtering method can be moving mean or Kalman filtering.
  • Qr is the theoretical rated capacity of the power battery.
  • the method before determining the current health state of the battery pack according to the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack, the method further includes:
  • the rated capacity of the battery pack is updated according to the entire pack capacity to complete capacity calibration.
  • the rated capacity can be obtained from the vehicle manufacturer or the battery manufacturer.
  • the method is used to calculate and update the entire battery pack capacity to avoid differences in battery rated capacity calculation due to inconsistency, and to ensure that each vehicle is calculated separately to improve accuracy.
  • the method further includes:
  • the current state of charge and the current state of health are output to the vehicle-side battery management system.
  • determining the current state of charge of the corresponding battery according to the charging data of the most recent charging segment and the current health state includes:
  • the current health status of the battery is determined based on the product of the current health status and the preset battery rated capacity. Charge the battery;
  • the current state of charge is calculated according to the current chargeable amount of electricity and the accumulated charged amount of electricity.
  • the latest charging segment is used to calculate the current battery's accurate initial SOC value (i.e., current state of charge), and the vehicle's initial SOC value and calculated SOH value are sent back to the vehicle's battery management system (BMS).
  • BMS updates the SOH value and the initial value of the SOC calculation based on the cloud calculation results, ensuring that the vehicle's BMS system accurately estimates and effectively controls the battery status.
  • FIG. 4 is a schematic diagram of the overall process of battery health status estimation in another embodiment of the present application.
  • Figure 5 is a schematic diagram of different charging curves and electromotive force calculations of the battery at
  • the charging data uploaded by the vehicle to the IoV cloud platform is extracted, and the extracted fields include current, voltage, time, battery SOC, cumulative charge, etc.
  • the extracted fields are cleaned and invalid data is removed.
  • the charging data is divided into m charging segments, each continuous charging period is regarded as a charging segment, and these charging segments are sorted by time, and the serial numbers are marked as 1, 2, ... m.
  • FIG. 6 is a schematic diagram of the calculation of the battery cell capacity in an embodiment of the present application.
  • each cell is calculated separately, and the R (SOC, T, I) obtained from the initial performance test is used to estimate the relationship between the estimated value EMF' of the EMF curve of the cell in the current charging segment and the cell SOC according to the current temperature T and current I; the charging starting SOC of different cell cells and the chargeable capacity Qm of the cell are calculated by seeking the optimal solution based on the EMF curve of the initial performance test and the EMF' curve of the charging segment; all cells are calculated in the same way to obtain the initial SOC of each cell in the current charging segment.
  • each monomer charging capacity Calculate the current chargeable capacity of the entire package Calculate the charging time of each charging segment Calculate the current chargeable capacity of the entire pack of batteries based on a certain filtering method, and calculate the current health status of the power battery based on the definition of SOH Where Qr is the theoretical rated capacity of the power battery, which can be obtained from the vehicle manufacturer or battery manufacturer.
  • this method is used to calculate and update the entire battery pack capacity to avoid differences in battery rated capacity calculation due to inconsistency, and to ensure that each vehicle is calculated separately to improve accuracy; based on the current SOH value, the latest charging fragment is used to calculate the accurate initial SOC value of the current battery, and the initial SOC value and calculated SOH value of the vehicle are sent back to the vehicle battery management system (BMS).
  • BMS vehicle battery management system
  • the BMS updates the SOH value and the initial value of the SOC calculation based on the cloud calculation results to ensure that the vehicle BMS system accurately estimates and effectively controls the battery status.
  • FIG. 7 is a module diagram of a battery health status estimation system in an embodiment of the present application, the system comprising: a charging data acquisition module 4551, used to obtain charging data of each battery cell in multiple charging segments within a preset time period, each of the charging segments corresponding to a continuous charging time period; an electromotive force determination module 4552, used to determine a first relationship between the state of charge and the electromotive force in the corresponding charging segment based on the charging data; an optimization module 4553, used to construct a target loss function based on the first relationship and a preset second relationship between the state of charge and the electromotive force, and perform optimization based on the target loss function to obtain the optimal rechargeable power of the corresponding charging segment when the target loss function is minimized; a state evaluation module 4554, used to determine the current health status of the battery pack based on the optimal rechargeable power of each of the charging segments, wherein the battery pack is composed of multiple battery cells.
  • a charging data acquisition module 4551 used to obtain charging data of each battery cell in multiple charging
  • the status evaluation module 4554 is also used to determine the current health state of the battery pack based on the optimal chargeable power of each charging segment, and also includes: obtaining the charging data of the most recent charging segment of the battery pack, and determining the current charge state of the corresponding battery pack based on the charging data of the most recent charging segment and the current health state; outputting the current charge state and the current health state to the vehicle-side battery management system.
  • the state evaluation module 4554 is further configured to: The method comprises: determining the current chargeable capacity of the battery pack according to the product of the current health status and the preset battery rated capacity; determining the cumulative charge capacity of the battery pack in the most recent charging segment according to the charging data of the most recent charging segment; and calculating the current charge state according to the current chargeable capacity and the cumulative charge capacity.
  • the system also includes a test data acquisition module, which is used to obtain the charging data of each battery cell in multiple charging segments within a preset time period, and also includes: obtaining test data of each battery under standard operating conditions, the test data including the charging capacity and voltage of the battery cell at different temperatures and currents, and determining the charge state of the battery cell at the corresponding time node according to the charging capacity; generating voltage curves at different temperatures, currents and charge states according to the voltage in the test data; determining the electromotive force curve of the corresponding battery cell at different charge states according to the voltage curve; and determining the internal resistance of the battery cell at different temperatures, currents and charge states according to the difference between the electromotive force curve and the voltage curve.
  • a test data acquisition module which is used to obtain the charging data of each battery cell in multiple charging segments within a preset time period, and also includes: obtaining test data of each battery under standard operating conditions, the test data including the charging capacity and voltage of the battery cell at different temperatures and currents, and determining the charge state of the battery cell
  • the state evaluation module 4554 is further used to determine the electromotive force curve of the corresponding battery cell at different charge states according to the voltage curve, including: interpolating the voltage curve to obtain curves with zero current at different temperatures as the electromotive force curves at different charge states.
  • the electromotive force determination module 4552 is also used to determine a first relationship between the state of charge and the electromotive force in the corresponding charging segment based on the charging data, including: selecting a point at the start of charging of each of the charging segments as the initial point, and obtaining the actual state of charge at the initial point as the initial state of charge; determining the state of charge at different time points of the corresponding charging segment based on the initial state of charge, the cumulative charged capacity and the chargeable capacity of the battery at different time nodes, wherein the cumulative charged capacity is obtained by integrating ampere-hours; determining the internal resistance of the battery cell at the corresponding time point based on the state of charge, current and temperature at the different time points; determining the electromotive force value at the corresponding time point based on the internal resistance of the battery cell, current and corresponding voltage value at the corresponding time point, and obtaining the first relationship between the state of charge and the electromotive force in the corresponding charging segment, wherein the initial state of charge, current, voltage value and
  • the status evaluation module 4554 is also used to determine the current health state of the battery pack based on the optimal chargeable power of each of the charging segments, including: filtering based on the optimal charge power of each of the charging segments to obtain the filtered charge power of the battery pack; determining the current health state of the battery pack based on the ratio of the charge power of the filtered battery pack to the rated capacity of the preset battery pack.
  • the state evaluation module 4554 is further configured to: Before determining the current health status of the battery pack by the ratio of the power level to the rated capacity of the preset battery pack, the method also includes: obtaining the charging data of the battery pack within a preset usage period when the battery pack is just put into use; obtaining the entire pack capacity of the battery pack based on the charging data of the battery pack; and updating the rated capacity of the battery pack based on the entire pack capacity to complete capacity calibration.
  • the optimization module 4553 is also used to construct a target loss function based on the first relationship and a preset second relationship between the state of charge and the electromotive force, and perform optimization based on the target loss function, including: constructing the target loss function based on the square difference between the first relationship and the second relationship; taking the initial state of charge and rated capacity uploaded by the system at the initial moment of each charging segment as the calculation starting point, and using the gradient descent method to obtain the optimal initial state of charge and the optimal chargeable capacity of the corresponding charging segment to minimize the value of the target loss function.
  • the battery health status estimation system can be implemented in the form of a computer program, and the computer program can be run on a computer device as shown in Figure 8.
  • the computer device includes: a memory, a processor, and a computer program stored in the memory and run on the processor.
  • Each module in the above battery health status estimation system can be implemented in whole or in part by software, hardware and their combination.
  • Each module can be embedded in or independent of the terminal's memory in the form of hardware, or can be stored in the terminal's memory in the form of software, so that the processor can call and execute the operations corresponding to each module.
  • the processor can be a central processing unit (CPU), a microprocessor, a single-chip microcomputer, etc.
  • a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: obtaining charging data of each battery cell in multiple charging segments within a preset time period, each charging segment corresponding to a continuous charging time period; determining a first relationship between the state of charge and the electromotive force in the corresponding charging segment according to the charging data; constructing a target loss function according to the first relationship and a preset second relationship between the state of charge and the electromotive force, optimizing according to the target loss function, and obtaining the optimal chargeable power of the corresponding charging segment when the target loss function is minimized; determining the current health state of a battery pack according to the optimal chargeable power of each charging segment, wherein the battery pack is composed of multiple battery cells.
  • the current health state of the battery pack is determined according to the optimal chargeable power of each charging segment, and further includes: obtaining charging data of the most recent charging segment of the battery pack, determining the current charge state of the corresponding battery pack according to the charging data of the most recent charging segment and the current health state; and inputting the current charge state and the current health state into the battery pack. Output to the vehicle-side battery management system.
  • the current state of charge of the corresponding battery pack is determined based on the charging data of the most recent charging segment and the current health status, including: determining the current chargeable amount of the battery pack based on the product of the current health status and a preset battery rated capacity; determining the cumulative charged amount of the battery pack in the most recent charging segment based on the charging data of the most recent charging segment; and calculating the current state of charge based on the current chargeable amount and the cumulative charged amount.
  • the method when the above-mentioned processor is executed, before obtaining the charging data of each battery cell in multiple charging segments within a preset time period, the method also includes: obtaining test data of each battery under standard operating conditions, the test data including the charging capacity and voltage of the battery cell at different temperatures and currents, and determining the charge state of the battery cell at the corresponding time node according to the charging capacity; generating a voltage curve at different temperatures, currents and charge states according to the voltage in the test data; determining the electromotive force curve of the corresponding battery cell at different charge states according to the voltage curve; and determining the internal resistance of the battery cell at different temperatures, currents and charge states according to the difference between the electromotive force curve and the voltage curve.
  • determining the electromotive force curve of the corresponding battery cell under different charge states according to the voltage curve includes: interpolating the voltage curve to obtain curves with zero current at different temperatures as the electromotive force curves under different charge states.
  • the first relationship between the state of charge and the electromotive force in the corresponding charging segment is determined according to the charging data, including: selecting a point at the beginning of the charging stage of each of the charging segments as the initial point, and obtaining the actual state of charge at the initial point as the initial state of charge; determining the state of charge at different time points of the corresponding charging segment according to the initial state of charge, the cumulative charged capacity and the chargeable capacity of the battery at different time nodes, wherein the cumulative charged capacity is obtained by integrating ampere-hours; determining the internal resistance of the battery cell at the corresponding time point according to the state of charge, current and temperature at the different time points; determining the electromotive force value at the corresponding time point according to the internal resistance of the battery cell at the corresponding time point, the current and the corresponding voltage value, and obtaining the first relationship between the state of charge and the electromotive force in the corresponding charging segment, wherein the initial state of charge, current, voltage value and temperature
  • the current health state of the battery pack is determined according to the optimal chargeable power of each charging segment, including: filtering based on the optimal chargeable power of each charging segment to obtain the filtered chargeable power of the battery pack;
  • the current health state of the battery pack is determined by a ratio of the battery capacity to the rated capacity of the preset battery pack.
  • the above-mentioned processor when executed, before determining the current health status of the battery pack based on the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack, it also includes: obtaining the charging data of the battery pack within a preset usage period when the battery pack is just put into use; obtaining the entire pack capacity of the battery pack based on the charging data of the battery pack; and updating the rated capacity of the battery pack based on the entire pack capacity to complete capacity calibration.
  • the target loss function is constructed according to the first relationship and the second relationship between the preset state of charge and the electromotive force, and the optimization is performed according to the target loss function, including: constructing the target loss function according to the square difference between the first relationship and the second relationship; taking the initial state of charge and the rated capacity uploaded by the system at the initial moment of each charging segment as the calculation starting point, and using the gradient descent method to obtain the optimal initial state of charge and the optimal chargeable capacity of the corresponding charging segment to minimize the value of the target loss function.
  • the above-mentioned computer device can be used as a server, including but not limited to an independent physical server, or a server cluster composed of multiple physical servers.
  • the computer device can also be used as a terminal, including but not limited to a mobile phone, a tablet computer, a personal digital assistant or a smart device, etc.
  • the computer device includes a processor, a non-volatile storage medium, an internal memory, a display screen and a network interface connected via a system bus.
  • the processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device.
  • the non-volatile storage medium of the computer device stores an operating system and a computer program.
  • the computer program can be executed by the processor to implement a battery health status estimation method provided in each of the above embodiments.
  • the internal memory in the computer device provides a cache operating environment for the operating system and computer program in the non-volatile storage medium.
  • the display interface can display data through the display screen.
  • the display screen can be a touch screen, such as a capacitive screen or an electronic screen, and can generate corresponding instructions by receiving a click operation acting on a control displayed on the touch screen.
  • FIG. 8 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer readable storage medium on which a computer program is stored.
  • the following steps are implemented: obtaining charging data of each battery cell in multiple charging segments within a preset time period, each charging segment corresponds to a continuous charging time period; determining a first relationship between the state of charge and the electromotive force in the corresponding charging segment based on the charging data; constructing a target loss function based on the first relationship and a preset second relationship between the state of charge and the electromotive force, optimizing based on the target loss function, and obtaining the optimal chargeable power of the corresponding charging segment when the target loss function is minimized; determining the current health state of the battery pack based on the optimal chargeable power of each charging segment, wherein the battery pack is composed of multiple battery cells.
  • the computer program when executed by a processor, after determining the current health state of the battery pack according to the optimal chargeable power of each charging segment, it also includes: obtaining the charging data of the most recent charging segment of the battery pack, determining the current charge state of the corresponding battery pack according to the charging data of the most recent charging segment and the current health state; and outputting the current charge state and the current health state to the vehicle-side battery management system.
  • the current state of charge of the corresponding battery pack is determined based on the charging data of the most recent charging segment and the current health status, including: determining the current chargeable amount of the battery pack based on the product of the current health status and a preset battery rated capacity; determining the cumulative charged amount of the battery pack in the most recent charging segment based on the charging data of the most recent charging segment; and calculating the current state of charge based on the current chargeable amount and the cumulative charged amount.
  • the program when the computer program is executed by a processor, before obtaining the charging data of each battery cell in multiple charging segments within a preset time period, the program also includes: obtaining test data of each battery under standard operating conditions, wherein the test data includes the charging capacity and voltage of the battery cell at different temperatures and currents, and determining the charge state of the battery cell at the corresponding time node according to the charging capacity; generating a voltage curve at different temperatures, currents and charge states according to the voltage in the test data; determining an electromotive force curve of the corresponding battery cell at different charge states according to the voltage curve; and determining the internal resistance of the battery cell at different temperatures, currents and charge states according to the difference between the electromotive force curve and the voltage curve.
  • the electromotive force curve of the corresponding battery cell under different charge states is determined according to the voltage curve, including: interpolating the voltage curve to obtain curves with zero current at different temperatures as the electromotive force curves under different charge states.
  • Determining a first relationship between the state of charge and the electromotive force in a corresponding charging segment includes: selecting a point at the charging start stage of each of the charging segments as an initial point, and obtaining a real state of charge at the initial point as an initial state of charge; determining the state of charge at different time points of the corresponding charging segment according to the initial state of charge, the cumulative charged power, and the chargeable power of the battery at different time nodes, wherein the cumulative charged power is obtained by integrating ampere-hours; determining the internal resistance of the battery cell at the corresponding time point according to the state of charge, current, and temperature at the different time points; determining the electromotive force value at the corresponding time point according to the internal resistance of the battery cell at the corresponding time point, the current, and the corresponding voltage value, and obtaining a first relationship between the state of charge and the electromotive force in the corresponding charging segment, wherein the initial state of charge, current,
  • the current health state of the battery pack is determined based on the optimal chargeable power of each charging segment, including: filtering based on the optimal chargeable power of each charging segment to obtain the filtered charge of the battery pack; determining the current health state of the battery pack based on the ratio of the charge power of the filtered battery pack to the rated capacity of the preset battery pack.
  • the instruction when executed by the processor, before determining the current health status of the battery pack based on the ratio of the charged power of the filtered battery pack to the rated capacity of the preset battery pack, it also includes: obtaining the charging data of the battery pack within a preset usage period when the battery pack is just put into use; obtaining the entire pack capacity of the battery pack based on the charging data of the battery pack; and updating the rated capacity of the battery pack based on the entire pack capacity to complete capacity calibration.
  • the target loss function when the instruction is executed by the processor, the target loss function is constructed according to the first relationship and the second relationship between the preset state of charge and the electromotive force, and the optimization is performed according to the target loss function, including: constructing the target loss function according to the square difference between the first relationship and the second relationship; taking the initial state of charge and the rated capacity uploaded by the system at the initial moment of each charging segment as the calculation starting point, and using the gradient descent method to obtain the optimal initial state of charge and the optimal chargeable capacity of the corresponding charging segment to minimize the value of the target loss function.
  • the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium.
  • the program When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.
  • the storage medium can be a disk, an optical disk, a read-only storage memory (ROM), etc.

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Abstract

一种电池健康状态估算方法、系统、设备和介质，包括：获取预设时间段内的各电池单体在多个充电片段的充电数据，每个充电片段对应一个连续的充电时间段（S300）；根据充电数据确定对应充电片段中荷电状态与电动势的第一关系式（S310）；根据第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据目标损失函数进行寻优，得到目标损失函数达到最小时对应充电片段的最优可充入电量（S320）；根据各充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池组成（S330）。该方法可有效保证电池健康状态估算的准确性。

Description

一种电池健康状态估算方法、系统、设备和介质 技术领域
本申请涉及动力电池领域，尤其涉及一种电池健康状态估算方法、系统、设备和介质。
背景技术
将康状态SOH(state of health)，SOH反映了动力电池当前容量的衰减程度，其主要表征参数是容量及内阻，目前电池管理系统一般将容量的衰减作为新能源车辆动力电池SOH的表征指标。SOH的精确估算不仅有助于动力电池的安全使用及维护，也可以进一步提升SOC估算精度，并且有利于二手车辆价值的评估。通常车载BMS会计算车辆当前的SOH值，但由于硬件条件的限制，车辆无法存储大量数据，同时SOH与电池管理系统策略紧密相关，为保证其准确性，触发车端更新SOH值的条件很苛刻，可能会引起车端不及时更新SOH值，导致电池管理系统对动力电池状态估算不准确甚至一些充放电策略不当的情况。
发明内容
鉴于以上现有技术存在的问题，本申请提出一种电池健康状态估算方法、系统、设备和介质，主要解决现有方法中电池健康状态估算准确性差的问题。
为了实现上述目的及其他目的，本申请采用的技术方案如下。
本申请提供一种电池健康状态估算方法，包括：
获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；
根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；
根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；
根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
在本申请一实施例中，根据各所述充电片段的最优可充入电量确定电池包的 当前健康状态之后，还包括：
获取所述电池包最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态；
将所述当前荷电状态和所述当前健康状态输出至车端电池管理系统。
在本申请一实施例中，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态，包括：
根据所述当前健康状态与预设电池额定容量的乘积确定所述电池包的当前可充入电量；
根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的累积充入电量；
根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
在本申请一实施例中，获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括：
获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态；
根据所述测试数据中的电压生成不同温度、电流以及荷电状态下的电压曲线；
根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线；
根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池单体内阻。
在本申请一实施例中，根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：
对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。
在本申请一实施例中，根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式，包括：
选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；
根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；
根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池内阻；
根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
在本申请一实施例中，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：
基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；
根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
在本申请一实施例中，根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：
在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；
根据所述电池包的充电数据获取所述电池包的整包容量；
根据所述整包容量更新所述电池包的额定容量以完成容量校准。
在本申请一实施例中，根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：
根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；
以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
本申请还提供一种电池健康状态估算系统，包括：
充电数据获取模块，用于获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；
电动势确定模块，用于根据所述充电数据确定对应充电片段中荷电状态与电 动势的第一关系式；
寻优模块，用于根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；
状态评估模块，用于根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
本申请还提供一种计算机设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现所述的电池健康状态估算方法的步骤。
本申请还提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现所述的电池健康状态估算方法的步骤。
如上所述，本申请一种电池健康状态估算方法、系统、设备和介质，具有以下有益效果。
本申请通过获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；根据各所述充电片段的最优可充入电量确定电池包的当前健康状态。本申请通过电池历史充电数据进行健康状态评估，可有效提高健康状态估算精度，不受温度和电压等条件限制，有利于基于估算的健康状态更新车端的初始荷电状态，以使电池管理系统提供更为合理的充放电策略。
附图说明
图1为本申请一实施例中电池健康状态估算系统的应用场景示意图。
图2是本申请一实施例种终端的结构示意图。
图3为本申请一实施例中电池健康状态估算方法的流程示意图。
图4为本申请另一实施例中电池健康状态估算的总体流程示意图。
图5为本申请一实施例中电池在某一温度下不同充电曲线及电动势计算示意图。
图6为本申请一实施例中电池单体容量的计算示意图。
图7为本申请一实施例中电池健康状态估算系统的模块图。
图8为本申请一实施例中设备的结构示意图。
具体实施方式
以下通过特定的具体实例说明本申请的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本申请的其他优点与功效。本申请还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本申请的精神下进行各种修饰或改变。需说明的是，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
需要说明的是，以下实施例中所提供的图示仅以示意方式说明本申请的基本构想，遂图式中仅显示与本申请中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。
现有技术中的健康状态估算方法通常通过采集动力电池直流充电时的数据，通过安时积分计算出满足优选条件的实时容量。计算每个月的平均容量与额定容量的比值得出相对SOH，随后通过多项式拟合的方式获得SOH的拟合曲线并持续更新。利用云端的电流数据进行安时积分存在误差，并且车端SOC估计本身存在估算的误差，利用车端SOC值去反推SOH值会导致误差累积，使得估算的SOH值不准确。
基于现有技术存在的问题，本申请提供一种电池健康状态估算方法、系统设备和介质，下面结合具体实施例对本申请技术方案进行详细阐述。
请参阅图1，图1为本申请一实施例中电池健康状态估算系统的应用场景示意图。车辆对应的车端系统可采集电动汽车每次充电的数据，如充电开始时间、结束时间，充电过程中电池的温度、电池电压、充电电流、充入电量等。车辆每次充电前可从云端服务器200处调取车辆的初始荷电状态作为当前充电开始阶段的初始荷电状态参考数据。车辆将采集的充电数据通过网络100上传至云端的电池数据管理平台，该电池数据管理平台设置于服务器200处，用于对车辆上传的充电数据进行整理，如将各历史充电数据按照每个连续的充电时间段分为多个充电片段，以使每个充电片段对应一个连续的充电时间段。还可进行数据清洗，去除无效数据或其他干扰数据。服务器200处可保存电池供应商上传的电池包鄂 额定容量、以及电池出厂前的测试数据等，基于测试数据生成对应电池包的电压曲线相对于容量的曲线以及电动势相对于容量的曲线。测试数据可针对不同温度以及电流条件的对电池在标准工况下的充放电数据进行统计得到。
在一实施例中，服务器200可以是独立的物理服务器，也可以是多个物理服务器构成的服务器集群或者分布式系统，还可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、CDN、以及大数据和人工智能平台等基础云计算服务的云服务器。
在一实施例中，也可在车端基于安时积分计算对应充电片段的累积充入电量，将累积充入电量上通过网络100上传至服务器200，避免由云端的服务器200进行累积充入电量计算的误差。在另一实施例中，终端可以是智能手机、平板电脑、笔记本电脑、台式计算机、智能音箱、智能手表、智能语音交互设备、智能家电和车载终端等，但并不局限于此。
参见图2，图2是本申请实施例提供的终端400的结构示意图，图2所示的终端400包括：至少一个处理器410、存储器450、至少一个网络接口420和用户接口430。终端400中的各个组件通过总线系统440耦合在一起。可理解，总线系统440用于实现这些组件之间的连接通信。总线系统440除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图2中将各种总线都标为总线系统440。
处理器410可以是一种集成电路芯片，具有信号的处理能力，例如通用处理器、数字信号处理器(DSP，Digital Signal Processor)，或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其中，通用处理器可以是微处理器或者任何常规的处理器等。
用户接口430包括使得能够呈现媒体内容的一个或多个输出装置431，包括一个或多个扬声器和/或一个或多个视觉显示屏。用户接口430还包括一个或多个输入装置432，包括有助于用户输入的用户接口部件，比如键盘、鼠标、麦克风、触屏显示屏、摄像头、其他输入按钮和控件。
存储器450可以是可移除的，不可移除的或其组合。示例性的硬件设备包括固态存储器，硬盘驱动器，光盘驱动器等。存储器250可选地包括在物理位置上远离处理器410的一个或多个存储设备。
存储器450包括易失性存储器或非易失性存储器，也可包括易失性和非易失 性存储器两者。非易失性存储器可以是只读存储器(ROM，Read Only Memory)，易失性存储器可以是随机存取存储器(RAM，Random Access Memory)。本申请实施例描述的存储器450旨在包括任意适合类型的存储器。
在一些实施例中，存储器450能够存储数据以支持各种操作，这些数据的示例包括程序、模块和数据结构或者其子集或超集，下面示例性说明。
操作系统451，包括用于处理各种基本系统服务和执行硬件相关任务的系统程序，例如框架层、核心库层、驱动层等，用于实现各种基础业务以及处理基于硬件的任务；
网络通信模块452，用于经由一个或多个(有线或无线)网络接口420到达其他计算设备，示例性的网络接口420包括：蓝牙、无线相容性认证(WiFi)、和通用串行总线(USB，Universal Serial Bus)等；
呈现模块453，用于经由一个或多个与用户接口430相关联的输出装置431(例如，显示屏、扬声器等)使得能够呈现信息(例如，用于操作外围设备和显示内容和信息的用户接口)；
输入处理模块454，用于对一个或多个来自一个或多个输入装置432之一的一个或多个用户输入或互动进行检测以及翻译所检测的输入或互动。
在一些实施例中，本申请实施例提供的装置可以采用软件方式实现，图2示出了存储在存储器450中的电池健康状态估算系统455，其可以是程序和插件等形式的软件，包括以下软件模块：充电数据获取模块4551、电动势确定模块4552、寻优模块4553和状态评估模块4554，这些模块是逻辑上的，因此根据所实现的功能可以进行任意的组合或进一步拆分。
将在下文中说明各个模块的功能。
在另一些实施例中，本申请实施例提供的系统可以采用硬件方式实现，作为示例，本申请实施例提供的系统可以是采用硬件译码处理器形式的处理器，其被编程以执行本申请实施例提供的电池健康状态估算方法，例如，硬件译码处理器形式的处理器可以采用一个或多个应用专用集成电路(ASIC，Application Specific Integrated Circuit)、DSP、可编程逻辑器件(PLD，Programmable Logic Device)、复杂可编程逻辑器件(CPLD，ComplexProgrammable Logic Device)、现场可编程门阵列(FPGA，Field-Programmable GateArray)或其他电子元件。
在一些实施例中，终端或服务器可以通过运行计算机程序来实现本申请实施例提供的电池健康状态估算方法。举例来说，计算机程序可以是操作系统中的原生程序或软件模块；可以是本地(Native)应用程序(APP，Application)，即需要在操作系统中安装才能运行的程序，如社交应用APP或者消息分享APP；也可以是小程序，即只需要下载到浏览器环境中就可以运行的程序；还可以是能够嵌入至任意APP中的小程序或者网页客户端程序。总而言之，上述计算机程序可以是任意形式的应用程序、模块或插件。
下面将结合本申请实施例提供的设备的示例性应用和实施，说明本申请实施例提供的电池健康状态估算方法。
请参阅图3，本申请提供一种电池健康状态估算方法，该方法包括以下步骤。
步骤S300，获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段。
在一实施例中，可通过云端平台存储车辆上传的充电数据，该充电数据可包括：充电时间、充电电流、电池电压、电池温度、累积充入电量等数据。
在一实施例中，获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括以下步骤：
步骤S301，获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态。
在一实施例中，可在项目开发过程中，测试电池在标准工况下的容量，测试电池在不同温度和不同电流工况下恒流充电曲线，记录其温度、充电电流、充入容量及电压，其中温度、充电电流范围应该大于电池使用的温度区间，并且充电电流在小电流区间设置应该密集一些，以保证后续计算电动势曲线的准确性。
步骤S302，根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线。
在一实施例中，根据公式U＝EMF+I·R(SOC,T,I)，用向外插值估计的方法估计该电池的电动势-容量曲线(EMF曲线)，并且根据充电曲线及EMF曲线计算电池内阻在不同SOC、不同温度、不同电流情况下的值R(SOC,T,I)。
步骤S303，根据所述电动势曲线与所述电压曲线的差值确定不同温度、电 流及荷电状态下的电池单体内阻。
在一实施例中，根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：
对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。
具体地，利用向外插值估计的方法估计该电池在电流为0时不同充入容量的电压曲线，该曲线即为电池的电动势-容量曲线(EMF曲线)，并且根据充电曲线及EMF曲线计算电池内阻在不同SOC、不同温度、不同电流情况下的值R(SOC,T,I)，式子中U表示电压，EMF表示电池电动势。在一实施例中，可使用外推方式初步估计电流为0时的电压，作为电动势EMF值。其中向外插值的方法可以采用线性插值或者其他的插值方法，采用该方法计算EMF曲线而不是静置求电压的原因是静置后测量电池的开路电压需要较长的测试时间，测量点的数量有限，利用充电曲线外插的方法计算EMF曲线可以大大节省测试时间。
步骤S304，根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池内阻。
在一实施例中，R(SOC,I,T)表示不同SOC，不同电流，不同温度下的电池内阻，其与电流的乘积即电压曲线和EMF曲线的差值，该值受电流大小影响较小，计算过程中可以忽略电流的影响。
进一步地，可提取车辆上传至车联网云平台的充电数据，提取的字段有电流，电压、时间、电池SOC、累计充入电量等，其中累计充入电量非国标(GBT32960)要求上传字段，可以要求车端新增上传该字段。
对提取到的字段做数据清洗，去掉无效的数据。对充电数据划分，将每一个连续的充电时段作为一个充电片段，共划分为m个充电片段，并将这些充电片段按时间排序，序号依次标记为1，2，...，m。
计算不同充电段在不同时间点的累计充电容量，其中车端计算累计充电容量的频率较高，准确性更高，也可以利用车联网云平台的数据来计算累计充电容量，采用安时积分法计算充入的容量，采用离散数据的积分方法。设充电数据一共有n行，将充电时间记为t1，t2，…，tn，电流记为I1，I2，…，In，则充入的电 量但是云端数据由于宽带原因，周期较长，计算的准确性一般。具体累积充入电量计算位置的选择可根据实际应用需求进行设置，这里不作限制。
步骤S310，根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式。
在一实施例中，根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式，包括：
选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；
根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；
根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池单体内阻；
根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
具体地，针对每一次充电数据，对每一个单体单独计算，在充电开始阶段选取一个点作为计算的初始点，该时刻电池真实SOC记做x0，选取初始点之后i时刻的点，记做xi，根据SOC的计算公式可以得到Qm表示当前该单体可充入电量，∫Idt表示x0到xi的充入电量，用累计充入电量计算或者采用云端电流时间安时积分的方法计算获得，则时刻i的估算电动势值EMF′(xi)＝U(xi)-I(xi)·R(xi，T，I(xi))，EMF′(xi)是关于x0和Qm的函数，其中U(xi)、I(xi)分别表示i时刻的电压和电流，R(xi，T，I(xi))表 示在i时刻的内阻估算值，其值是根据之前性能测试的R(SOC,I,T)表进行插值计算，根据测试获得的EMF曲线可以求得时刻i的电动势值为EMF(xi)。
步骤S320，根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量。
在一实施例中，根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：
根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；
以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
具体地，分别选取多个充电时刻点，通过求解最优解的方式计算x0和Qm。
其中，求解最优解的方法不一，以下对求解过程进行举例说明，但求最优解不仅仅局限于该方法。设损失函数为其中以初始时刻上传的电池SOC和整包额定容量为计算起点，利用梯度下降法求计算x0和Qm，使得损失函数L最小。
步骤S330，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
在一实施例中，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：
基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；
根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
具体地，对所有单体进行同样的计算，求得该次充电片段中各个单体的初始SOC为和各个单体充入容量计算当前整包的可充入容量
计算每一个充电片段的根据一定的滤波方法计算当前整包的电池可充入容量，根据SOH的定义计算该动力电池当前的健康状态其中表示经过滤波后的电池包容量，滤波方法可以采用移动均值或者卡尔曼滤波等方式，Qr为该动力电池的理论额定容量。
在一实施例中，根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：
在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；
根据所述电池包的充电数据获取所述电池包的整包容量；
根据所述整包容量更新所述电池包的额定容量以完成容量校准。
具体地，额定容量以从车辆制造商或者电池制造商获得。并且在车辆使用初期，利用该方法计算电池包的整包容量并更新，避免由于不一致性导致电池额定容量计算的差异，保证单车单独计算以提高准确性。
在一实施例中，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态之后，还包括：
获取电池包的最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池的当前荷电状态；
将所述当前荷电状态和所述当前健康状态输出至车端电池管理系统。
在一实施例中，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池的当前荷电状态，包括：
根据所述当前健康状态与预设电池额定容量的乘积确定所述电池的当前可 充入电量；
根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的的累积充入电量；
根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
具体地，根据当前计算SOH值，利用最新的一次充电片段，计算当前电池准确的初始SOC值(即当前荷电状态)，将车辆的初始SOC值和计算的SOH值回传给车辆电池管理系统(BMS)，BMS根据云端计算结果更新SOH值及SOC计算的初始值，保证车辆BMS系统对电池状态的准确估计和有效控制。
请参阅图4，图4为本申请另一实施例中电池健康状态估算的总体流程示意图。项目开发过程中，测试电池在标准工况下的容量，测试电池在不同温度和不同电流工况下恒流充电曲线，记录其温度、充电电流、充入容量及电压，其中温度、充电电流范围应该大于电池使用的温度区间，并且充电电流在小电流区间设置应该密集一些，以保证后续计算电动势曲线的准确性；根据公式U＝EMF+I·R(SOC,T,I)，用向外插值估计的方法估计该电池的电动势-容量曲线(EMF曲线)，并且根据充电曲线及EMF曲线计算电池内阻在不同SOC、不同温度、不同电流情况下的值R(SOC,T,I)。具体地，请参阅图5，图5为本申请一实施例中电池在某一温度下不同充电曲线及电动势计算示意图。
进一步地，提取车辆上传至车联网云平台的充电数据，提取的字段有电流，电压、时间、电池SOC、累计充入电量等。对提取到的字段做数据清洗，去掉无效的数据。对充电数据划分，将每一个连续的充电时段作为一个充电片段，共划分为m个充电片段，并将这些充电片段按时间排序，序号依次标记为1，2，…m。
请参阅图6，图6为本申请一实施例中电池单体容量的计算示意图。针对每一次充电数据，对每一个单体单独计算，利用初期性能测试获取的R(SOC,T,I)，根据当前的温度T、电流I，估算当次充电片段中该单体电芯EMF曲线估算值EMF’与单体SOC的关系；根据初期性能测试的EMF曲线和充电片段的EMF’曲线，通过求最优解的方式计算不同单体电芯的充电起始SOC和该单体可充入容量Qm；所有单体进行同样的计算，求得该次充电片段中各个单体的初始SOC 为和各个单体充入容量计算当前整包的可充入容量计算每一个充电片段的根据一定的滤波方法计算当前整包的电池可充入容量，根据SOH的定义计算该动力电池当前的健康状态其中Qr为该动力电池的理论额定容量，可以从车辆制造商或者电池制造商获得。并且在车辆使用初期，利用该方法计算电池包的整包容量并更新，避免由于不一致性导致电池额定容量计算的差异，保证单车单独计算以提高准确性；根据计算当前SOH值，利用最新的一次充电片段，计算当前电池准确的初始SOC值，将车辆的初始SOC值和计算的SOH值回传给车辆电池管理系统(BMS)，BMS根据云端计算结果更新SOH值及SOC计算的初始值，保证车辆BMS系统对电池状态的准确估计和有效控制。
请参阅图7，图7为本申请一实施例中电池健康状态估算系统的模块图，该系统包括：充电数据获取模块4551，用于获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；电动势确定模块4552，用于根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；寻优模块4553，用于根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；状态评估模块4554，用于根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
在一实施例中，状态评估模块4554还用于根据各所述充电片段的最优可充入电量确定电池包的当前健康状态之后，还包括：获取所述电池包最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态；将所述当前荷电状态和所述当前健康状态输出至车端电池管理系统。
在一实施例中，状态评估模块4554还用于根据所述最近一次充电片段的充 电数据以及所述当前健康状态确定对应电池包的当前荷电状态，包括：根据所述当前健康状态与预设电池额定容量的乘积确定所述电池包的当前可充入电量；根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的累积充入电量；根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
在一实施例中，系统还包括测试数据获取模块，用于获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括：获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态；根据所述测试数据中的电压生成不同温度、电流以及荷电状态下的电压曲线；根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线；根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池单体内阻。
在一实施例中，状态评估模块4554还用于根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。。
在一实施例中，电动势确定模块4552还用于根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式，包括：选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池单体内阻；根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
在一实施例中，状态评估模块4554还用于根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
在一实施例中，状态评估模块4554还用于根据所述滤波后的电池包的充入 电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；根据所述电池包的充电数据获取所述电池包的整包容量；根据所述整包容量更新所述电池包的额定容量以完成容量校准。
在一实施例中，寻优模块4553还用于根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
上述电池健康状态估算系统可以以一种计算机程序的形式实现，计算机程序可以在如图8所示的计算机设备上运行。计算机设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序。
上述电池健康状态估算系统中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于终端的存储器中，也可以以软件形式存储于终端的存储器中，以便于处理器调用执行以上各个模块对应的操作。该处理器可以为中央处理单元(CPU)、微处理器、单片机等。
如图8所示，为一个实施例中计算机设备的内部结构示意图。提供了一种计算机设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
在一实施例中，上述处理器执行时，所实现的根据各所述充电片段的最优可充入电量确定电池包的当前健康状态之后，还包括：获取所述电池包最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态；将所述当前荷电状态和所述当前健康状态输 出至车端电池管理系统。
在一实施例中，上述处理器执行时，所实现的根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态，包括：根据所述当前健康状态与预设电池额定容量的乘积确定所述电池包的当前可充入电量；根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的累积充入电量；根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
在一实施例中，上述处理器执行时，所实现的获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括：获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态；根据所述测试数据中的电压生成不同温度、电流以及荷电状态下的电压曲线；根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线；根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池单体内阻。
在一实施例中，上述处理器执行时，所实现的根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。
在一实施例中，上述处理器执行时，所实现的根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式，包括：选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池单体内阻；根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
在一实施例中，上述处理器执行时，所实现的根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；根据所述滤波后的电池包的充入电 量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
在一实施例中，上述处理器执行时，所实现的根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；根据所述电池包的充电数据获取所述电池包的整包容量；根据所述整包容量更新所述电池包的额定容量以完成容量校准。
在一实施例中，上述处理器执行时，所实现的根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
在一个实施例中，上述的计算机设备可用作服务器，包括但不限于独立的物理服务器，或者是多个物理服务器构成的服务器集群，该计算机设备还可用作终端，包括但不限手机、平板电脑、个人数字助理或者智能设备等。如图8所示，该计算机设备包括通过系统总线连接的处理器、非易失性存储介质、内存储器、显示屏和网络接口。
其中，该计算机设备的处理器用于提供计算和控制能力，支撑整个计算机设备的运行。计算机设备的非易失性存储介质存储有操作系统和计算机程序。该计算机程序可被处理器所执行，以用于实现以上各个实施例所提供的一种电池健康状态估算方法。计算机设备中的内存储器为非易失性存储介质中的操作系统和计算机程序提供高速缓存的运行环境。显示界面可通过显示屏进行数据展示。显示屏可以是触摸屏，比如为电容屏或电子屏，可通过接收作用于该触摸屏上显示的控件的点击操作，生成相应的指令。
本领域技术人员可以理解，图8中示出的计算机设备的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序， 所述计算机程序被处理器执行时实现以下步骤：获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
在一实施例中，该计算机程序被处理器执行时，所实现的根据各所述充电片段的最优可充入电量确定电池包的当前健康状态之后，还包括：获取所述电池包最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态；将所述当前荷电状态和所述当前健康状态输出至车端电池管理系统。
在一实施例中，该计算机程序被处理器执行时，所实现的根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态，包括：根据所述当前健康状态与预设电池额定容量的乘积确定所述电池包的当前可充入电量；根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的累积充入电量；根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
在一实施例中，该计算机程序被处理器执行时，所实现的获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括：获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态；根据所述测试数据中的电压生成不同温度、电流以及荷电状态下的电压曲线；根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线；根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池单体内阻。
在一实施例中，该计算机程序被处理器执行时，所实现的根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。
在一实施例中，该计算机程序被处理器执行时，所实现的根据所述充电数据 确定对应充电片段中荷电状态与电动势的第一关系式，包括：选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池单体内阻；根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
在一实施例中，该指令被处理器执行时，所实现的根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
在一实施例中，该指令被处理器执行时，所实现的根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；根据所述电池包的充电数据获取所述电池包的整包容量；根据所述整包容量更新所述电池包的额定容量以完成容量校准。
在一实施例中，该指令被处理器执行时，所实现的根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一非易失性计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)等。
上述实施例仅例示性说明本申请的原理及其功效，而非用于限制本申请。任何熟悉此技术的人士皆可在不违背本申请的精神及范畴下，对上述实施例进行修 饰或改变。因此，举凡所属技术领域中具有通常知识者在未脱离本申请所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本申请的权利要求所涵盖。

Claims (12)

  1. 一种电池健康状态估算方法，其特征在于，包括：
    获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；
    根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；
    根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；
    根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
  2. 根据权利要求1所述的电池健康状态估算方法，其特征在于，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态之后，还包括：
    获取所述电池包最近一个充电片段的充电数据，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态；
    将所述当前荷电状态和所述当前健康状态输出至车端电池管理系统。
  3. 根据权利要求2所述的电池健康状态估算方法，其特征在于，根据所述最近一次充电片段的充电数据以及所述当前健康状态确定对应电池包的当前荷电状态，包括：
    根据所述当前健康状态与预设电池额定容量的乘积确定所述电池包的当前可充入电量；
    根据所述最近一次充电片段的充电数据确定所述电池包的在所述最近一次充电片段中的累积充入电量；
    根据所述当前可充入电量与所述累积充入电量计算所述当前荷电状态。
  4. 根据权利要求1所述的电池健康状态估算方法，其特征在于，获取预设时间段内各电池单体在多个充电片段的充电数据之前，还包括：
    获取各电池在标准工况下的测试数据，所述测试数据包括电池单体在不同温度和电流下的充入容量以及电压，并根据所述充入容量确定对应时间节点电池单体的荷电状态；
    根据所述测试数据中的电压生成不同温度、电流以及荷电状态下的电压曲线；
    根据所述电压曲线确定对应电池单体的在不同荷电状态下的电动势曲线；
    根据所述电动势曲线与所述电压曲线的差值确定不同温度、电流及荷电状态下的电池单体内阻。
  5. 根据权利要求3所述的电池健康状态估算方法，其特征在于，根据所述电压曲线确定对应电池单体在不同荷电状态下的电动势曲线，包括：
    对所述电压曲线进行插值，得到不同温度下电流为零的曲线作为所述不同荷电状态下的电动势曲线。
  6. 根据权利要求1或4所述的电池健康状态估算方法，其特征在于，根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式，包括：
    选取每个所述充电片段的充电开始阶段的点作为初始点，并获取所述初始点的真实荷电状态作为初始荷电状态；
    根据所述初始荷电状态、累积充入电量及不同时间节点电池可充入电量确定对应充电片段不同时间点的荷电状态，其中所述累积充入电量通过安时积分得到；
    根据所述不同时间点的荷电状态、电流以及温度确定对应时间点的电池单体内阻；
    根据对应时间点的电池单体内阻、电流以及对应的电压值确定对应时间点的电动势值，得到对应充电片段中荷电状态与电动势的第一关系式，其中，所述初始荷电状态、电流、电压值以及温度包含于对应充电片段的充电数据中。
  7. 根据权利要求1所述的电池健康状态估算方法，其特征在于，根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，包括：
    基于各所述充电片段的最优充入电量进行滤波，得到滤波后的电池包充入电量；
    根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态。
  8. 根据权利要求7所述的电池健康状态估算方法，其特征在于，根据所述滤波后的电池包的充入电量与预设电池包的额定容量的比值确定所述电池包的当前健康状态之前，还包括：
    在电池包刚投入使用的预设使用时段内，获取电池包的充电数据；
    根据所述电池包的充电数据获取所述电池包的整包容量；
    根据所述整包容量更新所述电池包的额定容量以完成容量校准。
  9. 根据权利要求7所述的电池健康状态估算方法，其特征在于，根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，包括：
    根据所述第一关系式与所述第二关系式的平方差构建所述目标损失函数；
    以各充电片段初始时刻的系统上传的初始荷电状态以及额定容量为计算起点，采用梯度下降法求取对应充电片段的最优初始荷电状态以及最优可充入电量以使所述目标损失函数的值最小。
  10. 一种电池健康状态估算系统，其特征在于，包括：
    充电数据获取模块，用于获取预设时间段内各电池单体在多个充电片段的充电数据，每个所述充电片段对应一个连续的充电时间段；
    电动势确定模块，用于根据所述充电数据确定对应充电片段中荷电状态与电动势的第一关系式；
    寻优模块，用于根据所述第一关系式与预设的荷电状态与电动势的第二关系式构建目标损失函数，根据所述目标损失函数进行寻优，得到所述目标损失函数达到最小时对应充电片段的最优可充入电量；
    状态评估模块，用于根据各所述充电片段的最优可充入电量确定电池包的当前健康状态，其中电池包由多个电池单体组成。
  11. 一种计算机设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至9中任一项所述的电池健康状态估算方法的步骤。
  12. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至9中任一项所述的电池健康状态估算方法的步骤。
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CN115616421A (zh) * 2022-09-26 2023-01-17 重庆长安新能源汽车科技有限公司 一种电池健康状态估算方法、系统、设备和介质

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