WO2022068652A1 - 获取电池容量的方法、装置、存储介质及服务器 - Google Patents

获取电池容量的方法、装置、存储介质及服务器 Download PDF

Info

Publication number
WO2022068652A1
WO2022068652A1 PCT/CN2021/119637 CN2021119637W WO2022068652A1 WO 2022068652 A1 WO2022068652 A1 WO 2022068652A1 CN 2021119637 W CN2021119637 W CN 2021119637W WO 2022068652 A1 WO2022068652 A1 WO 2022068652A1
Authority
WO
WIPO (PCT)
Prior art keywords
capacity
charging
battery
current
initial
Prior art date
Application number
PCT/CN2021/119637
Other languages
English (en)
French (fr)
Inventor
廉玉波
凌和平
陈斯良
王峥峥
李君子
Original Assignee
比亚迪股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 比亚迪股份有限公司 filed Critical 比亚迪股份有限公司
Priority to EP21874313.6A priority Critical patent/EP4224183A4/en
Priority to KR1020237011519A priority patent/KR20230061520A/ko
Priority to JP2023519877A priority patent/JP2023543497A/ja
Priority to AU2021352848A priority patent/AU2021352848A1/en
Publication of WO2022068652A1 publication Critical patent/WO2022068652A1/zh
Priority to US18/128,703 priority patent/US20230236261A1/en

Links

Images

Classifications

    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods 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]
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • 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
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • Y—GENERAL 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging

Definitions

  • the present disclosure relates to the field of battery technology, and in particular, to a method, device, storage medium and server for obtaining battery capacity.
  • Electric vehicles are motor vehicles with power batteries as all or part of the power source.
  • the battery capacity of power batteries is an important factor affecting the performance of electric vehicles.
  • Accurate estimation of battery capacity can improve the estimation accuracy of SOC (State of Charge). , the prediction accuracy of peak power, and the estimation accuracy of cruising range. Therefore, in battery management systems, battery capacity is often used as an important indicator to characterize battery health.
  • the battery power of the battery can be calculated according to the charging and discharging current of the battery pack.
  • this method needs to detect the OCV (Open Circuit Voltage, open circuit voltage), and the OCV needs to be charged and discharged with a small current and can be obtained after it has been fully static. The acquisition time is longer, resulting in less efficient estimation of battery capacity.
  • OCV Open Circuit Voltage, open circuit voltage
  • the present disclosure provides a method, device, storage medium and server for obtaining battery capacity.
  • the present disclosure provides a method for obtaining a battery capacity, the method comprising: obtaining a plurality of initial charging parameters of the battery when the current charging process of the battery of the vehicle is effective charging; the effective charging represents a The variation range of the state of charge SOC of the battery in the current charging process includes a preset state of charge range, and the minimum charging temperature of the battery in the current charging process is greater than or equal to a preset temperature threshold; period; Obtain multiple actual charging parameters of the battery in the current charging process, and the current effective charging times corresponding to the current charging process; according to the multiple initial charging parameters, multiple actual charging parameters and all The current effective charging times are obtained, and the predicted battery capacity of the battery in the next effective charging is obtained.
  • the acquiring, according to a plurality of the initial charging parameters, a plurality of the actual charging parameters, and the current effective charging times obtains the predicted battery capacity of the battery during the next effective charging, including: according to the plurality of the The initial charging parameter and a plurality of the actual charging parameters are used to estimate the current maximum available capacity of the battery; according to the current maximum available capacity and the current effective charging times, a capacity correlation relationship is obtained, and the capacity correlation relationship includes predicting the battery The corresponding relationship between the capacity and the number of effective charging times; the predicted battery capacity of the battery during the next effective charging is obtained through the capacity correlation relationship.
  • the estimating the current maximum available capacity of the battery according to the plurality of initial charging parameters and the plurality of actual charging parameters includes: obtaining an initial capacity increment curve according to the initial charging parameters; According to the actual charging parameters, an actual capacity increment curve is obtained; according to the initial capacity increment curve and the actual capacity increment curve, the current maximum available capacity of the battery is estimated.
  • the estimating the current maximum available capacity of the battery according to the initial capacity increase curve and the actual capacity increase curve includes: acquiring a first value corresponding to a second peak value of the initial capacity increase curve.
  • the initial position information and the second initial position information corresponding to the third peak value obtain the first current position information corresponding to the second peak value of the actual capacity increment curve and the second current position information corresponding to the third peak value; an initial position information, the second initial position information, the first current position information, the second current position information, the initial charging parameter and the actual charging parameter, to estimate the current maximum available capacity of the battery .
  • obtaining the predicted battery capacity of the battery during the next effective charging process according to the capacity correlation relationship includes: obtaining the target charging times corresponding to the next effective charging process; according to the target charging times and the capacity; The association relationship is used to obtain the predicted battery capacity of the battery during the next effective charging.
  • the method before acquiring the capacity correlation relationship according to the current maximum available capacity and the current effective charging times, the method further includes: acquiring the history of each effective charging process before the current charging process.
  • the obtaining the capacity association relationship according to the current maximum available capacity and the current valid charging times includes: according to the current maximum available capacity, the The current effective charging times, the historical maximum available capacity, and the historical effective charging times, to obtain the capacity correlation.
  • the present disclosure provides an apparatus for obtaining battery capacity, the apparatus comprising: an initial parameter obtaining module, configured to obtain a plurality of initial charges of the battery under the condition that the current charging process of the battery of the vehicle is effective charging
  • the effective charge indicates that the variation range of the state of charge (SOC) of the battery in the current charging process includes a preset state of charge range, and the minimum charging temperature of the battery in the current charging process is greater than or is equal to the preset temperature threshold;
  • the actual parameter acquisition module is used to periodically acquire a plurality of actual charging parameters of the battery in the current charging process, and the current effective charging times corresponding to the current charging process;
  • the battery capacity acquisition module which is used to obtain the predicted battery capacity of the battery during the next effective charging according to a plurality of the initial charging parameters, a plurality of the actual charging parameters and the current effective charging times.
  • the battery capacity acquisition module is specifically configured to: estimate the current maximum available capacity of the battery according to a plurality of the initial charging parameters and a plurality of the actual charging parameters; For the current effective charging times, a capacity correlation relationship is obtained, and the capacity correlation relationship includes a corresponding relationship between the predicted battery capacity and the effective charging times; and the predicted battery capacity of the battery during the next effective charging is obtained through the capacity correlation relationship.
  • the battery capacity obtaining module is further configured to: obtain an initial capacity increment curve according to the initial charging parameter; obtain an actual capacity increment curve according to the actual charging parameter; and obtain an actual capacity increment curve according to the initial capacity increment curve and the actual capacity increment curve to estimate the current maximum available capacity of the battery.
  • the battery capacity acquisition module is further configured to: acquire first initial position information corresponding to the second peak of the initial capacity increment curve and second initial position information corresponding to the third peak; The first current position information corresponding to the second peak value of the capacity increment curve and the second current position information corresponding to the third peak value; according to the first initial position information, the second initial position information, the first current position information, the second current location information, the initial charging parameter, and the actual charging parameter to estimate the current maximum available capacity of the battery.
  • the battery capacity obtaining module is further configured to: obtain the target charging times corresponding to the next effective charging process; Predict battery capacity.
  • the device further includes: a historical parameter acquisition module, configured to acquire the historical maximum available capacity in each valid charging process before the current charging process and the historical valid charging times corresponding to the historical maximum available capacity;
  • the battery capacity obtaining module is further configured to: obtain the capacity association according to the current maximum available capacity, the current effective charging times, the historical maximum available capacity, and the historical effective charging times.
  • the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.
  • the present disclosure provides a server, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the steps of the method in the first aspect of the present disclosure .
  • the current charging process of the vehicle battery is effective charging, a plurality of initial charging parameters of the battery are obtained; the effective charging indicates that the state of charge SOC of the battery is in the current charging process
  • the variation range includes a preset state of charge range, and the minimum charging temperature of the battery in the current charging process is greater than or equal to a preset temperature threshold; periodically acquire multiple Actual charging parameters, and the current effective charging times corresponding to the current charging process; according to a plurality of the initial charging parameters, a plurality of the actual charging parameters, and the current effective charging times, obtain the next effective charging time of the battery the predicted battery capacity.
  • the battery does not need to be left to stand, and only the charging parameters of the battery are obtained during the effective charging process of the battery, and the predicted battery capacity of the battery in the next effective charging can be estimated, so that the efficiency of estimating the battery capacity can be improved.
  • FIG. 1 is a flowchart of a method for obtaining battery capacity according to an exemplary embodiment
  • FIG. 2 is a flowchart of another method for obtaining battery capacity according to an exemplary embodiment
  • FIG. 3 is a schematic diagram of an interval capacity integral value according to an exemplary embodiment
  • FIG. 4 is a schematic diagram showing a maximum available capacity according to an exemplary embodiment
  • FIG. 5 is a schematic structural diagram of an apparatus for obtaining battery capacity according to an exemplary embodiment
  • FIG. 6 is a schematic structural diagram of another apparatus for obtaining battery capacity according to an exemplary embodiment
  • Fig. 7 is a block diagram of a server according to an exemplary embodiment.
  • the present disclosure can be applied to the scenario of estimating the battery capacity.
  • the battery capacity reflects the current state of health of the battery. In practical applications, it cannot be fully discharged under laboratory conditions, the actual working conditions are complex and changeable, the battery is aging, and the battery material system is different. and other factors, the estimation of battery capacity has become a huge challenge and an important task.
  • a system identification algorithm can be used to obtain the OCV and capacity of the battery based on the battery mathematical model. If the battery pack resting time exceeds a predetermined time, the OCV is detected, and the change in the battery capacity in each time period is calculated and checked according to the OCV.
  • the table calculates the change of battery capacity, calculates the battery capacity attenuation weight value according to the two changes obtained by the above calculation, and calculates the current battery capacity according to the weight value and the battery capacity at the previous moment. In this way, the estimation efficiency of the battery capacity is low, and it is more dependent on the battery current, voltage, and temperature sampling accuracy, resulting in a low estimation accuracy of the capacitance capacity.
  • the present disclosure provides a method, device, storage medium and server for obtaining battery capacity, which can obtain a plurality of actual charging parameters of the battery and the current effective charging times corresponding to the current charging process during the charging process of the vehicle.
  • the predicted battery capacity of the battery in the next effective charging can be obtained according to a plurality of initial charging parameters, a plurality of actual charging parameters and the current effective charging times.
  • the battery does not need to be left to stand, and only the charging parameters of the battery are obtained during the effective charging process of the battery, so that the predicted battery capacity of the battery in the next effective charging can be estimated, so that the efficiency of estimating the battery capacity can be improved.
  • FIG. 1 is a flow chart of a method for obtaining battery capacity according to an exemplary embodiment. As shown in Figure 1, the method includes:
  • the battery may be a power battery pack
  • the effective charging indicates that the variation range of the SOC of the battery during the current charging process includes a preset state of charge range, and the minimum charging temperature of the battery during the current charging process is greater than or Equal to the preset temperature threshold.
  • the preset state of charge range and the preset temperature threshold may be determined according to the material of the battery, actual operating conditions, and the like.
  • the capacity increase curve of the battery includes three peaks, namely the first peak, the second peak and the third peak, according to the characteristics of the power lithium-ion battery and the empirical data of practical applications, it can be known that during the charging process of the battery, , the first peak usually appears in the range of 0-20% of the state of charge of the battery, the second peak and the third peak usually appear in the range of 20%-90% of the state of charge of the battery , and the user usually starts charging more than 20% of the state of charge range during actual use. Based on this, the present disclosure can obtain the next time the battery is valid according to the position information of the second peak and the third peak.
  • the preset state of charge range can be set as a range including the second peak value and the third peak value, for example, 20% to 90%, and the preset temperature threshold can be 10°C, The present disclosure does not limit this.
  • the SOC of the battery varies from 15% to 90% during the current charging process, and the minimum charging temperature of the battery during the current charging process is 15°C, it can be determined that the current charging process is effective charging ;
  • the SOC of the battery varies from 15% to 80% during the current charging process, or the minimum charging temperature of the battery during the current charging process is 7°C, it can be determined that the current charging process is not valid Charge.
  • the current charging process after the current charging process is completed, it can be determined whether the current charging process is effective charging, and in the case of determining that the current charging process is effective charging, multiple initial charging parameters of the battery are acquired.
  • the variation range of the SOC and the minimum charging temperature of the battery in the current charging process may be obtained, and it is determined that the variation range of the SOC includes the preset state of charge range, and the battery
  • the minimum charging temperature is greater than or equal to the preset temperature threshold
  • a plurality of initial charging parameters of the battery are acquired, and the plurality of initial charging parameters may be stored in the server in advance.
  • the initial charging parameters may be standard charging parameters obtained during the process of full discharge and full charging of the battery. After the battery is fully discharged and fully charged, the battery capacity of the battery can reach the maximum capacity, and the normal use process In the charging process of the battery, it is difficult to meet the conditions of full discharge and full charge. Therefore, the current maximum available capacity of the battery during normal use cannot reach the maximum capacity, and the current maximum available capacity will be less than the maximum capacity. If the battery If the manager uses the maximum capacity as the current maximum available capacity, a battery safety accident may be caused because the accuracy rate of the current maximum available capacity is too low. Therefore, the predicted battery capacity of the battery needs to be obtained.
  • the predicted battery capacity of the battery can be obtained through the initial charging parameter and the actual charging parameter.
  • the plurality of initial charging parameters can be acquired through a battery experimental test platform during the process of full discharge and full charging of the battery.
  • the full charging process may include:
  • the first rate, the first cut-off voltage, the first preset time period, the second rate, the second cut-off voltage, the second preset time period and the third rate can be determined according to the power of the battery
  • the core characteristics are preset, for example, the first rate may be 0.3C, the first cut-off voltage may be 2V, the first preset time period may be 1800s, the second rate may be 1C, and the second cut-off voltage It may be 3.75V, the second preset time period may be 300s, and the third rate may be 0.2C.
  • the initial charging parameters of the battery may be periodically collected, and the collection period may be determined according to the performance of the sampling circuit.
  • the better the performance of the sampling circuit the shorter the sampling period can be set, eg 50ms, the worse the performance of the sampling circuit, the longer the sampling period can be set, eg 100ms, which is not limited in the present disclosure.
  • the collection of the initial charging parameter is stopped. Afterwards, the initial charging parameters collected during the full charging process may be stored in the server.
  • the present disclosure can also determine whether the current charging process is valid charging during the current charging process of the battery. For example, at the beginning of the current charging process, the initial SOC of the battery and the charging temperature of the battery may be acquired, and then, during the current charging process, the current SOC of the battery and the charging temperature of the battery may be periodically acquired, If the range covered by the initial SOC and the current SOC includes the preset state of charge range, and the minimum charging temperature of the battery is less than the preset temperature threshold, it can be determined that the current charging process is effective charging, and the current charging process can be obtained.
  • the current SOC of the battery can be obtained periodically, and when it is determined that the current SOC reaches 90% and the minimum charging temperature of the battery is less than or equal to the preset temperature threshold during the process, it can be determined that the current charging process is Efficient charging. In this way, before the current charging process is completed, it can be determined that the current charging process is effective charging, and the initial charging parameters of the battery can be obtained, so that the efficiency of estimating the battery capacity can be improved.
  • the actual charging parameters of the battery may be periodically acquired, and the setting method of the acquisition period may refer to the acquisition period of the initial charging parameters, which will not be repeated here.
  • the current effective charging times corresponding to the current charging process can be obtained, and the current effective charging times can be determined according to the historical effective charging times.
  • the current effective charging times can be It is the number of times of effective charging in history plus 1. For example, if the number of times of effective charging in history is 10, the number of times of effective charging currently is 11.
  • the historical effective charging times can be increased by 1. For example, after the first effective charging is completed, the historical effective charging times are 1, and after the second effective charging is completed, the historical effective charging times are 1. The number of valid charges is 2.
  • S103 Acquire the predicted battery capacity of the battery during the next effective charging according to a plurality of initial charging parameters, a plurality of actual charging parameters, and the current effective charging times.
  • the target charging times corresponding to the next effective charging process can be determined according to the historical effective charging times, and then the target charging times corresponding to the next effective charging process can be determined according to the historical effective charging times.
  • the plurality of initial charging parameters, the plurality of actual charging parameters, the current effective charging times and the target charging times are used to obtain the predicted battery capacity of the battery during the next effective charging.
  • the predicted battery capacity of the battery at the next effective charging can be obtained according to a plurality of initial charging parameters, a plurality of actual charging parameters and the current effective charging times.
  • the battery does not need to be left to stand, and only the charging parameters of the battery are obtained during the effective charging process of the battery, so that the predicted battery capacity of the battery in the next effective charging can be estimated, so that the efficiency of estimating the battery capacity can be improved.
  • FIG. 2 is a flow chart of another method for obtaining battery capacity according to an exemplary embodiment. As shown in Figure 2, the method includes:
  • the battery may be a power battery pack
  • the effective charging indicates that the variation range of the SOC of the battery during the current charging process includes a preset state of charge range, and the minimum charging temperature of the battery during the current charging process is greater than or Equal to the preset temperature threshold.
  • the preset state of charge range and the preset temperature threshold may be determined according to the material of the battery, actual operating conditions, and the like. For example, since there are three peaks in the SOC range of the battery, namely the first peak, the second peak and the third peak, the present disclosure needs to obtain the position information of the second peak and the third peak according to the position information of the second peak and the third peak.
  • the preset state of charge range can be set to include the second peak and the third peak, for example, 20% to 90%, and the preset temperature threshold can be 10 °C, which is not limited in the present disclosure.
  • the SOC of the battery varies from 15% to 90% during the current charging process, and the minimum charging temperature of the battery during the current charging process is 15°C, it can be determined that the current charging process is effective charging ;
  • the SOC of the battery varies from 15% to 80% during the current charging process, or the minimum charging temperature of the battery during the current charging process is 7°C, it can be determined that the current charging process is not valid Charge.
  • the initial charging parameters may include initial charging voltage and initial charging current.
  • the actual charging parameters may include actual charging voltage and actual charging current.
  • an initial capacity increment curve may be acquired according to the initial charging parameters, and an actual capacity increment curve may be acquired according to the actual charging parameters, and According to the initial capacity increment curve and the actual capacity increment curve, the current maximum available capacity of the battery is estimated.
  • the initial capacity increment curve can be obtained through the following steps:
  • the preset voltage interval may be preset, for example, the preset voltage interval may be 5mV or 20mV, which is not limited in the present disclosure.
  • the capacity increment value is the capacity value corresponding to the unit voltage increment.
  • the initial capacity increment value can be calculated by the following formula:
  • ICi is the ith initial capacity increment value
  • Ii is the ith initial charging current
  • Vi is the ith initial charging voltage
  • the initial capacity can be generated by means of the related art according to the preset voltage interval, with the voltage as the abscissa and the capacity increment as the ordinate. Incremental curve.
  • the actual capacity increment curve may be obtained in the same manner as the initial capacity increment curve, which will not be repeated here.
  • the first initial position information corresponding to the second peak value of the initial capacity increment curve and the second initial position information corresponding to the third peak value may be acquired, and the The first current position information corresponding to the second peak value of the actual capacity increment curve and the second current position information corresponding to the third peak value, and based on the first initial position information, the second initial position information, and the first current position information , the second current location information, the initial charging parameter and the actual charging parameter to estimate the current maximum available capacity of the battery.
  • the initial interval capacity integral value can be obtained according to the initial charging parameter, the first initial position information and the second initial position information, and the initial interval
  • the capacity integral value is the capacity integral between the two peaks in the time interval between the second peak and the third peak.
  • the initial interval capacity integral value can be calculated by the following formula:
  • Qc is the initial interval capacity integral value
  • tII is the first initial position information
  • tIII is the second initial position information
  • Ii is the i-th initial charging current.
  • the initial capacity increment curve and the actual capacity increment curve can be preprocessed by means of averaging, low-pass filtering, etc. Filter out the noise in the initial capacity increase curve and the actual capacity increase curve, so that more accurate initial capacity increase curve and actual capacity increase curve can be obtained, so that according to the initial capacity increase curve and the actual capacity increase curve The predicted battery capacity estimated by the capacity curve is more accurate.
  • the initial interval capacity integral value is calculated by the slope corresponding to the second peak value and the third peak value, etc., which is not limited in the present disclosure.
  • FIG. 3 is a schematic diagram of an interval capacity integral value according to an exemplary embodiment, the abscissa is the effective charging times, and the ordinate is the interval capacity integral value. As shown in FIG. 3 , with the increase of the effective charging times, the interval The smaller and smaller the capacity integral value is, the larger the loss of the battery is.
  • the current maximum available capacity of the battery can be calculated by the following formula:
  • Qmax_j is the current maximum available capacity
  • j is the current effective charging times
  • Qc is the initial interval capacity integral value
  • Qs is the actual interval capacity integral value
  • C is the initial maximum available capacity
  • the initial maximum usable capacity can be calculated by the following formula:
  • C is the initial maximum available capacity
  • t1 is the initial sampling time corresponding to the initial charging parameter
  • t2 is the termination sampling time corresponding to the initial charging parameter
  • Ii is the ith initial charging current.
  • Fig. 4 is a schematic diagram showing a maximum usable capacity according to an exemplary embodiment, the abscissa is the number of effective charging times, and the ordinate is the maximum usable capacity. As shown in Fig. 4, as the number of effective charging times increases, the battery's The maximum usable capacity is getting smaller and smaller, indicating that the battery is wearing more and more.
  • the capacity relationship includes the corresponding relationship between the predicted battery capacity and the number of effective charging times.
  • the capacity correlation can be obtained according to the current maximum available capacity and the current effective charging times:
  • Qmax_j is the predicted battery capacity during effective charging
  • Xcharge is the number of effective charging times.
  • the capacity correlation is obtained by fitting.
  • the historical maximum available capacity and the current maximum available capacity in each valid charging process before the current charging process may be obtained.
  • the historical effective charging times corresponding to the historical maximum available capacity, and the capacity correlation is obtained according to the current maximum available capacity, the current effective charging times, the historical maximum available capacity, and the historical effective charging times.
  • the calculation method of the historical maximum available capacity may refer to the calculation method of the current maximum available capacity, which will not be repeated here.
  • the capacity correlation can be obtained by a fitting method, and the fitting method can include exponential fitting, linear fitting fitting, logarithmic fitting, polynomial fitting, power function fitting, etc., which are not limited in the present disclosure.
  • the fitting method can include exponential fitting, linear fitting fitting, logarithmic fitting, polynomial fitting, power function fitting, etc., which are not limited in the present disclosure.
  • the fitting method can include exponential fitting, linear fitting fitting, logarithmic fitting, polynomial fitting, power function fitting, etc.
  • the capacity correlation obtained by fitting may be formula (6) or formula (7):
  • y is the predicted battery capacity
  • x is the effective charging times
  • the target charging times corresponding to the next effective charging process can be obtained, and according to the target charging times and the capacity correlation, the predicted battery capacity of the battery during the next effective charging is obtained .
  • the target charging times can be substituted into the above formula (6) or formula (7), and the predicted battery capacity can be calculated.
  • the predicted battery can be calculated by formula (6).
  • the capacity is 144.121.
  • the capacity correlation relationship can be obtained according to the current maximum available capacity, the current effective charging times, the historical maximum available capacity and the historical effective charging times, and the predicted battery capacity of the battery at the next effective charging can be obtained through the capacity correlation relationship, In this way, the predicted battery capacity can be calculated only through the capacity correlation relationship, which can improve the efficiency of estimating the battery capacity.
  • the capacity correlation relationship is based on the charging parameters in the process of full discharge and full charging and all effective charges accumulated in history. Obtained from the parameters of the process, the accuracy of the capacity correlation relationship is higher, so that the accuracy of the predicted battery capacity obtained according to the capacity correlation relationship is also higher.
  • FIG. 5 is a schematic structural diagram of an apparatus for obtaining battery capacity according to an exemplary embodiment. As shown in Figure 5, the device includes:
  • the initial parameter acquisition module 501 is used to acquire a plurality of initial charging parameters of the battery under the condition that the current charging process of the vehicle battery is effective charging; the effective charging represents the state of charge SOC of the battery in the current charging process.
  • the variation range includes a preset state of charge range, and the minimum charging temperature of the battery during the current charging process is greater than or equal to a preset temperature threshold;
  • an actual parameter acquisition module 502 configured to periodically acquire a plurality of actual charging parameters of the battery in the current charging process, and the current effective charging times corresponding to the current charging process;
  • the battery capacity acquisition module 503 is configured to acquire the predicted battery capacity of the battery during the next effective charging according to a plurality of the initial charging parameters, a plurality of the actual charging parameters and the current effective charging times.
  • the battery capacity acquisition module 503 is specifically configured to: estimate the current maximum available capacity of the battery according to a plurality of the initial charging parameters and a plurality of the actual charging parameters; according to the current maximum available capacity and the current effective charging times, the capacity correlation relationship is obtained, and the capacity correlation relationship includes the corresponding relationship between the predicted battery capacity and the number of effective charging times; through the capacity correlation relationship, the predicted battery capacity of the battery during the next effective charging is obtained.
  • the battery capacity obtaining module 503 is further configured to: obtain an initial capacity increase curve according to the initial charging parameter; obtain an actual capacity increase curve according to the actual charging parameter; obtain an actual capacity increase curve according to the initial capacity increase curve and the The actual capacity increment curve, which estimates the current maximum usable capacity of the battery.
  • the battery capacity acquisition module 503 is further configured to: acquire the first initial position information corresponding to the second peak value of the initial capacity increment curve and the second initial position information corresponding to the third peak value; acquire the actual capacity increment curve.
  • the battery capacity obtaining module 503 is further configured to: obtain the target charging times corresponding to the next effective charging process; and obtain the predicted battery capacity of the battery during the next effective charging according to the target charging times and the capacity correlation .
  • FIG. 6 is a schematic structural diagram of another apparatus for acquiring battery capacity according to an exemplary embodiment. As shown in Figure 6, the device also includes:
  • a historical parameter acquisition module 504 configured to acquire the historical maximum available capacity in each valid charging process before the current charging process and the historical valid charging times corresponding to the historical maximum available capacity
  • the battery capacity obtaining module 503 is further configured to: obtain the capacity relationship according to the current maximum available capacity, the current effective charging times, the historical maximum available capacity, and the historical effective charging times.
  • the predicted battery capacity of the battery at the next effective charging can be obtained according to a plurality of initial charging parameters, a plurality of actual charging parameters and the current effective charging times.
  • the battery does not need to be left to stand, and only the charging parameters of the battery are obtained during the effective charging process of the battery, so that the predicted battery capacity of the battery in the next effective charging can be estimated, so that the efficiency of estimating the battery capacity can be improved.
  • FIG. 7 is a block diagram of a server 700 according to an exemplary embodiment.
  • the server 700 may be provided as a server. 7
  • the server 700 includes a processor 722 , which may be one or more in number, and a memory 732 for storing computer programs executable by the processor 722 .
  • the computer program stored in memory 732 may include one or more modules, each corresponding to a set of instructions.
  • the processor 722 may be configured to execute the computer program to perform the above-described method of obtaining battery capacity.
  • the server 700 may also include a power supply component 726, which may be configured to perform power management of the server 700, and a communication component 750, which may be configured to enable communications, eg, wired or wireless communications, of the server 700. . Additionally, the server 700 may also include an input/output (I/O) interface 758 . Server 700 may operate based on an operating system stored in memory 732, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, and the like.
  • a computer-readable storage medium comprising program instructions, the program instructions implementing the steps of the above-mentioned method for obtaining battery capacity when executed by a processor.
  • the computer-readable storage medium can be the above-mentioned memory 732 including program instructions, and the above-mentioned program instructions can be executed by the processor 722 of the server 700 to complete the above-mentioned method for obtaining battery capacity.
  • a computer program product comprising a computer program executable by a programmable apparatus, the computer program having, when executed by the programmable apparatus, for performing the above The code part of the method to get the battery capacity.

Landscapes

  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Tests Of Electric Status Of Batteries (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)

Abstract

一种获取电池容量的方法、装置、存储介质及服务器，方法包括：在车辆电池为有效充电的情况下，获取多个初始充电参数（S101）；有效充电表示电池的荷电状态SOC变化范围包括预设荷电状态范围，电池的最小充电温度大于或等于预设温度阈值；周期性获取电池的多个实际充电参数，以及当前充电过程对应的当前有效充电次数（S102）；根据多个初始充电参数、多个实际充电参数以及当前有效充电次数，获取电池在下一次有效充电时的预测电池容量（S103）。

Description

获取电池容量的方法、装置、存储介质及服务器
相关申请的交叉引用
本申请基于申请号为202011066177.3、申请日为2020年09月30日的中国专利申请《获取电池容量的方法、装置、存储介质及服务器》，并要求上述中国专利申请的优先权，上述中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本公开涉及电池技术领域，具体地，涉及一种获取电池容量的方法、装置、存储介质及服务器。
背景技术
电动汽车是以动力电池作为全部或部分动力来源的机动车辆，动力电池的电池容量是影响电动车性能的一个重要因素，准确估计电池容量可以提高SOC(State of Charge，荷电状态)的估计精度、峰值功率的预测精度以及续航里程的估计精度，因此，在电池管理系统中，电池容量常常作为表征电池健康状态的一个重要指标。
相关技术中，可以根据电池包的充放电电流计算电池的电池电量，但是，该方式需要检测OCV(Open Circuit Voltage，开路电压)，而OCV需要在小电流充放电且充分静置之后才能获取，获取时间较长，从而导致估算电池容量的效率较低。
发明内容
为了解决上述问题，本公开提供一种获取电池容量的方法、装置、存储介质及服务器。
第一方面，本公开提供一种获取电池容量的方法，所述方法包括：在车辆电池的当前充电过程为有效充电的情况下，获取所述电池的多个初始充电参数；所述有效充电表示所述电池的荷电状态SOC在所述当前充电过程中的变化范围包括预设荷电状态范围，且所述电池在所述当前充电过程中的最小充电温度大于或等于预设温度阈值；周期性获取所述电池在所述当前充电过程中的多个实际充电参数，以及所述当前充电过程对应的当前有效充电次数；根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量。
可选地，所述根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量包括：根据多个所述初 始充电参数和多个所述实际充电参数，估算所述电池的当前最大可用容量；根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系，所述容量关联关系包括预测电池容量和有效充电次数的对应关系；通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
可选地，所述根据多个所述初始充电参数和多个所述实际充电参数，估算所述电池的当前最大可用容量包括：根据所述初始充电参数，获取初始容量增量曲线；根据所述实际充电参数，获取实际容量增量曲线；根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量。
可选地，所述根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量包括：获取所述初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息；获取所述实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的第二当前位置信息；根据所述第一初始位置信息、所述第二初始位置信息、所述第一当前位置信息、所述第二当前位置信息、所述初始充电参数以及所述实际充电参数，估算所述电池的当前最大可用容量。
可选地，所述通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量包括：获取下一次有效充电过程对应的目标充电次数；根据所述目标充电次数和所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
可选地，在所述根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系前，所述方法还包括：获取所述当前充电过程之前的每次有效充电过程中的历史最大可用容量和所述历史最大可用容量对应的历史有效充电次数；所述根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系包括：根据所述当前最大可用容量、所述当前有效充电次数、所述历史最大可用容量以及所述历史有效充电次数，获取所述容量关联关系。
第二方面，本公开提供一种获取电池容量的装置，所述装置包括：初始参数获取模块，用于在车辆电池的当前充电过程为有效充电的情况下，获取所述电池的多个初始充电参数；所述有效充电表示所述电池的荷电状态SOC在所述当前充电过程中的变化范围包括预设荷电状态范围，且所述电池在所述当前充电过程中的最小充电温度大于或等于预设温度阈值；实际参数获取模块，用于周期性获取所述电池在所述当前充电过程中的多个实际充电参数，以及所述当前充电过程对应的当前有效充电次数；电池容量获取模块，用于根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量。
可选地，所述电池容量获取模块，具体用于：根据多个所述初始充电参数和多个所 述实际充电参数，估算所述电池的当前最大可用容量；根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系，所述容量关联关系包括预测电池容量和有效充电次数的对应关系；通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
可选地，所述电池容量获取模块，还用于：根据所述初始充电参数，获取初始容量增量曲线；根据所述实际充电参数，获取实际容量增量曲线；根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量。
可选地，所述电池容量获取模块，还用于：获取所述初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息；获取所述实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的第二当前位置信息；根据所述第一初始位置信息、所述第二初始位置信息、所述第一当前位置信息、所述第二当前位置信息、所述初始充电参数以及所述实际充电参数，估算所述电池的当前最大可用容量。
可选地，所述电池容量获取模块，还用于：获取下一次有效充电过程对应的目标充电次数；根据所述目标充电次数和所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
可选地，所述装置还包括：历史参数获取模块，用于获取所述当前充电过程之前的每次有效充电过程中的历史最大可用容量和所述历史最大可用容量对应的历史有效充电次数；所述电池容量获取模块，还用于：根据所述当前最大可用容量、所述当前有效充电次数、所述历史最大可用容量以及所述历史有效充电次数，获取所述容量关联关系。
第三方面，本公开提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现本公开第一方面所述方法的步骤。
第四方面，本公开提供一种服务器，包括：存储器，其上存储有计算机程序；处理器，用于执行所述存储器中的所述计算机程序，以实现本公开第一方面所述方法的步骤。
通过上述技术方案，在车辆电池的当前充电过程为有效充电的情况下，获取所述电池的多个初始充电参数；所述有效充电表示所述电池的荷电状态SOC在所述当前充电过程中的变化范围包括预设荷电状态范围，且所述电池在所述当前充电过程中的最小充电温度大于或等于预设温度阈值；周期性获取所述电池在所述当前充电过程中的多个实际充电参数，以及所述当前充电过程对应的当前有效充电次数；根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量。这样，该电池无需静置，仅在该电池进行有效充电过程中获取该电池的充电参数，即可估算该电池在下一次有效充电时的预测电池容量，从而可以提 高估算电池容量的效率。
本公开的其他特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
附图是用来提供对本公开的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本公开，但并不构成对本公开的限制。在附图中：
图1是根据一示例性实施例示出的一种获取电池容量的方法的流程图；
图2是根据一示例性实施例示出的另一种获取电池容量的方法的流程图；
图3是根据一示例性实施例示出的一种间隔容量积分值的示意图；
图4是根据一示例性实施例示出的一种最大可用容量的示意图；
图5根据一示例性实施例示出的一种获取电池容量的装置的结构示意图；
图6根据一示例性实施例示出的另一种获取电池容量的装置的结构示意图；
图7是根据一示例性实施例示出的一种服务器的框图。
具体实施方式
以下结合附图对本公开的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本公开，并不用于限制本公开。
在下文中的描述中，“第一”、“第二”等词汇，仅用于区分描述的目的，而不能理解为指示或暗示相对重要性，也不能理解为指示或暗示顺序。
首先，对本公开的应用场景进行说明。本公开可以应用于估算电池容量的场景，电池容量反应了电池当前的健康状态，因实际应用中无法达到实验室条件下的满充满放、实际工况复杂多变、电池老化、电池材料体系不同等因素，电池容量的估算成为一项巨大挑战和重要任务。
相关技术中，可以基于电池数学模型，利用系统辨识算法获取电池的OCV和容量，若电池包静置时间超过预定时间，则检测OCV，计算每个时间周期内电池容量的变化量以及根据OCV查表计算电池容量的变化量，根据上述计算得到的两个变化量计算电池容量衰减权重值，根据该权重值以及前一时刻的电池容量计算当前电池容量。这种方式下，电池容量的估算效率较低，并且对电池电流、电压，以及温度采样精度较为依赖，导致电容容量的估算精度较低。
为了解决上述存在的问题，本公开提供一种获取电池容量的方法、装置、存储介质及服务器，在车辆充电过程中可以获取电池的多个实际充电参数和该当前充电过程对应的当前有效充电次数，根据多个初始充电参数、多个实际充电参数以及该当前有效充电 次数，即可获取该电池在下一次有效充电时的预测电池容量。这样，该电池无需静置，仅在该电池进行有效充电过程中获取该电池的充电参数，即可估算该电池在下一次有效充电时的预测电池容量，从而可以提高估算电池容量的效率。
下面结合附图，对本公开的具体实施方式进行详细说明。
图1是根据一示例性实施例示出的一种获取电池容量的方法的流程图。如图1所示，该方法包括：
S101、在车辆电池的当前充电过程为有效充电的情况下，获取该电池的多个初始充电参数。
其中，该电池可以是动力电池组，该有效充电表示该电池的SOC在该当前充电过程中的变化范围包括预设荷电状态范围，且该电池在该当前充电过程中的最小充电温度大于或等于预设温度阈值。该预设荷电状态范围和该预设温度阈值可以根据该电池的材料、实际使用工况等确定。示例地，由于电池的容量增量曲线包括三个峰值，即第一峰值、第二峰值以及第三峰值，根据动力锂离子电芯的特性及实际应用的经验数据可知，在该电池充电过程中，该第一峰值通常出现在该电池的荷电状态范围的0～20％区间内，该第二峰值和该第三峰值通常出现在该电池的荷电状态范围的20％～90％区间内，而用户在实际使用过程中通常会在荷电状态范围的20％以上开始充电，基于此，本公开可以根据该第二峰值的位置信息和该第三峰值的位置信息获取该电池在下一次有效充电时的预测电池容量，因此，可以将该预设荷电状态范围设置为包括该第二峰值和该第三峰值的范围，例如20％～90％，该预设温度阈值可以是10℃，本公开对此不作限定。在该电池的SOC在该当前充电过程中的变化范围为15％～90％，且该电池在该当前充电过程中的最小充电温度为15℃的情况下，可以确定该当前充电过程为有效充电；在该电池的SOC在该当前充电过程中的变化范围为15％～80％，或者该电池在该当前充电过程中的最小充电温度为7℃的情况下，可以确定该当前充电过程不是有效充电。
在本步骤中，在该当前充电过程完成后，可以确定该当前充电过程是否为有效充电，在确定该当前充电过程为有效充电的情况下，获取该电池的多个初始充电参数。示例地，在该当前充电过程完成后，可以获取该当前充电过程中SOC的变化范围和该电池的最小充电温度，在确定该SOC的变化范围包括该预设荷电状态范围，并且该电池的最小充电温度大于或等于该预设温度阈值的情况下，获取该电池的多个初始充电参数，该多个初始充电参数可以预先存储在服务器中。
需要说明的是，该初始充电参数可以是在该电池满放满充过程中获取的标准充电参数，由于在该电池满放满充后，该电池的电池容量可以达到最大容量，而正常使用过程中，该电池的充电过程很难满足满放满充的条件，因此，该电池在正常使用过程中的当 前最大可用容量无法达到该最大容量，该当前最大可用容量会小于该最大容量，若电池管理器将该最大容量作为该当前最大可用容量，则会由于该当前最大可用容量准确率太低造成电池安全事故，因此，需要获取该电池的预测电池容量。这里，可以通过该初始充电参数和该实际充电参数，获取该电池的预测电池容量。其中，该多个初始充电参数可以在该电池满放满充过程中，通过电池实验测试平台获取。其中，该满放满充过程可以包括：
S1、在室温下将该电池以第一倍率恒流放电至该电池中每个单体电池的电压达到第一截止电压。
S2、将该电池静置第一预设时间段。
S3、在室温下将该电池以第二倍率恒流充电至该电池中每个单体电池的电压达到第二截止电压。
S4、将该电池静置第二预设时间段。
S5、在室温下将该电池以第三倍率恒流充电至该电池中的每个单体电池的电压达到该第二截止电压。
其中，该第一倍率、该第一截止电压、该第一预设时间段、该第二倍率、该第二截止电压、该第二预设时间段以及该第三倍率可以根据该电池的电芯特性预先设置，示例地，该第一倍率可以是0.3C，该第一截止电压可以是2V，该第一预设时间段可以是1800s，该第二倍率可以是1C，该第二截止电压可以是3.75V，该第二预设时间段可以是300s，该第三倍率可以是0.2C。
在该电池进入满放满充过程后，即该电池以第一倍率恒流放电开始后，可以周期性采集该电池的初始充电参数，该采集周期可以根据采样电路的性能确定，示例地，该采样电路的性能越好，该采样周期可以设置的越短，例如50ms，该采样电路的性能越差，该采样周期可以设置的越长，例如100ms，本公开对此不作限定。在该电池以第三倍率恒流充电至该电池中的每个单体电池的电压达到第二截止电压后，停止采集该初始充电参数。之后，可以将该满放满充过程中采集的该初始充电参数存储在服务器中。
需要说明的是，本公开也可以在该电池的当前充电过程中，确定该当前充电过程是否为有效充电。示例地，在该当前充电过程开始时，可以获取该电池的初始SOC和该电池的充电温度，之后，在该当前充电过程中，可以周期性获取该电池的当前SOC和该电池的充电温度，若该初始SOC和该当前SOC的所覆盖的范围包括该预设荷电状态范围，并且该电池的最小充电温度小于该预设温度阈值，则可以确定该当前充电过程为有效充电，可以获取该电池的多个初始充电参数，示例地，若该预设荷电状态范围为20％～90％，该当前充电过程开始时获取的该电池的初始SOC为15％，则在该当前充电 过程中，可以周期性获取该电池的当前SOC，在确定该当前SOC达到90％，并且在此过程中该电池的最小充电温度小于或等于该预设温度阈值的情况下，可以确定该当前充电过程为有效充电。这样，在该当前充电过程完成之前，就可以确定该当前充电过程是有效充电，并获取该电池的初始充电参数，从而可以提高估算电池容量的效率。
S102、周期性获取该电池在该当前充电过程中的多个实际充电参数，以及该当前充电过程对应的当前有效充电次数。
在本步骤中，在该当前充电过程开始后，可以周期性获取该电池的实际充电参数，该获取周期的设置方式可以参考该初始充电参数的采集周期，此处不再赘述了。另外，在确定该当前充电过程为有效充电的情况下，可以获取该当前充电过程对应的当前有效充电次数，该当前有效充电次数可以根据历史有效充电次数确定，示例地，该当前有效充电次数可以是该历史有效充电次数加1，例如，若该历史有效充电次数为10，则该当前有效充电次数为11。另外，该电池每完成一次有效充电，可以将该历史有效充电次数加1，例如，在完成第一次有效充电后，该历史有效充电次数为1，在完成第二次有效充电后，该历史有效充电次数为2。
S103、根据多个初始充电参数、多个实际充电参数以及该当前有效充电次数，获取该电池在下一次有效充电时的预测电池容量。
在本步骤中，在获取该多个初始充电参数、多个实际充电参数以及该当前有效充电次数后，可以根据该历史有效充电次数确定下一次有效充电过程对应的目标充电次数，之后，可以根据该多个初始充电参数、多个实际充电参数、该当前有效充电次数以及该目标充电次数，获取该电池在下一次有效充电时的预测电池容量。
采用上述方法，可以根据多个初始充电参数、多个实际充电参数以及该当前有效充电次数，获取该电池在下一次有效充电时的预测电池容量。这样，该电池无需静置，仅在该电池进行有效充电过程中获取该电池的充电参数，即可估算该电池在下一次有效充电时的预测电池容量，从而可以提高估算电池容量的效率。
图2是根据一示例性实施例示出的另一种获取电池容量的方法的流程图。如图2所示，该方法包括：
S201、在车辆电池的当前充电过程为有效充电的情况下，获取该电池的多个初始充电参数。
其中，该电池可以是动力电池组，该有效充电表示该电池的SOC在该当前充电过程中的变化范围包括预设荷电状态范围，且该电池在该当前充电过程中的最小充电温度大于或等于预设温度阈值。该预设荷电状态范围和该预设温度阈值可以根据该电池的材料、实际使用工况等确定。示例地，由于电池的荷电状态范围内存在三个峰值，即第一 峰值、第二峰值以及第三峰值，本公开需要根据该第二峰值的位置信息和该第三峰值的位置信息获取该电池在下一次有效充电时的预测电池容量，因此，可以将该预设荷电状态范围设置为包括第二峰值和第三峰值的范围，例如20％～90％，该预设温度阈值可以是10℃，本公开对此不作限定。在该电池的SOC在该当前充电过程中的变化范围为15％～90％，且该电池在该当前充电过程中的最小充电温度为15℃的情况下，可以确定该当前充电过程为有效充电；在该电池的SOC在该当前充电过程中的变化范围为15％～80％，或者该电池在该当前充电过程中的最小充电温度为7℃的情况下，可以确定该当前充电过程不是有效充电。另外，该初始充电参数可以包括初始充电电压和初始充电电流。
S202、周期性获取该电池在该当前充电过程中的多个实际充电参数，以及该当前充电过程对应的当前有效充电次数。
其中，该实际充电参数可以包括实际充电电压和实际充电电流。
S203、根据多个初始充电参数和多个实际充电参数，估算该电池的当前最大可用容量。
在本步骤中，在获取该多个初始充电参数和该多个实际充电参数后，可以根据该初始充电参数，获取初始容量增量曲线，根据该实际充电参数，获取实际容量增量曲线，并根据该初始容量增量曲线和该实际容量增量曲线，估算该电池的当前最大可用容量。
其中，可以通过以下步骤获取该初始容量增量曲线：
S1、获取预设电压间隔。
其中，该预设电压间隔可以是预先设置的，例如，该预设电压间隔可以是5mV，也可以是20mV，本公开对此不作限定。
S2、根据该多个初始充电参数中的多个初始充电电流和多个初始充电电压，获取多个初始容量增量值。
该容量增量值为单位电压增量对应的容量值。示例地，可以通过以下公式计算得到该初始容量增量值：
Figure PCTCN2021119637-appb-000001
其中，ICi为第i个初始容量增量值，Ii为第i个初始充电电流，Vi为第i个初始充电电压。
S3、根据该预设电压间隔和该多个容量增量值，生成该初始容量增量曲线。
在获取该预设电压间隔和该多个初始容量增量值后，可以以电压为横坐标，以容量 增量值为纵坐标，按照该预设电压间隔，通过相关技术的方式生成该初始容量增量曲线。
需要说明的是，在获取该初始容量增量曲线后，可以按照获取该初始容量增量曲线的方式获取该实际容量增量曲线，此处不再赘述了。
在获取该初始容量增量曲线和该实际容量增量曲线后，可以获取该初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息，获取该实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的第二当前位置信息，并根据该第一初始位置信息、该第二初始位置信息、该第一当前位置信息、该第二当前位置信息、该初始充电参数以及该实际充电参数，估算该电池的当前最大可用容量。
其中，在获取该第一初始位置信息和该第二初始位置信息后，可以根据该初始充电参数、该第一初始位置信息以及该第二初始位置信息，获取初始间隔容量积分值，该初始间隔容量积分值为该第二峰值和该第三峰值之间的时间间隔内两个峰值之间的容量积分。示例地，可以通过以下公式计算得到该初始间隔容量积分值：
Figure PCTCN2021119637-appb-000002
其中，Qc为该初始间隔容量积分值，tⅡ为该第一初始位置信息，tⅢ为该第二初始位置信息，Ii为第i个初始充电电流。
需要说明的是，在获取该初始容量增量曲线和该实际容量增量曲线后，可以通过均值算、低通滤波等方法对该初始容量增量曲线和该实际容量增量曲线进行预处理，滤除该初始容量增量曲线和该实际容量增量曲线中的噪声，从而可以得到更加精确的初始容量增量曲线和实际容量增量曲线，使得根据该初始容量增量曲线和该实际容量增量曲线估算的预测电池容量的准确率更高。
除上述根据该初始容量增量曲线的第二峰值和第三峰值的位置信息获取该初始间隔容量积分值外，还可以根据该第二峰值和该第三峰值之间的区域对应的面积、该第二峰值和该第三峰值对应的斜率等计算该初始间隔容量积分值，本公开对此不作限定。
在获取该第一当前位置信息和该第二当前位置信息后，可以根据该实际充电参数、该第一当前位置信息以及该第二当前位置信息，获取实际间隔容量积分值Qs，该实际间隔容量积分值的获取方式可以参考上述初始间隔容量积分值的获取方式，此处不再赘述了。图3是根据一示例性实施例示出的一种间隔容量积分值的示意图，横坐标为有效充电次数，纵坐标为间隔容量积分值，如图3所示，随着有效充电次数的增加，间隔容量积分值越来越小，表示该电池的损耗越来越大。
进一步地，在获取该初始间隔容量积分值和该实际间隔容量积分值后，可以通过以下公式计算得到该电池的当前最大可用容量：
Figure PCTCN2021119637-appb-000003
其中，Qmax_j为该当前最大可用容量，j为当前有效充电次数，Qc为该初始间隔容量积分值，Qs为该实际间隔容量积分值，C为初始最大可用容量。
该初始最大可用容量可以通过以下公式计算得到：
Figure PCTCN2021119637-appb-000004
其中，C为该初始最大可用容量，t1为该初始充电参数对应的起始采样时刻，t2为该初始充电参数对应的终止采样时刻，Ii为第i个初始充电电流。
图4是根据一示例性实施例示出的一种最大可用容量的示意图，横坐标为有效充电次数，纵坐标为最大可用容量，如图4所示，随着有效充电次数的增加，该电池的最大可用容量越来越小，表示该电池的损耗越来越大。
S204、根据该当前最大可用容量和该当前有效充电次数，获取容量关联关系。
其中，该容量关联关系包括预测电池容量和有效充电次数的对应关系。
在本步骤中，在获取该当前最大可用容量和该当前有效充电次数后，可以根据该当前最大可用容量和该当前有效充电次数获取该容量关联关系：
Figure PCTCN2021119637-appb-000005
其中，Qmax_j为有效充电时的预测电池容量，Xcharge为有效充电次数。
通过公式(5)的转换，可以得到预测电池容量和有效充电次数的对应关系Qmax_j＝g(Xcharge)。为了提高该预测电池容量的准确率，可以在获取该当前最大可用容量后，根据该当前最大可用容量、当前有效充电次数，以及该当前充电过程之前的所有有效充电过程对应的历史最大可用容量、历史有效充电次数，拟合得到该容量关联关系。
在一种可能的实现方式中，在根据该当前最大可用容量和该当前有效充电次数，获取容量关联关系前，可以获取该当前充电过程之前的每次有效充电过程中的历史最大可用容量和该历史最大可用容量对应的历史有效充电次数，根据该当前最大可用容量、该当前有效充电次数、该历史最大可用容量以及该历史有效充电次数，获取该容量关联关系。
其中，该历史最大可用容量的计算方式可以参考该当前最大可用容量的计算方式，此处不再赘述了。在获取该当前最大可用容量、该当前有效充电次数、该历史最大可用 容量以及该历史有效充电次数后，可以通过拟合方式获取该容量关联关系，该拟合方式可以包括指数拟合、线性拟合、对数拟合、多项式拟合、幂函数拟合等，本公开对此不作限定。这里，具体的拟合方式可以参考相关技术的实现方式，此处不再赘述了。
示例地，通过拟合方式得到的该容量关联关系可以是公式(6)或公式(7)：
y＝-0.0853x 2+0.1721x+133.87   (6)
y＝140.13e -0.012x   (7)
其中，y为该预测电池容量，x为该有效充电次数。
需要说明的是，上述公式(6)和公式(7)表示的该容量关联关系只是举例说明(根据实验数据得到的表达式)，针对不同的电芯、不同的使用环境、不同的拟合方式，会对应不同的容量关联关系，本公开对此不作限定。
S205、通过该容量关联关系，获取该电池在下一次有效充电时的预测电池容量。
在本步骤中，在获取该容量关联关系后，可以获取该下一次有效充电过程对应的目标充电次数，根据该目标充电次数和该容量关联关系，获取该电池在下一次有效充电时的预测电池容量。示例地，可以将该目标充电次数代入上述公式(6)或公式(7)，计算得到该预测电池容量，例如，若该目标充电次数为10，则通过公式(6)可以计算得到该预测电池容量为144.121。
采用上述方法，可以根据当前最大可用容量、当前有效充电次数、历史最大可用容量以及历史有效充电次数，获取容量关联关系，并通过该容量关联关系获取该电池在下一次有效充电时的预测电池容量，这样，仅通过该容量关联关系即可计算得到该预测电池容量，可以提高估算电池容量的效率，另外，该容量关联关系是基于满放满充过程中的的充电参数以及历史累积的所有有效充电过程的参数获取的，该容量关联关系的准确率更高，从而使得根据该容量关联关系获取的预测电池容量的准确率也更高。
图5根据一示例性实施例示出的一种获取电池容量的装置的结构示意图。如图5所示，该装置包括：
初始参数获取模块501，用于在车辆电池的当前充电过程为有效充电的情况下，获取该电池的多个初始充电参数；该有效充电表示该电池的荷电状态SOC在该当前充电过程中的变化范围包括预设荷电状态范围，且该电池在该当前充电过程中的最小充电温度大于或等于预设温度阈值；
实际参数获取模块502，用于周期性获取该电池在该当前充电过程中的多个实际充电参数，以及该当前充电过程对应的当前有效充电次数；
电池容量获取模块503，用于根据多个该初始充电参数、多个该实际充电参数以及该当前有效充电次数，获取该电池在下一次有效充电时的预测电池容量。
可选地，该电池容量获取模块503，具体用于：根据多个该初始充电参数和多个该实际充电参数，估算该电池的当前最大可用容量；根据该当前最大可用容量和该当前有效充电次数，获取容量关联关系，该容量关联关系包括预测电池容量和有效充电次数的对应关系；通过该容量关联关系，获取该电池在下一次有效充电时的预测电池容量。
可选地，该电池容量获取模块503，还用于：根据该初始充电参数，获取初始容量增量曲线；根据该实际充电参数，获取实际容量增量曲线；根据该初始容量增量曲线和该实际容量增量曲线，估算该电池的当前最大可用容量。
可选地，该电池容量获取模块503，还用于：获取该初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息；获取该实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的第二当前位置信息；根据该第一初始位置信息、该第二初始位置信息、该第一当前位置信息、该第二当前位置信息、该初始充电参数以及该实际充电参数，估算该电池的当前最大可用容量。
可选地，该电池容量获取模块503，还用于：获取下一次有效充电过程对应的目标充电次数；根据该目标充电次数和该容量关联关系，获取该电池在下一次有效充电时的预测电池容量。
可选地，图6根据一示例性实施例示出的另一种获取电池容量的装置的结构示意图。如图6所示，该装置还包括：
历史参数获取模块504，用于获取该当前充电过程之前的每次有效充电过程中的历史最大可用容量和该历史最大可用容量对应的历史有效充电次数；
该电池容量获取模块503，还用于：根据该当前最大可用容量、该当前有效充电次数、该历史最大可用容量以及该历史有效充电次数，获取该容量关联关系。
通过上述装置，可以根据多个初始充电参数、多个实际充电参数以及该当前有效充电次数，获取该电池在下一次有效充电时的预测电池容量。这样，该电池无需静置，仅在该电池进行有效充电过程中获取该电池的充电参数，即可估算该电池在下一次有效充电时的预测电池容量，从而可以提高估算电池容量的效率。
关于上述实施例中的装置，其中各个模块执行操作的具体方式已经在有关该方法的实施例中进行了详细描述，此处将不做详细阐述说明。
图7是根据一示例性实施例示出的一种服务器700的框图。例如，服务器700可以被提供为一服务器。参照图7，服务器700包括处理器722，其数量可以为一个或多个，以及存储器732，用于存储可由处理器722执行的计算机程序。存储器732中存储的计 算机程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理器722可以被配置为执行该计算机程序，以执行上述的获取电池容量的方法。
另外，服务器700还可以包括电源组件726和通信组件750，该电源组件726可以被配置为执行服务器700的电源管理，该通信组件750可以被配置为实现服务器700的通信，例如，有线或无线通信。此外，该服务器700还可以包括输入/输出(I/O)接口758。服务器700可以操作基于存储在存储器732的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM，LinuxTM等等。
在另一示例性实施例中，还提供了一种包括程序指令的计算机可读存储介质，该程序指令被处理器执行时实现上述的获取电池容量的方法的步骤。例如，该计算机可读存储介质可以为上述包括程序指令的存储器732，上述程序指令可由服务器700的处理器722执行以完成上述的获取电池容量的方法。
在另一示例性实施例中，还提供一种计算机程序产品，该计算机程序产品包含能够由可编程的装置执行的计算机程序，该计算机程序具有当由该可编程的装置执行时用于执行上述的获取电池容量的方法的代码部分。
以上结合附图详细描述了本公开的优选实施方式，但是，本公开并不限于上述实施方式中的具体细节，在本公开的技术构思范围内，可以对本公开的技术方案进行多种简单变型，这些简单变型均属于本公开的保护范围。另外需要说明的是，在上述具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合，为了避免不必要的重复，本公开对各种可能的组合方式不再另行说明。
此外，本公开的各种不同的实施方式之间也可以进行任意组合，只要其不违背本公开的思想，其同样应当视为本公开所公开的内容。

Claims (14)

  1. 一种获取电池容量的方法，其特征在于，所述方法包括：
    在车辆电池的当前充电过程为有效充电的情况下，获取所述电池的多个初始充电参数；所述有效充电表示所述电池的荷电状态SOC在所述当前充电过程中的变化范围包括预设荷电状态范围，且所述电池在所述当前充电过程中的最小充电温度大于或等于预设温度阈值；
    周期性获取所述电池在所述当前充电过程中的多个实际充电参数，以及所述当前充电过程对应的当前有效充电次数；
    根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量。
  2. 根据权利要求1所述的方法，其特征在于，所述根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量包括：
    根据多个所述初始充电参数和多个所述实际充电参数，估算所述电池的当前最大可用容量；
    根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系，所述容量关联关系包括预测电池容量和有效充电次数的对应关系；
    通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
  3. 根据权利要求2所述的方法，其特征在于，所述根据多个所述初始充电参数和多个所述实际充电参数，估算所述电池的当前最大可用容量包括：
    根据所述初始充电参数，获取初始容量增量曲线；
    根据所述实际充电参数，获取实际容量增量曲线；
    根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量。
  4. 根据权利要求3所述的方法，其特征在于，所述根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量包括：
    获取所述初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息；
    获取所述实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的 第二当前位置信息；
    根据所述第一初始位置信息、所述第二初始位置信息、所述第一当前位置信息、所述第二当前位置信息、所述初始充电参数以及所述实际充电参数，估算所述电池的当前最大可用容量。
  5. 根据权利要求2所述的方法，其特征在于，所述通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量包括：
    获取下一次有效充电过程对应的目标充电次数；
    根据所述目标充电次数和所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
  6. 根据权利要求2至5任一项所述的方法，其特征在于，在所述根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系前，所述方法还包括：
    获取所述当前充电过程之前的每次有效充电过程中的历史最大可用容量和所述历史最大可用容量对应的历史有效充电次数；
    所述根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系包括：
    根据所述当前最大可用容量、所述当前有效充电次数、所述历史最大可用容量以及所述历史有效充电次数，获取所述容量关联关系。
  7. 一种获取电池容量的装置，其特征在于，所述装置包括：
    初始参数获取模块，用于在车辆电池的当前充电过程为有效充电的情况下，获取所述电池的多个初始充电参数；所述有效充电表示所述电池的荷电状态SOC在所述当前充电过程中的变化范围包括预设荷电状态范围，且所述电池在所述当前充电过程中的最小充电温度大于或等于预设温度阈值；
    实际参数获取模块，用于周期性获取所述电池在所述当前充电过程中的多个实际充电参数，以及所述当前充电过程对应的当前有效充电次数；
    电池容量获取模块，用于根据多个所述初始充电参数、多个所述实际充电参数以及所述当前有效充电次数，获取所述电池在下一次有效充电时的预测电池容量。
  8. 根据权利要求7所述的装置，其特征在于，所述电池容量获取模块，具体用于：
    根据多个所述初始充电参数和多个所述实际充电参数，估算所述电池的当前最大可用容量；
    根据所述当前最大可用容量和所述当前有效充电次数，获取容量关联关系，所述容量关联关系包括预测电池容量和有效充电次数的对应关系；
    通过所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
  9. 根据权利要求8所述的装置，其特征在于，所述电池容量获取模块，还用于：
    根据所述初始充电参数，获取初始容量增量曲线；
    根据所述实际充电参数，获取实际容量增量曲线；
    根据所述初始容量增量曲线和所述实际容量增量曲线，估算所述电池的当前最大可用容量。
  10. 根据权利要求9所述的装置，其特征在于，所述电池容量获取模块，还用于：
    获取所述初始容量增量曲线的第二峰值对应的第一初始位置信息和第三峰值对应的第二初始位置信息；
    获取所述实际容量增量曲线的第二峰值对应的第一当前位置信息和第三峰值对应的第二当前位置信息；
    根据所述第一初始位置信息、所述第二初始位置信息、所述第一当前位置信息、所述第二当前位置信息、所述初始充电参数以及所述实际充电参数，估算所述电池的当前最大可用容量。
  11. 根据权利要求8所述的装置，其特征在于，所述电池容量获取模块，还用于：
    获取下一次有效充电过程对应的目标充电次数；
    根据所述目标充电次数和所述容量关联关系，获取所述电池在下一次有效充电时的预测电池容量。
  12. 根据权利要求8至11任一项所述的装置，其特征在于，所述装置还包括：
    历史参数获取模块，用于获取所述当前充电过程之前的每次有效充电过程中的历史最大可用容量和所述历史最大可用容量对应的历史有效充电次数；
    所述电池容量获取模块，还用于：
    根据所述当前最大可用容量、所述当前有效充电次数、所述历史最大可用容量以及所述历史有效充电次数，获取所述容量关联关系。
  13. 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，该程序被处 理器执行时实现权利要求1-6中任一项所述方法的步骤。
  14. 一种服务器，其特征在于，包括：
    存储器，其上存储有计算机程序；
    处理器，用于执行所述存储器中的所述计算机程序，以实现权利要求1-6中任一项所述方法的步骤。
PCT/CN2021/119637 2020-09-30 2021-09-22 获取电池容量的方法、装置、存储介质及服务器 WO2022068652A1 (zh)

Priority Applications (5)

Application Number Priority Date Filing Date Title
EP21874313.6A EP4224183A4 (en) 2020-09-30 2021-09-22 METHOD AND APPARATUS FOR OBTAINING BATTERY CAPACITY, STORAGE MEDIUM AND SERVER
KR1020237011519A KR20230061520A (ko) 2020-09-30 2021-09-22 배터리 용량을 획득하기 위한 방법 및 장치, 저장 매체 및 서버
JP2023519877A JP2023543497A (ja) 2020-09-30 2021-09-22 電池容量を取得する方法、装置、記憶媒体及びサーバ
AU2021352848A AU2021352848A1 (en) 2020-09-30 2021-09-22 Method and apparatus for obtaining battery capacity, storage medium and server
US18/128,703 US20230236261A1 (en) 2020-09-30 2023-03-30 Method and device for acquiring battery capacity, storage medium, and server

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011066177.3 2020-09-30
CN202011066177.3A CN114325432B (zh) 2020-09-30 2020-09-30 获取电池容量的方法、装置、存储介质及服务器

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/128,703 Continuation US20230236261A1 (en) 2020-09-30 2023-03-30 Method and device for acquiring battery capacity, storage medium, and server

Publications (1)

Publication Number Publication Date
WO2022068652A1 true WO2022068652A1 (zh) 2022-04-07

Family

ID=80949547

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/119637 WO2022068652A1 (zh) 2020-09-30 2021-09-22 获取电池容量的方法、装置、存储介质及服务器

Country Status (7)

Country Link
US (1) US20230236261A1 (zh)
EP (1) EP4224183A4 (zh)
JP (1) JP2023543497A (zh)
KR (1) KR20230061520A (zh)
CN (1) CN114325432B (zh)
AU (1) AU2021352848A1 (zh)
WO (1) WO2022068652A1 (zh)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115184805A (zh) * 2022-06-21 2022-10-14 东莞新能安科技有限公司 电池健康状态获取方法、装置、设备及计算机程序产品
CN117767506B (zh) * 2024-02-21 2024-05-14 东莞市时实电子有限公司 基于数据驱动的新能源汽车电源适配器功率自适应方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120150465A1 (en) * 2010-12-14 2012-06-14 Sony Corporation Electronic device, battery pack, and method of computing battery pack capacity
CN103823191A (zh) * 2013-12-03 2014-05-28 天津航空机电有限公司 一种计算锂离子电池组可用剩余容量的方法
CN105068009A (zh) * 2015-07-14 2015-11-18 盐城工学院 电池循环寿命预测方法
CN107045104A (zh) * 2016-11-29 2017-08-15 北京交通大学 一种钛酸锂电池容量的在线估计方法
CN110568361A (zh) * 2019-09-12 2019-12-13 华中科技大学 一种动力电池健康状态的预测方法
US20200203780A1 (en) * 2018-12-21 2020-06-25 Samsung Electronics Co., Ltd. Method and system for predicting onset of capacity fading in a battery

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110764009B (zh) * 2019-10-31 2021-09-28 重庆长安新能源汽车科技有限公司 一种电动汽车的电池组循环容量及健康状态的估算方法
CN110988695A (zh) * 2019-12-24 2020-04-10 珠海银隆电器有限公司 动力电池健康状态评估方法、装置、存储介质及电子设备

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120150465A1 (en) * 2010-12-14 2012-06-14 Sony Corporation Electronic device, battery pack, and method of computing battery pack capacity
CN103823191A (zh) * 2013-12-03 2014-05-28 天津航空机电有限公司 一种计算锂离子电池组可用剩余容量的方法
CN105068009A (zh) * 2015-07-14 2015-11-18 盐城工学院 电池循环寿命预测方法
CN107045104A (zh) * 2016-11-29 2017-08-15 北京交通大学 一种钛酸锂电池容量的在线估计方法
US20200203780A1 (en) * 2018-12-21 2020-06-25 Samsung Electronics Co., Ltd. Method and system for predicting onset of capacity fading in a battery
CN110568361A (zh) * 2019-09-12 2019-12-13 华中科技大学 一种动力电池健康状态的预测方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4224183A4 *

Also Published As

Publication number Publication date
JP2023543497A (ja) 2023-10-16
EP4224183A1 (en) 2023-08-09
CN114325432B (zh) 2023-04-07
US20230236261A1 (en) 2023-07-27
AU2021352848A1 (en) 2023-06-08
KR20230061520A (ko) 2023-05-08
CN114325432A (zh) 2022-04-12
EP4224183A4 (en) 2024-03-27

Similar Documents

Publication Publication Date Title
US11269011B2 (en) Method, device, system for estimating remaining charging time and storage medium
KR102452548B1 (ko) 배터리 열화 상태 추정장치, 그를 포함한 시스템 및 그 방법
Murnane et al. A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries
US20220099753A1 (en) Method For Predicting Service Life Of Retired Power Battery
WO2022068652A1 (zh) 获取电池容量的方法、装置、存储介质及服务器
WO2019144646A1 (zh) 电池的电量状态估算方法、装置及电子设备
CN109991554B (zh) 一种电池电量检测方法、装置及终端设备
CN108550928B (zh) 电动汽车、充放电设备、电池soh的检测方法及系统
JP2018082618A (ja) バッテリ充電方法、バッテリ充電情報生成方法、及びバッテリ充電装置
EP2851700B1 (en) Method and terminal for displaying capacity of battery
WO2020259096A1 (zh) 电池的许用功率估算方法、装置、系统和存储介质
CN111929596A (zh) 获取电池容量的方法、装置、存储介质和电子设备
CN103969587A (zh) 一种混合动力车用动力电池soc估算方法
WO2020198118A1 (en) Methods, systems, and devices for estimating and predicting a remaining time to charge and a remaining time to discharge of a battery
CN110376536B (zh) 电池系统soh检测方法、装置、计算机设备和存储介质
Li et al. A new parameter estimation algorithm for an electrical analogue battery model
CN111707955B (zh) 电池剩余寿命的估算方法、装置和介质
CN109613445A (zh) 一种估计动力电池的健康状态的方法和装置
CN112630661B (zh) 一种电池荷电状态soc估算方法和装置
CN114371408B (zh) 电池荷电状态的估算方法、充电曲线的提取方法及装置
WO2023197939A1 (zh) Ocv-soc标定及估算方法、装置、介质和车辆
US11579201B2 (en) Method and system for identifying third-order model parameters of lithium battery based on likelihood function
CN113484762A (zh) 电池健康状态估算方法、装置、设备及存储介质
CN110646742A (zh) 一种动力电池soh获取方法、系统及相关组件
CN112394290A (zh) 电池包soh的估算方法、装置、计算机设备和存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21874313

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2023519877

Country of ref document: JP

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 20237011519

Country of ref document: KR

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2021874313

Country of ref document: EP

Effective date: 20230502

ENP Entry into the national phase

Ref document number: 2021352848

Country of ref document: AU

Date of ref document: 20210922

Kind code of ref document: A