WO2023221589A1 - 一种电池健康状态的检测方法、装置及系统 - Google Patents

一种电池健康状态的检测方法、装置及系统 Download PDF

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Publication number
WO2023221589A1
WO2023221589A1 PCT/CN2023/078219 CN2023078219W WO2023221589A1 WO 2023221589 A1 WO2023221589 A1 WO 2023221589A1 CN 2023078219 W CN2023078219 W CN 2023078219W WO 2023221589 A1 WO2023221589 A1 WO 2023221589A1
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battery
charging
charge
data
new energy
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PCT/CN2023/078219
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English (en)
French (fr)
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廖增成
沈小杰
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深圳市道通合创数字能源有限公司
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Publication of WO2023221589A1 publication Critical patent/WO2023221589A1/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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • 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
    • 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

Definitions

  • the present application relates to the technical field of battery detection, and in particular to a method, device and system for detecting battery health status.
  • Lithium batteries are the power source of electric vehicles. Due to their complex working environment and electrochemical mechanisms that are difficult to accurately simulate, rapid detection technology and equipment for the health status of lithium batteries has always been an urgent problem to be solved. Moreover, not only car owners urgently need to know the health status of their vehicle batteries, but also the second-hand car market, vehicle maintenance sites, battery cascade utilization manufacturers, etc. are also in urgent need of breakthroughs in rapid lithium battery detection technology.
  • SOH battery state of charge
  • the currently commonly used method is to use the battery management system (Battery Management System, BMS) data collected by the vehicle end, and query the open circuit voltage-state of charge (SOC) relationship obtained by querying the offline experimental data through the cell voltage. -OCV) curve, thereby obtaining the battery's state of charge (State of Charge, SOC) at the beginning and end of charging and the accumulated power during the charging process, and estimating the current available capacity of the battery.
  • BMS Battery Management System
  • SOC open circuit voltage-state of charge
  • the inventor found that there are at least the following problems in the above related technologies: the above method of estimating the available capacity of the battery has many deficiencies in engineering applications. In order to obtain the charging initial state of charge SOC more accurately, Sufficient resting time is required to eliminate battery polarization voltage before charging, and most vehicles, especially commercial vehicles, cannot meet the conditions of sufficient resting before charging.
  • Embodiments of the present application provide a method, device and system for detecting battery health status.
  • embodiments of the present application provide a method for detecting battery health status, which is applied to new energy vehicles.
  • the new energy vehicles are equipped with rechargeable batteries.
  • the method includes : Acquire vehicle charging and discharging data in the new energy vehicle; judge whether the battery is sufficiently rested according to the vehicle charging and discharging data to determine the resting state of the battery; select according to the resting state of the battery Different models calculate the current available capacity of the battery; based on the current available capacity, the current battery health status of the new energy vehicle is calculated.
  • determining whether the battery is sufficiently rested based on the vehicle charging and discharging data to determine the resting state of the battery includes: obtaining resting data of the new energy vehicle before charging; According to the resting data, calculate the resting time of the new energy vehicle before charging; obtain the preset sufficient resting time; determine whether the resting time is greater than the sufficient resting time; if so, the battery The battery has been allowed to rest sufficiently before charging; if not, the battery has not been allowed to rest sufficiently before charging.
  • selecting different models to calculate the current available capacity of the battery according to the resting state of the battery includes: obtaining vehicle charge and discharge data during use of the new energy vehicle, and selecting different models to calculate the current available capacity of the battery. Obtain the starting and ending state of charge of the battery during charging; calculate the total charging capacity of the battery through the ampere-hour integration method according to the vehicle charging data; calculate the total charging capacity of the battery according to the difference between the total charging capacity and the starting and ending state of charge of the battery value to calculate the current available capacity of the battery.
  • selecting different models to obtain the starting and ending state of charge during charging of the battery includes: when the battery has been sufficiently rested before charging, obtaining the starting time of charging of the battery through a rest correction model.
  • the initial state of charge of the battery; when the battery is not sufficiently rested before charging, the initial state of charge of the battery at the beginning of charging is obtained through a depolarization model.
  • obtaining the initial state of charge at the beginning of charging of the battery through a static correction model includes: obtaining the cell voltage at the beginning of charging of the battery according to the vehicle charging data; For the cell voltage at the start of charging of the battery, query the open circuit voltage-state of charge relationship curve to obtain the initial state of charge of the battery at the start of charging.
  • obtaining the initial state of charge of the battery at the beginning of charging through a depolarization model includes: obtaining the voltage at the end of discharge of the new energy vehicle, and obtaining the end of discharge of the new energy vehicle. and the discharge data during the time when the state of charge is a preset percentage before the end of discharge; obtain the depolarization model of the battery; substitute the discharge data into the depolarization model for prediction, and calculate the depolarization time of the battery when it is fully resting Voltage: According to the voltage when the battery is fully resting, query the open circuit voltage-state of charge relationship curve to obtain the initial state of charge when charging of the battery begins.
  • obtaining the depolarization model of the battery includes: obtaining experimental data of a battery of the same type or model as the battery in the new energy vehicle; preprocessing the experimental data; Based on the preprocessed experimental data, a data feature project is established; and the depolarization model is trained based on the data feature project.
  • obtaining experimental data of a battery of the same type or model as the battery in the new energy vehicle includes: fully charging the experimental battery and letting it stand fully; setting the discharge current of the experimental battery and Discharge to a set power value; let the experimental battery fully stand; set the discharge current of the experimental battery again and discharge to the next initial power value until the experimental battery is fully discharged; record the discharge process battery voltage, discharge current, discharge temperature, and sampling time to obtain the experimental data.
  • the charging data includes charging start time, charging end time, and real-time current value during the charging process.
  • the total charge of the battery is calculated through the ampere-hour integration method. Capacity, the calculation formula is:
  • Q charge represents the total charging capacity
  • t 1 represents the charging start time
  • t 2 represents the charging end time
  • I represents the real-time current value during the charging process.
  • the currently available capacity of the battery is calculated based on the difference between the total charged capacity and the starting and ending states of charge of the battery, and the calculation formula is:
  • Q total represents the current available capacity of the battery
  • Q charge represents the total charging capacity
  • SOC end represents the state of charge of the battery at the end of charging
  • SOC beg represents the state of charge of the battery at the beginning of charging. state.
  • the current battery health status of the new energy vehicle is calculated based on the currently available capacity, and the calculation formula is:
  • SOH represents the current battery health status of the new energy vehicle
  • Q total represents the current available capacity of the battery
  • Q normal represents the nominal capacity of the battery
  • a detection device including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a Instructions executed by the at least one processor, the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect above.
  • embodiments of the present application also provide a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause the computer to execute The method described in the first aspect above.
  • inventions of the present application also provide a computer program product.
  • the computer program product includes a computer program stored on a computer-readable storage medium.
  • the computer program includes program instructions. When the program instructions are executed by the computer, the computer is caused to execute the method described in the first aspect above.
  • embodiments of the present application also provide a battery health status detection system, including: a new energy vehicle, the new energy vehicle is provided with a rechargeable battery; as in the second aspect The detection device is connected to the new energy vehicle and is used to detect the battery health status of the battery.
  • the embodiments of this application provide a method, device and system for detecting battery health status.
  • the system includes new energy vehicles, new The energy vehicle is equipped with a rechargeable battery.
  • This method first obtains the vehicle charge and discharge data in the new energy vehicle, and then determines whether the battery is sufficiently rested based on the vehicle charge and discharge data to determine the battery's The resting state, then select different models to calculate the current available capacity of the battery according to the resting state of the battery, and finally calculate the current battery health status of the new energy vehicle based on the current available capacity.
  • the detection method provided can calculate the current available capacity of the battery regardless of whether the vehicle is fully stationary, thereby calculating the current battery health status of the new energy vehicle and achieving rapid detection of the battery health status.
  • Figure 1 is a flow chart of a method for detecting battery health status provided in Embodiment 1 of the present application;
  • FIG. 2 is a sub-flow chart of step S200 in the detection method shown in Figure 1;
  • FIG. 3 is a sub-flow chart of step S300 in the detection method shown in Figure 1;
  • Figure 4 is a sub-flow chart of step S310 in the detection method shown in Figure 3;
  • Figure 5 is an open circuit voltage-state of charge relationship curve provided by an embodiment of the present application.
  • FIG. 6 is another sub-flow chart of step S310 in the detection method shown in Figure 3;
  • Figure 7 is a schematic diagram of the depolarization model provided by the embodiment of the present application.
  • Figure 8 is a sub-flow chart of step S312b in the detection method shown in Figure 6;
  • Figure 9 is a sampling example diagram of the lithium battery discharge-standing experiment sampling time and sampled battery voltage provided by the embodiment of the present application.
  • FIG. 10 is a hardware structure diagram of a battery health status detection device provided in Embodiment 2 of the present application.
  • FIG. 11 is a schematic structural diagram of a battery health status detection system provided in Embodiment 3 of the present application.
  • Embodiments of the present application provide a method for detecting battery health status.
  • the detection method can be applied to new energy vehicles.
  • the new energy vehicles are equipped with rechargeable batteries.
  • Figure 1 shows the present invention.
  • the application embodiment provides a flow of a method for detecting battery health status. The method includes but is not limited to the following steps:
  • Step S100 Obtain vehicle charging and discharging data in the new energy vehicle
  • the charging data and discharge data of the vehicle battery in the new energy vehicle it is necessary to obtain the charging data and discharge data of the vehicle battery in the new energy vehicle to further determine whether the new energy vehicle is sufficiently stationary, and based on the existing charging data and discharge data Data is used to calculate the battery's current available capacity and battery health status.
  • the charge and discharge data include current and temperature during the charge and discharge process, battery cell voltage and state of charge at each moment during the charge and discharge process, rest time before charging, data sampling time, etc.
  • the vehicle charge and discharge data can be collected through the battery management system BMS in the new energy vehicle.
  • Step S200 Based on the vehicle charging and discharging data, determine whether the battery is sufficiently rested to determine the resting state of the battery;
  • Step S210 Obtain the resting data of the new energy vehicle before charging
  • Step S220 Calculate the resting time before charging of the new energy vehicle based on the resting data
  • Step S230 Obtain the preset sufficient rest time
  • Step S240 Determine whether the resting time is greater than the sufficient resting time; if so, jump to step S250; if not, jump to step S260;
  • Step S250 The battery has been fully allowed to stand before charging
  • Step S260 The battery is not allowed to stand sufficiently before charging.
  • the way to determine whether the battery has been sufficiently rested before charging is to obtain the resting time t s of the vehicle before charging based on the resting data, and determine it based on the preset vehicle resting time ta , when t s > t a , it is determined that the battery has been fully rested before charging, and then jumps to step S311a to query and obtain the charging initial charge state. If t s ⁇ t a , it is determined that the battery is charged If the battery is not allowed to stand sufficiently before, it needs to jump to step S311b to calculate the charging initial charge state.
  • the preset sufficient resting time can be preset or calculated based on expert experience, laboratory data, or big data analysis of historical vehicle data, and can be set according to actual needs.
  • Step S300 Select different models to calculate the current available capacity of the battery according to the resting state of the battery
  • two different models are selected and used to calculate the battery charging process using the ampere-hour integration method and the depolarization model.
  • the state of charge of the battery can be calculated, so that the current available capacity of the battery can be calculated.
  • Figure 3 shows a sub-process of step S300 in the detection method shown in Figure 1.
  • choose different models to calculate the current available capacity of the battery including:
  • Step S310 Obtain the vehicle charging and discharging data during the use of the new energy vehicle, and select different models to obtain the starting and ending state of charge during the charging process of the battery;
  • Step S311a According to the vehicle charging data, obtain the cell voltage at the starting time of charging of the battery;
  • Step S312a Query the open circuit voltage-state of charge relationship curve according to the cell voltage at the beginning of charging of the battery to obtain the initial state of charge of the battery at the beginning of charging.
  • the cell voltage at the starting time of battery charging can be obtained. Then, based on the cell voltage at the starting time of battery charging, the query is as shown in Figure 5
  • the open circuit voltage-state-of-charge relationship curve shown below, that is, the OCV-SOC curve, can be used to obtain the vehicle charging initial charge state SOC beg .
  • Step S311b Obtain the voltage of the new energy vehicle at the end of discharge, and obtain the discharge data between the end of discharge and the time when the state of charge of the new energy vehicle is a preset percentage before the end of discharge;
  • Figure 7 shows the schematic diagram of the depolarization model.
  • the preset percentage moment is the moment of 5% state of charge before the end of vehicle discharge as an example, where , t 1 is the time when the vehicle discharge ends with 5% SOC, t 2 is the time when the vehicle discharge ends, and t 3 is the time when the vehicle is fully resting after the end of discharge.
  • t 1 is the time when the vehicle discharge ends with 5% SOC
  • t 2 is the time when the vehicle discharge ends
  • t 3 is the time when the vehicle is fully resting after the end of discharge.
  • Step S312b Obtain the depolarization model of the battery
  • the depolarization model of the battery is obtained to further calculate the state of charge, wherein the depolarization model needs to be obtained by analyzing and training the experimental data of the same battery or battery of the same type before executing the detection method of the present application.
  • Figure 8 shows a sub-process of step S312b in the detection method shown in Figure 6.
  • Obtaining the depolarization model of the battery includes:
  • Step S3121b Obtain experimental data of batteries of the same type or model as the batteries in the new energy vehicle;
  • Obtaining experimental data for batteries of the same type or model as the battery in the new energy vehicle includes: converting the experimental battery to Fully charge and let it sit fully; set the discharge current of the experimental battery and discharge it to a set power value; let the experimental battery fully stand; set the discharge current of the experimental battery again and discharge it to the next initial power level. value until the experimental battery is discharged; record the battery voltage, discharge current, discharge temperature, and sampling time during the discharge process to obtain the experimental data.
  • Step 9 shows a sampling example of the lithium battery discharge-standing experiment sampling time and sampled battery voltage
  • the specific sampling steps can be: Step 1) Fully charge the target battery and let it stand fully. At this time, the battery is 100% SOC; Step 2) Set the current to 0.1C for discharge, and discharge to the initial power value - 5% SOC; Step 3) Let it stand fully. In this application, the rest time is 0.5 hours; Step 4) Cycle through steps 2) and 3) until the battery SOC is 0; it should be noted that Figure 8 is only an example of recording the battery voltage.
  • experimental data such as battery voltage, discharge current, discharge temperature, and sampling time need to be recorded simultaneously, and some also need to be carried out under working conditions of different currents and different temperatures, for example, different current working conditions It can be set to 0.1C, 0.2C, 0.3C, 0.4C, 0.5C, and different temperature conditions can be set to 0°C, 10°C, 35°C, 45°C.
  • Step S3122b Preprocess the experimental data
  • the experimental data After obtaining the experimental data, the experimental data also needs to be preprocessed to facilitate the next step of feature establishment.
  • the preprocessing of the data includes abnormal data processing, charging and discharging, and static working condition annotation, etc.
  • Step S3123b Establish a data feature project based on the preprocessed experimental data
  • the embodiment of this application only uses the discharge end voltage, average discharge rate, The four characteristics of discharge duration and average discharge temperature are because too many characteristics may increase the correlation between the characteristics and also increase the complexity of the depolarization model. When necessary, more features can be added according to actual needs. Multiple data features.
  • Step S3124b Train the depolarization model according to the data feature engineering.
  • the polarization voltage y obtained by subtracting the discharge end voltage from the voltage after each full discharge is counted, and an appropriate regression model is selected to establish the depolarization model.
  • an appropriate regression model is selected to establish the depolarization model.
  • the depolarization model After screening the data of n segments of the vehicle that meet sufficient static conditions, and converting the data set into matrix form, the depolarization model can be expressed as:
  • the depolarization model After normalizing the data and converting the data into the distribution of [0,1], the depolarization model can be expressed as:
  • the loss function of the depolarization model is defined as:
  • Step S313b Substitute the discharge data into the depolarization model for prediction, and calculate the voltage when the battery is fully resting;
  • u a represents the voltage at the time t 3 when the battery is fully rested
  • u end represents the voltage at the time t 2 when the discharge of the new energy vehicle ends
  • up represents the polarization voltage
  • the embodiment of the present application uses a depolarization model to directly predict the voltage u a of the vehicle at time t 3 through the data between t 1 and t 2 without requiring the vehicle to rest between t 2 and t 3 .
  • Step S314b Query the open-circuit voltage-state-of-charge relationship curve according to the voltage at the moment when the battery is fully rested to obtain the initial state-of-charge when charging of the battery begins.
  • step S314b After the voltage u a of the battery at the time of sufficient rest is obtained through the prediction and calculation in the above step S314b, the same as step S312a, by querying the open circuit voltage-state of charge relationship curve or open circuit voltage-state of charge shown in Figure 5 According to the relationship table, the vehicle charging initial charge state SOC beg can be obtained.
  • Step S320 Calculate the total charging capacity of the battery through the ampere-hour integration method according to the vehicle charging data
  • the charging data includes charging start time, charging end time, and real-time current value during charging.
  • the total charging capacity of the battery is calculated through the ampere-hour integration method. The calculation formula is:
  • Q charge represents the total charging capacity
  • t 1 represents the charging start time
  • t 2 represents the charging end time
  • I represents the real-time current value during the charging process.
  • Step S330 Calculate the current available capacity of the battery based on the difference between the total charging capacity and the starting and ending states of charge of the battery.
  • the state of charge of the battery at the end of charging can be calculated by querying the charging data.
  • the current available capacity of the battery is obtained. Specifically, the current available capacity of the battery is calculated based on the difference between the total charging capacity and the starting and ending state of charge of the battery.
  • the calculation formula is:
  • Q total represents the current available capacity of the battery
  • Q charge represents the total charging capacity
  • SOC end represents the state of charge of the battery at the end of charging
  • SOC beg represents the starting point at the beginning of charging of the battery. State of charge, SOC end -SOC beg represents the difference between the starting and ending states of charge of the battery.
  • Step S400 Calculate the current battery health status of the new energy vehicle based on the currently available capacity.
  • the nominal capacity Q normal of the battery is obtained through the basic information of the vehicle battery, and the new energy vehicle can be calculated by combining the current available capacity Q total of the battery calculated in step S300.
  • the current battery health status The basic information of the battery can be obtained based on the model and type of the battery. Specifically, the current battery health status of the new energy vehicle is calculated based on the current available capacity, and the calculation formula is:
  • SOH represents the current battery health status of the new energy vehicle
  • Q total represents the current available capacity of the battery
  • Q normal represents the nominal capacity of the battery
  • the embodiment of the present application also provides a detection device. Please refer to FIG. 10 , which shows the hardware structure of the detection device 10 capable of executing the battery health status detection method described in FIGS. 1 to 9 .
  • the detection device 10 includes: at least one processor 11; and a memory 12 communicatively connected to the at least one processor 11.
  • one processor 11 is taken as an example.
  • the memory 12 stores instructions that can be executed by the at least one processor 11, so The instructions are executed by the at least one processor 11 so that the at least one processor 11 can perform the battery health status detection method described in FIGS. 1 to 9 .
  • the processor 11 and the memory 12 may be connected through a bus or other means. In FIG. 10 , the connection through a bus is taken as an example.
  • the memory 12 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the battery health status detection method in the embodiment of the present application/ module.
  • the processor 11 executes the non-volatile software programs, instructions and modules stored in the memory 12 to execute various functional applications and data processing of the server, that is, to implement the battery health status detection method of the above method embodiment.
  • the memory 12 may include a storage program area and a storage data area, wherein the storage program area may store an operating system and an application program required for at least one function; the storage data area may store data created according to the use of the battery health status detection device, etc. .
  • the memory 12 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the memory 12 optionally includes memories remotely located relative to the processor 11 , and these remote memories can be connected to a battery health status detection device through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • the one or more modules are stored in the memory 12, and when executed by the one or more processors 11, perform the battery health status detection method in any of the above method embodiments, for example, perform the above-described The method steps of Figures 1 to 9.
  • Embodiments of the present application also provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, for example, executing the above-described diagram. Method steps 1 to 9.
  • Embodiments of the present application also provide a computer program product, which includes a computing program stored on a computer-readable storage medium.
  • the computer program includes program instructions.
  • the program instructions When the program instructions are executed by a computer, the computer is caused to execute the above.
  • the method for detecting the battery health status in any method embodiment for example, performs the method steps of FIG. 1 to FIG. 9 described above.
  • An embodiment of the present application provides a battery health status detection system. Please refer to Figure 11 , which shows the structure of a battery health status detection system provided by an embodiment of the present application.
  • the battery health status detection system 100 It includes: a new energy vehicle 20, which is provided with a rechargeable battery BAT; a detection device 10 as described in Embodiment 2, which is connected to the new energy vehicle 20 for Detect the battery health status of the battery BAT.
  • the detection device 10 may be a device, module or unit installed in the new energy vehicle 20 , or may be a device independently provided with the new energy vehicle 20 , or may be It is a device, module or unit in automobile diagnostic equipment.
  • the detection device 10 and the new energy vehicle 20 must at least be able to establish a communication/communication connection.
  • the actual structure and installation location of the detection device 10 , connection methods, etc. can be set according to the needs of actual application scenarios, and do not need to be limited by the embodiments of this application.
  • the detection system provided by the embodiment of this application uses the detection method shown in Embodiment 1 to detect the battery health status of the vehicle.
  • the vehicle does not need to be left standing for a long time before charging. After charging, the battery health status can be estimated immediately and accurately without the need for external connections.
  • Additional battery detection equipment can obtain vehicle charge and discharge data through the battery management system BMS in the new energy vehicle 20 through only one real charge and discharge behavior of the primary battery BAT, thereby calculating the current health status of the vehicle battery and achieving rapid detection.
  • the embodiments of the present application provide a method, device and system for detecting battery health status.
  • the system includes a new energy vehicle.
  • the new energy vehicle is equipped with a rechargeable battery.
  • the method first obtains the vehicle in the new energy vehicle. charge and discharge data, and then based on the vehicle charge and discharge data, determine whether the battery is sufficiently rested to determine the resting state of the battery, and then select different models to calculate the battery's resting state based on the resting state of the battery.
  • the current available capacity is finally calculated based on the current available capacity.
  • the current battery health status of the new energy vehicle is calculated.
  • the detection method provided by the embodiment of the present application can calculate the current available capacity of the battery regardless of whether the vehicle is fully stationary, thereby Calculate the current battery health status of new energy vehicles and achieve rapid detection of battery health status.
  • the device embodiments described above are only illustrative.
  • the units described as separate components may or may not be physically separated.
  • the components shown as units may or may not be physically separate.
  • the unit can be located in one place, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware.
  • the programs can be stored in computer-readable storage media. When the programs are executed, When doing so, it may include the processes of the above method embodiments.
  • the storage medium can be a disk, Optical disc, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access Memory, RAM), etc.

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Abstract

一种电池健康状态的检测方法、装置及系统，该系统包括新能源汽车（20），新能源汽车（20）中设置有可充放电的电池，该方法首先获取新能源汽车（20）中车辆充放电数据（S100），然后根据车辆充放电数据，判断电池是否充分静置，以确定电池的静置状态（S200），接着根据电池的静置状态，选择不同模型计算电池的当前可用容量（S300），最后根据当前可用容量，计算新能源汽车（20）当前的电池健康状态（S400）。上述检测方法无论车辆是否充分静置，都能够准确计算出电池的当前可用容量，从而计算出新能源汽车（20）当前的电池健康状态，实现电池健康状态的快速检测。

Description

一种电池健康状态的检测方法、装置及系统
本申请要求于2022年5月19日提交中国专利局、申请号为202210555717.7、申请名称为“一种电池健康状态的检测方法、装置及系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及电池检测技术领域，特别涉及一种电池健康状态的检测方法、装置及系统。
背景技术
近些年来，随着电动汽车的飞速发展，电动汽车在汽车市场上的占有率逐年提升。锂电池作为电动汽车的动力来源，由于其复杂的工作环境和较难准确模拟的电化学机理，锂电池健康状态的快速检测技术及设备一直是亟待解决的难题。并且，不仅车主迫切需要了解自己车辆电池的健康状态，二手车市场、车辆维修站点、电池梯次利用厂商等也急需锂电池快速检测技术的突破。
目前，多数电池健康状态(State of Charge，SOH)的估算方式是通过离线测试同批次电池的相关实验数据，再通过曲线拟合或者插值方法进一步估算电池当前可用容量，通过与电池出厂标称容量比值从而估算出电池的当前健康状态SOH的。
其中，针对可用容量的估算，目前常用的方式是利用车端采集的电池管理系统(Battery Management System，BMS)的数据，通过单体电压查询离线实验数据获得的开路电压-荷电状态关系(SOC-OCV)曲线，从而得到电池的充电起止荷电状态(State of Charge，SOC)及充电过程中累计电量，估算出当前电池的可用容量。
在实现本申请实施例过程中，发明人发现以上相关技术中至少存在如下问题：上述对电池的可用容量的估算方式在工程应用中存在诸多不足，为了较准确获得充电起始荷电状态SOC，在充电前需要足够的静置时间去消除电池极化电压，而大多数车辆特别是营运车辆在充电前无法满足充分静置的条件。
发明内容
本申请实施例提供了一种电池健康状态的检测方法、装置及系统。
本申请实施例的目的是通过如下技术方案实现的：
为解决上述技术问题，第一方面，本申请实施例中提供了一种电池健康状态的检测方法，应用于新能源汽车，所述新能源汽车中设置有可充放电的电池，所述方法包括：获取所述新能源汽车中车辆充放电数据；根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态；根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量；根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态。
在一些实施例中，所述根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态，包括：获取所述新能源汽车充电前的静置数据；根据所述静置数据，计算所述新能源汽车充电前的静置时间；获取预设的充分静置时间；判断所述静置时间是否大于所述充分静置时间；若是，则所述电池充电前已充分静置；若否，则所述电池充电前未充分静置。
在一些实施例中，所述根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量，包括：获取所述新能源汽车使用过程中的车辆充放电数据，选择不同模型以获取所述电池充电过程中起止荷电状态；根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量；根据所述充入总容量和所述电池起止荷电状态差值，计算所述电池的当前可用容量。
在一些实施例中，所述选择不同模型以获取所述电池充电过程中起止荷电状态，包括：在所述电池充电前已充分静置时，通过静置修正模型获取所述电池充电开始时的起始荷电状态；在所述电池充电前未充分静置时，通过去极化模型获取所述电池充电开始时的起始荷电状态。
在一些实施例中，所述通过静置修正模型获取所述电池充电开始时的起始荷电状态，包括：根据所述车辆充电数据，获取所述电池充电起始时刻的单体电压；根据所述电池充电起始时刻的单体电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
在一些实施例中，所述通过去极化模型获取所述电池充电开始时的起始荷电状态，包括：获取所述新能源汽车放电结束时刻的电压，以及获取所述新能源汽车放电结束及放电结束前荷电状态为预设百分比时刻间的放电数据；获取所述电池的去极化模型；将所述放电数据代入所述去极化模型进行预测，计算出电池充分静置时刻的电压；根据所述电池充分静置时刻的电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
在一些实施例中，所述获取所述电池的去极化模型，包括：获取与所述新能源汽车中的电池同类型或同款的电池的实验数据；对所述实验数据进行预处理；根据所述预处理后的实验数据，建立数据特征工程；根据所述数据特征工程进行去极化模型的训练。
在一些实施例中，所述获取与所述新能源汽车中的电池同类型或同款的电池的实验数据，包括：将实验电池充满电并充分静置；设置所述实验电池的放电电流并放电至一设定电量值；将所述实验电池进行充分静置；再次设置所述实验电池的放电电流并放电至下一电量初始值，直至所述实验电池放完电；记录所述放电过程中的电池电压、放电电流、放电温度、采样时间，以得到所述实验数据。
在一些实施例中，所述充电数据包括充电起始时间、充电结束时间、充电过程中的实时电流值，所述根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量，计算公式为：
其中，Qcharge表示所述充入总容量，t1表示所述充电起始时间，t2表示所述充电结束时间，I表示所述充电过程中实时电流值。
在一些实施例中，所述根据所述充入总容量和所述电池起止荷电状态差值，计算所述电池的当前可用容量，计算公式为：
其中，Qtotal表示所述电池的当前可用容量，Qcharge表示所述充入总容量，SOCend表示所述电池的充电结束时的荷电状态，SOCbeg表示所述电池充电开始时的荷电状态。
在一些实施例中，所述根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态，计算公式为：
其中，SOH表示所述新能源汽车当前的电池健康状态，Qtotal表示所述电池的当前可用容量，Qnormal表示所述电池的标称容量。
为解决上述技术问题，第二方面，本申请实施例提供了一种检测装置，包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如上第一方面所述的方法。
为解决上述技术问题，第三方面，本申请实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令用于使计算机执行如上第一方面所述的方法。
为解决上述技术问题，第四方面，本申请实施例还提供了一种计算机程序产品，所述计算机程序产品包括存储在计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行如上第一方面所述的方法。
为解决上述技术问题，第五方面，本申请实施例还提供了一种电池健康状态的检测系统，包括：新能源汽车，所述新能源汽车中设置有可充放电的电池；如第二方面所述的检测装置，所述检测装置与所述新能源汽车连接，用于检测所述电池的电池健康状态。
与现有技术相比，本申请的有益效果是：区别于现有技术的情况，本申请实施例中提供了一种电池健康状态的检测方法、装置及系统，该系统包括新能源汽车，新能源汽车中设置有可充放电的电池，该方法首先获取所述新能源汽车中车辆充放电数据，然后根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态，接着根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量，最后根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态，本申请实施例提供的检测方法无论车辆是否充分静置，都能够计算出电池的当前可用容量，从而计算出新能源汽车当前的电池健康状态，实现电池健康状态的快速检测。
附图说明
一个或多个实施例中通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件/模块和步骤表示为类似的元件/模块和步骤，除非有特别申明，附图中的图不构成比例限制。
图1是本申请实施例一提供的一种电池健康状态的检测方法的流程图；
图2是图1所示检测方法中步骤S200的一子流程图；
图3是图1所示检测方法中步骤S300的一子流程图；
图4是图3所示检测方法中步骤S310的一子流程图；
图5是本申请实施例提供的开路电压-荷电状态关系曲线图；
图6是图3所示检测方法中步骤S310的另一子流程图；
图7是本申请实施例提供的去极化模型的原理图；
图8是图6所示检测方法中步骤S312b的一子流程图；
图9是本申请实施例提供的锂电池放电-静置实验采样时间和采样的电池电压的一种采样示例图；
图10是本申请实施例二提供的一种电池健康状态的检测装置的硬件结构图；
图11是本申请实施例三中提供的一种电池健康状态的检测系统的结构示意图。
具体实施方式
下面结合具体实施例对本申请进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本申请，但不以任何形式限制本申请。应当指出的是，对本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进。这些都属于本申请的保护范围。
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，如果不冲突，本申请实施例中的各个特征可以相互结合，均在本申请的保护范围之内。另外，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。
除非另有定义，本说明书所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本说明书中在本申请的说明书中所使用的术语只是为了描述具体的实施方式的目的，不是用于限制本申请。本说明书所使用的术语“和/或”包括一个或多个相关的所列项目的任意的和所有的组合。
此外，下面所描述的本申请各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
具体地，下面结合附图，对本申请实施例作进一步阐述。
实施例一
本申请实施例提供了一种电池健康状态的检测方法，该检测方法能够应用于新能源汽车中，所述新能源汽车中设置有可充放电的电池，请参见图1，其示出了本申请实施例提供的一种电池健康状态的检测方法的流程，所述方法包括但不限于以下步骤：
步骤S100：获取所述新能源汽车中车辆充放电数据；
在本申请实施例中，首先，需要获取所述新能源汽车中车辆电池的充电数据和放电数据，以进一步用于确定所述新能源汽车是否充分静置，并根据已有的充电数据和放电数据来计算出电池的当前可用容量和电池健康状态。其中，所述充放电数据包括充放电过程中的电流、温度，充放电过程中各时刻的电池单体电压和荷电状态，充电前的静置时间，数据采样时间等。且有，所述车辆充放电数据可通过所述新能源汽车中的电池管理系统BMS采集。
步骤S200：根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态；
在本申请实施例中，获取到所述车辆的充放电数据后，需要判断所述电池是否充分静置，从而对于充分静置和未充分静置两种状态，采用不同的方式进行下一步的计算，具体地，请参见图2，其示出了图1所示检测方法中步骤S200的一子流程，所述根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态，包括：
步骤S210：获取所述新能源汽车充电前的静置数据；
步骤S220：根据所述静置数据，计算所述新能源汽车充电前的静置时间；
步骤S230：获取预设的充分静置时间；
步骤S240：判断所述静置时间是否大于所述充分静置时间；若是，则跳转至步骤S250；若否，则跳转至步骤S260；
步骤S250：所述电池充电前已充分静置；
步骤S260：所述电池充电前未充分静置。
在本申请实施例中，判断所述电池充电前是否充分静置的方式为，根据静置数据获取车辆充电前的静置时间ts，结合预先设定的车辆充分静置时间ta来判断，当ts＞ta时，确定所述电池充电前已充分静置，接下来跳转至步骤S311a查询获取充电起始电荷状态即可，若ts＜ta，则确定所述电池充电前未充分静置，则需要跳转至步骤S311b计算充电起始电荷状态。其中，所述预设的充分静置时间可根据专家经验、实验室数据、或者历史车辆数据的大数据分析等预先设置或计算得到，具体可根据实际需要进行设置。
步骤S300：根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量；
在本申请实施例中，针对电池充电前已充分静置和电池充电前未充分静置两种状态，选择并通过安时积分法和去极化模型两种不同的模型计算出电池充电过程中的的荷电状态，从而可以计算出所述电池的当前可用容量，具体地，请参见图3，其示出了图1所示检测方法中步骤S300的一子流程，所述根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量，包括：
步骤S310：获取所述新能源汽车使用过程中的车辆充放电数据，选择不同模型以获取所述电池充电过程中起止荷电状态；
一方面，所述选择不同模型以获取所述电池充电过程中起止荷电状态，在所述电池充电前已充分静置时，通过静置修正模型获取所述电池充电开始时的起始荷电状态；具体地，请参见图4，其示出了图3所示检测方法中步骤S310的一子流程，所述通过静置修正模型获取所述电池充电开始时的起始荷电状态，包括：
步骤S311a：根据所述车辆充电数据，获取所述电池充电起始时刻的单体电压；
步骤S312a：根据所述电池充电起始时刻的单体电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
在本申请实施例中，首先，通过分析所述车辆充放电数据，可以得到电池充电起始时刻的单体电压，然后，根据所述电池充电起始时刻的单体电压，查询如图5所示的开路电压-荷电状态关系曲线，即OCV-SOC曲线，即可得到车辆充电起始电荷状态SOCbeg。
另一方面，所述选择不同模型以获取所述电池充电过程中起止荷电状态，在所述电池充电前未充分静置时，通过去极化模型预测所述电池充电开始时的起始荷电状态；具体地，请参见图6，其示出了图3所示检测方法中步骤S310的另一子流程，所述通过去极化模型获取所述电池充电开始时的起始荷电状态，包括：
步骤S311b：获取所述新能源汽车放电结束时刻的电压，以及获取所述新能源汽车放电结束及放电结束前荷电状态为预设百分比时刻间的放电数据；
在本申请实施例中，请一并参见图7，其示出了去极化模型的原理图，图7中以预设百分比时刻为车辆放电结束前5％荷电状态的时刻为例，其中，t1为车辆放电结束前5％SOC的时刻，t2为车辆放电结束时刻，t3为车辆放电结束后充分静置时刻，首先，需要获取所述新能源汽车放电结束时刻t2的电压，以及获取所述新能源汽车放电结束前荷电状态为预设百分比时刻t1的电压。
步骤S312b：获取所述电池的去极化模型；
进一步地，获取电池的去极化模型以进一步计算荷电状态，其中，所述去极化模型需要在执行本申请的检测方法之前通过对同款电池或者同类型电池实验数据分析训练得到，具体地，请参见图8，其示出了图6所示检测方法中步骤S312b的一子流程，所述获取所述电池的去极化模型，包括：
步骤S3121b：获取与所述新能源汽车中的电池同类型或同款的电池的实验数据；
其中，需要在特定实验条件下进行放电-静置实验获取去极化模型实验数据，所述获取与所述新能源汽车中的电池同类型或同款的电池的实验数据，包括：将实验电池充满电并充分静置；设置所述实验电池的放电电流并放电至一设定电量值；将所述实验电池进行充分静置；再次设置所述实验电池的放电电流并放电至下一电量初始值，直至所述实验电池放完电；记录所述放电过程中的电池电压、放电电流、放电温度、采样时间，以得到所述实验数据。
具体地，请参见图9，其示出了锂电池放电-静置实验采样时间和采样的电池电压的一种采样示例， 其采样步骤具体可以是：步骤1)将目标电池充满电并充分静置，此时电池为100％SOC；步骤2)设置电流为0.1C进行放电，放电至电量初始值-5％SOC；步骤3)进行充分静置，本申请中取静置时间为0.5小时；步骤4)循环进行步骤2)和3)，直至电池SOC为0；需要说明的是，图8仅为记录电池电压的一种示例，在实验过程中，需要同步记录电池电压、放电电流、放电温度、采样时间等实验数据，且有，还需要在不同电流及不同温度的工况条件下进行，例如，不同电流工况可设置为0.1C、0.2C、0.3C、0.4C、0.5C，不同温度工况可设置为0℃、10℃、35℃、45℃。
步骤S3122b：对所述实验数据进行预处理；
在获取到所述实验数据之后，还需要对实验数据进行预处理，从而方便进行下一步的特征建立，所述对数据进行预处理，包括异常数据处理、充放电及静置工况标注等。
步骤S3123b：根据所述预处理后的实验数据，建立数据特征工程；
在本申请实施例中，由于电池的去极化过程受放电工况的诸多因素影响，因此，需要选择几种典型数据特征，来建立数据特征工程，所述数据特征可以优选为放电结束电压、平均放电倍率、放电时长、放电平均温度，记为xi(i＝1,2,3,4)。需要说明的是，在建立所述数据特征工程的过程中，特征工程的建立可以不仅限于上述四种影响电池去极化的典型数据特征，本申请实施例仅采用放电结束电压、平均放电倍率、放电时长、放电平均温度这四种特征，是由于过多的特征可能会导致特征之间相关性增加，同时也会增加去极化模型的复杂度，在必要时，也可根据实际需要增加更多数据特征。
步骤S3124b：根据所述数据特征工程进行去极化模型的训练。
最后，统计每次充分放电静置后电压减放电结束电压得到的极化电压y，并选取合适的回归模型进行去极化模型的建立，其中，在建立所述数据特征工程的过程中，可深入研究去极化算法模型来提升模型预测极化电压精度；例如，选择线性回归模型时，去极化模型可表示为：
y＝ω0+ω1x1+φ2x2+ω3x3+ω4x4+ξ
筛选该车辆n段符合充分静置条件的数据，将数据集转化成矩阵形式后，所述去极化模型可表示为：
对数据进行归一化处理，将数据转化在[0,1]的分布后，所述去极化模型可表示为：
其中，定义所述去极化模型模型的损失函数为：
通过参数调优、模型训练、模型优化，使得所述损失函数L(ω)的值最小化，最终评估出最优去极化模型，作为本申请实施例提供的检测方法在电池充电前未充分静置时计算电池起始荷电状态的去极化模型。
步骤S313b：将所述放电数据代入所述去极化模型进行预测，计算出电池充分静置时刻的电压；
请继续参见上述图7，根据去极化模型的原理可知，当车辆在t2时刻停止放电后其电压为uend，由于锂电池极化的作用，电压会逐渐上升直到充分静置时刻t3，此时电压为极化后电压ua，电压从t2时刻到t3时刻区间电压上升值为极化电压up，也即是，可以得到所述电池充分静置时刻的电压的计算公 式为：
ua＝uend+up
其中，ua表示所述电池充分静置时刻t3的电压，uend表示所述新能源汽车放电结束时刻t2的电压，up表示所述极化电压。
本申请实施例采用去极化模型可通过t1和t2之间数据，直接预测出车辆t3时刻电压ua，而无需车辆t2到t3之间静置时间。
步骤S314b：根据所述电池充分静置时刻的电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
在通过上述步骤S314b预测计算得到所述电池充分静置时刻的电压ua后，与步骤S312a相同地，通过查询如图5所示的开路电压-荷电状态关系曲线或开路电压-荷电状态关系表，即可得到车辆充电起始电荷状态SOCbeg。
步骤S320：根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量；
所述充电数据包括充电起始时间、充电结束时间、充电过程中的实时电流值，所述根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量，计算公式为：
其中，Qcharge表示所述充入总容量，t1表示所述充电起始时间，t2表示所述充电结束时间，I表示所述充电过程中实时电流值。
步骤S330：根据所述充入总容量和所述电池起止荷电状态的差值，计算所述电池的当前可用容量。
在通过步骤S320计算得到所述充入总容量，并通过步骤S310计算或预测计算得到电池的起始荷电状态后，通过充电数据查询所述电池的充电结束时的荷电状态，即可计算出所述电池的当前可用容量，具体地，所述根据所述充入总容量和所述电池起止荷电状态的差值，计算所述电池的当前可用容量，计算公式为：
其中，Qtotal表示所述电池的当前可用容量，Qcharge表示所述充入总容量，SOCend表示所述电池的充电结束时的荷电状态，SOCbeg表示所述电池充电开始时的起始荷电状态，SOCend-SOCbeg表示所述电池起止荷电状态的差值。
步骤S400：根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态。
在本申请实施例中，通过所述车辆电池的基本信息，获取所述电池的标称容量Qnormal，结合步骤S300计算得到的电池的当前可用容量Qtotal，即可计算得到所述新能源汽车当前的电池健康状态，其中，所述电池的基本信息，可根据所述电池的型号和类型等查询得到。具体地，所述根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态，计算公式为：
其中，SOH表示所述新能源汽车当前的电池健康状态，Qtotal表示所述电池的当前可用容量，Qnormal表示所述电池的标称容量。
实施例二
本申请实施例还提供了一种检测装置，请参见图10，其示出了能够执行图1至图9所述电池健康状态的检测方法的检测装置10的硬件结构。
所述检测装置10包括：至少一个处理器11；以及，与所述至少一个处理器11通信连接的存储器12，图10中以一个处理器11为例。所述存储器12存储有可被所述至少一个处理器11执行的指令，所 述指令被所述至少一个处理器11执行，以使所述至少一个处理器11能够执行上述图1至图9所述的电池健康状态的检测方法。所述处理器11和所述存储器12可以通过总线或者其他方式连接，图10中以通过总线连接为例。
存储器12作为一种计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如本申请实施例中的电池健康状态的检测方法对应的程序指令/模块。处理器11通过运行存储在存储器12中的非易失性软件程序、指令以及模块，从而执行服务器的各种功能应用以及数据处理，即实现上述方法实施例电池健康状态的检测方法。
存储器12可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据电池健康状态的检测装置的使用所创建的数据等。此外，存储器12可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器12可选包括相对于处理器11远程设置的存储器，这些远程存储器可以通过网络连接至电池健康状态的检测装置。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器12中，当被所述一个或者多个处理器11执行时，执行上述任意方法实施例中的电池健康状态的检测方法，例如，执行以上描述的图1至图9的方法步骤。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本申请实施例所提供的方法。
本申请实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如，执行以上描述的图1至图9的方法步骤。
本申请实施例还提供了一种计算机程序产品，包括存储在计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时时，使所述计算机执行上述任意方法实施例中的电池健康状态的检测方法，例如，执行以上描述的图1至图9的方法步骤。
实施例三
本申请实施例提供了一种电池健康状态的检测系统，请参见图11，其示出了本申请实施例提供的一种电池健康状态的检测系统的结构，所述电池健康状态的检测系统100包括：新能源汽车20，所述新能源汽车20中设置有可充放电的电池BAT；如实施例二所述的检测装置10，所述检测装置10与所述新能源汽车20连接，用于检测所述电池BAT的电池健康状态。
需要说明的是，所述检测装置10可以是设置在所述新能源汽车20内的装置、模块或单元，或者，也可以是独立与所述新能源汽车20另外设置的设备，或者，也可以是汽车诊断设备中的装置、模块或单元等，所述检测装置10与所述新能源汽车20之间至少需要能够建立通信/通讯连接，具体地，所述检测装置10的实际结构、设置位置、连接方式等可根据实际应用场景的需要进行设置，不需要拘泥于本申请实施例的限定。
本申请实施例提供的检测系统采用实施例一所示检测方法对车辆的电池健康状态进行检测，车辆充电前无需长时间静置，充电结束后即可立即并准确估算电池健康状态，且无需外接额外电池检测设备，仅仅通过一次电池BAT的一次真实充放电行为即可通过新能源汽车20中的电池管理系统BMS获取车辆充放电数据，从而计算得到车辆电池当前健康状态，实现快速检测。
本申请实施例中提供了一种电池健康状态的检测方法、装置及系统，该系统包括新能源汽车，新能源汽车中设置有可充放电的电池，该方法首先获取所述新能源汽车中车辆充放电数据，然后根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态，接着根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量，最后根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态，本申请实施例提供的检测方法无论车辆是否充分静置，都能够计算出电池的当前可用容量，从而计算出新能源汽车当前的电池健康状态，实现电池健康状态的快速检测。
需要说明的是，以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
通过以上的实施方式的描述，本领域普通技术人员可以清楚地了解到各实施方式可借助软件加通用硬件平台的方式来实现，当然也可以通过硬件。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，所述的存储介质可为磁碟、 光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(Random Access Memory,RAM)等。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；在本申请的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本申请的不同方面的许多其它变化，为了简明，它们没有在细节中提供；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (13)

  1. 一种电池健康状态的检测方法，应用于新能源汽车，所述新能源汽车中设置有可充放电的电池，所述方法包括：
    获取所述新能源汽车中车辆充放电数据；
    根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态；
    根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量；
    根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态。
  2. 根据权利要求1所述的检测方法，所述根据所述车辆充放电数据，判断所述电池是否充分静置，以确定所述电池的静置状态，包括：
    获取所述新能源汽车充电前的静置数据；
    根据所述静置数据，计算所述新能源汽车充电前的静置时间；
    获取预设的充分静置时间；
    判断所述静置时间是否大于所述充分静置时间；
    若是，则所述电池充电前已充分静置；
    若否，则所述电池充电前未充分静置。
  3. 根据权利要求1所述的检测方法，所述根据所述电池的静置状态，选择不同模型计算所述电池的当前可用容量，包括：
    获取所述新能源汽车使用过程中的车辆充放电数据，选择不同模型以获取所述电池充电过程中起止荷电状态；
    根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量；
    根据所述充入总容量和所述电池起止荷电状态的差值，计算所述电池的当前可用容量。
  4. 根据权利要求3所述的检测方法，所述选择不同模型以获取所述电池充电过程中起止荷电状态，包括：
    在所述电池充电前已充分静置时，通过静置修正模型获取所述电池充电开始时的起始荷电状态；
    在所述电池充电前未充分静置时，通过去极化模型预测所述电池充电开始时的起始荷电状态。
  5. 根据权利要求4所述的检测方法，所述通过静置修正模型获取所述电池充电开始时的起始荷电状态，包括：
    根据所述车辆充电数据，获取所述电池充电起始时刻的单体电压；
    根据所述电池充电起始时刻的单体电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
  6. 根据权利要求4所述的检测方法，所述通过去极化模型获取所述电池充电开始时的起始荷电状态，包括：
    获取所述新能源汽车放电结束时刻的电压，以及获取所述新能源汽车放电结束及放电结束前荷电状态为预设百分比时刻间的放电数据；
    获取所述电池的去极化模型；
    将所述放电数据代入所述去极化模型进行预测，计算出电池充分静置时刻的电压；
    根据所述电池充分静置时刻的电压，查询开路电压-荷电状态关系曲线，以得到所述电池充电开始时的起始荷电状态。
  7. 根据权利要求6所述的检测方法，所述获取所述电池的去极化模型，包括：
    获取与所述新能源汽车中的电池同类型或同款的电池的实验数据；
    对所述实验数据进行预处理；
    根据所述预处理后的实验数据，建立数据特征工程；
    根据所述数据特征工程进行去极化模型的训练。
  8. 根据权利要求7所述的检测方法，所述获取与所述新能源汽车中的电池同类型或同款的电池的实验数据，包括：
    将实验电池充满电并充分静置；
    设置所述实验电池的放电电流并放电至一设定电量值；
    将所述实验电池进行充分静置；
    再次设置所述实验电池的放电电流并放电至下一电量初始值，直至所述实验电池放完电；
    记录所述放电过程中的电池电压、放电电流、放电温度、采样时间，以得到所述实验数据。
  9. 根据权利要求3-8任一项所述的检测方法，
    所述充电数据包括充电起始时间、充电结束时间、充电过程中的实时电流值，
    所述根据所述车辆充电数据，通过安时积分法计算所述电池的充入总容量，计算公式为：
    其中，Qcharge表示所述充入总容量，t1表示所述充电起始时间，t2表示所述充电结束时间，I表示所述充电过程中实时电流值。
  10. 根据权利要求9所述的检测方法，
    所述根据所述充入总容量和所述电池起止荷电状态的差值，计算所述电池的当前可用容量，计算公式为：
    其中，Qtotal表示所述电池的当前可用容量，Qcharge表示所述充入总容量，SOCend表示所述电池的充电结束时的荷电状态，SOCbeg表示所述电池充电开始时的荷电状态。
  11. 根据权利要求10所述的检测方法，
    所述根据所述当前可用容量，计算所述新能源汽车当前的电池健康状态，计算公式为：
    其中，SOH表示所述新能源汽车当前的电池健康状态，Qtotal表示所述电池的当前可用容量，Qnormal表示所述电池的标称容量。
  12. 一种检测装置，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1-11任一项所述的方法。
  13. 一种电池健康状态的检测系统，包括：
    新能源汽车，所述新能源汽车中设置有可充放电的电池；
    如权利要求12所述的检测装置，所述检测装置与所述新能源汽车连接，用于检测所述电池的电池健康状态。
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