WO2023169134A1 - 电池soh值计算模型生成方法、计算方法、装置和系统 - Google Patents

电池soh值计算模型生成方法、计算方法、装置和系统 Download PDF

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Publication number
WO2023169134A1
WO2023169134A1 PCT/CN2023/075285 CN2023075285W WO2023169134A1 WO 2023169134 A1 WO2023169134 A1 WO 2023169134A1 CN 2023075285 W CN2023075285 W CN 2023075285W WO 2023169134 A1 WO2023169134 A1 WO 2023169134A1
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Prior art keywords
battery
model
soh
soh value
models
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PCT/CN2023/075285
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English (en)
French (fr)
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王霞
吴元和
陈狄松
叶庆丰
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宁德时代新能源科技股份有限公司
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Publication of WO2023169134A1 publication Critical patent/WO2023169134A1/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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the present application relates to the field of battery technology, and in particular to a battery SOH value calculation model generation method, calculation method, device and system.
  • the purpose of this application is to provide a battery SOH value calculation model generation method, calculation method, device and system.
  • the second model in the method can be used to calculate the battery SOH value, and can improve the accuracy of calculating the battery SOH value.
  • embodiments of the present application provide a method for generating a battery SOH value calculation model.
  • the method includes: obtaining n first SOH values of n groups of batteries, where n ⁇ 1; obtaining m first models, and Obtain the second SOH value of the n groups of batteries under the m first models, where m ⁇ 1; according to the m first models, the n first SOH values and (m*n) The second SOH value is used to determine the second model.
  • a second model for calculating battery SOH is constructed through different first models.
  • the information provided by the different first models can be comprehensively utilized, thereby improving Accurate and reliable estimation of battery SOH value.
  • determining the second model according to the m first models, the n first SOH values and (m*n) second SOH values includes: according to the n The first SOH values and the (m*n) second SOH values are used to obtain the square of the fitting error of each of the batteries under the m first models; the minimum sum of the squares of the fitting errors of each of the batteries is target, and use the least squares method to obtain the weight coefficient corresponding to each of the first models; obtain the second model based on the m first models and each of the weight coefficients.
  • the optimal weight coefficients of m first models in the second model are obtained, which can effectively reduce the number of different first models.
  • the deviation when estimating the battery SOH value can optimize the results of the battery SOH value estimation and improve the accuracy and robustness of calculating the battery SOH value.
  • obtaining the second model based on the m first models and each weight coefficient includes: constructing the second model Y through the following formula:
  • yi is the i-th first model
  • p i is the weight coefficient of the i-th first model in the second model.
  • a method of constructing a combined prediction model is provided.
  • different first models can be synthesized, thereby improving the accuracy of estimating the battery SOH value. sex.
  • the goal is to minimize the sum of squares of each of the fitting errors and using the least squares method to obtain the weight coefficient corresponding to each of the first models, including: through the following formula
  • y it is the first SOH value of the tth group of batteries
  • e it is the first SOH value of the t-th group of batteries and the t-th group of batteries under the i-th first model
  • i is an integer greater than or equal to 1 and less than or equal to m
  • t is an integer greater than or equal to 1 and less than or equal to n.
  • a method of calculating the weight coefficients of different first models is provided.
  • the subsequent application process can quickly obtain different first models based on the first SOH value, the second SOH value and the different first models.
  • the weight coefficient of the model thereby increasing the speed of determining the second model.
  • obtaining the n first SOH values of n groups of batteries includes: obtaining the n first SOH values through a battery cycle charge method.
  • the n first SOH values corresponding to n groups of batteries are calculated by using the battery cycle power method, which can improve the accuracy of calculating the actual SOH value of the battery, thereby obtaining a more accurate actual SOH value, which is beneficial to Subsequent calibration can improve the accuracy of subsequent construction of a second model for calculating the SOH value of the battery to be tested.
  • the first SOH value is the difference between 5 times the discharge capacity of the battery within the life cycle range and the cumulative discharge capacity of the battery, and 5 times the discharge capacity of the battery within the life cycle range. Quantity ratio.
  • the first SOH value is determined based on 5 times the discharge capacity of the battery within the life cycle and the cumulative discharge capacity of the battery, which can improve the accuracy of the first SOH value, thereby improving the accuracy of the second model. .
  • the first model represents the corresponding relationship between at least one influencing factor and SOH.
  • the estimated SOH values under different influencing factors can be obtained through m first models.
  • the influencing factor is one of battery open circuit voltage, ohmic internal resistance, polarization resistance, polarization capacitance, battery temperature, current rate, and battery state of charge.
  • this application also provides a battery SOH value calculation method.
  • the method includes: obtaining a battery SOH value calculation model, where the battery SOH value calculation model is a second model generated by any one of the methods in the first aspect; based on The second model calculates the SOH value of the battery to be tested.
  • the SOH value of the battery to be tested is calculated based on the second model.
  • the second model can comprehensively utilize the information provided by different first models, thereby improving the accuracy and reliability of calculating the SOH value of the battery to be tested. sex.
  • calculating the SOH value of the battery to be tested based on the second model includes: obtaining the measured values of each influencing factor of the battery to be tested; inputting the measured values of each influencing factor into the Second model, obtain the SOH value of the battery to be tested output by the second model.
  • the measured values of each influencing factor of the battery to be tested are input into the second model to obtain the SOH value of the battery to be tested.
  • the second model comprehensively utilizes the first model under different influencing factors to calculate the SOH value of the battery. This can improve the accuracy of measuring the battery SOH value using the internal resistance method.
  • the present application also provides a control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores information that can be used by the at least one processor. Instructions executed by the processor, the instructions being executed by the at least one processor, so that the at least one processor can perform the method as described in any one of the first aspect, and/or as any one of the second aspect the method described.
  • the present application also provides a battery management system, which includes the control device described in the third aspect.
  • the present application also provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to cause the computer to execute any one of the above first aspects. method, and/or the method described in any one of the second aspects above.
  • the present application also provides a computer program product, the computer program product including a computer program stored on a computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer , causing the computer to execute the method described in the first aspect above, and/or the method described in the second aspect above.
  • Figure 1 is a method for generating a battery SOH value calculation model provided by an embodiment of the present application. Process diagram;
  • FIG. 2 is a schematic flow chart of step S30 in Figure 1 provided by the embodiment of the present application.
  • FIG 3 is a schematic flow chart of step S10 in Figure 1 provided by the embodiment of the present application.
  • Figure 4 is a schematic structural block diagram of a control device provided by an embodiment of the present application.
  • an embodiment means that a particular feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application.
  • the appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those skilled in the art understand, both explicitly and implicitly, that the embodiments described herein may be combined with other embodiments.
  • multiple refers to two or more (including two), and similarly, “multiple groups” refers to two or more groups (including two groups).
  • SOH estimation methods can be roughly divided into experimental-based methods and model-based methods. methods and data-driven methods.
  • Methods based on experiments include the cumulative power look-up table method, the empirical formula method, the electrochemical impedance spectroscopy (EIS) method, the capacity growth analysis (Incremental Capacity Analysis, ICA) or the differential voltage analysis (Dynamic Voltage Analysis, DVA) method. wait.
  • the cumulative power look-up table method obtains the relationship between the cumulative charge/discharge data and SOH through prior experiments, and then looks up the current battery's cumulative charge/discharge data in the table to obtain the battery's SOH. This method is simple, but due to the actual operation of the battery There are differences between the working conditions and the experimental working conditions. The decay speeds of the two are different.
  • the data obtained by directly looking up the table is not accurate, and the prior experiments often take a long time.
  • the empirical formula method is to fit the experimental data with an empirical formula to obtain the battery SOH attenuation formula, and then use this formula to calculate the current SOH of the battery.
  • the shortcomings of the empirical formula method are similar to the cumulative power look-up table method, and inappropriate fitting formulas are also will bring additional errors.
  • the EIS method determines the impedance based on the spectrum of the battery to obtain the aging degree of the battery, but this method only stays at the stage of offline detection of SOH.
  • ICA is similar to DVA. They both use the characteristic that the constant current charge/discharge curves of different aging levels are different. The disadvantage is that the error caused by the sensor cannot be avoided.
  • the model-based method mainly uses the equivalent circuit model of the battery for parameter identification to obtain the internal resistance of the battery, and then uses relevant formulas to calculate SOH. This method only relies on the internal resistance of the battery for calculation, and requires high accuracy in estimating the internal resistance of the battery. .
  • the data-based method refers to using machine learning methods such as statistical learning and neural networks to estimate battery SOH.
  • the general steps are to use sensor measurement quantities (such as voltage, current, temperature, etc.) and SOH to establish a model, and then train the model based on battery charge and discharge data. parameters, and finally use the trained model to estimate battery SOH.
  • sensor measurement quantities such as voltage, current, temperature, etc.
  • SOH sensor measurement quantities
  • the disadvantage of this method is that training the model requires a relatively large amount of data, and due to various reasons, the training set and the prediction data are not necessarily the same distribution.
  • Embodiments of the present application provide a battery SOH value calculation model generation method, calculation method, device and system.
  • a second model is constructed through a plurality of different first models, and the battery SOH value is calculated using the second model.
  • the second model calculates the battery SOH value by integrating different first models, which can improve the accuracy of calculating the battery SOH value.
  • inventions of the present application provide a method for generating a battery SOH value calculation model. Please refer to Figure 1.
  • the method includes:
  • Step S10 Obtain n first SOH values of n groups of batteries, where n ⁇ 1;
  • the n first SOH values are the actual SOH values of the corresponding batteries at the current moment.
  • a battery refers to any type of energy storage component used to store electrical energy.
  • it can be a single battery cell, a battery module composed of multiple battery cells, or a battery pack containing one or more battery modules.
  • the shape of the battery can have corresponding shapes according to the needs of the actual situation, such as cylinder, rectangular parallelepiped, etc.
  • the battery can be, but is not limited to, a battery in a mobile phone, a tablet, a laptop, an electric toy, an electric tool, a battery car, an electric vehicle, a ship, a spacecraft, etc.
  • electric toys can include fixed or mobile electric toys, such as game consoles, electric car toys, electric ship toys, electric airplane toys, etc.
  • spacecraft can include airplanes, rockets, space shuttles, spaceships, etc.
  • multiple battery cells in the battery module can be connected in series, in parallel, or in mixed connection.
  • Mixed connection means that multiple battery cells are connected in series and in parallel.
  • the battery modules that make up the battery pack can also be connected in series, parallel, or mixed.
  • the battery pack or battery module may also include other structures in addition to the battery cells, for example, a bus component for realizing electrical connection between multiple battery cells.
  • Step S20 Obtain m first models, and obtain the second SOH values of the n groups of batteries under the m first models, where m ⁇ 1;
  • (m*n) second SOH values are SOH estimated values corresponding to n groups of batteries under m first models.
  • Step S30 Determine a second model based on the m first models, the n first SOH values and (m*n) second SOH values.
  • a second model can be constructed through n first SOH values, (m*n) second SOH values and m first models.
  • the second model includes different influencing factors and SOH values. corresponding relationship; finally, the second model can be used to calculate the SOH value of the battery to be tested.
  • a second model for predicting and estimating the battery SOH value is constructed through different first models.
  • the information provided by the different first models can be comprehensively utilized.
  • the method for calculating the battery SOH value provided in the embodiments of the present application can improve the accuracy and reliability of estimating the battery SOH value.
  • the step S30 includes:
  • Step S31 According to the n first SOH values and the (m*n) second SOH values, obtain the square of the fitting error of each battery under the m first models;
  • Step S32 Taking the minimum sum of squares of the fitting errors as the goal, and using the least squares method to obtain the weight coefficient corresponding to each of the first models;
  • Step S33 Obtain the second model based on the m first models and each weight coefficient.
  • Q is the objective function.
  • the optimal weight vector is obtained by solving mathematical programming under the least squares principle for the sum of squares of fitting errors.
  • the optimal weight vector includes the optimal weights of m first models in the second model. coefficient, the above method can effectively reduce the deviation of different first models when estimating the battery SOH value, optimize the results of the SOH value estimation of the battery to be tested, and improve the accuracy and robustness of calculating the battery SOH value. And, through The weight coefficients of different first models can be quickly calculated. In subsequent applications, the weight coefficients of different first models can be quickly obtained based on the first SOH value, the second SOH value and different first models, thereby increasing the speed of building the second model. , to speed up the estimation of the SOH value of the battery to be tested.
  • obtaining the second model based on the m first models and each weight coefficient includes: constructing the second model Y through the following formula:
  • yi is the i-th first model
  • p i is the weight coefficient of the i-th first model in the second model.
  • a method of constructing a combined prediction model is provided. Different first models are combined to obtain a second model through weighted combination, so that the second model can synthesize different first models and subsequently use the second model.
  • the estimate can be improved Calculate the accuracy of battery SOH value.
  • step S10 includes:
  • Step S11 Obtain the n first SOH values through the battery cycle power method.
  • the first SOH value is the difference between the 5 times discharge capacity of the battery within the life cycle range and the cumulative discharge capacity of the battery, and the ratio of the 5 times discharge capacity of the battery within the life cycle range.
  • Q is the cumulative discharge capacity of the i-th battery group
  • Qt is the discharge capacity of the i-th battery group within the life cycle.
  • the cumulative discharge capacity is the cumulative sum of the battery discharge capacity of the i-th group of batteries before the current time.
  • the discharge capacity of the i-th battery group within the life cycle range can be calculated by the ampere-hour integration method.
  • the life cycle range can refer to the time range in which the battery SOH value drops from 100% to 80%. In actual applications, the life cycle range can be set according to actual needs, and there is no need to adhere to the limitations in this embodiment.
  • the accuracy of calculating the actual SOH value of the battery can be improved, thereby obtaining a more accurate actual SOH value, which is beneficial to subsequent Calibration is performed to improve the accuracy of subsequent construction of a second model of the computational battery.
  • the first model represents the corresponding relationship between at least one influencing factor and SOH.
  • the influencing factors may be battery open circuit voltage, ohmic internal resistance, polarization resistance, polarization capacitance, battery temperature, current rate, battery state of charge (State of Charge, SOC), battery humidity one of them.
  • the battery open circuit voltage refers to the open circuit voltage of the battery equal to the difference between the positive electrode potential and the negative electrode potential of the battery when the battery is open (that is, when no current flows through the two poles).
  • Battery Internal resistance includes ohmic resistance and polarization resistance exhibited by electrodes during electrochemical reactions. The sum of ohmic resistance and polarization resistance is the internal resistance of the battery.
  • Ohmic resistance consists of electrode material, electrolyte, diaphragm resistance and contact resistance of various parts.
  • Polarization resistance refers to the resistance caused by polarization during electrochemical reactions, including resistance caused by electrochemical polarization and concentration polarization.
  • Polarization capacitance represents the capacitive reactance produced by the battery during the polarization process.
  • Battery temperature refers to the temperature of the environment where the battery is located.
  • Battery humidity refers to the humidity of the environment where the battery is located.
  • the current rate generally refers to the ratio of the maximum discharge current of a lithium battery to the battery capacity above a certain specified voltage platform.
  • the flexibility in constructing m first models can be improved, thereby improving the flexibility and adaptability in constructing a second model for calculating the battery SOH value.
  • the internal resistance of the battery is one of the most important characteristic parameters of the battery, that is, the SOH value can be calculated through the internal resistance of the battery.
  • Battery internal resistance is an important parameter that characterizes battery SOH and battery operating status. It is a major indicator of the ease of electrons and ions transporting within the electrode. Therefore, an equivalent circuit model can be established to estimate the SOH value based on the internal resistance method. Under this internal resistance method, four influencing factors can be selected: open circuit voltage, ohmic internal resistance, polarization resistance and polarization capacitance, then , the SOH value can be calculated by measuring the open circuit voltage, ohmic internal resistance, polarization resistance and polarization capacitance.
  • first models can be selected, namely the first model y1, the first model y2, the first model y3 and the first model y4.
  • the first model y1 represents the corresponding relationship between ohmic internal resistance, polarization resistance and polarization capacitance and SOH under the influence of open circuit voltage
  • the first model y2 represents the relationship between open circuit voltage, polarization resistance and SOH under the influence of ohmic internal resistance.
  • the first model y3 represents the open circuit voltage, ohmic internal resistance and polarity under the influence of polarization resistance.
  • the first model type y4 represents the corresponding relationship between open circuit voltage, ohmic internal resistance and polarization resistance and SOH under the influence of polarizing capacitance.
  • this second model comprehensively uses the first model under different influencing factors to calculate the battery SOH value, thereby improving the accuracy of measuring the battery SOH value using the internal resistance method.
  • a second model is constructed through the first model of the internal resistance method under different influencing factors, which can improve the accuracy of measuring the SOH value using the internal resistance method. Subsequent Helps the battery work efficiently.
  • the impact factor can be selected according to actual needs, and there is no need to be limited to the limitations in this embodiment.
  • inventions of the present application provide a battery SOH value calculation method.
  • the method includes: obtaining a battery SOH value calculation model, where the battery SOH value calculation model is a second model generated by any method in the first aspect; Based on the second model, the SOH value of the battery to be tested is calculated.
  • a second model generated by any method in the first aspect can be obtained.
  • the second model includes the corresponding relationship between different influencing factors and the SOH value.
  • the battery to be tested is calculated. SOH value.
  • the SOH value of the battery to be tested is calculated based on the second model.
  • the second model can comprehensively utilize the information provided by different first models, thereby improving the calculation of the battery to be tested. Accuracy and reliability of measuring battery SOH value.
  • calculating the SOH value of the battery to be tested based on the second model includes: obtaining the measured values of each influencing factor of the battery to be tested; inputting the measured values of each influencing factor into the The second model is used to obtain the SOH value of the battery to be tested output by the second model.
  • the second model includes influencing factors such as: open circuit voltage, ohmic internal resistance, polarization resistance and polarization capacitance, then the open circuit voltage, ohmic internal resistance, polarization resistance and polarization capacity of the battery to be tested can be measured. After changing the capacitance and inputting it into the second model, the SOH value of the battery to be tested can be obtained.
  • the measured values of each influencing factor of the battery to be tested are input into the second model to obtain the SOH value of the battery to be tested.
  • the second model comprehensively utilizes the first model under different influencing factors to calculate the SOH value of the battery. This can improve the accuracy of measuring the battery SOH value using the internal resistance method.
  • the embodiment of the present application also provides a control device, please refer to Figure 4.
  • the control device 10 includes: at least one processor 11; and a memory 12 communicatively connected with the at least one processor 11, Figure 4 A processor 11 is used as an example.
  • the memory 12 stores instructions that can be executed by the at least one processor 11, and the instructions are executed by the at least one processor 11, so that the at least one processor 11 can perform the above-mentioned steps of FIGS. 1 to 3.
  • the processor 11 and the memory 12 may be connected through a bus or other means. In FIG. 4 , 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 the battery SOH value in the first embodiment of the present application.
  • the processor 11 executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory 12, that is, realizing the first party as above.
  • the memory 12 may include a program storage area and a data storage area, where the program storage 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 pixel correction 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 the control 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, execute the method for generating the battery SOH value calculation model in the above method embodiment of the first aspect, and/ Or the battery SOH value calculation method in the method embodiment of the second aspect above, for example, perform the method steps of FIGS. 1 to 3 described above, and/or the method described in the embodiment of the second aspect above.
  • embodiments of the present application further provide a battery management system, which includes the control device described in the third aspect.
  • the battery management system refers to the electronic system used to manage the battery and ensure that the battery can operate normally.
  • the control device in the battery management system constructs a second model for calculating the battery SOH value through different first models, and/or calculates the battery SOH value based on the second model, and can comprehensively utilize the information provided by the different first models, This improves the accuracy and reliability of estimating battery SOH value.
  • the battery management system further includes at least one set of batteries. control The devices are respectively connected to the batteries.
  • embodiments of the present application also provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to cause the computer to execute any one of the first aspects. The method described in any one of the second aspects.
  • embodiments of the present application also provide a computer program product, including a computing program stored on a non-volatile computer-readable storage medium.
  • the computer program includes program instructions.
  • the program instructions When the program instructions are executed by a computer From time to time, the computer is caused to execute the method for generating the battery SOH value calculation model as in any method embodiment of the first aspect, and/or the method for calculating the battery SOH value as in any method embodiment of the second aspect, for example, perform the above description The method steps of Figures 1 to 3.
  • 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 separated.
  • 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 computer software products can be stored in computer-readable storage media, such as ROM/RAM, disks. , optical disk, etc., including a number of instructions to use at least one computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

Abstract

一种电池SOH值计算模型生成方法、计算方法、装置和系统，电池SOH值计算模型生成方法包括获取n组电池的n个第一SOH值，其中，n≥1（S10）；获取m个第一模型，并得到n组电池在m个第一模型下的第二SOH值，其中，m≥1（S20）；根据m个第一模型、n个第一SOH 值和（m*n）个第二SOH值，确定第二模型（S30）。电池SOH值计算方法通过不同的第一模型构建出对电池SOH值估计的第二模型，第二模型能够综合利用不同第一模型所提供的信息，从而提高计算待测电池SOH值的精确度和可靠性。

Description

电池SOH值计算模型生成方法、计算方法、装置和系统
相关申请
本申请要求于2022年3月7日申请的、申请号为202210216543.1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
本申请涉及电池技术领域，特别涉及一种电池SOH值计算模型生成方法、计算方法、装置和系统。
背景技术
随着社会的发展和进步，传统能源越来越接近于枯竭，因此开发新能源势在必行，包括核能、太阳能和可燃冰等多种新型能源将是未来能源科技发展的趋势，也将成为电动汽车动力的来源。
随着锂离子电池在新能源汽车上的广泛应用，电池健康状态(State of Health，SOH)越来越多的受到工业界和学术界的关注，如何对电池SOH进行较为准确的预测，是目前急需解决的问题。
申请内容
本申请的目的是提供一种电池SOH值计算模型生成方法、计算方法、装置和系统，该方法中的第二模型可用于计算电池SOH值，并能提高计算电池SOH值的准确性。
第一方面，本申请实施方式提供一种电池SOH值计算模型的生成方法，该方法包括：获取n组电池的n个第一SOH值，其中，n≥1；获取m个第一模型，并得到所述n组电池在所述m个第一模型下的第二SOH值，其中，m≥1；根据所述m个第一模型、所述n个第一SOH值和(m*n) 个所述第二SOH值，确定第二模型。
本申请实施例的技术方案中，通过不同的第一模型构建出对电池SOH计算的第二模型，后续在对电池SOH值进行估计时，能够综合利用不同第一模型所提供的信息，从而提高估算电池SOH值的精确度和可靠性。
在一些实施例中，所述根据所述m个第一模型、所述n个第一SOH值和(m*n)个所述第二SOH值，确定第二模型，包括：根据所述n个第一SOH值和所述(m*n)个第二SOH值，得到各所述电池在m个所述第一模型下的拟合误差平方；以各所述拟合误差平方和最小为目标、并采用最小二乘法得到各所述第一模型对应的权重系数；根据所述m个第一模型和各所述权重系数，得到所述第二模型。
在本申请上述实施例中，通过对拟合误差平方和在最小二乘原理下求解数学规划，得到m个第一模型在第二模型中的最优权重系数，能有效降低不同的第一模型在进行估计电池SOH值时的偏差，能够优化对电池SOH值估计的结果，提高了计算电池SOH值的精度和稳健性。
在一些实施例中，所述根据所述m个第一模型和各所述权重系数，得到所述第二模型，包括：通过以下公式构建所述第二模型Y：
其中，yi为第i个所述第一模型，pi为第i个所述第一模型在所述第二模型中的权重系数。
在本申请上述实施例中，提供了一种构建组合预测模型的方式，通过对不同的第一模型进行加权组合得到第二模型，可以综合不同的第一模型，从而提高估算电池SOH值的准确性。
在一些实施例中，所述以各所述拟合误差平方和最小为目标、并采用最小二乘法得到各所述第一模型对应的权重系数，包括：通过以下公 式计算得到各所述权重系数：其中，R＝[1,1,...1]T，
yit为第t组电池的所述第一SOH值，为第t组电池在第i个所述第一模型下的所述第二SOH值，eit为第t组电池的所述第一SOH值与第t组电池在第i个所述第一模型下的所述第二SOH值的拟合误差，i为大于等于1且小于等于m的整数，t为大于等于1且小于等于n的整数。
在本申请上述实施例中，提供了一种计算不同的第一模型的权重系数的方式，后续应用过程可快速根据第一SOH值、第二SOH值和不同的第一模型、得到不同第一模型的权重系数，从而提高确定第二模型的速度。
在一些实施例中，所述获取n组电池的n个第一SOH值，包括：通过电池循环电量法获取所述n个第一SOH值。
在本申请上述实施例中，通过使用电池循环电量法计算得到n组电池对应的n个第一SOH值，可以提高计算电池实际的SOH值的精度，从而获取较为准确的实际SOH值，有利于后续进行标定，从而能提高后续构建计算待测电池SOH值的第二模型的准确性。
在一些实施例中，所述第一SOH值为电池在生命周期范围内的5倍放电量与电池的累计放电量的差，与电池在生命周期范围内的5倍放电 量的比值。
在本申请上述实施例中，通过电池在生命周期范围内的5倍放电量与电池的累计放电量确定第一SOH值，可以提高第一SOH值的精度，从而能够提高第二模型的准确性。
在一些实施例中，所述第一模型表征至少一种影响因子与SOH的对应关系。
在本申请上述实施例中，通过用第一模型表征影响因子与SOH的对应关系，可以通过m个第一模型来求得在不同影响因子下的SOH估计值。
在一些实施例中，所述影响因子为电池开路电压、欧姆内阻、极化电阻、极化电容、电池温度、电流倍率、电池荷电状态中的其中一种。
在本申请上述实施例中，提供了多种影响因子，提高了构建m个第一模型的灵活性，从而提高构建计算电池SOH值的第二模型的灵活性和适应性。
第二方面，本申请还提供一种电池SOH值计算方法，该方法包括：获取电池SOH值计算模型，所述电池SOH值计算模型为第一方面中任一项方法生成的第二模型；基于所述第二模型，计算待测电池的SOH值。
本申请实施例的技术方案中，基于第二模型计算待测电池的SOH值，该第二模型能够综合利用不同第一模型所提供的信息，从而提高计算待测电池SOH值的精确度和可靠性。
在一些实施例中，所述基于所述第二模型，计算待测电池的SOH值，包括：获取待测电池的各影响因子的测量值；将所述各影响因子的测量值输入至所述第二模型，得到所述第二模型输出的所述待测电池的SOH值。
在本申请上述实施例中，将待测电池各影响因子的测量值输入第二模型，获得待测电池的SOH值，该第二模型综合利用不同影响因子下的第一模型计算电池SOH值，从而能提高用内阻法衡量电池SOH值的准确性。
第三方面，本申请还提供一种控制装置，该控制装置包括：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第一方面任意一项所述的方法，和/或如第二方面任意一项所述的方法。
第四方面，本申请还提供一种电池管理系统，该电池管理系统包括如第三方面所述的控制装置。
第五方面，本申请还提供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任意一项所述的方法，和/或如上第二方面任意一项所述的方法。
第六方面，本申请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，和/或如上第二方面所述的方法。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。
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是本申请实施例提供的一种电池SOH值计算模型的生成方法的 流程示意图；
图2是本申请实施例提供的图1中的步骤S30的一种流程示意图；
图3是本申请实施例提供的图1中的步骤S10的一种流程示意图；
图4是本申请实施例提供的一种控制装置的结构框图示意图。
具体实施方式
下面将结合附图对本申请技术方案的实施例进行详细的描述。以下实施例仅用于更加清楚地说明本申请的技术方案，因此只作为示例，而不能以此来限制本申请的保护范围。
除非另有定义，本文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；本文中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。
在本申请实施例的描述中，技术术语“第一”“第二”等仅用于区别不同对象，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量、特定顺序或主次关系。在本申请实施例的描述中，“多个”的含义是两个以上，除非另有明确具体的限定。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
在本申请实施例的描述中，术语“多个”指的是两个以上(包括两个)，同理，“多组”指的是两组以上(包括两组)。
目前，SOH估计方法可以概略地分为基于实验的方法、基于模型的 方法和基于数据驱动的方法三类。
基于实验的方法有累计电量查表法、经验公式法、电化学阻抗谱(Electrochemical Impedance Spectroscopy，EIS)法和容量增长分析(Incremental Capacity Analysis，ICA)或微分电压分析(Dynamic Voltage Analysis，DVA)法等。其中，累计电量查表法通过事先实验获得累计充/放电数据与SOH之间的关系，再将当前电池的累计充/放电数据查表，得到电池的SOH，该方法简单，但是由于电池实际运行工况与实验工况有差异，两者衰减速度不同，直接查表得到的数据不精确，而且事先的实验往往耗时较长。经验公式法是将实验数据用经验公式拟合，得到电池SOH衰减公式，再利用该公式计算当前电池的SOH，经验公式法的缺点与累计电量查表法类似，而且不合适的拟合公式还会带来额外误差。EIS法根据电池的频谱确定阻抗，从而得到电池的老化程度，但是该方法只停留在离线检测SOH的阶段。ICA和DVA类似，都是利用不同老化程度的恒流充/放电曲线有差异这一特性，其缺点是不能避免传感器带来的误差。
基于模型的方法主要是利用电池的等效电路模型进行参数辨识，得到电池的内阻，再利用相关公式计算SOH，该方法仅仅依赖于电池内阻进行计算，对电池内阻估算精度要求很高。
基于数据的方法是指采用统计学习、神经网络等机器学习方法估算电池SOH，其一般步骤为利用传感器测量量(如电压、电流、温度等)与SOH建立模型，再根据电池充放电数据训练模型参数，最后用训练出的模型估算电池SOH，这类方法的缺点是，训练模型需要比较大的数据量，而且由于各种原因，训练集与预测数据不一定同分布。
综上，如何对电池SOH值进行较为准确的进行预测，是目前急需解决的问题。
本申请实施例提供一种电池SOH值计算模型生成方法、计算方法、装置和系统，该方法中通过多个不同的第一模型构建第二模型，用第二模型计算电池SOH值，相比于单独使用不同的第一模型，第二模型通过综合不同的第一模型计算电池SOH值，能提高计算电池SOH值的准确性。
第一方面，本申请实施例提供一种电池SOH值计算模型的生成方法，请参阅图1，该方法包括：
步骤S10：获取n组电池的n个第一SOH值，其中，n≥1；
在本申请实施例中，n个第一SOH值为对应的电池在当前时刻实际的SOH值。
电池是指任何类型的，用于存储电能的储能组件。例如，可以是单个的电池单体，也可以是多个电池单体组成的电池模组，还可以是包含了一个或者多个电池模组的电池包。电池的外形可以根据实际情况的需要而具有相应的形状，比如，圆柱体、长方体等。
电池可以为但不限于手机、平板、笔记本电脑、电动玩具、电动工具、电瓶车、电动汽车、轮船、航天器等等中的电池。其中，电动玩具可以包括固定式或移动式的电动玩具，例如，游戏机、电动汽车玩具、电动轮船玩具和电动飞机玩具等等，航天器可以包括飞机、火箭、航天飞机和宇宙飞船等等。
在一些实施例中，电池模组中的多个电池单体之间可串联或并联或混联，混联是指多个电池单体中既有串联又有并联。组成电池包的电池模组之间也可以是串联或并联或混联。电池包或者电池模组中还可以包括除电池单体以外的其他结构，例如，用于实现多个电池单体之间的电连接的汇流部件。
步骤S20：获取m个第一模型，并得到所述n组电池在所述m个第一模型下的第二SOH值，其中，m≥1；
在本申请实施例中，(m*n)个第二SOH值为n组电池对应在m个第一模型下的SOH估计值。
步骤S30：根据所述m个第一模型、所述n个第一SOH值和(m*n)个所述第二SOH值，确定第二模型。
在本申请实施例中，可以通过n个第一SOH值、(m*n)个第二SOH值和m个第一模型构建出第二模型，该第二模型包括了不同影响因子与SOH值的对应关系；最后，可利用第二模型对待测电池的SOH值进行计算。
在本申请实施例中，通过不同的第一模型构建出对电池SOH值预测估计的第二模型，后续在对电池SOH值进行估计时，能够综合利用不同第一模型所提供的信息，相比于单独使用不同的第一模型进行估计电池SOH值，本申请实施例提供的计算电池SOH值的方法能提高估算电池SOH值的精确度和可靠性。
在其中一些实施例中，请参阅图2，所述步骤S30包括：
步骤S31：根据所述n个第一SOH值和所述(m*n)个第二SOH值，得到各所述电池在m个所述第一模型下的拟合误差平方；
步骤S32：以各所述拟合误差平方和最小为目标、并采用最小二乘法得到各所述第一模型对应的权重系数；
步骤S33：根据所述m个第一模型和各所述权重系数，得到所述第二模型。
具体的，通过n个第一SOH值和(m*n)个第二SOH值，可以得到拟合误差矩阵：
其中，yit为第t组电池的第一SOH值，为第t组电池在第i个第一模型下的第二SOH值，eit为第t组电池的第一SOH值与第t组电池在第i个第一模型下的第二SOH值的拟合误差，i为大于等于1且小于等于m的整数，t为大于等于1且小于等于n的整数。
接着，建立目标函数如下：
其中，Q为目标函数。
以及，建立约束条件如下：
定义R＝[1,1,...1]T，记P＝[p1,p2,...pm]；
那么，由约束条件可以得到：

对上式采用拉格朗日乘子法，从而求得最优权重向量为：
目标函数最小值为
在本申请实施例中，通过对拟合误差平方和在最小二乘原理下求解数学规划，得到最优权重向量，该最优权重向量包括m个第一模型在第二模型中的最优权重系数，上述方式能有效降低不同第一模型在进行估计电池SOH值时的偏差，能够优化对待测电池SOH值估计的结果，提高了计算电池SOH值的精度和稳健性。并且，通过可快速计算不同第一模型的权重系数，后续应用中可快速根据第一SOH值、第二SOH值和不同的第一模型、得到不同第一模型的权重系数，从而提高构建第二模型的速度，加快估计待测电池SOH值的速度。
在其中一些实施例中，所述根据所述m个第一模型和各所述权重系数，得到所述第二模型，包括：通过以下公式构建所述第二模型Y：
其中，yi为第i个所述第一模型，pi为第i个所述第一模型在所述第二模型中的权重系数。
在本申请实施例中，提供了一种构建组合预测模型的方式，将不同的第一模型通过加权组合得到第二模型，使第二模型可以综合不同的第一模型，后续利用该第二模型进行计算待测电池的SOH值时，可提高估 算电池SOH值的准确性。
在其中一些实施例中，请参阅图3，所述步骤S10包括：
步骤S11：通过电池循环电量法获取所述n个第一SOH值。
在其中一些实施例中，所述第一SOH值为电池在生命周期范围内的5倍放电量与电池的累计放电量的差，与电池在生命周期范围内的5倍放电量的比值。
具体的，通过以下公式计算第i组电池的第一SOH值SOH1:
SOH1＝1-Q/(5Qt)；
其中，Q为第i组电池的累计放电量，Qt为第i组电池在生命周期范围内的放电量。累计放电量即为第i组电池在截止到当前时间前电池放电电量的累加和。第i组电池在生命周期范围内的放电量可以通过安时积分法计算得到，生命周期范围可以是指电池SOH值从100％下降至80％的时间范围。实际应用中，生命周期范围可根据实际需要进行设置，在此不需拘泥于本实施例中的限定。
在本申请实施例中，通过使用电池循环电量法计算得到n组电池对应的n个第一SOH值，可以提高计算电池实际的SOH值的精度，从而获取较为准确的实际SOH值，有利于后续进行标定，从而能提高后续构建计算电池的第二模型的准确性。
在其中一些实施例中，所述第一模型表征至少一个影响因子与SOH的对应关系。本申请通过用第一模型表征影响因子与SOH的对应关系，可以通过m个第一模型来求得在不同影响因子下的SOH估计值。
具体的，在其中一些实施例中，影响因子可以为电池开路电压、欧姆内阻、极化电阻、极化电容、电池温度、电流倍率、电池荷电状态(State of Charge，SOC)、电池湿度中的其中一种。
其中，电池开路电压是指电池的开路电压等于电池在断路时(即没有电流通过两极时)电池的正极电极电势与负极的电极电势之差。电池 内阻包括欧姆电阻和电极在电化学反应时所表现的极化电阻。欧姆电阻与极化电阻之和为电池内阻。欧姆电阻由电极材料、电解液、隔膜电阻及各部分零件的接触电阻组成。极化电阻是指电化学反应时由于极化引起的电阻，包括电化学极化和浓差极化引起的电阻。极化电容是表示电池在极化过程中所产生的容抗。电池温度是指电池所处环境的温度。电池湿度是指电池所处环境的湿度。电流倍率一般是指在某规定电压平台之上锂电最大放电电流与电池容量之比。SOC是用来反映电池的剩余容量状况的物理量，其数值定义为电池剩余容量占电池容量的比值，即SOC＝Qc/CI，其中，Qc为电池剩余的容量，CI为电池以恒定电流大小I放电时所具有的容量。
在本申请实施例中，通过提供多种影响因子，可提高构建m个第一模型时的灵活性，从而提高构建计算电池SOH值的第二模型的灵活性和适应性。
可以理解的是，在对电池的SOH值进行预测时，电池内阻是电池最为重要的特性参数之一，即可以通过电池内阻来计算SOH值。电池内阻是表征电池SOH以及电池运行状态的重要参数，是衡量电子和离子在电极内传输难易程度的主要标志。因此，可以建立电路等效模型，基于内阻法对SOH值进行估计，在该内阻法下可以选取四个影响因子分别为：开路电压、欧姆内阻、极化电阻和极化电容，那么，SOH值可通过测量开路电压、欧姆内阻、极化电阻和极化电容后进行计算得到。
接着，可以选取四个第一模型，分别为第一模型y1、第一模型y2、第一模型y3和第一模型y4。具体的，第一模型y1表征在开路电压影响下，欧姆内阻、极化电阻和极化电容与SOH的对应关系；第一模型y2表征在欧姆内阻影响下，开路电压、极化电阻和极化电容与SOH的对应关系；第一模型y3表征在极化电阻影响下，开路电压、欧姆内阻和极 化电容与SOH的对应关系；第一模型型y4表征在极化电容影响下，开路电压、欧姆内阻和极化电阻与SOH的对应关系。
接着，在得到n组电池的第一SOH值后，分别根据第一模型y1、第一模型y2、第一模型y3和第一模型y4计算得到n组电池的4n个第二SOH值；然后，根据各第一SOH值和各第二SOH值计算得到拟合误差矩阵，并以拟合误差平方和最小为目标、采用最小二乘法得到第一模型y1的第一权重系数p1、第一模型y2的第二权重系数p2、第一模型y3的第三权重系数p3、第一模型y4的第四权重系数p4。最后，得到第二模型为Y＝p1y1+p2y2+p3y3+p4y4。在该第二模型中，表明了SOH与电池开路电压、欧姆内阻、极化电阻和极化电容的对应关系。
通过上述方式构建出不同影响因子下、电池内阻与SOH值的关系，相比于直接利用内阻法进行计算电池SOH值，该第二模型综合利用不同影响因子下的第一模型进行计算电池SOH值，从而能提高用内阻法衡量电池SOH值的准确性。在本申请实施例中基于电池内阻法测量SOH值的基础上，通过不同的影响因子下的内阻法第一模型构建第二模型，可以提高利用内阻法测量SOH值的准确性，后续有利于电池高效工作。实际应用中，影响因子可根据实际需要进行选取，在此不需拘泥于本实施例中的限定。
第二方面，本申请实施例提供一种电池SOH值计算方法，该方法包括：获取电池SOH值计算模型，所述电池SOH值计算模型为第一方面中任一项方法生成的第二模型；基于所述第二模型，计算待测电池的SOH值。
在本申请实施例中，可获取如上第一方面中任一方法生成的第二模型，该第二模型包括了不同影响因子与SOH值的对应关系，基于该第二模型计算出待测电池的SOH值。
本申请实施例的技术方案中，基于第二模型计算待测电池的SOH值，该第二模型能够综合利用不同第一模型所提供的信息，从而提高计算待 测电池SOH值的精确度和可靠性。
在其中一些实施例中，所述基于所述第二模型，计算待测电池的SOH值，包括：获取待测电池的各影响因子的测量值；将所述各影响因子的测量值输入至所述第二模型，得到所述第二模型输出的所述待测电池的SOH值。
在本实施例中，例如第二模型包括的影响因子有：开路电压、欧姆内阻、极化电阻和极化电容，则可以测量待测电池的开路电压、欧姆内阻、极化电阻和极化电容后，输入至该第二模型中，即可得到待测电池的SOH值。
在本申请上述实施例中，将待测电池各影响因子的测量值输入第二模型，获得待测电池的SOH值，该第二模型综合利用不同影响因子下的第一模型计算电池SOH值，从而能提高用内阻法衡量电池SOH值的准确性。
第三方面，本申请实施例还提供一种控制装置，请参阅图4，该控制装置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4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3所述的电池SOH值计算模型的生成方法，和/或执行如上第二方面所述的电池SOH值计算方法。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4中以通过总线连接为例。
存储器12作为一种非易失性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，如本申请如上第一方面实施例中的电池SOH值计算模型的生成方法，和/或本申请如上第二方面实施例中的电池SOH值计算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，即实现如上第一方 面方法实施例中电池SOH值计算模型的生成方法，和/或实现如上第二方面方法实施例中电池SOH值计算方法。
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像素校正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。在其中一些实施例中，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控制装置。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，执行上述第一方面方法实施例中的电池SOH值计算模型的生成方法，和/或上述第二方面方法实施例中的电池SOH值计算方法，例如，执行以上描述的图1至图3的方法步骤，和/或以上第二方面实施例中描述的方法。
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。
第四方面，本申请实施例还提供一种电池管理系统，该电池管理系统包括如第三方面所述的控制装置。该电池管理系统是指用于管理电池，确保电池能够正常运行的电子系统。该电池管理系统中的控制装置通过不同的第一模型构建出对电池SOH值计算的第二模型，和/或基于第二模型计算电池SOH值，能够综合利用不同第一模型所提供的信息，从而提高估算电池SOH值的精确度和可靠性。
在其中一些实施例中，该电池管理系统还包括至少一组电池。控制 装置分别与所述电池进行连接。
第五方面，本申请实施例还提供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第一方面任意一项所述的方法，和/或第二方面任意一项所述的方法。
第六方面，本申请实施例还提供了一种计算机程序产品，包括存储在非易失性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，使所述计算机执行如第一方面任意方法实施例中的电池SOH值计算模型的生成方法，和/或如第二方面任意方法实施例中的电池SOH值计算方法，例如，执行以上描述的图1至图3的方法步骤。
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，本领域的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。基于这样的理解，上述技术方案本质上或者说对相关技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用至少一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各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，或者对其中部分或者全部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，其均应涵盖在本申请的权利要求和说明书的范围当中。尤其是，只要不存在结构冲突，各个实施例中所提到的各项技术特征均可以任意方式组合起来。本申请并不局限于文中公开的特定实施例，而是包括落入权利要求的范围内的所有技术方案。

Claims (13)

  1. 一种电池SOH值计算模型的生成方法，其中，包括：
    获取n组电池的n个第一SOH值，其中，n≥1；
    获取m个第一模型，并得到所述n组电池在所述m个第一模型下的第二SOH值，其中，m≥1；
    根据所述m个第一模型、所述n个第一SOH值和(m*n)个所述第二SOH值，确定第二模型。
  2. 根据权利要求1所述的方法，其中，所述根据所述m个第一模型、所述n个第一SOH值和(m*n)个所述第二SOH值，确定第二模型，包括：
    根据所述n个第一SOH值和所述(m*n)个第二SOH值，得到各所述电池在m个所述第一模型下的拟合误差平方；
    以各所述拟合误差平方和最小为目标、并采用最小二乘法得到各所述第一模型对应的权重系数；
    根据所述m个第一模型和各所述权重系数，得到所述第二模型。
  3. 根据权利要求2所述的方法，其中，所述根据所述m个第一模型和各所述权重系数，得到所述第二模型，包括：
    通过以下公式构建所述第二模型Y：
    其中，yi为第i个所述第一模型，pi为第i个所述第一模型在所述第二模型中的权重系数。
  4. 根据权利要求2或3所述的方法，其中，所述以各所述拟合误差平方和最小为目标、并采用最小二乘法得到各所述第一模型对应的权 重系数，包括：
    通过以下公式计算得到各所述权重系数：
    其中，
    yit为第t组电池的所述第一SOH值，为第t组电池在第i个所述第一模型下的所述第二SOH值，eit为第t组电池的所述第一SOH值与第t组电池在第i个所述第一模型下的所述第二SOH值的拟合误差，i为大于等于1且小于等于m的整数，t为大于等于1且小于等于n的整数。
  5. 根据权利要求1-4任意一项所述的方法，其中，所述获取n组电池的n个第一SOH值，包括：
    通过电池循环电量法获取所述n个第一SOH值。
  6. 根据权利要求1-5任意一项所述的方法，其中，所述第一SOH值为电池在生命周期范围内的5倍放电量与电池的累计放电量的差，与电池在生命周期范围内的5倍放电量的比值。
  7. 根据权利要求1-6任意一项所述的方法，其中，所述第一模型 表征至少一种影响因子与SOH的对应关系。
  8. 根据权利要求7所述的方法，其中，所述影响因子为电池开路电压、欧姆内阻、极化电阻、极化电容、电池温度、电流倍率、电池荷电状态中的其中一种。
  9. 一种电池SOH值计算方法，其中，包括：
    获取电池SOH值计算模型，所述电池SOH值计算模型为权利要求1-8中任一项方法生成的第二模型；
    基于所述第二模型，计算待测电池的SOH值。
  10. 根据权利要求9所述的电池SOH值计算方法，其中，所述基于所述第二模型，计算待测电池的SOH值，包括：
    获取待测电池的各影响因子的测量值；
    将所述各影响因子的测量值输入至所述第二模型，得到所述第二模型输出的所述待测电池的SOH值。
  11. 一种控制装置，其中，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1-8任意一项所述的方法，和/或使所述至少一个处理器能够执行如权利要求9或10所述的方法。
  12. 一种电池管理系统，其中，包括如权利要求11所述的控制装置。
  13. 一种计算机可读存储介质，其中，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权利要求1-8任意一项所述的方法，和/或如权利要求9或10所述的方法。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117406125A (zh) * 2023-12-15 2024-01-16 山东派蒙机电技术有限公司 电池健康状态确认方法、装置、设备及存储介质

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130110429A1 (en) * 2010-03-23 2013-05-02 Taiji Mitsuyama Battery internal state estimating apparatus and battery internal state estimating method
CN106842045A (zh) * 2017-01-20 2017-06-13 北京理工大学 一种基于自适应权重方法的电池多模型融合建模方法和电池管理系统
CN107015155A (zh) * 2017-03-24 2017-08-04 江门市地尔汉宇电器股份有限公司 一种电动车电池soh的测算方法及装置
CN109725266A (zh) * 2018-12-29 2019-05-07 蜂巢能源科技有限公司 一种电池健康状态soh的计算方法及装置
CN111337832A (zh) * 2019-12-30 2020-06-26 南京航空航天大学 一种动力电池多维度融合soc和soh在线联合估算的方法
CN111948560A (zh) * 2020-07-30 2020-11-17 西安工程大学 基于多因子评估模型的锂电池健康状态估算方法
CN112858918A (zh) * 2021-01-15 2021-05-28 长沙理工大学 基于优化多因子的动力锂离子电池健康状态在线估计策略
CN113009349A (zh) * 2021-04-09 2021-06-22 哈尔滨工业大学 一种基于深度学习模型的锂离子电池健康状态诊断方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130110429A1 (en) * 2010-03-23 2013-05-02 Taiji Mitsuyama Battery internal state estimating apparatus and battery internal state estimating method
CN106842045A (zh) * 2017-01-20 2017-06-13 北京理工大学 一种基于自适应权重方法的电池多模型融合建模方法和电池管理系统
CN107015155A (zh) * 2017-03-24 2017-08-04 江门市地尔汉宇电器股份有限公司 一种电动车电池soh的测算方法及装置
CN109725266A (zh) * 2018-12-29 2019-05-07 蜂巢能源科技有限公司 一种电池健康状态soh的计算方法及装置
CN111337832A (zh) * 2019-12-30 2020-06-26 南京航空航天大学 一种动力电池多维度融合soc和soh在线联合估算的方法
CN111948560A (zh) * 2020-07-30 2020-11-17 西安工程大学 基于多因子评估模型的锂电池健康状态估算方法
CN112858918A (zh) * 2021-01-15 2021-05-28 长沙理工大学 基于优化多因子的动力锂离子电池健康状态在线估计策略
CN113009349A (zh) * 2021-04-09 2021-06-22 哈尔滨工业大学 一种基于深度学习模型的锂离子电池健康状态诊断方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117406125A (zh) * 2023-12-15 2024-01-16 山东派蒙机电技术有限公司 电池健康状态确认方法、装置、设备及存储介质
CN117406125B (zh) * 2023-12-15 2024-02-23 山东派蒙机电技术有限公司 电池健康状态确认方法、装置、设备及存储介质

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