WO2024036737A1 - 一种动力电池老化状态评估与退役筛选方法及系统 - Google Patents

一种动力电池老化状态评估与退役筛选方法及系统 Download PDF

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WO2024036737A1
WO2024036737A1 PCT/CN2022/125720 CN2022125720W WO2024036737A1 WO 2024036737 A1 WO2024036737 A1 WO 2024036737A1 CN 2022125720 W CN2022125720 W CN 2022125720W WO 2024036737 A1 WO2024036737 A1 WO 2024036737A1
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battery
screening
batteries
voltage
power battery
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French (fr)
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段彬
黄鹏
张承慧
康永哲
商云龙
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Shandong University
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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]
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3828—Arrangements for monitoring battery or accumulator variables, e.g. SoC using current integration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the invention belongs to the technical field of battery evaluation and screening, and relates to a power battery aging state evaluation and decommissioning screening method and system.
  • Power batteries are the "heart” of electric vehicles and new energy storage systems.
  • lithium-ion batteries are widely used due to their advantages such as high energy density, long cycle life and high reliability.
  • Battery capacity is the most direct parameter that reflects the aging state of the battery.
  • Battery capacity is less than 80% of the rated capacity, its performance will not be able to meet the application needs of electric vehicles, but it still has high remaining capacity and economic value, so it is unreasonable to directly discard the retired batteries of electric vehicles, and its echelon Utilization or secondary utilization has become an inevitable trend and attracted widespread attention.
  • Battery cascade utilization can be applied to different scenarios according to actual needs, such as new energy storage systems, low-speed electric vehicles, electric bicycles, etc.
  • the current screening methods for power batteries are mainly divided into two categories: the first category is direct measurement methods. First, observe the appearance characteristics of the battery to simply judge the battery integrity; then obtain the voltage data through charging and discharging tests, and then classify and screen the voltage values. This method has good accuracy, but the entire testing process is complex, time-consuming and cumbersome, and has poor economic efficiency, making it difficult to apply on a large scale.
  • Another type of screening method utilizes the correlation characteristics of capacity and internal resistance for classification.
  • the commonly used methods have poor adaptability and cannot meet the requirements of many types of batteries with widely different characteristics, including LiNCM, LiFePO 4 , etc.
  • the Chinese invention patent application proposes a screening method based on five battery parameters: DC internal resistance RDC, Coulombic efficiency CE, capacity retention rate SOH, temperature rise ⁇ T and voltage increment ⁇ V as indicators.
  • DC internal resistance RDC DC internal resistance
  • Coulombic efficiency CE capacity retention rate SOH
  • temperature rise ⁇ T temperature rise ⁇ T
  • voltage increment ⁇ V voltage increment
  • the peak value of the battery's incremental capacity curve has an obvious mapping relationship with battery aging, and has received much research in battery capacity estimation. Usually multiple battery peaks are used as capacity indicators, and the estimation effect is very good. However, multiple peaks will cause the problem of too long battery charging time. The capacity estimation effect at one peak is often very unsatisfactory.
  • the present invention proposes a power battery aging state assessment and decommissioning screening method and system.
  • the present invention can effectively reduce the detection time, reduce the test cost of screening, and improve the accuracy of assessment and screening.
  • the present invention adopts the following technical solutions:
  • a power battery aging state assessment and retirement screening method for screening retired power batteries including the following steps:
  • the batteries screened at the second level are screened at the third level based on the DC internal resistance of the battery.
  • the test data includes voltage and current data during the charging process of the power battery.
  • the set time is greater than or equal to the time required for the power battery to be charged to exhibit the first index and the second index.
  • the specific process of second-level screening includes: deriving the incremental capacity curve by derivation of the battery's capacity-voltage curve, extracting the first peak value of the filtered incremental capacity curve as the first index, and using The first indicator determines the battery category;
  • the abscissa value of the first indicator is greater than the preset value, it is a ternary lithium battery; otherwise, it is a lithium iron phosphate battery.
  • the specific process of using the second index to determine the consistency of each category of batteries includes using at least one trained random forest model to determine the consistency of each category of batteries based on the second index.
  • the training process of the random forest model includes:
  • multiple features are randomly extracted from the feature set without replacement as the basis for splitting each node on the decision tree.
  • a complete decision tree is generated from top to bottom, and it is repeated until Multiple decision trees are obtained, and each decision tree is combined to form a random forest model.
  • the process of obtaining the DC internal resistance of the battery includes: for lithium iron phosphate batteries, after completing the charging task, discharge to the first set voltage and then let it stand for a period of time. voltage value, calculate the DC internal resistance of the battery; for ternary batteries, after obtaining the capacity characteristics, continue charging to the second set voltage and then let it sit for a period of time. According to the second set voltage and the voltage value after resting, calculate Get the DC internal resistance of the battery.
  • a power battery aging status assessment and decommissioning screening system including:
  • the first screening module is configured to perform first-level screening based on the appearance and voltage data of the power battery
  • the second screening module is configured to obtain the charging test data of the power battery within the set time after the first level screening, perform derivation and secondary derivation based on the capacity-voltage curve of the battery, and extract the derivation curves respectively. Set the first peak index and the second index to determine the battery category and consistency respectively to achieve the second level of screening;
  • the third screening module is configured to perform third-level screening on the batteries after the second-level screening based on the DC internal resistance of the battery.
  • the present invention constructs the first index and the second index, and only needs to charge the battery until it exhibits the first index and the second index. It can greatly shorten the test time, reduce the data required for the test, and improve the screening speed of retired batteries.
  • the invention obtains the voltage and current data of the power battery in a short time and quickly processes it to complete the battery aging performance evaluation. It is fast, highly accurate and adaptable, and can be widely used in fields such as retired battery screening.
  • the present invention can complete multiple levels of retired battery screening using basic appearance data, capacity characteristic indicators and DC internal resistance respectively.
  • a large number of experimental tests show that the evaluation and screening method proposed by the present invention can effectively reduce testing time while ensuring a high accuracy rate.
  • Figure 1 is the process of obtaining new indicators for power battery aging state assessment
  • Figure 2 is the random forest algorithm process for decommissioned batteries
  • Figure 3 is the incremental capacity curve and capacity-voltage second-order derivative curve of multiple retired ternary lithium batteries
  • Figure 4 is the incremental capacity curve and capacity-voltage second-order derivative curve of multiple retired lithium iron phosphate batteries
  • Figure 5 is a diagram of the sorting results of retired ternary lithium batteries
  • Figure 6 is a diagram of the sorting results of retired lithium iron phosphate batteries
  • Figure 7 is a diagram of the charge and discharge consistency of the retired ternary lithium battery before and after reorganization
  • Figure 8 is a diagram of the charge and discharge consistency effects of retired lithium iron phosphate batteries before and after reorganization
  • Figure 9 is a graph of the screening accuracy of retired ternary batteries at different dv intervals
  • Figure 10 is a graph of the screening accuracy of retired lithium iron phosphate batteries at different dv intervals
  • Figure 11 is a graph of the screening accuracy of retired lithium iron phosphate batteries at different sampling frequencies
  • Figure 12 is a graph of the screening accuracy of retired ternary batteries at different sampling frequencies.
  • a power battery aging state assessment method and a retirement screening method are provided.
  • the acquisition process of new indicators of aging state and the random forest algorithm process are shown in Figures 1 and 2 respectively, specifically including the following processes:
  • the appearance of the battery can be collected by a camera or other shooting equipment, and automatically separated through an image processing model.
  • the image processing model can use an existing intelligent algorithm model to identify whether the battery casing is damaged and exclude damaged batteries.
  • manual selection can also be performed.
  • the sampling frequency of the data is 1 Hz.
  • the special charging in this section means that the battery only needs to be charged until the power battery can be charged to show the first indicator and the second indicator.
  • the incremental capacity curve is obtained by deriving the capacity-voltage curve of the battery. After Gaussian filtering, the first peak of the incremental capacity curve is extracted as one of the aging indicators (i.e., the first index); the incremental capacity curve is calculated A new curve is derived, and after Gaussian filtering, the first peak of the new curve is extracted as a new indicator (i.e., the second indicator) for evaluating the aging state.
  • Q is the capacity obtained by the battery ampere-hour integration method
  • t 1 is the charging start time
  • t 2 is the charging end time
  • i(t) is the current value during the charging process.
  • Q(t) and V(t) represent the electricity quantity and terminal voltage at time t respectively.
  • Q(j) and V(j) are the discrete forms of Q(t) and V(t) respectively.
  • n is the sampling interval.
  • Q’(t) represents the derivative value of the electric charge function at time t
  • Q’(j) is the discrete form of Q’(t)
  • n is the sampling interval.
  • the training process of retirement screening based on random forest algorithm includes:
  • the training sample sets of these two retired batteries can be used to train two random forest models respectively, and the training process of the two models is consistent, including:
  • the training set is T and consists of N samples.
  • the feature set be F including the first index and the second index, and the category set be C.
  • the retired battery capacity-voltage curve is processed twice to extract features that characterize the battery capacity; the extracted features include: the coordinates of the first peak point of the incremental capacity curve and the capacity-voltage curve. The coordinates of the first peak point of the second derivative curve. The lower the capacity of the retired battery, the position of the peak point moves toward the lower right, as shown in Figures 3 and 4.
  • P 1 and P 2 are the peak points of the two curves of retired ternary batteries respectively;
  • the capacity characteristics of l batteries are extracted based on the incremental capacity curve and the capacity-voltage second-order derivative curve of retired ternary batteries to form a training set ⁇ (P 1,1 ,P 1,2 ,C 1 ), ( P 2,1 ,P 2,2 ,C 2 ),...,(P l,1 ,P l,2 ,C l ) ⁇ ;
  • the curve extracts the capacity characteristics of k batteries (P i,3 ,P i,4 ,C 1 ) to form a training set ⁇ (P 1,3 ,P 1,4 ,C 2 ), (P 2,3 ,P 2, 4 ,SOH),...,(P k,3 ,P k,4 ,C k ) ⁇ .
  • the random forest model 1 is trained based on the retired lithium iron phosphate battery data set, and the random forest model 2 is trained based on the retired ternary battery data set.
  • the random forest model consists of multiple independent decision trees, and bootstrap sampling is used to obtain n training sample subsets for training n decision trees, and finally a random forest is generated.
  • the output function of the retired battery screening model based on random forest is specifically:
  • C represents the actual category set
  • D j (T) is the estimated category of the j-th decision tree
  • F (.) is a 0-1 judgment function
  • argmax (.) outputs the category number with the most votes.
  • 222 retired lithium iron phosphate batteries and 103 retired ternary batteries were selected to form a sample set for training and testing.
  • the specific numbers of training set samples and test set samples are shown in Tables 2 and 3.
  • Table 2 Number of training samples and test samples of retired ternary batteries.
  • each type of retired battery is divided into three categories according to the size and density of capacity. A part of retired batteries is selected for each category for training. By comparing the actual classification results and the predicted classification results, the proposed classification results are compared. Testing of methods. The screening results of different types of retired batteries are shown in Figures 5 and 6. The classification accuracy of the retired ternary battery test set is 97.14%, and the classification accuracy of the lithium iron phosphate battery test set is 97.26%. The overall screening accuracy of retired batteries is as high as 97.22%, and only three out of 108 retired batteries were misclassified. In addition, since it is a small segment of charging test, the test time is greatly reduced.
  • test result evaluation function is:
  • ⁇ represents the number of retired batteries in the test set
  • C′ ⁇ represents the predicted classification number of the ⁇ -th retired battery
  • C ⁇ represents the actual classification number of the ⁇ -th retired battery.
  • the third level of screening is performed by extracting DC internal resistance.
  • Both batteries can use the DC internal resistance at the above voltage point as the internal resistance of the entire battery.
  • 0.1V the voltage interval for pulse discharge: every time the discharge decreases by 0.1V, let it sit for a period of time and measure the DC internal resistance of the battery. It is found that the DC internal resistance of the lithium iron phosphate battery does not change significantly in the low voltage range. It can be reduced to 3.3V.
  • the DC internal resistance is used as the internal resistance of the battery to reduce the test time; while for the ternary battery, the internal resistance in the high voltage area does not change significantly during the charging process, so using the DC internal resistance at 4.0V as the battery internal resistance can reduce the test time.
  • Both batteries can use the DC internal resistance at the above voltage point as the internal resistance of the entire battery.
  • 0.1V the voltage interval for pulse discharge: every time the discharge decreases by 0.1V, let it sit for a period of time and measure the DC internal resistance of the battery. It is found that the DC internal resistance of the lithium iron phosphate battery does not change significantly in the low voltage range. It can be reduced to 3.3V.
  • the DC internal resistance is used as the internal resistance of the battery to reduce the test time; while for the ternary battery, the internal resistance in the high voltage area does not change significantly during the charging process, so using the DC internal resistance at 4.0V as the battery internal resistance can reduce the test time.
  • U(t 1 ) represents the voltage value at time t 1
  • U(t 1 +1) represents the voltage value at the 1st s after time t 1
  • I(t 1 ) is the current value at time t 1 .
  • retired ternary batteries and lithium iron phosphate batteries with similar performance are recombined respectively, and each module is formed by four battery cells connected in series.
  • the voltage inconsistency between battery cells is verified through a simple full charge and discharge test.
  • the results are shown in Figures 7 and 8. It can be seen from the figure that the battery consistency after reorganization has been significantly improved compared to the previous initial module.
  • a post-reorganization verification process is also included, in which retired ternary batteries and lithium iron phosphate batteries with similar performance are connected in series and recombined, and then normal charge and discharge tests are performed to verify the consistency between the batteries after screening and reorganization.
  • a power battery aging state assessment and decommissioning screening system including:
  • a power battery aging status assessment and decommissioning screening system including:
  • the first screening module is configured to perform first-level screening based on the appearance and voltage data of the power battery
  • the second screening module is configured to obtain the charging test data of the power battery within the set time after the first level screening, perform derivation and secondary derivation based on the capacity-voltage curve of the battery, and extract the derivation of the curve respectively. Set the first peak index and the second index to determine the battery category and consistency respectively to achieve the second level of screening;
  • the third screening module is configured to perform third-level screening on the batteries after the second-level screening based on the DC internal resistance of the battery.
  • the retired battery can obtain the incremental capacity curve and the capacity-voltage second-order derivative curve of the battery at different voltage intervals. Although the two curves will change to some extent, the proposed power battery aging state assessment method and its retirement screening technology are still applicable. The screening accuracy of retired battery tests for 100 times is shown in Figures 9 and 10.
  • retired batteries can obtain battery voltage and current data at different sampling frequencies, and the proposed power battery aging state assessment method and its retirement screening technology are still applicable.
  • the screening accuracy of retired battery tests for 100 times is shown in Figures 11 and 12.
  • embodiments of the present invention may be provided as methods, systems, or computer program products.
  • the invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.
  • the invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions
  • the device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device.
  • Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

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Abstract

一种动力电池老化状态评估与退役筛选方法及系统，方法包括：根据动力电池的外观以及电压数据进行第一层次筛选；获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；基于电池的直流内阻对第二层次筛选后的电池进行第三层次筛选。筛选方法可以有效降低检测时间，降低测试成本，提升评估和筛选精度。

Description

一种动力电池老化状态评估与退役筛选方法及系统
本发明要求于2022年8月17提交中国专利局、申请号为202210985717.0、发明名称为“一种动力电池老化状态评估与退役筛选方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本发明中。
技术领域
本发明属于电池评估和筛选技术领域，涉及一种动力电池老化状态评估与退役筛选方法及系统。
背景技术
本部分的陈述仅仅是提供了与本发明相关的背景技术信息，不必然构成在先技术。
动力电池是电动汽车和新能源储能系统的“心脏”，尤其是锂离子电池具有高能量密度、长循环寿命和高可靠性等优点而被广泛采用。但是，随着动力电池的不断老化，其性能将逐渐下降。电池容量是反应电池老化状态最直接参数。当电池容量低于额定容量的80％时，其性能将无法满足电动汽车的应用需求，但仍具有较高的剩余容量和经济价值，所以将电动汽车退役电池直接丢弃是不合理的，其梯次利用或二次利用已成为必然趋势并引起广泛关注。电池梯次利用可根据实际需求应用于不同场景，如新能源储能系统、低速电动汽车、电动自行车等，其中在新能源储能系统中的应用主要用于解决新能源消纳，支撑新能源稳定发展。综上所述，动力电池从生产制造到梯次利用都需要综合考虑应用场景、经济性和环境等多个因素来完成状态检测和评估。
事实上，动力电池的内部性能差异主要表现在内阻和最大可用容量两个方面。据此，当前对动力电池的筛选方法主要分为两类：第一类是直接测量方法。首先观察电池的外观特征，可以简单判断电池完整性；然后通过充电和放电测试获得电压数据，通过电压值分类筛选。该方法准确性好，但是整个测试过程工序复杂，耗时且繁琐，而且经济性差，难以大规模应用。另一类筛选方法利用容量和内阻的相关特征进行分类。但常用的方法适应性差，不能满足包括LiNCM、LiFePO 4等电池种类多、特性差异大的要求。
中国发明专利申请(申请号202010273168.5)提出一种以电池的直流内阻RDC、库伦效率CE、容量保持率SOH、温升ΔT和电压增量ΔV五个电池参数作为指标进行筛选。存在多参数特征获取周期长、缺乏实际验证等问题。中国发明专利申请(202010610610.9)通过测量的电池容量、内阻、温度三个参数进行多层次筛选，但整个筛选过程测试时间过长，尤其是容量指标的获取需要完整的充电过程，并且温度参数的获取会增加筛选过程的成本问题。
电池的增量容量曲线的峰值与电池老化有明显的映射关系，在电池容量估计中获得较多的研究。通常以多个电池峰值作为容量指标，估计效果很好，但是多个峰值就会带来电池充电时间过长的问题，一个峰值下的容量估计效果往往很不理想。
发明内容
本发明为了解决上述问题，提出了一种动力电池老化状态评估与退役筛选方法及系统，本发明可以有效降低检测时间，降低筛选的测试成本，提升评估和筛选精度。
根据一些实施例，本发明采用如下技术方案：
一种动力电池老化状态评估与退役筛选方法，用于筛选退役动力电池，包括以下步骤：
根据动力电池的外观以及电压数据进行第一层次筛选；
获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；
基于电池的直流内阻对第二层次筛选后的电池进行第三层次筛选。
作为可选择的实施方式，所述测试数据包括动力电池充电过程中的电压、电流数据。
作为可选择的实施方式，所述设定时间大于等于动力电池能够充电至表现出第一指标和第二指标所需的时间。
作为可选择的实施方式，第二层次筛选的具体过程包括：以电池的容量-电压曲线进行求导得到增量容量曲线，提取滤波后增量容量曲线的第一个峰值作为第一指标，利用第一指标判断电池类别；
对增量容量曲线进行求导得到新曲线，提取滤波后新曲线的第一个峰值作为第二指标，利用第二指标确定各类别电池的一致性，筛选出一致性小于设定值的各同类别电池。
作为进一步限定的实施方式，若第一指标所在横坐标值大于预设值，则为三元锂电池，否则为磷酸铁锂电池。
作为进一步限定的实施方式，利用第二指标确定各类别电池的一致性的具体过程包括，利用至少一训练后的随机森林模型，根据第二指标确定每个类别电池的一致性。
作为进一步限定的实施方式，所述随机森林模型的训练过程包括：
通过对部分不同类型的退役电池充电获取电压、电流数据，并利用安时积分法获得退役电池真实的容量值；
利用一部分充电片段的容量-电压数据提取第一指标和第二指标，通过第一指标坐标位置判断电池类型，分别组成不同电池训练样本集，有放回地抽取多个样本，作为一个训练子集；
对于训练子集，从特征集中无放回地随机抽取多个特征，作为决策树上的每个节点分裂的依据，从根结点开始，自上而下生成一个完整的决策树，不断重复直到得到多个决策树，将各决策树组合起来，形成随机森林模型。
作为可以选择的方案，所述电池直流内阻的获取过程包括：对于磷酸铁锂电池，完成充电任务之后放电到第一设定电压后静置一段时间，根据第一设定电压和静置后的电压值，计算得到电池的直流内阻；对于三元电池，获取容量特征之后继续充电到第二设定电压后静置一段时间，根据第二设定电压和静置后的电压值，计算得到电池的直流内阻。
一种动力电池老化状态评估与退役筛选系统，包括：
第一筛选模块，被配置为根据动力电池的外观以及电压数据进行第一层次筛选；
第二筛选模块，被配置为获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；
第三筛选模块，被配置为基于电池的直流内阻对第二层次筛选后的电池进行 第三层次筛选。
与现有技术相比，本发明的有益效果为：
本发明构建了第一指标和第二指标，只要充电至电池表现出第一指标和第二指标即可，能够大幅缩小测试时间，减少测试所需数据，提高退役电池的筛选速度。
本发明在短时间内获取动力电池的电压、电流数据并快速处理完成电池老化性能评估，速度快、精度高、适应性强，可推广应用于退役电池筛选等领域。
本发明分别利用外观基础数据、容量特性指标和直流内阻可完成多个层次的退役电池筛选。大量的实验测试表明本发明提出的评估和筛选方法有效地减少测试时间，同时保证很高的准确率。通过将性能接近退役电池重组之后进行充放电测试，发现相比于初始模组，重组之后电池单体之间的电压一致性有明显改善。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。
图1是动力电池老化状态评估新指标的获取过程；
图2是退役电池的随机森林算法流程；
图3是多个退役的三元锂电池增量容量曲线和容量-电压二阶导数曲线图；
图4是多个退役的磷酸铁锂电池增量容量曲线和容量-电压二阶导数曲线图；
图5是退役的三元锂电池的分选结果图；
图6是退役的磷酸铁锂电池的分选结果图；
图7是退役的三元锂电池重组前和重组后充放电一致性效果图；
图8是退役的磷酸铁锂电池重组前和重组后充放电一致性效果图；
图9是退役的三元电池在不同dv间隔下的筛选准确率图；
图10是退役的磷酸铁锂电池在不同dv间隔下的筛选准确率图；
图11是退役的磷酸铁锂电池在不同采样频率下的筛选准确率图；
图12是退役的三元电池在不同采样频率下的筛选准确率图。
具体实施方式：
下面结合附图与实施例对本发明作进一步说明。
应该指出，以下详细说明都是例示性的，旨在对本发明提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
实施例一
提供了动力电池老化状态评估方法及其退役筛选方法，其中老化状态新指标的获取流程以及随机森林算法流程分别如图1、2所示，具体包括如下过程：
(1)通过拆解退役电池模组为退役电池单体，随后观察电池外观、电压以及自放电率，将其中一部分电池挑选出来直接回收。
在一部分实施例中，电池外观可以由摄像头等拍摄设备采集，经过图像处理模型自动分别，图像处理模型选用现有智能算法模型即可，以识别电池外壳是否存在破损，排除存在破损的电池。
当然，在部分实施例中，也可以人工挑选。
另外，还需要挑出电压和自放电率小于设定值的电池。
(2)通过特殊充电获取退役电池电压、电流数据，并进行预处理得到增量容量曲线和容量-电压二阶导数曲线，本实施例中，数据的采样频率为1Hz。
本部分的特殊充电是指只需将电池充电至动力电池能够充电至表现出第一指标和第二指标后即可。
本实施例中，测试工况具体步骤如下：
以1/3C恒流充电到增量容量曲线的第一个峰值点(即第一指标)，充电截止；
根据第一个峰值点的横坐标是否大于设定值(本实施例中为3.6V)，以判断电池的类型；
如果是磷酸铁锂电池则放电到3.3.V，截止放电，记录3.3V下直流内阻值；
如果是三元电池则继续充电到4.0V，截止充电，记录4.0V下直流内阻值；
(3)基于电池的充电数据提取电池容量相关的特征；
通过对电池的容量-电压曲线进行求导得到增量容量曲线，经过高斯滤波之后提取增量容量曲线的第一个峰值作为其中一个老化指标(即第一指标)；对增量容量曲线进行求导得到新曲线，经过高斯滤波之后，提取新曲线的第一个峰值作为评估老化状态的新指标(即第二指标)。
具体的，电池容量的计算公式如下所示：
Figure PCTCN2022125720-appb-000001
式中：Q为电池安时积分法得到的容量，t 1为充电起始时刻，t 2为充电终止时刻，i(t)是充电过程中的电流值。
退役电池增量容量曲线的计算公式如下：
Figure PCTCN2022125720-appb-000002
式中：Q(t)和V(t)分别表示t时刻的电量和端电压。Q(j)和V(j)分别是Q(t)和V(t)的离散形式。此外，n是采样间隔。
评估老化状态新指标的计算公式如下所示：
Figure PCTCN2022125720-appb-000003
其中Q’(t)表示电量函数在t时刻的导数值，Q’(j)是Q’(t)的离散形式，n是采样间隔。
基于随机森林算法的退役筛选的训练过程包括：
通过对部分不同类型的退役电池充电获取电压、电流数据，并利用安时积分法获得退役电池真实的容量值；
仅利用少量充电片段的容量-电压数据提取上述特征，通过增量容量曲线的峰值坐标位置判断电池类型，分别组成磷酸铁锂电池训练样本集和三元锂电池训练样本集；
这两种退役电池的训练样本集可以分别用于训练两个随机森林模型，并且两个模型训练过程一致，包括：
为了描述方便，假设训练集为T，由N个样本组成，设特征集为F包含第一指标和第二指标，类别集合为C。
(a)从容量为N的训练集T中，采用自助抽样法(bootstrap)，即有放回地抽取N个样本，作为一个训练子集T k；
(b)对于训练子集T k，从特征集F中无放回地随机抽取m个特征，其中 m＝log 2M(向上取整)，其中M表示特征集的维度，作为决策树上的每个节点分裂的依据，从根结点开始，自上而下生成一个完整的决策树D k，不需要剪枝；
(c)重复n次步骤(a)和(b)，得到n个训练子集T 1,T 2,…,T n，并生成决策树D 1,D 2,…,D n，将n个决策树组合起来，形成随机森林。
作为一种典型实施例，本实施例中对退役电池容量-电压曲线进行两次处理来提取表征电池容量的特征；提取的特征包括：增量容量曲线的第一个峰值点坐标和容量-电压二阶导数曲线的第一个峰值点坐标。退役电池的容量越低，峰值点的位置向着右下方移动，如图3、4所示。
通过求取特征与容量之间的皮尔逊相关性系数，表明特征与容量的相关性较强，具体结果如表1所示。P 1和P 2分别是退役三元电池的两个曲线的峰值点；P 3和P 4是退役磷酸铁锂电池的两个曲线的峰值点，其中P i_x表示峰值点横坐标，P i_y表示峰值点纵坐标，(i＝1,2,3,4)。
表1提取特征与容量之间的相关性系数
Figure PCTCN2022125720-appb-000004
本实施案例中，基于退役三元电池的增量容量曲线以及容量-电压二阶导数曲线提取l个电池的容量特征构成训练集{(P 1,1,P 1,2,C 1),(P 2,1,P 2,2,C 2),…,(P l,1,P l,2,C l)}；基于退役磷酸铁锂电池的增量容量曲线以及容量-电压二阶导数曲线提取k个电池的容量特征(P i,3,P i,4,C 1)构成训练集{(P 1,3,P 1,4,C 2),(P 2,3,P 2,4,SOH),…,(P k,3,P k,4,C k)}。
基于退役的磷酸铁锂电池数据集训练随机森林模型一，基于退役的三元电池数据集训练随机森林模型二。随机森林模型由多个独立的决策树组成，并且将 bootstrap取样得到n个训练样本子集用于训练n个决策树，最后生成随机森林。
将测试集输入到训练好的基于随机森林的退役电池筛选模型中，让每个决策树对测试集进行决策，然后采用多数投票法对决策结果投票，最终决定退役电池的类别号，如图2所示。本实施例中，基于随机森林的退役电池筛选模型的输出函数具体为：
Figure PCTCN2022125720-appb-000005
其中，C表示实际类别集，D j(T)是第j个决策树的估计类别，F(.)是一个0-1判断函数，argmax(.)输出票数最多的类别号。
本实施例中选取了222节退役的磷酸铁锂电池和103节退役的三元电池组成样本集进行训练和测试，其中具体的训练集样本和测试集样本数目如表2，3所示。
表2退役的三元电池训练样本和测试样本数目.
Figure PCTCN2022125720-appb-000006
表3退役的磷酸铁锂电池训练样本和测试样本数目
Figure PCTCN2022125720-appb-000007
为了展示所提出方法的分类效果，依据容量的大小和密集程度将每种退役电池分成三类，每一类都选取一部分退役电池用于训练，通过对比实际分类结果和预测分类结果实现对所提出方法的检验。不同种类的退役电池筛选结果如图5、6 所示，其中退役的三元电池测试集的分类正确率为97.14％，磷酸铁锂电池测试集分类正确率为97.26％。退役电池整体上的筛选准确率高达97.22％，108个退役电池中仅有三个被错误分类。此外，由于是小片段的充电测试，所以测试测时间大幅度缩减。
测试结果评估函数为：
Figure PCTCN2022125720-appb-000008
其中，λ表示测试集中退役电池的个数，C′ μ表示第μ个退役电池的预测分类号，C μ表示第μ个退役电池的实际分类号。
基于电池容量分类的结果，通过提取直流内阻进行第三层次筛选。
两种电池可以利用上述电压点下的直流内阻作为整个电池的内阻。通过以0.1V作为电压间隔进行脉冲放电：放电每减少0.1V，静置一段时间测量电池的直流内阻，发现磷酸铁锂电池在低电压范围内直流内阻变化不明显，可以将3.3V下直流内阻作为电池内阻，减少测试时间；而对于三元电池在充电过程中高电压区域内阻变化不明显，所以将4.0V下的直流内阻作为电池内阻，可以减少测试时间。
两种电池可以利用上述电压点下的直流内阻作为整个电池的内阻。通过以0.1V作为电压间隔进行脉冲放电：放电每减少0.1V，静置一段时间测量电池的直流内阻，发现磷酸铁锂电池在低电压范围内直流内阻变化不明显，可以将3.3V下直流内阻作为电池内阻，减少测试时间；而对于三元电池在充电过程中高电压区域内阻变化不明显，所以将4.0V下的直流内阻作为电池内阻，可以减少测试时间。
直流内阻的计算公式如下所示：
Figure PCTCN2022125720-appb-000009
U(t 1)表示t 1时刻的电压值，U(t 1+1)表示t 1时刻后静置第1s的电压值，I(t 1)为t 1时刻的电流值。
本实施例中，通过将性能相近的退役三元电池和磷酸铁锂电池分别重组，每个模组由四个电池单体串联形成。通过简单的完全充放电测试来验证电池单体之间的电压不一致性，结果如图7、8所示。图中可以看出，重组之后电池一致性相比之前的初始模组有明显的改善。
在部分实施例中，还包括重组后验证过程，将性能相近的退役三元电池、磷酸铁锂电池分别串联重组后进行正常充放电测试，验证筛选重组之后的电池之间的一致性。
实施例二
在一个或多个实施方式中，公开了动力电池老化状态评估和退役筛选系统，包括：
一种动力电池老化状态评估与退役筛选系统，包括：
第一筛选模块，被配置为根据动力电池的外观以及电压数据进行第一层次筛选；
第二筛选模块，被配置为获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；
第三筛选模块，被配置为基于电池的直流内阻对第二层次筛选后的电池进行第三层次筛选。
需要说明的是，上述各模块的具体实现方式已经在实施例一中进行了说明， 此处不再详述。
实施例三
在一个或多个实施方式中，退役电池可在不同电压间隔下获取电池的增量容量曲线以及容量-电压二阶导数曲线。虽然两个曲线会有一定变化，但所提出的动力电池老化状态评估方法及其退役筛选技术依然适用。退役电池测试100次的筛选准确率如图9、10所示。
实施例四
在一个或多个实施方式中，退役电池可以在不同采样频率下获取电池的电压、电流数据，并且所提出的动力电池老化状态评估方法及其退役筛选技术依然适用。退役电池测试100次的筛选准确率如图11、12所示。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
上述虽然结合附图对本发明的具体实施方式进行了描述，但并非对本发明保护范围的限制，所属领域技术人员应该明白，在本发明的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围以内。

Claims (10)

  1. 一种动力电池老化状态评估与退役筛选方法，其特征是，包括以下步骤：
    根据动力电池的外观以及电压数据进行第一层次筛选；
    获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；
    基于电池的直流内阻对第二层次筛选后的电池进行第三层次筛选。
  2. 如权利要求1所述的一种动力电池老化状态评估与退役筛选方法，其特征是，所述测试数据包括动力电池充电过程中的电压、电流数据。
  3. 如权利要求1所述的一种动力电池老化状态评估与退役筛选方法，其特征是，所述设定时间大于等于动力电池能够充电至表现出第一指标和第二指标所需的时间。
  4. 如权利要求1所述的一种动力电池老化状态评估与退役筛选方法，其特征是，第二层次筛选的具体过程包括：以电池的容量-电压曲线进行求导得到增量容量曲线，提取滤波后增量容量曲线的第一个峰值作为第一指标，利用第一指标判断电池类别；
    对增量容量曲线进行求导得到新曲线，提取滤波后新曲线的第一个峰值作为第二指标，利用第二指标确定各类别电池的一致性，筛选出一致性小于设定值的各同类别电池。
  5. 如权利要求4所述的一种动力电池老化状态评估与退役筛选方法，其特征是，若第一指标所在横坐标值大于预设值，则为三元锂电池，否则为磷酸铁锂电池。
  6. 如权利要求4所述的一种动力电池老化状态评估与退役筛选方法，其特征 是，利用第二指标确定各类别电池的一致性的具体过程包括，利用至少一训练后的随机森林模型，根据第二指标确定每个类别电池的一致性。
  7. 如权利要求6所述的一种动力电池老化状态评估与退役筛选方法，其特征是，所述随机森林模型的训练过程包括：
    通过对部分不同类型的退役电池充电获取电压、电流数据，并利用安时积分法获得退役电池真实的容量值；
    利用一部分充电片段的容量-电压数据提取第一指标和第二指标，通过第一指标坐标位置判断电池类型，分别组成不同电池训练样本集，有放回地抽取多个样本，作为一个训练子集；
    对于训练子集，从特征集中无放回地随机抽取多个特征，作为决策树上的每个节点分裂的依据，从根结点开始，自上而下生成一个完整的决策树，不断重复直到得到多个决策树，将各决策树组合起来，形成随机森林模型。
  8. 如权利要求1所述的一种动力电池老化状态评估与退役筛选方法，其特征是，所述电池直流内阻的获取过程包括：对于磷酸铁锂电池，完成充电任务之后放电到第一设定电压后静置一段时间，根据第一设定电压和静置后的电压值，计算得到电池的直流内阻；对于三元电池，获取容量特征之后继续充电到第二设定电压后静置一段时间，根据第二设定电压和静置后的电压值，计算得到电池的直流内阻。
  9. 如权利要求8所述的一种动力电池老化状态评估与退役筛选方法，其特征是，所述电池直流内阻的获取过程包括：将各类电池设定电压下的直流内阻作为整个电池的内阻。
  10. 一种动力电池老化状态评估与退役筛选系统，其特征是，包括：
    第一筛选模块，被配置为根据动力电池的外观以及电压数据进行第一层次筛选；
    第二筛选模块，被配置为获取第一层次筛选后的动力电池的设定时间内充电的测试数据，基于电池的容量-电压曲线进行求导和二次求导，分别提取求导后曲线的设定峰值第一指标和第二指标，分别用于判断电池类别和一致性，实现第二层次筛选；
    第三筛选模块，被配置为基于电池的直流内阻对第二层次筛选后的电池进行第三层次筛选。
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