| 基于剩余充电电量的锂离子电池模组内短路在线定量诊断算法 |
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| 引用本文： | 来鑫,李彬,孟正,李相俊,靳文涛,汪湘晋,马瑜涵,郑岳久. 基于剩余充电电量的锂离子电池模组内短路在线定量诊断算法[J]. 工程科学学报, 2023, 45(1): 158-168. DOI: 10.13374/j.issn2095-9389.2021.08.02.002 |
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| 作者姓名： | 来鑫 李彬 孟正 李相俊 靳文涛 汪湘晋 马瑜涵 郑岳久 |
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| 作者单位： | 1.上海理工大学机械工程学院，上海 200093 |
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| 基金项目： | 国家电网公司科技项目（3A-20-304-008） |
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| 摘 要： | 通过对锂离子电池内短路的在线诊断可以有效预防热失控的发生。本文利用锂离子电池模组的充电曲线提出一种基于剩余充电电量的内短路在线定量诊断算法，并对该算法在不同的电压采集精度与采样周期、温度变化、老化程度等条件下进行仿真与实验验证。结果表明所提出的算法在一定条件下能准确定量地诊断出内短路电阻:(1) 对于10 Ω级别的严重内短路，即使在10 mV的采集精度、10 s的采样周期、变温度条件下也能得到很高的诊断精度。对于100 Ω级别的早期内短路，所诊断的内短路阻值比实际值偏小，诊断时间变长。为了提高早期内短路诊断的精度与时效性，电压采集精度与采样频率应该分别在1 mV 与 1 Hz 以上；(2) 电池老化会降低内短路的诊断精度，但是对于10 Ω级别的内短路影响很小。极端温度变化同样会影响内短路定量诊断精度，极端高温下的诊断误差比极端低温下的诊断误差要大，在极限低温(–20 ℃)下的内短路内阻的诊断误差在6%以内。研究结论为提高锂离子内短路的定量诊断精度具有重要意义。
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| 关 键 词： | 锂离子电池 内短路 剩余充电电量 故障诊断 电池安全 |
| 收稿时间： | 2021-08-02 |
Online quantitative diagnosis algorithm for the internal short circuit of a lithium-ion battery module based on the remaining charge capacity |
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| Affiliation: | 1.College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China2.China Electric Power Research Institute, Beijing 100192, China3.State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, China |
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| Abstract: | Lithium-ion batteries are widely used in energy storage and new energy electric vehicles due to their superior performance, but the internal short circuit problem of lithium-ion batteries is a safety hazard during usage for energy storage and vehicle battery packs. If it cannot be detected in time, the deepening of the internal short circuit will be accompanied by an increase in heat, which will cause thermal runaway and lead to safety accidents. Diagnosing whether the battery pack has an internal short circuit and quantitatively estimating the short circuit resistance of the battery cell that has the internal short circuit can effectively prevent the occurrence of thermal runaway. This study proposes a quantitative diagnosis algorithm of Internal short circuit (ISC) based on the remaining charge capacity based on the charging curve of the lithium-ion battery module. The simulation and experimental verification of the algorithm are carried out under the conditions of different voltage acquisition accuracies, sampling periods, temperatures, and aging degrees. The results show that the proposed algorithm can accurately and quantitatively diagnose the ISC under certain conditions: (1) For serious ISC of 10 Ω level, high diagnosis accuracy can be obtained even under the conditions of 10 mV acquisition accuracy, 10 s sampling period, and variable temperature. For early ISC of 100 Ω level, the ISC resistance is smaller than the actual value and the diagnosis time is longer. To improve the accuracy and timeliness of early ISC diagnosis, the voltage acquisition accuracy, and sampling frequency should be higher than 1 mV and 1 Hz, respectively. (2) Battery aging will reduce the accuracy of ISC diagnosis, but it has little effect on the 10 Ω level ISC, and the diagnostic error of the ISC resistance is less than 6% even at an extremely low temperature (?20 ℃). The conclusions are of great significance to improve the accuracy of quantitative diagnosis of ISC for lithium-ion batteries. |
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