云端数据驱动的锂电池故障分级预警研究

doi:10.19562/j.chinasae.qcgc.2023.09.016

Abstract

Abstract:

At present， there is no effective method for unsupervised fault warning for vehicle cloud data with unspecified fault types. Therefore， this paper proposes a multi-level fault warning method for lithium-ion batteries driven by cloud data. Firstly， the features suitable for the characteristics of cloud data are selected through mechanism analysis， and six types of differential entropy feature sets are constructed for multiple mixed clustering to achieve the score evaluation of battery health. Then， temperature information is introduced in to distinguish heat-related faults and the warning level division criteria are constructed to determine the battery fault status. Finally， five field failure cases are used for validation. The results show that the method can accurately identify faults and distinguish fault types， and is ahead of its time and highly adaptable.

Key words: lithium-ion battery, unsupervised fault warning, differential entropy feature sets, warning level division

Wenchao Guo,Lin Yang,Zhongwei Deng,Jilin Li,Zhixian Fan. Research on Multi-level Fault Warning Method for Lithium-ion Batteries Driven by Cloud Data[J].Automotive Engineering, 2023, 45(9): 1677-1687.

Figures/Tables 13

References 25

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特征范围	自身差值特征	平均偏差特征	熵值特征
电压信息/V	放电压差 Vd_dis	放电电压均差 *aveVd*_dis	放电压熵 *VEn*_dis
电压信息/V	充电压差 Vd_cha	充电电压均差 *aveVd*_cha	充电压熵 *VEn*_cha
温度信息/℃	放电温差 Td_dis	放电温度均差 *aveTd*_dis	放电温熵 *TEn*_dis
温度信息/℃	充电温差 Td_cha	充电温度均差 *aveTd*_cha	充电温熵 *TEn*_cha

现场故障案例	电池类型	单体数量	采样间隔/s	采样精度	保帧率/%	使用场景
故障对象No.1 （热失控故障）	三元锂离子电池	92	10	电压： ±10 mV 电流： ±0.1 A 温度： ±1 ℃	20.7	乘用车
故障对象No.2 （潜在故障）	三元锂离子电池	144	10	电压： ±10 mV 电流： ±0.1 A 温度： ±1 ℃	8.7	客车
故障对象No.3 （潜在故障）	三元锂离子电池	89	30	电压： ±10 mV 电流： ±0.1 A 温度： ±1 ℃	6.5	乘用车
故障对象No.4 （潜在故障）	磷酸铁锂电池	192	10	电压： ±10 mV 电流： ±0.1 A 温度： ±1 ℃	21.6	换电重卡
故障对象No.5 （潜在故障）	磷酸铁锂电池	228	10	电压： ±20 mV 电流： ±0.05 A 温度： ±1 ℃	35.0	储能电站

诊断结果	异常单体或模组	是否有热失控风险	维护建议
对象No.1	单体#7 单体#67	有	紧急更换单体#67；非紧急更换单体#7
对象No.2	10号模组	无	紧急更换10号模组
对象No.3	单体#83	无	紧急更换单体#83
对象No.4	单体#33	无	非紧急更换单体#33
对象No.5	2号模组	无	紧急更换2号模组

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Research on Multi-level Fault Warning Method for Lithium-ion Batteries Driven by Cloud Data

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