基于混合高斯-隐马尔可夫模型的动力电池实时热失控检测  

A Real-time Thermal Runaway Detection Method of Power Battery Based on Guassian Mixed Model and Hidden Markov Model

作　　者：廉玉波 凌和平 王钧斌 潘华 谢朝 Lian Yubo;Ling Heping;Wang Junbin;Pan Hua;Xie Zhao(BYD Auto Industry Company Limited,Shenzhen 518118)

机构地区：[1]比亚迪汽车工业有限公司,深圳518118

出　　处：《汽车工程》2023年第1期139-146,共8页Automotive Engineering

摘　　要：随着电动汽车在我国的发展,动力电池的安全性能成为评价电动汽车综合产品力的重要指标,其中动力电池热失控的检测对乘车人员的安全至关重要。针对传统热失控检测方法在实际应用中难以准确做出判断的问题,从电池传感器直接观测的电压、电流、时间等参数中提取状态特征向量,使用混合高斯模型对特征进行最优化筛选。分别对动力电池不同的安全状态评估其混合概率分布,通过BW方法建立隐马尔可夫模型,利用维特比算法对当前观测序列计算相似概率来判断当前电池的健康状况。实验结果表明,隐马尔可夫模型对动力电池热失控的识别较常见时序检测方法更为准确,可以实现在无需电化学仪器检测的前提下达到初步热失控风险检测的目的,提升安全检测效率,降低检测成本。With the development of the electric vehicle industry in China, the safety performance of the EV batteries has became one of the most important guidelines scaling the overall product value, whereas the detection of the battery thermal runaway is the most concerned safety issue of passengers. To solve the problem of the low accuracy of the conventional thermal runaway detection method, this paper extracts the state feature vectors from the voltage, current, time and other parameters directly observed by the battery sensors and optimizes and screen the features using the Gaussian mixed model. The mixed probability distribution of different safety states of the power battery is evaluated and the hidden Markov method is established by the BW algorithm. The current battery health status is judged by calculating the similar probability of the current observation sequence using the Viterbi algorithm. The experiment results show that the hidden Markov model can more accurately predict the thermal runaway status in real-time compared with the traditional time series detection method. It can realize preliminary thermal runaway risk detection without additional electrochemical testing experiment on the batteries, which improves the safety detection efficiency and reduces the detection cost.

关 键 词：电池热失控 实时预警 隐马尔科夫模型 混合高斯模型 机器学习 

分 类 号：U469.72[机械工程—车辆工程]