混合动力汽车电池内部状态预测的贝叶斯极限学习机方法

摘要/Abstract

摘要： 针对混合动力汽车（HEV）电池内部状态预测问题，引入贝叶斯极限学习机（BELM）方法。对BELM的基本原理进行了详细介绍，在高级车辆仿真软件ADVISOR中采集HEV电池的各项性能参数，包括电压、电流、温度和内阻等。基于此，将BELM应用于电池的荷电状态（SOC）和健康状态（SOH）的预测，同时考虑电池老化对内部状态预测效果的影响。BELM预测结果表明：所设计的预测模型具有较高的精度，能够实时准确地预测出电池的SOC和SOH值。

关键词: 贝叶斯极限学习机, 混合动力汽车, 荷电状态, 健康状态

Abstract: BELM was proposed based on approach to predict the battery's internal states of HEVs. The basic principles of BELM were described in detail, and the performance parameters of battery were collected under advanced vehicle simulator(ADVISOR) including voltages, currents, temperatures and so on. Then the BELM was used in the predictions of SOC and SOH, at the same time, the influences of aging battery were taken into account. The results of BELM indicate that the prediction model possesses higher prediction accuracy, and achieves real-time and accurate SOC and SOH predictions with high effectiveness.

Key words: Bayesian extreme learning machine（BELM）, hybrid electric vehicle（HEV）, state of charge（SOC）, state of health（SOH）

中图分类号:

TM912

王琪, 孙玉坤, 倪福银, 陈泰洪, 陈连玉, 罗印升. 混合动力汽车电池内部状态预测的贝叶斯极限学习机方法[J]. 中国机械工程.

Wang Qi, Sun Yukun, Ni Fuyin, Chen Taihong, Chen Lianyu, Luo Yinsheng. Prediction of Internal States of Battery in HEV by BELM[J]. China Mechanical Engineering.

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