Abstract：Aiming at the problems of low prediction accuracy and poor online adaptability for stateofcharge(SOC)of li-ion battery, an improved incremental relevance vector machine(RVM) model is proposed to predict the SOC of li-ion battery online. The measured voltage, discharge current and surface temperature are selected as the model input, and the SOC is used as the model output, both of them are constructed the training set of the model.A fast-sequence sparse Bayesian learning algorithm is chosen to train RVM,and theincremental learning relevance vector machine model was established by connecting RVM algorithm with incremental learning method to research the prediction for SOC of li-ion battery online. The study found that this model can guarantee a higher prediction accuracy by adjusting the kernel parameters automatically. The result of the experiment indicates that the kernel parameters can control the prediction accuracy and calculation efficiency of the algorithm,and the IRVM algorithmhas the characteristics of high prediction accuracy, fast calculation speed and strong universality, in terms of prodiction and application of the SOC li-ion battery, the algorithm can provide a reference for it.
范兴明, 王超, 张鑫, 高琳琳, 刘华东. 基于增量学习相关向量机的锂离子电池SOC预测方法[J]. 电工技术学报, 2019, 34(13): 2700-2708.
Fan Xingming, Wang Chao, Zhang Xin, GaoLinlin, Liu Huadong. A Prediction Method of Li-ion Batteries SOC Based on Incremental Learning Relevance Vector Machine. Transactions of China Electrotechnical Society, 2019, 34(13): 2700-2708.
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2019, Vol. 34 
