Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

作     者：Wentao Ma Yiming Lei Xiaofei Wang Badong Chen Wentao Ma;Yiming Lei;Xiaofei Wang;Badong Chen

作者机构：School of Electrical EngineeringXi'an University of TechnologyXi’an 710048ShaanxiChina School of MicroelectronicsXi'an Jiaotong UniversityXi’an 710049ShaanxiChina Institute of Artificial Intelligence and Robotics(IAIR)Xi'an Jiaotong UniversityXi’an 710049ShaanxiChina 

出 版 物：《Journal of Energy Chemistry》 (能源化学（英文版）)

年 卷 期：2023年第80卷第5期

页      面：768-784,I0016页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 

基　　金：supported by the National Key R.D Program of China(2021YFB2401904) the Joint Fund project of the National Natural Science Foundation of China(U21A20485) the National Natural Science Foundation of China(61976175) the Key Laboratory Project of Shaanxi Provincial Education Department Scientific Research Projects(20JS109)。 

主　　题：SOC estimation Long short term memory model Mixture kernel mean p-power error Heap-based-optimizer Lithium-ion battery Non-Gaussian noisy measurement data 

摘      要：The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.