A Fast Capacity Estimation Approach for Retired Lithium-ion Batteries
作者单位：School of Control Science and Engineering Shandong University
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：12[管理学] 083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：retired batteries capacity estimation support vector machine(SVM) particle swarm optimization(PSO)
摘 要：Capacity is one of the most critical parameters of lithium-ion batteries（LIBs）. Retired batteries generally also have 70%-80% of the nominal capacity, so it can bring considerable benefits through echelon use. However, the existing methods cannot simultaneously meet the capacity estimation accuracy and efficiency required for large-scale retired batteries. So this may lead to costing excessive time and energy. To solve the above problems, a fast and accurate capacity evaluation method based on support vector machine（SVM） is proposed in this paper. And the parameters（penalty coefficient and kernel function width） of the SVM model are optimized by the particle swarm optimization（PSO）. In order to expand the scope of application,capacity evaluation models in three different SOC are established. The inputs of the model are sampling voltages selected from the featured charging curve at three cases, and the output is the battery capacity. Experimental results demonstrate that the maximum error in the first case is less than 3.02%, and the errors in the other two cases are 1.88% and 1.97% respectively.