Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation - Xingbo Zhang; Kui Chen; Zhou Long; Yang Luo; Yang Li; Jiamin Zhu; Kai Liu; Guoqiang Gao; Guangning Wu (IET Energy Systems Integration)
Abstract In order to guarantee the durability and security of electric vehicles (EV), the ageing estimation of lithium‐ion batteries (LIBs) is of great practical significance. Lithium inventory is an important indicator for assessing the LIB ageing process. Incremental capacity (IC), particle swarm optimisation (PSO) and support vector machine (SVM) are proposed to estimate the LIBs lithium inventory. Firstly, the IC curve that reflect the electrochemical reaction is analysed, and the middle peak of IC curve that characterises the material phase transition point is selected to represent the LIB lithium inventory. IC curve is smoothed by the Savitzky–Golay method to eliminate noise. Three features of the charging voltage curve are selected as the LIB health feature, and the correlation between three features and the lithium inventory is analysed by using the grey relation analysis method. Then, the mapping relationship between the lithium inventory and three health features is established based on SVM. PSO is used to optimise SVM kernel and penalty parameters to improve the precision of LIBs lithium inventory estimation. Finally, the proposed method is verified by three ageing experiments of LIBs. The results show that the proposed method can precisely estimate the lithium inventory of different LIBs.