Identification of the Li+ initial inserted rate of electrode materials in Li-ion batteries: Based on Multi-Objective Genetic Algorithm

In this article, Li⁺ initial inserted rate and stoichiometric window of electrode materials in Li-ion batteries are identified using Multi-Objective Genetic Algorithm (MOGA). The article is motivated by the problem of fitting both the OCP model and incremental capacity analysis (ICA) curve, which comes from OCP curve and has higher parameter sensitivity, of batteries to those experimental data. A dynamic weight coefficient is also proposed to deal with the multi-objective problem by transforming the MOGA to GA. LiCoO₂ and LiFePO₄ systems are investigate.