Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming

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@Article{Li:2024:egyr,

• author = "Xuebin Li and Zhao Jin and Shengqun Li and Daiwei Yu and Jun Zhang and Wenjin Zhang",
• title = "Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming",
• journal = "Energy Reports",
• year = "2024",
• volume = "12",
• pages = "368--380",
• keywords = "genetic algorithms, genetic programming, Lithium-ion battery, State of health (SOH), Multiobjective grey wolf optimization (MOGWO), Genetic programming (GP), Decision making",
• ISSN = "2352-4847",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352484724003937",
• DOI = "doi:10.1016/j.egyr.2024.06.038",
• abstract = "Predicting the State of Health (SOH) of the Lithium-ion battery with higher accuracy and reduced cost is a challenging and crucial task for ensuring its reliability and safety. To achieve this, a two-stage prediction framework is proposed to find a concise expression of SOH using the health features of the battery through metaheuristic algorithms and genetic programming (GP).In Stage-I, three conflicting objectives are considered concurrently: the root-mean-square error (RMSE) of SOH prediction, the health features selected, and the expressional complexity of the battery capacity. The wrapper structure is used for SOH prediction, where a binary multi-objective grey wolf optimisation (Binary MOGWO) algorithm is employed to select features and generate the Pareto set. Genetic programming is used to calculate the SOH value using symbolic regression models. In Stage-II, the final compromise solution is filtered from the Pareto set through decision-making approaches. The relationships between selected features and the capacity of the battery are investigated. The NASA Prognostics Center of Excellence battery dataset is chosen to verify the effectiveness of the proposed framework. The simulation results show that the features found through the framework can provide SOH predictions in the form of symbolic equations with higher accuracy, lower cost, and reduced complexity",

}

Genetic Programming entries for Xuebin Li Zhao Jin Shengqun Li Daiwei Yu Jun Zhang Wenjin Zhang

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