Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model

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@Article{Yao:2020:ACC,

• author = "Hang Yao and Xiang Jia and Qian Zhao and Zhi-Jun Cheng and Bo Guo",
• title = "Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model",
• journal = "IEEE Access",
• year = "2020",
• volume = "8",
• pages = "95333--95344",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ACCESS.2020.2995899",
• ISSN = "2169-3536",
• abstract = "State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate.",
• notes = "Also known as \cite{9097168}",

}

Genetic Programming entries for Hang Yao Xiang Jia Qian Zhao Zhi-Jun Cheng Bo Guo

Citations