An Information Appraisal Procedure: Endows Reliable Online Parameter Identification to Lithium-Ion Battery Model

作者：Xinghao Du, Jinhao Meng, Yingmin Zhang, Xinrong Huang, Shunliang Wang, Ping Liu, Tianqi Liu · 发表于：IEEE Transactions on Industrial Electronics · 年份：2021 · DOI：10.1109/tie.2021.3091920 · 被引用次数：86 · 研究领域：Advanced Battery Technologies Research、Advancements in Battery Materials、Advanced Battery Materials and Technologies

Online parameter identification is vital for boosting the accuracy of the battery equivalent circuit model (ECM) under dynamic profiles. However, traditional recursive least squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This article thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer–Rao lower bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a multiple adaptive forgetting factors RLS method.