A comprehensive overview and comparison of parameter benchmark methods for lithium-ion battery application

• DOI: 10.1016/j.est.2023.108197
• Publication Date: 2023-07-04T12:16:03Z
• Authors: Jichang Peng; Jinhao Meng; Ji Wu; Zhongwei Deng; Mingqiang Lin; Shuai Mao; Daniel-Ioan Stroe
• Keywords: Equivalent circuit model; /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy; Battery management system; Parameter benchmark; Lithium-ion battery; 0202 electrical engineering, electronic engineering, information engineering; Battery modeling; 02 engineering and technology; /dk/atira/pure/sustainabledevelopmentgoals/partnerships; name=SDG 17 - Partnerships for the Goals; 7. Clean energy; Battery energy storage system

To deal with the indeterminacy of the renewable energy in power system, electrochemical energy storage system is a promising solution for improving the flexibility of grid. As lithium-ion (Li-ion) battery-based energy storage system (BESS) including electric vehicle (EV) will dominate this area, accurate and cost-efficient battery model becomes a fundamental task for the functionalities of energy management. Equivalent circuit model (ECM) has been treated as a good trade-off between complexity and accuracy for Li-ion batteries modeling. Meanwhile, the resistance and capacitance in ECM cannot be constant values considering the effects of state of charge (SOC), C-rate and temperature. Hence, extensive parameters identification methods have been proposed to adapt the ECM models to various operating conditions. Online parameter identification is often sensitive to the measurement noise from sensors, while offline methods can usually provide more reliable parameters due to the high-precision laboratory facilities and well-predefined procedures. In this thread, offline parameter identification can both initialize the battery model and act as a benchmark for online application. This work reviews and analyzes the parameter identification for Li-ion battery models in both frequency and time domains. Three typical offline identification methods are introduced as the benchmark method, and further validated on hybrid pulse power characterization (HPPC) test and different driving cycles. By analyzing the variations of the parameters and the modeling accuracy, the recommendations of those methods for real applications are given as a conclusion. The discussion and results in this research can benefit the BESS energy management and system design, and further help the popularization of the Li-ion battery in EV and smart grid.