Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey

• DOI: 10.1016/j.est.2021.102572
• Publication Date: 2021-05-12T22:02:05Z
• Authors: Bo Yang; Junting Wang; Pulin Cao; Tianjiao Zhu; Hongchun Shu; Jiao Chen; Jin Zhang; Jiawei Zhu
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 7. Clean energy

Abstract State-of-charge (SoC) estimation is of great importance for electric vehicles (EVs) optimum operation, while highly dynamic operation environment makes this task extremely thorny. By far, considerable researches have been done in modeling and approaches to accurately estimate SoC for lithium-ion batteries (LiBs) used in EVs. Nevertheless, existing reviews are either ambiguous in classifications or incomplete in methodologies, especially lack of detailed evaluation. To solve these issues, this work undertakes a comprehensive survey on various models and methods utilized in LiBs SoC estimation, which aims to more specifically guide automobile engineers and researchers utilize these techniques in an effective manner and facilitate future research works. Enumeration method is used for review screening to select the most representative papers in an application-oriented manner. Firstly, five modeling techniques are outlined, which are significant in improving estimation precision. Then, six categories along with twenty-one evaluation criteria are summarized for quantitative performance verification. Moreover, various SoC estimation methods in six categories are thoroughly discussed, among which ninety-three methods are carefully elaborated in table for a systematic evaluation and fair comparison. Meanwhile, this work gives insight to SoC estimation methods with emphasis on their major benefits/limitations and application conditions. Lastly, important recommendations of future researches are disclosed in the hope of providing some inspirations to propel EVs applications. All in all, this work provides a state-of-the-art one-stop handbook of LiBs SoC estimation used in EVs.