Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. (October 2019)

• Record Type: Journal Article
• Authors: Shrivastava, Prashant; Soon, Tey Kok; Idris, Mohd Yamani Idna Bin; Mekhilef, Saad
• Is Part Of: Renewable & sustainable energy reviews. Volume 113(2019)
• Publication Date: 2019-10
• Subjects: Lithium-ion battery -- Battery model -- State-of-charge -- Kalman filter -- Electric vehicle -- Battery management system -- Estimation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.06.040
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Carbon impression and the growing reliance on fossil fuels are two unique concerns for world emission regulatory agencies. These issues have placed electric vehicles (EVs) powered by lithium-ion batteries (LIBs) on the forefront as alternative vehicles. The LIB has noticeable features, including high energy and power density, compared with other accessible electrochemical energy storage systems. However, LIB is exceedingly nonlinear and dynamic; therefore, it generally requires an accurate online state-of-charge (SOC) estimation algorithm for real-time applications. Accurate battery modelling is an essential and primary requirement of online SOC estimation to simulate the dynamics. In this paper, different modelling methods suitable for online SOC estimation are discussed, and four groups of available online SOC estimation approaches are reviewed. After the general survey, the study explores the available Kalman filter (KF) family algorithms suitable for model-based online SOC estimation. The mathematical process and limitations of different KF family algorithms are analysed in depth and discussed. Moreover, challenging steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application. The on-going research is propelled by KF-based online SOC estimation approaches distinctly emphasised through reviewing various studies for future research progression. Highlights: Online SOC estimation methods are reviewed, discussed and classified. Battery modelling methods employed in commercial BMS for online SOC estimation are described. Key features and limitations of suitable Kalman filter family algorithms for model-based online SOC estimation are discussed. Challenging steps involved in the implementation of Kalman filter family. Open research issues for future innovation in online SOC estimation using Kalman filter family are provided.