A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. (October 2020)

• Record Type: Journal Article
• Authors: Wang, Yujie; Tian, Jiaqiang; Sun, Zhendong; Wang, Li; Xu, Ruilong; Li, Mince; Chen, Zonghai
• Is Part Of: Renewable & sustainable energy reviews. Volume 131(2020)
• Publication Date: 2020-10
• Subjects: Battery modeling -- State estimation -- Energy storage -- Battery management -- Lithium-ion battery; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2020.110015
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs. Highlights: Battery modeling methods are systematically overviewed. Battery state estimation methods are reviewed and discussed. Future research challenges and outlooks are disclosed. Battery management scheme based on big data and cloud computing is proposed.