A review of equalization strategies for series battery packs: variables, objectives, and algorithms. (December 2019)

• Record Type: Journal Article
• Authors: Feng, Fei; Hu, Xiaosong; Liu, Jianfei; Lin, Xianke; Liu, Bo
• Is Part Of: Renewable & sustainable energy reviews. Volume 116(2019)
• Publication Date: 2019-12
• Subjects: Series battery packs -- Equalization strategies -- Equalization objectives -- Equalization variables -- Energy storage -- Electric vehicles; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.109464
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Inconsistency in the internal parameters and external environments of lithium-ion cells after they are connected as a battery pack may greatly limit the pack's capacity, power capability, and lifetime. Equalization management systems (EMSs) are necessary to mitigate such inter-cell inconsistency. Due to the complexity of application scenarios and the cost of EMSs, the speed, accuracy, and stability of EMSs need to be comprehensively considered. Unlike the research on equalization circuit architectures, studies on equalization control strategies are far from mature. This paper, for the first time, presents a critical summary and analysis of currently available equalization strategies. They are elaborated and categorized based on the main components of a controller formulation, including equalization variables, equalization objectives, and equalization algorithms. Finally, some insights and thinking about technical challenges and opportunities for the development of equalization strategies are provided. This work could be beneficial to the selection and design of appropriate control strategies for different equalization applications. It spurs new expansive research routes and forward-looking visions for researchers and practitioners in this field. Highlights: Existing equalization strategies are classified according to different criteria. Both equalization variables and objectives are divided into four categories. Equalization algorithms are divided into three categories. Advantages and disadvantages of each strategy are summarized. Main challenges and prospects are highlighted.