Active Balancing of Lithium-Ion Batteries Using Graph Theory and A-Star Search Algorithm

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 4; pp. 2587 - 2599
• Main Authors: Dong, Guangzhong, Yang, Fangfang, Tsui, Kwok-Leung, Zou, Changfu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Balancing; Batteries; Cell balancing; Energy dissipation; Equalization; Estimation; Graph theory; Heterogeneity; Ice; Integrated circuit modeling; Liapunov direct method; Lithium; Lithium-ion batteries; lithium-ion battery; parallel operation; power converter; Pulse width modulation; Rechargeable batteries; Search algorithms; Shortest-path problems; Stability analysis; state estimation; State of charge; Topology
• ISSN: 1551-3203; 1941-0050; 1941-0050
• DOI: 10.1109/TII.2020.2997828

The heterogeneity of cells in a battery pack is inevitable but brings high risks of premature failure and even safety hazards. Accordingly, for safe and long-life operation, it is necessary to adjust the state of charge (SOC) of all in-pack cells to the same level. To address this problem, this article first proposes a battery SOC observer and analyzes its stability and convergence analysis using the Lyapunov direct method. Different to most available estimators is that the proposed method does not require the information of cell capacities. Then, after modeling the equalization system as a directed graph, the equalization problem is cast as a path searching problem. Finally, an A-star algorithm subject to balancing constraints is proposed to find the shortest path in this graph, corresponding to the most efficient SOC equalization. Experimental results show that the steady-state error of the proposed observer is less than <inline-formula><tex-math notation="LaTeX">2\%</tex-math></inline-formula>. It also demonstrates that the A-star algorithm can decrease the balancing time and energy loss during the balancing process by 9.59% and 19.5%, respectively, relative to the mean-difference-average method.