| Summary: | Safely and efficiently managing a battery pack consisting of hundreds to thousands of battery cells is a critical but challenging task due to commonly observed uncertainties, e.g. temperature, battery degradation and SOC estimation inaccuracy. This paper proposes a robust and efficient most significant cell methodology that estimates the battery pack SOC depending on the determined most significant cells. The estimation adopting this methodology is robust to variations of temperature, battery degradation and battery cell SOC estimation inaccuracy. A battery pack simulator and a real battery pack designed for electric vehicles were used as prototypes to illustrate the high performance, robustness and effectiveness of the proposed methodology. Moreover, the proposed algorithm requires light computational effort, making it suitable for real-time operation.
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