Developing simple and accurate state estimators for battery packs is important but technically challenging due to not only the high number of batteries requiring monitoring but also the uncertainties brought by the inherent cell inconsistency and the additional equalisation hardware. We here propose a low-computational “leader-follower” framework to achieve state-of-charge (SoC) and state-of-health (SoH) estimations for all series-connected cells within a pack. It basically uses an enhanced algorithm to handle a selected battery (“leader”) and updates the states of the remainders (“followers”) with lightweight calibrators. Specifically, a revised extreme-learning machine equipped with two gradient correctors is first developed to estimate the SoC of the “leader” adaptively, followed by a simple yet effective definition-based approach for its SoH. The states of the “followers”, on the other hand, are calibrated based upon the relationships among voltage, SoC, and a concept called balancing-current-ratio (BCR). Battery-in-the-loop experiments show that when the computational time is reduced by 83% compared to the benchmarks, the typical estimation error of all cells in a pack can still be bounded by 2.5% and 1.25% for SoC and SoH, respectively. Given the low computational burden of our algorithm, it can be easily transplanted to applications with different system configurations.
The first author would like to thank the continuing support from the Guangzhou HKUST Fok Ying Tung Research Institute during Hong Kong’s unrest and the outbreak of the Covid-19.
This work is supported, in part, by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06), Hong Kong Research Grant Council under grant 16208520 , the National Natural Science Foundation of China (51977131), the Natural Science Foundation of Shanghai (19ZR1435800), Guangdong Scientific and Technological Project, China (2019A050516002), and the Foshan-HKUST Projects Program, China (FSUST20-FYTRI12F).
© 2022 Elsevier B.V.
- Leader–follower strategy
- Lithium-ion battery pack
- State-of-charge estimation
- State-of-health estimation