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Open AccessArticle

Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares

by Woosuk Sung 1,* and Jaewook Lee 2
1
School of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, Korea
2
School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1237; https://doi.org/10.3390/electronics8111237
Received: 6 October 2019 / Revised: 21 October 2019 / Accepted: 28 October 2019 / Published: 29 October 2019
This paper presents a computationally efficient state-of-health (SOH) estimator that is readily applicable to automotive battery management systems (BMSs). The proposed scheme uses a recursive estimator to improve the original scheme based on a batch estimator. In the batch process, state estimation requires significantly longer CPU time than data measurement, and the original scheme may fail to satisfy real-time guarantees. To prevent this problem, we apply recursive least-squares. By replacing the batch process to solve the normal equation with a recursive update, the proposed scheme can spread CPU utilization and reduce memory footprint. The benefits of the recursive estimator are quantitatively validated by comparing its CPU time and memory footprint with those of the batch estimator. A similar level of SOH estimation accuracy is achievable with over 60% less memory usage, and the CPU time stabilizes around 5 ms. This enables implementation of the proposed scheme in automotive BMSs. View Full-Text
Keywords: lithium-ion battery; battery management system; electric vehicle; state-of-health; recursive least-squares lithium-ion battery; battery management system; electric vehicle; state-of-health; recursive least-squares
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Sung, W.; Lee, J. Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares. Electronics 2019, 8, 1237.

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