Battery state-of-charge estimator design based on the least-squares support vector machine

Cavanini, Luca and Majecki, Pawel and Grimble, Michael and van der Molen, Gerrit M; (2024) Battery state-of-charge estimator design based on the least-squares support vector machine. In: 2024 IEEE International Conference on Automation Science and Engineering (CASE 2024). IEEE, ITA, pp. 711-716. ISBN 9798350358513 (https://doi.org/10.1109/CASE59546.2024.10711506)

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Abstract

The design and development of a data-driven algorithm for battery State-of-Charge estimation is presented. The estimation of battery SoC is important in the development of Battery Management Systems. The proposed approach exploits the Least-Squares Support Vector Machine data-driven estimation paradigm and statistical methods. The algorithm’s computational complexity is reduced by using a data pruning procedure. The optimization of the SVM-based estimator is performed by using a Particle Swarm Optimization method. The design approach proposed to develop to estimator is validated using a simulation model of the battery and an Estimator Design Tool in MATLAB software which provides a user-friendly interface for the different algorithms that may be used in the estimator design. The approach is applicable to a wide range of applications including automotive systems.

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