State-of-charge and state-of-health prediction of lead-acid batteries with genetic algorithms

Abstract : In this paper, a state of charge (SoC) and state of health (SoH) estimator is presented for lead-acid batteries. The estimation strategy is based on adaptive control theory for online parameters identification. To speed up the estimator's convergence, the adaptation law is replaced by a genetic algorithm (GA). Therefore, robustness to parameters variation is also achieved and thus, accurate prediction with battery aging. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Results show high convergence and highlight the performance of the proposed estimator in predicting the SoC and SoH with high accuracy.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02156645
Contributeur : Christine Rouil <>
Soumis le : vendredi 14 juin 2019 - 15:18:55
Dernière modification le : samedi 15 juin 2019 - 01:35:24

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  • HAL Id : hal-02156645, version 1

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Hicham Chaoui, Suruz Miah, Amrane Oukaour, Hamid Gualous. State-of-charge and state-of-health prediction of lead-acid batteries with genetic algorithms. 2015 IEEE Transportation Electrification Conference and Expo (ITEC), Jun 2015, Dearborn, United States. pp.1-6. ⟨hal-02156645⟩

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