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Communication dans un congrès
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.
Keywords :
battery terminal current measurement
battery terminal voltage measurement
lead acid battery state-of-charge prediction
lead acid battery state-of-health prediction
genetic algorithm
lead acid battery SoH estimator
lead acid battery SoC estimator convergence
adaptive control theory
online parameter identification
battery aging
https://hal-normandie-univ.archives-ouvertes.fr/hal-02156645
Contributeur : Christine Rouil <>
Soumis le : vendredi 14 juin 2019 - 15:18:55
Dernière modification le : vendredi 10 avril 2020 - 12:06:07
Contributeur : Christine Rouil <>
Soumis le : vendredi 14 juin 2019 - 15:18:55
Dernière modification le : vendredi 10 avril 2020 - 12:06:07
