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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    Title :

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


    Contributors:


    Publication date :

    2015-06-01


    Size :

    848745 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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