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

Hicham Chaoui (1) , Suruz Miah (2) , Amrane Oukaour (1) , Hamid Gualous (1)

1 LUSAC - Laboratoire Universitaire des Sciences Appliquées de Cherbourg
2 University of Ottawa [Ottawa]

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.

Mots clés: genetic algorithm; lead acid battery SoH estimator; lead acid battery SoC estimator convergence; adaptive control theory; online parameter identification; battery aging; battery terminal voltage measurement; battery terminal current measurement; lead acid battery state-of-charge prediction; lead acid battery state-of-health prediction

Domaines: Sciences de l'ingénieur [physics]; Sciences de l'environnement; Physique [physics]; Matériaux; Milieux fluides et réactifs

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, ⟨10.1109/itec.2015.7165782⟩. ⟨hal-02156645⟩