Methods for state of health estimation for lithium-ion batteries: An essential review

EEIS, ENSET Mohammedia, University Hassan II Casablanca, Morocco

^* e-mail: houda.rhdifa-etu@etu.univh2c.ma

Abstract

Electric vehicles (EVs) are a practical and suitable choice for reducing the pollution rate caused by combustible engines of conventional cars. The lithium-ion batteries (LIB) serve as a support for energy storage in EVs owing to their benefits and advantages. To ensure their optimal performance and working under safe conditions the state of health SOH of battery has to be accurately estimated. In this paper, the main estimation techniques, namely, model-based, and data-driven approaches are explained with a brief look at their several stages. Thus, two examples are presented for each method: neural networks (NN) and support vector machines (SVM) for data-driven, the combination of variable forgetting factor recursive least squares (VFF-RLS) with adaptive unscented Kalman filter (AUKF) and particle swarm optimization (PSO), genetic algorithm (GA), particle filter (PF), recursive least squares (RLS) for model-based method to show how each method is applied. Finally, a list of advantages and drawbacks of some parameter identification and SOH estimation methods is prepared, and then some other related works are referred to.