Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health
Jamila Hemdani,
Laid Degaa,
Moez Soltani,
Nassim Rizoug,
Achraf Jabeur Telmoudi,
Abdelkader Chaari
Affiliations
Jamila Hemdani
Department of Electrical Engineering, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, Tunis BP 56-1008, Tunisia
Laid Degaa
Laboratoire des Systèmes et Energies Embarqués pour les Transports, Higher School of Aeronautical Techniques and Automobile Construction (ESTACA), Parc Universitaire Laval-Changé, Rue Georges Charpak, 53000 Laval, France
Moez Soltani
Department of Electrical Engineering, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, Tunis BP 56-1008, Tunisia
Nassim Rizoug
Laboratoire des Systèmes et Energies Embarqués pour les Transports, Higher School of Aeronautical Techniques and Automobile Construction (ESTACA), Parc Universitaire Laval-Changé, Rue Georges Charpak, 53000 Laval, France
Achraf Jabeur Telmoudi
Department of Electrical Engineering, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, Tunis BP 56-1008, Tunisia
Abdelkader Chaari
Department of Electrical Engineering, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, Tunis BP 56-1008, Tunisia
The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium–iron–phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental results showed that the proposed methodology can well learn the characteristic relationship of battery discharge capacities as well as its state of health (SOH), where the battery life cycle changes as the battery ages with time and cycles.
