State of Health Prediction of Lithium-Ion Battery Using Machine Learning Algorithms
Jamila Hemdani, Laid Degaa, Nassim Rizoug, Abdelkader Châari
Abstract
In the last years have seen an increasing usage of Electrical Vehicle (EV). To guarantee safe and reliable operation, it's necessary to possess the capability to monitor, in real time, the state of health (SOH) of the battery. This paper presents a deep learning method which utilizes a Deep Neural network (DNN) for cell-level capacity estimation based on the voltage, current, and State Of Charge. First, a multi-physical models of the battery is done to extract input and output data for the different learning and testing phases. Second, two machine learning algorithms, including DNN and Convolution Neural Network (CNN), are used to predict SOH. Mean Absolute Error (MAE) and Mean Square Error (MSE) are selected as the evaluation index. The results show that the proposed algorithm DNN has the weakest error, which makes it possible to accurately predict the SOH and to have a better stability.