Accurate real-time estimation of battery State of Charge (SOC) and State of Health (SOH) is essential for electric vehicle (EV) performance and safety. We propose a hybrid framework combining deep learning with physics insights using LFP cell data. Our core model is a series CNN-TCN-DNN network trained on raw voltage/current signals; replacing measured temperature with cumulative charge ($\int I d t$) as an input improves accuracy. The model is tested under input noise and bias to ensure robustness. For example, it consistently achieves SOC errors below 2% MAPE and more accurate SOH tracking, enabling more reliable range prediction.

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.

The interest of research and automotive industries is concentrating progressively on the Electric Vehicles (EV) which are a global transportation development currently in order to achieve considerable carbon emission reductions. Electric batteries are the essential component of the EV and precise remaining useful life prediction is the main to ensure its reliability. As a result, the inside workings of these battery systems must be fully included. There is presently no precise model for predicting an EV battery's aging. This paper presents an intelligent method for estimating the State Of Charge (SOC) of the battery.