Accurate State of Charge (SoC) estimation is essential for the efficient management of Li-ion batteries, especially under varying operating conditions. Traditional filtering approaches rely on predefined battery models, requiring prior knowledge of internal dynamics, which may introduce inaccuracies in real-world applications. In this study, we propose a data-driven SoC estimation method based on a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with a self-attention mechanism, designed to identify and prioritize key time steps in the battery’s charge-discharge cycle. The model takes voltage, current, and temperature as inputs and has been validated across multiple battery profiles under diverse temperature conditions. Experimental results demonstrate that the proposed approach achieves an estimation accuracy of approximately 98%, with a Root Mean Squared Error (RMSE) below 1.7, significantly improving the reliability of SoC predictions. By using both past and future states, along with attention-driven feature weighting, the proposed model enhances SoC estimation robustness across different operating scenarios.

State of charge (SoC) estimation is a critical aspect of managing lithium-ion (Li-ion) batteries in electric vehicles (EVs). Various Artificial Intelligence (AI) techniques have been used to enhance SoC estimation accuracy, such as convolutional neural networks, recurrent neural networks, generative networks, and more. This article provides a thorough survey of AI-based models utilized for SoC estimation in Li-ion batteries, synthesizing findings from diverse studies. By comparing the performance of different models : classical Machine Learning, convolutional, recurrent and generative, insights into the effectiveness and limitations of various AI approaches are highlighted. The review aims to guide future research efforts toward developing robust and accurate SoC estimation methods crucial for optimizing EV battery management systems.

Thanks to their interesting characteristics in terms of energetic performances and safety, Li-ion batteries Lithium Ferro-Phosphate (LFP) type are increasingly embedded in Electric Vehicles (EV). Hence a great interest to guarantee a high autonomy of the vehicle respecting the specific behavior of LFP batteries with accurate state of charge (SoC) monitoring. In this paper, we propose a full polynomial parameters-model of the battery first order model. The proposed model combined with the Extended Kalman Filter (EKF) is then used to estimate accurately the SoC of LFP battery. This monitoring method has been validated for Dynamic Discharge Pulse (DDP) profile, thus a high accuracy of SoC estimation is recorded, in fact an average error about 0.05% of SoC is obtained.