State of health lithium battery estimation using deep learning

Abstract

The State of Health (SOH) of a lithium-ion battery is a critical metric used to evaluate the performance and degradation level of a battery compared to its original condition. It reflects the battery’s ability to store and deliver energy effectively, and is often expressed as a percentage of its initial capacity. Accurate SOH estimation is crucial for ensuring battery reliability, safety, and efficiency, especially in sectors like electric vehicles (EVs), renewable energy storage, and portable electronics. Traditional SOH estimation methods rely on electrochemical models, equivalent circuit models (ECMs), and statistical algorithms, which often require deep domain knowledge, detailed physical parameters, and controlled conditions. These approaches can be limited by their inability to handle real-world operating variability and may not scale well to different battery types or usage patterns. To address these limitations, deep learning (DL) techniques have emerged as a promising alternative. Deep learning models, such as:     Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) networks Convolutional Neural Networks (CNNs) Transformer architectures ...are capable of automatically learning the underlying patterns in large, complex datasets without the need for explicit modeling of the battery’s internal behavior. These models can process multi-dimensional time-series data such as voltage, current, and temperature during charge/discharge cycles to estimate the SOH with high accuracy. LSTM networks, in particular, have shown strong performance due to their ability to model time-dependent degradation trends over many cycles. Similarly, CNNs have been effectively used to extract features from battery data streams. Some advanced models even combine different deep learning architectures into hybrid frameworks to improve robustness and precision     High accuracy in nonlinear and dynamic systems Adaptability to different battery chemistries and usage conditions Real-time, data-driven decision-making capability Reduced need for domain-specific knowledge or handcrafted features However, deep learning approaches are not without challenges. These include:     The need for large, high-quality labeled datasets for training Lack of transparency or interpretability in model predictions (black-box nature) High computational cost during training or deployment Generalization issues when applied to unseen or rare operating conditions Recent research is addressing these limitations through transfer learning, explainable AI (XAI), and model compression techniques to make models more interpretable and suitable for embedded systems in real-world Battery Management Systems (BMS).