SOCXAI: Leveraging CNN and SHAP Analysis for Battery SOC Estimation and Anomaly Detection

In the domain of battery energy storage systems for Electric Vehicles (EVs) applications and beyond, the adoption of machine learning techniques has surfaced as a notable strategy for battery modeling. Machine learning models are primarily utilized for forecasting the forthcoming state of batteries, with a specific focus on analyzing the State-of-Charge (SOC). Additionally, these models are employed to assess the State-of-Health (SOH) and predict the Remaining Useful Life (RUL) of batteries. Moreover, offering clear explanations for abnormal battery usage behavior is crucial, empowering users with insights needed for informed decision-making, build trust in the system, and ultimately enhance overall satisfaction. This paper presents SOCXAI, a novel algorithm designed for precise estimation of batteries’s SOC. Our proposed model utilizes a Convolutional Neural Network (CNN) architecture to efficiently estimate the twenty five future values of SOC, rather than a single value. We also incorporate a SHApley Additive exPlanations (SHAP)-based post-hoc explanation method into our method focusing on the current feature values for deeper prediction insights. Furthermore, to detect abnormal battery usage behavior, we employ a 2-dimensional matrix profile-based approach on the time series of current values and their corresponding SHAP values. This methodology facilitates the detection of discords, which indicate irregular patterns in the battery usage. Our extensive empirical evaluation, using diverse real-world benchmarks, demonstrates our approach effectiveness, showcasing its superiority over state-of-the-art algorithms.