Machine Learning, Deep Learning, Computer Vision and Cybersecurity in Battery Technologies: A Comprehensive Review

Abstract

Batteries, especially lithium-ion types, are fundamental to electric vehicles, large-scale energy storage, and portable electronics, leading to increasingly complex management systems. This review compiles recent studies (2019–2025) on four AI-related fields applied to battery technology: machine learning (ML) and deep learning (DL) for estimating state-of-charge (SOC), state-of-health (SOH), and remaining useful life (RUL); computer vision (CV) for manufacturing defect detection and thermal diagnostics; and cybersecurity for battery management systems (BMS), electric-vehicle supply equipment (EVSE), and vehicle-to-grid (V2G) interfaces. We outline the methodology for literature search and selection, present a taxonomy of algorithms and public datasets used across these areas, and compile performance metrics from selected studies into comparative tables and figures. The reviewed research shows that hybrid and optimized deep learning models (e.g., PSO-LSTM) achieve the highest accuracy in SOH estimation; vision models based on convolutional neural networks and transformers are increasingly employed for non-destructive defect and thermal inspections; and deep-learning-based intrusion detection is becoming the primary approach for securing BMS and charging networks. However, systematic reviews indicate that much of this evidence relies more on simulations than real-world deployments. We conclude with an outline of open challenges, including data scarcity, lack of standardization, domain transfer issues, real-time processing limits, and the discrepancy between simulated and actual cyber-attacks. Finally, we propose future research directions, emphasizing multimodal data fusion combining electrical, visual, and network traffic data for comprehensive battery health and security monitoring.