AI-Driven Energy Management Architecture for Electric Vehicles: Anomaly Detection, Battery Optimization, and Stakeholder-Centric Analytics

Abstract

EVs need smart energy management systems to be efficient, have long battery life, and be able to operate safely in uncertain real-world scenarios. Conventional rule-based and optimization methods find it difficult to accommodate nonlinear battery models, thermal limits, stochastic driving models and unforeseen anomalies like sensor drift, actuator failures and cyber-distorted measurements. In order to overcome these shortcomings, this paper presents a single AI-based anomaly-sensitive Safe Reinforcement Learning (Safe-RL) energy management framework of EVs. The structure is an anomaly detector based on Temporal Convolutional Network (TCN), a constrained Safe-RL control, a safety projection layer, and a fallback policy in the event of faults, all with the assistance of comprehensive electro-thermal battery modeling and degradation-constrained optimization.
The sensor/actuator faults are detected with an accuracy of 96.8%, ROC-AUC of 0.985, and a detection latency of less than a second. Safe-RL controller maximizes charging/discharging power and strictly adheres to SOC, thermal and power limits using a rapid quadratic-programming safety layer. High-fidelity EV simulation environment with domain randomization and 500+ injected anomaly scenarios were used to evaluate the system. Findings indicate that the suggested approach will save up to 14.1% of energy, 16.6% of the daily charging cost, and 31.7% of long-term battery degradation with zero safety breaches. The system can be seen to be highly resilient with cost increase of only 3.6% under faults, which is significantly lower than MPC and standard RL baselines. On the whole, the suggested architecture is a strong, secure, and very effective solution to the next-generation EV energy management.