An Intelligent Cyber–Physical Framework for Electric Bus Batteries: Combining DQN Smart Charging with LSTM Digital Twins

The operational reliability of electric buses depends heavily on the thermal stability and longevity of their battery packs. In urban public transit, highly dynamic driving cycles characterized by severe current surges during regenerative braking and prolonged constant-current charging phases induce massive localized thermal stress. Traditional battery management systems often fail to dynamically balance energy recovery with the physical constraints of cell matrices, leading to accelerated degradation and potential safety hazards. This paper proposes an integrated cyber–physical framework that merges intelligent control with advanced prognostic monitoring and predictive thermal management. The core of the control layer is a deep Q-network (DQN) agent designed for real-time smart charging and dynamic regenerative braking current limitation (smart derating). This control loop is tightly integrated with a long short-term memory (LSTM) network-based digital twin (DT) operating at the edge level to mirror complex electro-thermal dynamics and track the battery state of health (SOH). To ensure transparency and proactive thermal control, a nonlinear model predictive control (NMPC) strategy is implemented, utilizing SHapley Additive exPlanations (SHAP) to map core heat generation and dynamically adjust for internal thermal lag. The predictive NMPC-xAI loop maintained cell temperatures within a tight, optimal envelope, preventing delayed under-cooling and reducing peak temperature fluctuations.