Battery packs develop faults over time, many of which are difficult to detect early. For instance, cooling system blockages raises temperatures but may not trigger alerts until protection limits are exceeded. This work presents a model-based method for early thermal fault detection and identification in battery packs. By comparing measured and estimated temperatures, the method identifies faults including failed sensors, coolant pump malfunctions, and flow blockages. The core is a high-accuracy temperature estimation model, integrating a physics-based thermal model with a neural network, achieves a root mean square error of 0.39 °C and a maximum error of 1 °C under a US06 discharge and 6C charge at 15 °C. Tested on a 72-cell air-cooled pack, the method detects faults using only eight temperature sensors within 13 to 45 minutes, with zero false detections in 11 testing cycles. This approach enables early fault alerts, enhancing reliability and safety in electric vehicles.

This paper presents a novel approach to battery thermal management control in Electric Vehicles (EVs), focusing on the establishment of a power loss model that incorporates temperature and aging effects on internal resistance, thereby enabling accurate estimation of battery power loss for optimized Battery Thermal Management Systems (BTMS). Additionally, the paper introduces a BTMS design capable of both heating and cooling, aiming to maintain optimal battery temperature and enhance battery efficiency and longevity. The proposed methodology includes an offline optimization layer to improve battery longevity and BTMS energy efficiency and an online control layer to maintain a safe battery temperature operation. The adaptability of this BTMS design for real-time applications in various climates is achieved by integrating discharge rate information from the drive cycle. This results in a two-level, driving-aware BTMS control system tailored to varying driving patterns specific to commuter applications. Consequently, this research significantly advances EV battery thermal management by addressing key challenges such as reducing power loss estimation error by up to 28%, optimizing temperature regulation, improving power efficiency by up to 7 kJ for different drive cycles, and enhancing battery aging by more than 3% per life cycle, while ensuring adaptability to various driving patterns for commuters.

Despite the promising performance of deep reinforcement learning (DRL)-based energy management systems (EMSs) for electrified vehicles, persistent safety concerns regarding control actions hinder their real-world deployment. This article proposes a safe DRL-based EMS for hybrid electric vehicles (HEVs) that guarantee zero safety violations during both training and deployment. A model-based safety layer is developed with prior knowledge to filter unsafe actions with minimal disruption to agent exploration. This safety metric evaluation is embedded into the reward function via a Lagrangian relaxation (LR) method, enabling adaptive penalization of constraint violations and prompting efficient policy learning. A safe soft actor–critic (SSAC) EMS is then trained under stochastic conditions, including random initial battery state-of-charge (SOC) and multimodal driving cycles. The approach is validated through simulation and battery-in-the-loop (BIL) experiments. The proposed reward formulation accelerates safety-aware policy learning by 38.5% compared to conventional methods. In BIL tests, SSAC-EMS achieves over 95% fuel efficiency relative to the offline dynamic programming (DP) benchmark under unseen driving cycles and initial SOCs, with actions consistently respecting safety constraints. Compared to the adaptive equivalent consumption minimization strategy (AECMS), SSAC-EMS improves fuel economy by 6%–10% while delivering more stable SOC regulation and smoother engine operation.