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Due to traffic congestion and environmental pollution, connected automated vehicle (CAV) technologies based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure communication (V2I) have gained increasing attention from both academia and industry. Connected hybrid electric vehicles (CHEVs) offer great opportunities to reduce vehicular operating costs and emissions. However, in complex traffic scenarios, high-quality real-time energy management of CHEVs remains a technical challenge. To address the challenge, this paper proposes a hierarchical eco-driving strategy that consists of speed planning and energy management layers. At the upper layer, by leveraging the real-time traffic data provided by vehicle-to-everything (V2X) communication, dynamic traffic constraints are predicted by the traffic flow predictor developed based on the Hankel dynamic mode decomposition algorithm (H-DMD).

Accurate fault diagnosis is critical to the safe and efficient operation of lithium-ion battery systems. However, various faults in battery systems are difficult to detect and isolate due to their similar features. This paper proposes a model-based multi-fault diagnosis method to detect and isolate the current, voltage, and temperature sensor faults, short circuit faults, and connection faults in the lithium-ion battery systems. An electro-thermal model with fault information is established and used to construct the structural model. Structural analysis theory is applied to design diagnostic tests sensitive to multiple faults. To improve the accuracy and robustness of residual generation, the adaptive extended Kalman filter is introduced to battery state estimation. The multi-fault detection and isolation are implemented using residual evaluation based on the cumulative sum algorithm.