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Unlike passenger electric vehicles, heavy-duty electric-drive hybrid vehicles operate for long periods under high-load conditions. The continuous heat rejection of the engine-generator set through the engine high-temperature coolant loop makes fan/pump power directly interact with the direct-current (DC) bus power balance and generator–battery power split. Therefore, cooling power scheduling is part of energy management, rather than only a thermal-protection task. This paper proposes a dual-time-scale predictive hierarchical electro-thermal coordination strategy, termed DTP-HETC, for thermal-load reshaping in heavy-duty electric-drive hybrid vehicles. A shared-encoder dual-head bidirectional long short-term memory (BiLSTM) predictor is used to capture short-horizon traction fluctuations and long-horizon engine-loop thermal evolution. The predicted thermal risk is quantified as thermal debt and converted into thermal buffer intensity B T k , which further adapts the thermal-load-shaping weights and the control-grid resolution of dynamic programming-based model predictive control (DP-MPC). By using this information, DTP-HETC advances part of the cooling effort from boundary-triggered response to thermal-risk formation intervals, smoothing cooling-induced DC-bus disturbances and coordinating fan/pump power with generator–battery power allocation. Simulation and hardware-in-the-loop tests on a 220-ton series hybrid mining truck show that, under sustained heavy-load uphill operation, DTP-HETC reduces equivalent fuel consumption by 12.16% and lowers peak engine-loop coolant temperature by 2.53 °C compared with a rule-based baseline. The mean execution time is 33.7 ms, confirming controller-level real-time feasibility. These results indicate that DTP-HETC provides an effective thermal-load reshaping approach for improving energy efficiency and thermal reliability in heavy-duty electric-drive hybrid vehicles.
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Xiong et al. (2026) studied this question.
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