Optimization of a PHEV Adaptive Energy-Thermal Management Coupling Strategy Considering the Vehicle Energy Demand and Driving Mode Under Cold Weather

JM Ni and Y Liu and XY Shi and R Huang and Z Xu and YJ Wang and Y Lu and ZW Chen, INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 26, 1537-1556 (2025).

DOI: 10.1007/s12239-025-00234-8

The energy management system (EMS) and thermal management system (TMS) have a great impact on the economy of plug-in hybrid electric vehicles (PHEVs). In this paper, the warm-up process at low temperature is taken as an example to explore the co-optimization problem of EMS and TMS strategy. The energy-heat-cost model of the whole vehicle is firstly constructed and the total cost during the driving process is considered, including the equivalent fuel consumption, electric power consumption, and the cost caused by battery life degradation. Subsequently, the optimization problem is established and the Ivy algorithm (IVYA) is introduced to solve it. Then subsequent investigation focuses on the impact of driving distance, driving style and driving mode. According to the SOC trajectory, the driving distance reflects the whole vehicle energy demand, thus the strategy is influenced. The optimization results are analyzed. The results show that (1) the parameters for EMS are influenced by energy demand and driving mode; (2) the battery heating power for TMS is also influenced by energy demand and driving mode; (3) the engine waste heat in coolant can be used to heat cabin as early as possible. Finally, based on the optimization results, an adaptive EMS- TMS strategy based on driving style and predicted energy demand is proposed, and the total energy demand and the initial mode are introduced as the basis of the adaptive strategy. The cost performances of the proposed adaptive strategy are compared with the original scheme and the equivalent fuel consumption minimization strategy without considering the temperature effect in the charge-depleting (CD) mode, the charge-sustaining (CS) mode and the CD-CS transition mode. It is found that the proposed adaptive strategy has good performance in different modes, and the maximum total cost optimization rate reaches 6.63%, 12.58% and 29.88% for CD, CS and CD-CS transition mode, respectively comparing with the worst scenarios.

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