Optimization of energy flow in thermal management of electric vehicles based on real vehicle testing and digital twin simulation

The thermal system is one of the most critical parts of electric vehicles since its performance directly affects the vehicle's energy consumption and emissions. The current digital twin technology in the auto industry lacks a thermodynamic model focusing on energy consumption, and the control strategy lacks an adversary model, which does not allow for an in-depth discussion of energy consumption without sufficient use of data. This paper aims to study the optimization of the thermal system coupled energy flow in electric vehicles with digital twin technologies. The vehicle thermal management digital twin technology discussed in this paper establishes the digital twin model of the vehicle thermodynamic system that focuses on energy consumption. Meanwhile, vehicle energy flow experiments are carried out to analyze the energy consumption of key components under typical high energy consumption conditions. Accordingly, this research optimizes the control logic to optimize energy consumption and enhance vehicle ranges without changing the hardware architecture. The results show that after the optimization of the control strategy, the electricity consumption under high-temperature, low-temperature, and dehumidification conditions is reduced by 890 W, 456 W, and 1594.9 W, respectively. The vehicle driving mileages are increased by 12.51 km, 6.16 km, and 22.42 km, respectively.