Co-Optimization of Charging Strategies and Route Planning for Variable-Ambient-Temperature Long-Haul Electric Vehicles Based on an Electrochemical–Vehicle Dynamics Model

Vehicle electrification is one of the main development directions within the automobile industry. However, due to the range limit of electric vehicles, electric vehicle users generally have range anxiety, especially toward long-haul driving. Therefore, there is an urgent need to effectively coordinate route planning and charging during long-haul driving, especially considering factors such as insufficient charging facilities, long charging times, battery aging, and changes in energy consumption under variable-temperature environments. In this study, the goal is to collaboratively optimize route planning and charging strategies. To achieve this goal, a mixed-integer nonlinear model is developed to minimize the total system cost, an electrochemical model is applied to accurately track the battery state, and a two-layer IACO-SA is proposed. Finally, the highway network in five provinces of China is adopted as an example to compare the optimal scheme results of our model with those of three other models. The comparison results prove the effectiveness of the proposed model and solution algorithm for the collaborative optimization of route planning and charging strategies of electric vehicles during long-haul driving.