This research proposes to tackle two important issues in long‐distance travel for electric vehicles (EVs): range anxiety and driver fatigue. A new route planning approach integrating the perception of psychological and physiological states is presented. This model quantifies range anxiety by integrating conditions such as battery depletion, charging station coverage, and traffic congestion and explicitly considers the evolution of driver fatigue influenced by continuous driving behavior and circadian rhythms, as well as a time‐overlapping strategy for simultaneously arranging charging and rest tasks. To efficiently solve the route planning problem for large‐scale road networks, this research develops an optimization approach using an A ∗ ‐guided adaptive genetic algorithm (A ∗ ‐AGA), which integrates heuristic search and evolutionary optimization. Simulation experiments on typical long‐distance routes demonstrate that the approach is highly effective in reducing driver anxiety and fatigue, optimizing the total travel time, ensuring the route′s feasibility, and greatly improving the long‐distance EV driving experience.
Jia et al. (Thu,) studied this question.