Recharging and battery swapping are of great significance for extending the driving range of autonomous vehicles (AVs). However, if an AV cannot recharge or swap batteries in a timely manner, the consequences are more serious than for a traditional human-driven vehicle, as there is a lack of human assistance in an AV. To address this challenge, this study proposes the joint routing optimization of AVs under recharging and battery-swapping modes. Firstly, a multi-objective model is defined for the joint routing optimization problem of AVs, which minimizes the total distance, idling time, and charging waiting time of AVs while meeting all user demands. The user demand is described as a directed arc consisting of a departure node and a destination at random locations and times, and the AVs need to plan their routes to sequentially access all user demand arcs and recharge or swap batteries in a timely manner. Secondly, an improved artificial plant community (APC) algorithm is proposed to solve the NP-hard problem, including a recharging scheme and a hybrid scheme comprising recharging and swapping. In the seeding operation, random seeds are generated to enhance global search capabilities, and optimal solution learning is added in the fruiting operation to improve local search capabilities. In the growing operation, population optimization is strengthened to improve convergence performance. Thirdly, a benchmark test set was developed based on a real scenario in Wuhan, China. Compared to some baseline algorithms, the results show that the proposed APC algorithm exhibits better performance in solving the NP-hard problem.
Cai et al. (Thu,) studied this question.