Optimization of Electric Vehicle Charging Routes Using Fuzzy Inference and Enhanced Gray Wolf Algorithm

Guanlong Jia, Qianqian Pang, Xintong Lv, Feng Niu, Xiaoxue Wang

Abstract


Electric vehicles (EVs) help lower carbon emissions but face challenges from varied charging prices and long wait times. This study optimizes EV charging route planning to minimize user costs under diverse pricing and diverse driver preferences. Vague user preferences are converted into explicit weightings by employing a fuzzy inference system, and a total cost function covering economic, time, and carbon costs is defined. The optimal charging route is then determined by applying Depth-First Search combined with the Enhanced Gray Wolf Algorithm (EGWA). The method is tested on a single EV and a cluster of EVs. The results demonstrate that the combination of EGWA and fuzzy inference system can select the most compliant charging path for the user, thus effectively reducing the user's cost. Tests on single EV show EGWA consistently achieves the lowest comprehensive cost across weight combinations. Specifically, EGWA lowers costs by 12.47% on average compared to GWO, 9.27% over IWOA, and 5.08% over LPGA. EGWA also demonstrates faster convergence and better stability. These results highlight the benefit of integrating fuzzy modeling with metaheuristic optimization to support drivers in choosing cost-effective and environmentally friendly routes. The proposed method provides practical guidance for intelligent EV route planning systems.

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References


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DOI: https://doi.org/10.31449/inf.v49i25.9882

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