Enhancing electric vehicles efficiency: A heuristic approach

GSEM Professor Nicolas Zufferey has co-authored an article in the top tier journal Transportation Research Part C: Emerging Technologies alongside Jaehee Jeong, Bissan Ghaddar, and Jatin Nathwani. The study investigates the barriers to electric vehicles adoption, focusing on the uncertainty in energy consumption. To address this challenge, the authors develop an adaptive robust optimization framework designed to minimize the maximum potential energy consumption, while maintaining timely service delivery and preventing battery depletion. The proposed solution involves a heuristic algorithm based on column-and-constraint generation, complemented by variable neighborhood search and an alternating direction method to efficiently solve the model within reasonable computational limits.

ABSTRACT

Electric vehicles (EVs) have been highly favoured as a mode of transportation in recent years. EVs offer numerous benefits over traditional fuel-based vehicles, particularly in terms of the environmental impact. Although electric vehicles offer several advantages, there are certain restrictions that limit their usage. One of the significant issues is the uncertainty in their driving range. The driving range of EVs is closely related to their energy consumption, which is highly affected by exogenous and endogenous factors. Since those factors are unpredictable, uncertainty in EVs’ energy consumption should be considered for efficient operation. This paper proposes a two-stage adaptive robust optimization framework for the electric vehicle routing problem. The objective is to minimize the worst-case energy consumption while guaranteeing that services are delivered at the appointed time windows without battery level deficiency. We postulate that EVs can be recharged on route, and the charging amount can be adjusted depending on the circumstances. A column-and-constraint generation based heuristic algorithm, which is coupled with variable neighborhood search and alternating direction algorithm, is proposed to solve the resulting model. The computational results show the economic efficiency and robustness of the proposed model, and that there is a tradeoff between the total required energy and the risk of failing to satisfy all customers’ demand.

Access the study: Adaptive robust electric vehicle routing under energy consumption uncertainty

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