Joint electric vehicle routing and battery health management integrating an explicit state of charge model

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@Article{LONGHITANO:2024:cie,

• author = "Pedro Dias Longhitano and Christophe Berenguer and Benjamin Echard",
• title = "Joint electric vehicle routing and battery health management integrating an explicit state of charge model",
• journal = "Computer \& Industrial Engineering",
• volume = "188",
• pages = "109892",
• year = "2024",
• ISSN = "0360-8352",
• DOI = "doi:10.1016/j.cie.2024.109892",
• URL = "https://www.sciencedirect.com/science/article/pii/S0360835224000135",
• keywords = "genetic algorithms, genetic programming, Battery health management, Degradation modeling, Electric vehicle routing, Multi-objective optimization",
• abstract = "Although fleet management has been extensively explored in transportation science, the rise of electromobility imposes several scientific challenges and opportunities. So far, few attempts were made to include battery degradation in the Electric Vehicle Routing Problem (EVRP). To do it realistically, it is necessary to model State of Charge (SoC), however most versions of routing problems use oversimplified SoC models or consider only energy consumption which leads to less robust solutions overall. In this work, a method for estimating battery degradation, which relies on a realistic SoC model is presented and incorporated into a new version of the electric vehicle routing problem. In this version, not only battery degradation is integrated, but also the possibility of limiting different vehicle parameters, such as maximum vehicle speed and acceleration. Due to the extra computational complexity related to the SoC and degradation models, a genetic algorithm capable of solving the aforementioned extended EVRP is presented. Finally, through different numerical experiments, the advantages of the proposed methodology are shown",

}

Genetic Programming entries for Pedro Dias Longhitano Christophe Berenguer Benjamin Echard

Citations