Battery-Aware Electric Truck Delivery Route Exploration

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dc.contributor.authorBaek, Donkyuko
dc.contributor.authorChen, Yukaiko
dc.contributor.authorChang, Naehyuckko
dc.contributor.authorMacii, Enricoko
dc.contributor.authorPoncino, Massimoko
dc.date.accessioned2020-07-23T00:55:13Z-
dc.date.available2020-07-23T00:55:13Z-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.created2020-07-20-
dc.date.issued2020-04-
dc.identifier.citationENERGIES, v.13, no.8-
dc.identifier.issn1996-1073-
dc.identifier.urihttp://hdl.handle.net/10203/275612-
dc.description.abstractThe energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which use a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance x residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study.-
dc.languageEnglish-
dc.publisherMDPI-
dc.titleBattery-Aware Electric Truck Delivery Route Exploration-
dc.typeArticle-
dc.identifier.wosid000538041800239-
dc.identifier.scopusid2-s2.0-85084111524-
dc.type.rimsART-
dc.citation.volume13-
dc.citation.issue8-
dc.citation.publicationnameENERGIES-
dc.identifier.doi10.3390/en13082096-
dc.contributor.localauthorChang, Naehyuck-
dc.contributor.nonIdAuthorBaek, Donkyu-
dc.contributor.nonIdAuthorChen, Yukai-
dc.contributor.nonIdAuthorMacii, Enrico-
dc.contributor.nonIdAuthorPoncino, Massimo-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorElectric Truck Simulator-
dc.subject.keywordAuthorElectric Vehicle (EV)-
dc.subject.keywordAuthorVehicle Routing Problem (VRP)-
dc.subject.keywordAuthorTraveling Salesman Problem (TSP)-
dc.subject.keywordAuthorleast-energy routing algorithm-
dc.subject.keywordAuthorenergy efficiency-
dc.subject.keywordAuthorEV batteries-
dc.subject.keywordAuthormetric evaluation-
dc.subject.keywordPlusRANGE-
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