Location-Scheduling Optimization Problem to Design Private Charging Infrastructure for Electric Vehicles

Koháni, Michal; Czimmermann, Peter; Váňa, Michal; Cebecauer, Matej; Buzna, Ľuboš

doi:10.1007/978-3-319-94767-9_8

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…The paper that is closer to our problem is that by Kohani et al (2018) [13]. The paper contributes a novel method for deploying charging infrastructure in large urban areas for electric vehicle fleets, utilizing GPS traces to identify suitable charging station locations and formulating an optimization model for efficient deployment.…”

Section: Literature Reviewmentioning

confidence: 90%

Exploration of approaches to locate charging stations for a private electric delivery fleet

Kunapareddy,

Batta

2024

Preprint

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This paper explores approaches to find optimal locations for charging stations in the context of a private delivery company that is attempting to electrify its fleet of delivery vehicles. Although the possible total set of customers is known in advance, the organization has uncertainty in realizing the actual customers on a particular day and this ambiguity leads to uncertainty in the location of charging stations. To address this, we divide the routes based on their frequencies as frequent and infrequent. We further consider three sets of frequent routes and develop three sets of models and heuristics that determine the locations of the charging stations. The first model maximizes the number of chargers located on arcs which are common to multiple routes. The second model explores the possibility of locating chargers at the customer locations as a combinatorial problem and solves it to optimality. The last model, which considers routes with chargers on the last arcs of two or more different routes, consolidates those stations. To showcase the efficacy and efficiency of our proposed approaches, we present results obtained by applying these models to various instances. We find that, although the number of routes that can fall into the first set is comparatively less, the algorithm reduces the chargers that should be located in those routes. The total time spent on routes in the second set is reduced by almost 15 percent on average. For the last set, the important metric while evaluating the efficacy of the model is the increase in travel time, which is relatively small for the number of chargers that are being consolidated. These findings can serve as valuable insights for decision-makers planning to electrify their fleet.

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Section: Literature Reviewmentioning

confidence: 90%

Exploration of approaches to locate charging stations for a private electric delivery fleet

Kunapareddy,

Batta

2024

Preprint

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“…Using a different approach, [46] instead saves the locations of the charge requests when vehicles report they are low on charge and then solves a K-means problem to locate the charging stations. Going beyond the use of simulations to provide the two phases, [18] studies where to place charging stations by reviewing GPS data for vehicle trajectories from real world systems and using where they tend to park and spend time idling to guide station location decisions. Some researchers have studied charge station location outside of the context of a two-phase simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

Real time operation of high-capacity electric vehicle ridesharing fleets

Zalesak

Samaranayake

2021

Transportation Research Part C: Emerging Technologies

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“…Discussion of appropriate thresholds for identifying stops of the trips is available in [43]. We used similar methods for extracting the trips from GPS data as in [44]. A description of travel time modelling (from station to patient) can be found in [29].…”

Section: Literature Reviewmentioning

confidence: 99%

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Buzna

Czimmermann

2021

Mathematics

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The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity.

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Location-Scheduling Optimization Problem to Design Private Charging Infrastructure for Electric Vehicles

Cited by 8 publications

References 29 publications

Exploration of approaches to locate charging stations for a private electric delivery fleet

Exploration of approaches to locate charging stations for a private electric delivery fleet

Real time operation of high-capacity electric vehicle ridesharing fleets

On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

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