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Finding minimumcost paths for electric vehicles. Paper presented at the Electric Vehicle
 Conference (IEVC), 2012 IEEE International
, 2012
"... AbstractModern routeguidance software for conventional gasolinepowered vehicles does not consider refueling since gasoline stations are ubiquitous and convenient in terms of both accessibility and use. The same technology is insufficient for electric vehicles (EVs), however, as charging stations ..."
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AbstractModern routeguidance software for conventional gasolinepowered vehicles does not consider refueling since gasoline stations are ubiquitous and convenient in terms of both accessibility and use. The same technology is insufficient for electric vehicles (EVs), however, as charging stations are much more scarce and a suggested route may be infeasible given an EV's initial charge level. Recharging decisions may also have significant impacts on the total travel time and longevity of the battery, which can be costly to replace, so they must be considered when planning EV routes. In this paper, the problem of finding a minimumcost path for an EV when the vehicle must recharge along the way is modeled as a dynamic program. It is proven that the optimal control and state space are discrete under mild assumptions, and two different solution methods are presented.
Review Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap
, 2014
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Energyoptimal routes for electric vehicles
 In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13
, 2013
"... Abstract. We study the problem of electric vehicle route planning, where an important aspect is computing paths that minimize energy consumption. Thereby, any method must cope with specific properties, such as recuperation, battery constraints (over and undercharging), and frequently changing cost ..."
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Abstract. We study the problem of electric vehicle route planning, where an important aspect is computing paths that minimize energy consumption. Thereby, any method must cope with specific properties, such as recuperation, battery constraints (over and undercharging), and frequently changing cost functions (e. g., due to weather conditions). This work presents a practical algorithm that quickly computes energyoptimal routes for networks of continental scale. Exploiting multilevel overlay graphs [26, 31], we extend the Customizable Route Planning approach [8] to our scenario in a sound manner. This includes the efficient computation of profile queries and the adaption of bidirectional search to battery constraints. Our experimental study uses detailed consumption data measured from a production vehicle (Peugeot iOn). It reveals for the network of Europe that a new cost function can be incorporated in about five seconds, after which we answer random queries within 0.3ms on average. Additional evaluation on an artificial but realistic [22, 36] vehicle model with unlimited range demonstrates the excellent scalability of our algorithm: Even for longrange queries across Europe it achieves query times below 5ms on average—fast enough for interactive applications. Altogether, our algorithm exhibits faster query times than previous approaches, while improving (metricdependent) preprocessing time by three orders of magnitude. 1
Minimum cost path problem for plugin hybrid electric vehicles
 Transportation Research Part E: Logistics and Transportation Review
, 2015
"... Abstract We introduce a practically important and theoretically challenging problem: finding the minimum cost path for plugin hybrid electric vehicles (PHEVs) in a network with refueling and battery switching stations, considering electricity and gasoline as sources of energy with different cost s ..."
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Abstract We introduce a practically important and theoretically challenging problem: finding the minimum cost path for plugin hybrid electric vehicles (PHEVs) in a network with refueling and battery switching stations, considering electricity and gasoline as sources of energy with different cost structures and limitations. We show that this problem is NPcomplete even though its electric vehicle and conventional vehicle special cases are polynomially solvable. We propose three solution techniques: (1) a mixed integer quadratically constrained program that incorporates nonfuel costs such as vehicle depreciation, battery degradation and stopping, (2) a dynamic programming based heuristic and (3) a shortest path heuristic. We conduct extensive computational experiments using both real world road network data and artificially generated road networks of various sizes and provide significant insights about the effects of driver preferences and the availability of battery switching stations on the PHEV economics. In particular, our findings show that increasing the number of battery switching stations may not be enough to overcome the range anxiety of the drivers.
Incentive Based MultiObjective Optimization in Electric Vehicle Navigation including Battery Charging
"... Abstract: This paper proposes a framework for a navigation system of electric vehicles (EVs) that minimizes the expectation of the energy consumption of the entire users while enhancing the Quality of Life (QoL), i.e., reducing the travel time and cost in our context, of individual users. To this en ..."
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Abstract: This paper proposes a framework for a navigation system of electric vehicles (EVs) that minimizes the expectation of the energy consumption of the entire users while enhancing the Quality of Life (QoL), i.e., reducing the travel time and cost in our context, of individual users. To this end, we provide users optional flexibility of selecting a preferable route based on the individual travel time and cost among the multiple candidates indicated by the system, while use an incentive approach to make users select a route that requires as low energy consumption as possible. We show by numerical simulations on Chukyo Area in Japan that the proposed method is effective.
EVPlanning: Electric Vehicle Itinerary Planning
"... AbstractLot of efforts have been done to pave the way to sustainable mobility, in order to solve pollution problems and fuel shortage. The use of electric vehicles (EV) is considered as one of the best ecologic and economic solution. However, autonomy barriers and limitations slow the progress and ..."
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AbstractLot of efforts have been done to pave the way to sustainable mobility, in order to solve pollution problems and fuel shortage. The use of electric vehicles (EV) is considered as one of the best ecologic and economic solution. However, autonomy barriers and limitations slow the progress and the deployment of this technology. In this paper, we propose an advanced electric vehicles' fleet management architecture. This architecture provides economic itineraries planning for electric vehicles. The best routes, in term of electric power consumption, are computed based on the collected information about road topology (elevation variations, source, destination, etc.), weather conditions, vehicle characteristics, driver profile, traffic conditions and electric charging stations positions. In case of battery drop, new routes passing through the nearest available charging stations are recalculated and provided to the driver.
SpeedConsumption Tradeoff for Electric Vehicle Route Planning *
"... Abstract We study the problem of computing routes for electric vehicles (EVs) in road networks. Since their battery capacity is limited, and consumed energy per distance increases with velocity, driving the fastest route is often not desirable and may even be infeasible. On the other hand, the ener ..."
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Abstract We study the problem of computing routes for electric vehicles (EVs) in road networks. Since their battery capacity is limited, and consumed energy per distance increases with velocity, driving the fastest route is often not desirable and may even be infeasible. On the other hand, the energyoptimal route may be too conservative in that it contains unnecessary detours or simply takes too long. In this work, we propose to use multicriteria optimization to obtain Pareto sets of routes that trade energy consumption for speed. In particular, we exploit the fact that the same road segment can be driven at different speeds within reasonable intervals. As a result, we are able to provide routes with low energy consumption that still follow major roads, such as freeways. Unfortunately, the size of the resulting Pareto sets can be too large to be practical. We therefore also propose several nontrivial techniques that can be applied online at query time in order to speed up computation and filter insignificant solutions from the Pareto sets. Our extensive experimental study, which uses a realworld energy consumption model, reveals that we are able to compute diverse sets of alternative routes on continental networks that closely resemble the exact Pareto set in just under a secondseveral orders of magnitude faster than the exhaustive algorithm.
Optimal Routing for Plugin Hybrid Electric Vehicles
"... energyefficient routing, plugin hybrid electric vehicles, approximation schemes ..."
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energyefficient routing, plugin hybrid electric vehicles, approximation schemes
Adaptive Routing and Recharging Policies for Electric Vehicles
, 2015
"... Planning a trip with an electric vehicle requires consideration of both battery dynamics and the availability of charging infrastructure. Recharging costs for an electric vehicle, which increase as the battery’s charge level increases, are fundamentally different than refueling costs for convention ..."
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Planning a trip with an electric vehicle requires consideration of both battery dynamics and the availability of charging infrastructure. Recharging costs for an electric vehicle, which increase as the battery’s charge level increases, are fundamentally different than refueling costs for conventional vehicles, which do not depend on the amount of fuel already in the tank. Furthermore, the viability of any route requiring recharging is sensitive to the availability of charging stations along the way. In this paper, we study the problem of finding an optimal adaptive routing and recharging policy for an electric vehicle in a network. Each node in the network represents a charging station and has an associated probability of being available at any point in time or occupied by another vehicle. We develop efficient algorithms for finding an optimal a priori routing and recharging policy and then present solution approaches to an adaptive problem that build on a priori policy. We present two heuristic methods for finding adaptive policies – one with adaptive recharging decisions only and another with both adaptive routing and recharging decisions. We then further enhance our solution approaches to a special case of grid network. We conduct numerical experiments to demonstrate the empirical performance of our solutions.
Solving RiskSensitive POMDPs With and Without Cost Observations
"... Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in RiskSensitive POMDPs (RSPOMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some userdefined cost threshold. In this paper, un ..."
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Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in RiskSensitive POMDPs (RSPOMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some userdefined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new searchbased algorithm to solve RSPOMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with realworld data.