Long-Trip Optimization of Charging Strategies for Battery Electric Vehicles

A major drawback of battery electric vehicles is their small range. For long-distance coverage, stops for charging are inevitable. Because of emerging technologies, charging durations can be reduced to 30 min; this reduction means that short stops for charging are possible, even during a long trip. In this study, the problem of finding optimal charging strategies was formulated as a time-dependent, bicriteria, shortest-path problem. The intention was to provide a new feature for navigation by computing for a predefined route when and how long a battery must be charged to reach a destination quickly and reliably (i.e., without the risk of an empty battery). Two algorithms for solving this problem are explained. A case study analyzed the performance of these two algorithms and the impact of several modifications of the underlying graph-based representation of charging possibilities. For practical relevance to be obtained, historical traffic data were used to estimate travel times and energy consumption within the case study, along with real-time traffic information from an online stream hosted by a professional traffic data provider.

[1]  Donald B. Johnson,et al.  Efficient Algorithms for Shortest Paths in Sparse Networks , 1977, J. ACM.

[2]  Konrad Reif,et al.  On the computation of the energy-optimal route dependent on the traffic load in Ingolstadt , 2013 .

[3]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[4]  Silviu-Iulian Niculescu,et al.  Energy optimal real-time navigation system: Application to a hybrid electrical vehicle , 2013, ITSC.

[5]  Emil Klafszky,et al.  Determination of shortest path in a network with time-dependent edge-lengths 1 , 1972 .

[6]  E. Martins On a multicriteria shortest path problem , 1984 .

[7]  T. M. Sweda,et al.  Finding minimum-cost paths for electric vehicles , 2012, 2012 IEEE International Electric Vehicle Conference.

[8]  Martin Treiber,et al.  How Much Does Traffic Congestion Increase Fuel Consumption and Emissions? Applying Fuel Consumption Model to NGSIM Trajectory Data , 2008 .

[9]  R Bellman,et al.  On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.

[10]  M. Kostreva,et al.  Time Dependency in Multiple Objective Dynamic Programming , 1993 .

[11]  Sepideh Pourazarm,et al.  Optimal routing of electric vehicles in networks with charging nodes: A dynamic programming approach , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[12]  Sabine Storandt,et al.  Quick and energy-efficient routes: computing constrained shortest paths for electric vehicles , 2012, IWCTS '12.

[13]  Martin Sachenbacher,et al.  The optimal routing problem in the context of battery-powered electric vehicles , 2010 .

[14]  Stefan Funke,et al.  Optimal Route Planning for Electric Vehicles in Large Networks , 2011, AAAI.

[15]  Ariel Orda,et al.  Minimum weight paths in time-dependent networks , 1991, Networks.

[16]  Horst W. Hamacher,et al.  Algorithms for time-dependent bicriteria shortest path problems , 2006, Discret. Optim..

[17]  Andrew V. Goldberg,et al.  Computing Point-to-Point Shortest Paths from External Memory , 2005, ALENEX/ANALCO.