Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime
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ORIGINAL ARTICLE
Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime Theresia Perger
· Hans Auer
Received: 22 July 2019 / Accepted: 28 August 2020 © The Author(s) 2020
Abstract In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of T. Perger () · H. Auer Institute of Energy Systems and Electrical Drives, Energy Economics Group (EEG), TU Wien, Gusshausstrasse 25-29 E370-3, 1040, Wien, Austria e-mail: [email protected] H. Auer e-mail: [email protected]
charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types. Keywords Energy efficiency · Electric vehicles · Route planning · Yen algorithm · Multi-objective optimization · Battery lifetime
Nomenclature Vehicle parameters m cw A SoC w/ h v
Mass Drag coefficient Cross-sectional area State of charge of the battery Width/height Velocity
Energy Efficiency
Road parameters s Length of a road section q Slope of a road section in % α Slope of a road section in rad External parameters T Outside temperature g Gravitational constant ρ Air density fR Rolling resistance coefficient Efficiencies ηd ηm /ηg ηinv ηacc ηhc ηcha /ηdis
Final drive Motor/generator DC-AC inverter Accessories Heating and cooling Battery charging/discharging
Topography data Rearth φ/ψ x/y z
Earth radius Coordinates in longitude/latitude Distances Height difference
Energy consumption Froll Rolling resistance Air resist
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