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3139. Improving Route Planning of Electric Vehicles by Considering Road Gradients and Regenerative Braking

Invited abstract in session TC-26: Sustainability in Distribution and Transportation, stream Combinatorial Optimization.

Tuesday, 12:30-14:00
Room: 012 (building: 208)

Authors (first author is the speaker)

1. Sina Rastani
Operations Management and Decision Sciences, Sheffield University Management School
2. Merve Keskin
Operations Management and Decision Sciences, Sheffield University Management School
3. Tugce Yuksel
Faculty of Engineering and Natural Sciences, Sabanci University
4. Bülent Çatay
Faculty of Engineering and Natural Sciences, Sabanci University

Abstract

With the increasing greenhouse gas emissions, the use of electric vehicles (EVs) in logistics has increased. In contrast to conventional vehicles, EVs use batteries to store energy, and they often stop at stations to recharge. The energy consumed per unit distance traveled depends on several factors including vehicle load, speed, and road gradient.
Traversing an arc with a positive gradient requires more energy compared to an arc on a flat network. This is further amplified when the EV carries a heavy load while moving uphill. Conversely, if the driver presses the brake pedal to maintain a constant speed while moving downhill, it can regain energy through regenerative braking technology. The impact of road gradients and regenerative braking may be significant for companies that carry heavy loads in hilly regions. Therefore, an approach that incorporates both cargo weight and road network terrain may improve the route planning of EVs. With this aim, this study addresses an extension of the Electric Vehicle Routing Problem with Time Windows considering a comprehensive energy consumption function that incorporates the cargo load, road gradient, and regenerative braking.
We formulate the problem as a mixed integer linear program, solve small-size instances with Gurobi, and develop a Large Neighborhood Search to tackle the large-size instances. Numerical results show that considering the road gradient along with the cargo weight can significantly influence the routing decisions.

Keywords

Status: accepted

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