Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

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 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.

Multi-objective optimization of Electric Vehicle Routing Problem using Bat Algorithm

“…The proposed method outputs the feasible routes in less energy terms for the electric vehicle fleet when tested on different benchmark problems of EVRP. Assuming the three Optimization variables as the energy consumption, the cyclic lifetime of battery and the travel time in the Yen's algorithm, a new method is proposed in [17] which solves multi-objective EVRP. An efficient electric vehicle assignment control scheme that is user oriented is presented in [18].…”

Optimal Transport and Seismic Rays

“…This capability, known as regenerative braking, allows them to recharge their battery while in motion, resulting in negative costs in terms of energy consumption. In terrains with varied topography, efficient route planning is therefore crucial, as downhill segments offer opportunities for energy recovery through deceleration [28].…”

Data Driven Energy Economy Prediction for Electric City Buses Using Machine Learning

“…In addition, electrification of the powertrain brings many other advantages, such as a reduced noise level or pollution [7]- [10]. On the downside, the battery charging time of an electric bus is significantly longer than the refueling time of a diesel bus, while the opposite is true for the range [11]. Ultimately, widespread electrification of the mobility sector is one of the most positive actions that can be taken in terms of climate change and sustainability, but more research is needed to ensure efficient operation, as it also poses significant challenges.…”