Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Abstract

Electric vehicle (EV) charging problem impedes its wide scale commercial adoption. In this study, the authors address this problem through an ant colony optimisation based multiobjective routing algorithm that is dedicated to accommodate EV trips. By using connectivity, EVs communicate with other vehicles and infrastructure components to transmit information in real time for finding the best route, and for intelligently recharging on the move using an inductively coupled power transfer system. Such connected EVs are capable of adapting each trip with the lowest travel time and/or the lowest recharge cost along with an optimal recharge plan to prevent a battery drain. As a case study, a real world roadway network in Charleston, South Carolina was simulated to examine the performance of the routing strategy. Simulation analysis revealed that connected EVs can reduce not only the total travel time and the energy consumption, but also the recharged volume of electricity and corresponding cost, thus significantly relieving the concerns of range anxiety of EV drivers. This routing approach potentially leads to a reduction in the EV battery capacity requirement, which in turn can reduce the cost of energy storage systems to a reasonable level.

Ant Colony Optimized Routing Strategy for Electric Vehicles

“…The routing algorithm for conventional vehicles, called conventional vehicle routing (CVR), defines a function using the distance and time costs [17]. The other routing algorithm for EVs, called compare another ACO (CA-ACO), defines a function using the time and energy costs [18]. The energy refers to the cost of charging.…”

“…For the 6 km path, we selected the pairs (0, 4), (2,14), and (3,18) as the source and destination. For the 8 km path, we selected (1,18), (2,17), and (5, 22) and for the 10 km path (0, 17), (0, 23), and (1,19) were selected. In this experiment, we set = 1 for the log function, a Gaussian distribution for the speed function, and a 10 km path length as default.…”

Path Planning Method for Electric Vehicles Based on Freeway Network

“…Liu et al [31] studied how to make an appointment charging strategy to ensure that electric vehicles can charge with shorter charging time and lower charging cost when charging requirements are put forward. Li et al [32] communicated vehicles with infrastructure components and real-time transmission of information, in order to find the best route. Taking an actual road network in Charleston, South Carolina, as the simulation road network, the results show that the electric vehicle network can not only reduce the total driving time and energy consumption but also reduce the charging power and the corresponding cost, so as to significantly alleviate the mileage anxiety of electric vehicle drivers.…”

Optimal Placement of wireless charging lanes in road networks

“…Routing algorithms that take dynamic charging into account have also be developed. An ant colony optimization based multiobjective routing algorithm that utilizes V2V and V2I communications systems to determine the best route considering the current battery charge is developed in [48].…”