Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles

Electric vehicles (EVs) are the emerging solution for pollution-free transportation systems in the modern era. Battery-operated motor-powered electric vehicles serve the purpose of transportation with in-time recharging ability. Routing and traversing locations using EVs demand optimal route selection for retaining delay and power of the vehicle. This manuscript proposes a fitness-ant colony optimization (FACO)-based route optimization for improving the driving range of EVs. FACO works in two phases: conditional route discovery and range-sustained traversing to control delay and to deprive EV failures because of earlier power drain. In range-sustained phase, the fitness of the traversing route is framed by considering the inputs of power and travel time of the EV for ensuring construction of optimal touring paths. The EVs are directed to traverse the paths defined through ACO, after which the available paths are further attuned to identify the most efficient route depending on the braking and battery power of the vehicle. This optimization balances both power and its variants along with travel time for improving the driving range of the EV before it actually drains out. The optimization technique is assessed using arbitrary road and delivery point simulation with real-time configurations to demonstrate the effectiveness of the proposed method. Experimental results demonstrate the consistency of the proposed FACO by increasing driving distance and delivery point visits. This optimization also achieves lesser power depletion retaining higher charging level with lesser waiting time.

Automated summary

This paper presents a fitness-ant colony optimization (FACO)-based route optimization method for electric vehicles (EVs), aiming to improve the driving range. By considering the inputs of power and travel time, the optimal route is selected. Experimental results demonstrate that the proposed method increases the driving distance and the number of delivery point visits, while reducing power depletion and waiting time.