A meta-heuristic-based energy efficient route modeling for EVs integrating start/stop and recapturing energy effect. (April 2023) - Kumar, Ashwani; Kumar, Ravinder; Aggarwal, Ashutosh; Bedi, Jatin
Abstract: The research interest in electric vehicles (EVs) has recently sparked exponentially on account of curbing carbon emissions and paucity of fuel resources. No doubt, these vehicles are ecologically sustainable and move smoothly at a moderate speed that is economical on fuel, but their limited range due to the battery reserve demands an efficient use of energy resources. An immediate solution to overcome this problem lies in regulating the EV route, with optimum energy usage. Thus, this study presents a mathematical model that accounts for vehicle energy consumption with the vehicle's start/stop energy expenditure. To achieve the objective of maximizing the energy-efficiency, it also introduces Amplified-ACO ( A 2 C O ), a routing algorithm based on Ant Colony Optimization (ACO) principles that make use of the probabilistic selection model ( D S 2 relies on Distance, Speed, and State-of-Charge). This study simulates the proposed model over the real-time map of Chandigarh (India) and provides a comparative study against some of the nature-inspired meta-heuristic approaches such as classical ACO and Particle Swarm Optimization (PSO). The proposed model justifies its significance under various parameters, viz. energy consumption, travel time, total travel distance, and remaining SoC. Highlights: Proposed a model for energy consumption of EVs incorporating several critical effects. Regenerative braking converts kinetic energy into usable or storable energy. Amplified ACO with a probabilistic selection model is proposed for improved performance. Comparative analysis is conducted among proposed & popular nature inspired algorithms.