Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique. Issue 10 (7th June 2021)

• Record Type: Journal Article
• Authors: Devendiran, Ramkumar; Kasinathan, Padmanathan; Ramachandaramurthy, Vigna K.; Subramaniam, Umashankar; Govindarajan, Uma; Fernando, Xavier
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 10(2021)
• Page Start: 5816
• Page End: 5844
• Publication Date: 2021-06-07
• Subjects: battery charging -- charging scheduling -- charging station -- electric vehicle -- optimization -- VANET; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22531
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The coordination of modern transportation system depends heavily on intelligent techniques, information assortment, and its analysis. Sensors play a crucial role in information assortment in charging scheduling of electric vehicles (EVs). EVs are destined to become inevitable due to their innate economic contribution, climate improvement, and social attributes as per United Nation's sustainable development goals. Innovation in EV has gained the interest of many researchers since it is one of the novel green transportation sectors. Moreover, EVs are essential to preserve conventional fuels and to maximize the utilization of renewable sources. Nevertheless, EVs have short driving ranges due to their battery limitation, which hinders the reliability. The charging stations (CS) for EVs are also unevenly distributed. This paper presents a novel strategy to schedule the charging points in EV CSs. The goal is to determine the convenient CS for EVs through Vehicular Ad‐hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases, such as driving, charge planning, charging scheduling, and battery charging. Charging scheduling was designed using a newly developed optimization strategy, exponential Harris Hawks optimization (Exponential HHO) algorithm, which combines two algorithms, Harris Hawks optimization (HHO) and exponential weighted moving average (EWMA). Furthermore, the fitness function was also newly devised by considering parameters such as average waiting time, remaining energy, number of EVs, and distance. The proposed Exponential HHO was validated using VANET simulation and the performance was improved with maximum remaining energy of 52.709 Whr, minimal distance of 27.256 km, and a maximum average waiting time of 0.352 min in comparison with existing methods. To be specific, the proposed Exponential HHO yielded better improvement, especially when considering a large number of vehicles.