Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle

Abstract

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies.

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

“…Designing and implementing a new metaheuristic optimization algorithm takes time, but there are several pressing basic needs for them that motivate academics to develop a new algorithm [26]. Some new research papers have focused on integrating these algorithms with features of artificial intelligence (AI) to improve their performance [27][28][29]. Many academic papers, including [30,31], have described their fundamental properties and advantages as follows:…”

“…where Ng expresses the generators' number, T symbols the overall scheduling hours, P i (t) is the ith unit power generated at instant t, I i (t) represents the switching status (ON/OFF) of ith unit at instant t, F i (P i (t)) is the ith unit fuel cost which is given in Equation ( 27), S i (t) is the ith unit start-up cost, which is presented as S i (t) = Sh i i f T i,o f f (t) ≤ T i,Down + T i,cold Sc i i f T i,o f f (t) > T i,Down + T i,cold (28) where Sh i and Sc i are the hot and cold start-up costs, T i,off (t) is the duration of ith unit's continuous inactivity, T i,Down is the minimum downtime of ith unit, and T i,cold is the cold start-up time of the ith unit.…”

An Electric Vehicle Charging Method Considering Multiple Power Exchange Modes’ Coordination

“…Considering the number of charging EVs is random, the optimization object is the minimum cost of users at each period as shown in (18). In (19), the object SOC value of the EV battery is constrained within the normal range. EV parking period is defined and constrained as shown in (20).…”

“…B2V and H2V are both smart charging technology that allows bidirectional power flow between EV batteries and microgrids. In the microgrids, flexible loads and renewable power generation (RPG) are considered [14][15][16][17][18][19][20]. The increasing EVs are integrated into the RPG systems to overcome power and environmental limitations.…”