Energy Management and Optimization of LargeScale Electric Vehicle Charging on the Grid
Abstract
:1. Introduction
 This review investigates the uncontrolled charging problem of largescale EVs and the impact on the grid electricity distribution system.
 It identifies research gaps from previously published papers in related fields where the uncontrolled charging of EVs is a major concern for optimization.
 It identifies improved control strategies that will minimize the high energy consumption cost and grid impact from the uncontrolled charging of largescale EVs.
 The insight derived from this study provides a valuable recommendation that will guide government policy on the need to promote energy management strategies that will enhance the electricity grid operations and promote the adoption of EVs that will drive the electrification of the transport sector.
 It contributes to the United Nation Sustainable Development Goals No. 7, which advocates for affordable and clean energy; No. 11, which promotes efforts towards sustainable cities and communities; No. 12, which encourages responsible consumption and production; and No. 13, which advocates for climate change actions that will minimize global greenhouse gas (GHG) emissions.
2. Related Work
2.1. Electric Vehicle Charging Problem
2.2. Electric Vehicle Charging Control Methods and Grid Energy Management
2.2.1. AntBased Optimization for Electric Vehicle Charge Management
2.2.2. Artificial Bee Colony
2.2.3. Genetic Algorithms
2.2.4. Particle Swarm Optimization
2.2.5. Neural NetworkBased Optimization
2.2.6. Hybrid Optimization Strategy
2.2.7. Controlled Charging of Electric Vehicles Using Social Spider Algorithm
2.2.8. Reinforcement Learning
2.2.9. Load Modelling
2.2.10. Alternating Direction Method of Multipliers
2.2.11. Monte Carlo Method
3. Research Gaps Identified in the Literature
3.1. Load Modelling Based on Grid Capacity Constraints
3.2. Forecasting EV Charging Demand
3.3. Dynamic Load Management
4. Conclusions and Future Research Direction
 Development of an integrated multilevel energy management optimization approach to solve the uncontrolled largescale EV charging problem on the grid electricity distribution system.
 Considering the flexibility potential that is available with largescale EV capacity to offer ancillary services, we recommend further research in this area for an optimal vehicletogrid business model.
 EV battery degradation [70] due to the frequent charging is a major concern for EV owners; as it discourages them from participating in ancillary services that could be beneficial as an example, in grid voltage control. This perception is also a major drawback for EVs to participate in various V2G applications. Further research should be conducted to address this concern.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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EV Brand  Battery Capacity  Range  Energy Consumption  Charger Type  Charger Power  

AC  DC  
Tesla Model 3  60 kWh  380 km  151 Wh/km  Type 2  11 kWAC (6 h15)  170 kWDC (25 min) 
Hyundai IONIQ6  58 kWh  360 km  150 Wh/km  Type2  11 kWAC (6 h)  175 kWDC (17 min) 
Renault Megane ETech  60 kWh  365 km  164 Wh/km  Type 2  22 kWAC (3 h15)  129 kWDC (30 min) 
Peugeote308 SW  54 kWh  300 km  170 Wh/km  Type 2  11 kWAC (5 h30)  100 kWDC (28 min) 
Fiat 500e Hatchback  24 kWh  135 km  158 Wh/km  Type 2  11 kWAC (2 h30)  50 kWDC (24 min) 
Mini Cooper SE  32.6 kWh  180 km  161 Wh/km  Type 2  11 kWAC (3 h15)  49 kWDC (29 min) 
Step 1. Establish the Objective Function Step 2. Set Parameters Based on Ant Population, Number of Iterations, etc. Step 3. Randomly Initialize Ants from the Colony Step 4. Initialize the Transition Probabilities of Ants Based on Pheromone Trails Step 5. While (Terminate Condition Not Satisfied) do Step 6. Construct Solutions Step 7. Pheromone Update Step 8 Output Best Solution for Fitness Step 9. End While 
References  Methods/Techniques  Objectives  Problem Addressed 

[10,11,12,13]  AntBased Optimization 


[14,15,16,17,18]  Artificial Bee Colony 


[19,20,21,22,23,24]  Genetic Algorithm 


[25,26,27,28,29]  Particle Swarm Optimization 


[30,31,32,33,34,35,36,37]  Neural Network 


[44,45,46,47]  Social Spider Optimization 


[48,49,50,51,52,53]  Reinforcement Learning 


[12,54,55,56,57,58]  Load Modelling Techniques 


[59,60,61,62,63,64,65]  ADMM Technique 


[66,67,68]  Monte Carlo Simulation 


[38,39,40,41,42,43]  Hybrid Optimization: PSO, GA, Dynamic Programming, Monte Carlo, Hybrid Crow Search 


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Kene, R.O.; Olwal, T.O. Energy Management and Optimization of LargeScale Electric Vehicle Charging on the Grid. World Electr. Veh. J. 2023, 14, 95. https://doi.org/10.3390/wevj14040095
Kene RO, Olwal TO. Energy Management and Optimization of LargeScale Electric Vehicle Charging on the Grid. World Electric Vehicle Journal. 2023; 14(4):95. https://doi.org/10.3390/wevj14040095
Chicago/Turabian StyleKene, Raymond O., and Thomas O. Olwal. 2023. "Energy Management and Optimization of LargeScale Electric Vehicle Charging on the Grid" World Electric Vehicle Journal 14, no. 4: 95. https://doi.org/10.3390/wevj14040095