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Optimal electric vehicle charge scheduling algorithm using war strategy optimization approach.

Vikramgoud Madaram1, Pabitra Kumar Biswas1, Chiranjit Sain2

  • 1Department of Electrical and Electronics Engineering, National Institute of Technology Mizoram, Aizwal, 796012, India.

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|September 18, 2024
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Summary

A new War Strategy Optimization (WSO) algorithm significantly reduces electric vehicle (EV) charging costs and waiting times. This innovative approach enhances EV charging management efficiency, especially with limited infrastructure.

Keywords:
Charge schedulingElectric vehicleHarris Hawk optimizationWar strategy algorithm

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Operations Research

Background:

  • Electric vehicle (EV) adoption is rapidly increasing, necessitating efficient charging management systems.
  • Current EV charging scheduling algorithms face challenges in optimizing both cost and waiting time, particularly in areas with limited infrastructure.

Purpose of the Study:

  • To propose a novel charge scheduling algorithm inspired by military strategies.
  • To evaluate the effectiveness of the War Strategy Optimization (WSO) algorithm in reducing EV charging costs and waiting times.

Main Methods:

  • Developed a charge scheduling algorithm based on War Strategy Optimization (WSO) principles (attack, defense, soldier assignment).
  • Validated the WSO algorithm in a simulated geographic area with multiple nodes, charging stations, and varying numbers of EVs and charging piles.
  • Utilized MATLAB for experimental validation and comparison with existing algorithms like First Come First Serve (FCFS), Chaotic Harris Hawk Optimization (CHHO), and Harris Hawk Optimization (HHO).

Main Results:

  • The WSO algorithm demonstrated significant improvements, reducing average charging costs by up to 13.67% and average waiting time by up to 83.25% compared to FCFS.
  • Compared to CHHO and HHO, WSO achieved reductions in waiting time by 11.17% and 39.09%, respectively, and in charging costs by 3.61% and 12.45%, respectively.
  • The algorithm proved particularly effective in scenarios with limited charging infrastructure.

Conclusions:

  • The proposed WSO algorithm offers a superior solution for EV charging management, enhancing efficiency and cost-effectiveness.
  • WSO's ability to optimize EV allocation, minimize waiting times, and reduce charging costs makes it a promising approach for real-world applications.
  • The findings highlight the potential of strategy-inspired algorithms in addressing complex optimization problems in sustainable transportation.