RESEARCH PAPER
Efficiency and Reliability: Optimization of Energy Management in Electric Vehicles Apply Monarch Butterfly Algorithm and Fuzzy Logic Control
1
Department of Artificial Intelligence and Data Science, R. M. K. College of Engineering and Technology, Tiruvallur, Tamilnadu, India
2
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
3
Department of Physics, S.A. Engineering College, Thiruverkadu, Tamilnadu, India
4
Department of Electronics and Communication Engineering, St.Joseph's College of Engineering, Chennai, Tamilnadu, India
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2024-11-18
Final revision date: 2024-12-08
Acceptance date: 2025-01-31
Online publication date: 2025-02-09
Publication date: 2025-02-09
Corresponding author
Josephin Shermila P
Department of Artificial Intelligence and Data Science, R. M. K. College of Engineering and Technology, Tiruvallur, 601206, Tamilnadu, India
Department of Artificial Intelligence and Data Science, R. M. K. College of Engineering and Technology, Tiruvallur, 601206, Tamilnadu, India
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):200691
HIGHLIGHTS
- Using Monarch butterfly optimization and controlled fuzzy logic for energy optimization.
- Energy consumption is reduced with such solutions as optimised efficiency of EVs.
- Systems are adjustable to a dynamic driving environment.
- PV assisted EV’s optimized HESS design comprising battery and supercapacitor.
KEYWORDS
Electric VehiclesEnergy ConsumptionEfficiencyEmissionsEnergy ManagementMonarch Butterfly Optimization (MBO)
TOPICS
ABSTRACT
This paper proposes a Monarch Butterfly Optimization (MBO)-based energy management strategy and a fuzzy logic-based nonlinear controller for electric vehicles (EVs). The MBO algorithm optimizes energy sharing between the battery, electric motor, and regenerative braking system, while the fuzzy logic controller compensates for nonlinearities and uncertainties in EV operations. The objectives are to minimize energy wastage, reduce emissions, and enhance efficiency. The MBO algorithm tunes the fuzzy logic controller to meet energy demands under varying driving conditions. Simulation results show that the proposed approach outperforms traditional methods in efficiency and reliability. The paper also explores the combination of MBO and fuzzy logic for hybrid energy storage systems in photovoltaic-powered EVs, focusing on optimizing battery and supercapacitor performance. The main goal is to improve energy management system performance for cost-effective operation and enhanced EV stability and reliability.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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