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Related Experiment Video

Updated: Jun 7, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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Electric vehicle power system in intelligent manufacturing based on soft computing optimization.

Shangyi Zhao¹, Ming Guo¹

¹School of automotive engineering, Wuhan university of technology, Wuhan City, 430070, China.

|November 11, 2024

Summary

This study uses a Genetic Algorithm (GA) to optimize electric vehicle performance, improving dynamic capabilities by over 7% and reducing power loss by 5%. The enhanced model allows electric vehicles to travel further on the same charge.

Keywords:

Electric automobile Genetic algorithm Multi-objective optimization NSGA-II Power system Soft computing

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Area of Science:

Computer Engineering
Automatic Control
Soft Computing

Background:

Soft computing effectively addresses uncertainties and complexities in engineering.
Genetic Algorithms (GA) are powerful tools for solving intricate optimization challenges.
Electric vehicle (EV) performance optimization is crucial for efficiency and sustainability.

Purpose of the Study:

To cooperatively optimize electric vehicle dynamic and economic performance using soft computing.
To enhance EV energy management strategies and power system configurations.
To leverage advanced algorithms for improved EV operational efficiency.

Main Methods:

Investigation and analysis of EV power system structure and energy management.
Application of the improved non-dominated sorting genetic algorithm II (NSGA-II) for parameter optimization.
Integration of a fuzzy logic controller for online GA parameter adjustment.

Main Results:

Optimized EVs demonstrated a >7% improvement in dynamic performance.
Acceleration time was significantly reduced, and power loss decreased by 5%.
The model enabled EVs to achieve longer travel distances under identical power conditions.

Conclusions:

The study presents a novel approach for EV performance enhancement through soft computing.
The optimized model offers a valuable reference for intelligent manufacturing in the EV sector.
Future EVs can be more efficient, energy-saving, and environmentally friendly.