Dual-Objective Function Optimization for Vehicle Performance and Fuel Economy Using MATLAB

This article provides a comprehensive overview of optimization equipment utilizing MATLAB-based dual-objective functions for vehicle performance and fuel economy. In the automotive industry, optimization design plays a critical role in enhancing both vehicle performance and economic efficiency. We delve into methodologies for implementing multi-objective optimization in MATLAB, which typically involves defining fitness functions using vehicle dynamics models (e.g., acceleration time, maximum speed) and fuel consumption models (e.g., driving cycle simulations). Key algorithms like Pareto-front optimization or weighted-sum approaches are employed to balance competing objectives. The discussion covers the characteristics and advantages of optimization equipment, including automated parameter tuning via MATLAB's Global Optimization Toolbox and real-world application strategies. Through case studies, we demonstrate how to integrate these techniques with simulation tools like Simulink for co-simulation validation. Readers will gain insights into practical MATLAB implementation—such as using fgoalattain for goal attainment optimization or genetic algorithms for non-linear constraints—and learn to apply these solutions effectively in automotive engineering to contribute to advanced vehicle performance and fuel economy.