Dimensionality Reduction-Based Multi-Step Optimization Method for Multi-Parameter Driving Cycles

With the advancement of automotive energy-saving and emission-reduction technologies, constructing multi-parameter driving cycles that accurately represent real-world driving behavior has become crucial for vehicle development, performance evaluation, and energy management strategy optimization. These cycles typically incorporate key parameters such as vehicle velocity, acceleration, and road grade, providing a comprehensive description of both the vehicle operational states and the external environment. However, constructing such multi-parameter driving cycles faces challenges including high-dimensional state spaces, complex constraints, and high computational costs. To address these challenges, this study proposes a dimensionality reduction-based multi-step optimization method designed to significantly reduce modeling complexity and improve generation efficiency. The method achieves effective dimensionality reduction through a joint low-dimensional representation of state transitions and conditional constraints, establishes a comprehensive evaluation framework integrating distributional consistency and statistical features, and employs a multi-step genetic algorithm for efficient iterative optimization of cycle sequences. Validation results demonstrate that the proposed method can reliably generate highly representative multi-parameter driving cycles, exhibiting substantial advantages over conventional baseline methods as regards modeling efficiency, sequence generation speed, and accuracy. The framework shows good extensibility and adaptability, offering an effective solution for constructing multi-parameter driving cycles across different vehicle types and driving scenarios.