Self-Adaptive Hyper-Heuristic Markov Chain Evolution for Generating Vehicle Multi-Parameter Driving Cycles

Abstract

Representative driving cycles are very important for testing energy consumption and pollutant emission, optimizing control strategy, and designing power resource components of vehicles. Multi-parameter representative driving cycles are needed to improve vehicles' adaptability to the driving environment. Thus, high-dimensional driving cycles need to be efficiently generated. Additionally, the generation method should be flexible enough to facilitate use by automobile engineers. This study introduces a hyper-heuristic framework into Markov chain evolution (MCE). A boundary variable is introduced to refine strategies, and multiple evolution strategies are proposed for self-adaptivity. Then an evaluation function based on the desired driving cycles is designed using allocation and update mechanisms. Finally, an efficient framework is established for generating multi-parameter driving cycles. As an example, the generation efficiency of this framework is increased by 63.25% over the standard MCE method through collecting real-world driving data and considering representative driving cycles with three parameters (velocity, acceleration, and road slope). Analyzing the proportions of hyper-heuristic evolution strategies indicates the self-adaptivity of this method. Compared with an adaptive MCE method with two strategies, the proposed method has greater running efficiency and application flexibility.