High-Efficiency Driving Cycle Generation Using a Markov Chain Evolution Algorithm

At present, driving cycles generated by Markov chain (MC) based methods have been used extensively in the evaluation of vehicle fuel and emissions, for designing the size of power components, and the optimization of energy management strategies. To study the adaptability of vehicles to driving cycles, it is necessary to generate representative driving cycles with a large number of vehicles and road parameters to reflect the actual driving environment. However, under this condition, the time efficiency of the MC simulation is very low, which makes it difficult to generate driving cycles with the required representativeness. Combined with random sampling, the evolution mechanism of a genetic algorithm (GA) can achieve this goal more efficiently than conventional random sampling. Therefore, the authors propose a novel MC evolution (MCE) algorithm that improves the efficiency of the MC by combining stochastic sampling with evolution. Genetic strategies are designed to satisfy the state transition of driving cycles, and the new GA is employed to optimize the stochastic sequences of the MC. Three-parameter (velocity, acceleration, and grade) driving cycles with different lengths are generated using the MCE from collected highway driving data. The validity and feasibility of the proposed method are evaluated by a statistical analysis. A comparison of the MCE with other methods, including the MC stochastic method and a traditional GA, reveals that the proposed method yields driving cycles with remarkably improved representativeness and efficiency.


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  • Accession Number: 01710922
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 21 2019 1:59PM