Second Optimization Method of Neural Network Driving Condition Identification Based on the Genetic Algorithm

Research Area: Volume 11, Issue 4, July 2022 Year: 2022
Type of Publication: Article Keywords: Plug-in Hybrid Electric Vehicle, Driving Cycle Prediction, Energy Management Strategy, Equivalent Fuel Consumption Minimization
Authors:
  • Shipeng Li
  • Ruijun Liu
  • Dapai Shi
  • Jinlong Yang
  • Junjie Guo
  • Qingling Cai
Journal: IJEIR Volume: 11
Number: 4 Pages: 57-70
Month: July
ISSN: 2277-5668
Abstract:
In order to solve the problem that the low recognition rate of the driving condition reduces the control effect of the whole vehicle energy control strategy, this paper proposes an intelligent identification method of the driving condition which uses a genetic algorithm (GA) to optimize the back propagation neural network (BPNN) intelligent identification of driving conditions. First, 21 typical driving conditions are classified according to the dimension reduction characteristic values and the comprehensive driving condition is constructed. Second, the typical driving condition identification model is established by the K-means clustering and simulated. Then, the K-means condition identification model is optimized by the BPNN method and the BPNN condition identification model are simulated. Finally, the genetic algorithm is used to optimal the BPNN condition identification model, and the second optimization model of the BPNN condition identification (GA-BPNN) is established and simulated in the MATLAB environment. The main contribution of the paper is that GA-BPNN condition identification can accurately identify future driving conditions. Results show that compared with the traditional K-means clustering method and BPNN intelligent identification method, the GA-BPNN driving condition intelligent identification method can further improve the condition identification accuracy, and the driving conditions recognition accuracy reaches 93%.

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