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Automotive Engineering ›› 2023, Vol. 45 ›› Issue (12): 2338-2347.doi: 10.19562/j.chinasae.qcgc.2023.12.016

Special Issue: 智能网联汽车技术专题-控制2023年

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CAN Bus Load Rate Optimization Based on Improved Continuous Hopfield Neural Network

Zhicheng He1,Leihao Du2,Enlin Zhou1(),Gaofeng Qin2,Jin Huang3   

  1. 1.Hunan University,State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Changsha  410082
    2.SAIC GM Wuling Automobile Co. ,Ltd. ,Liuzhou  545007
    3.School of Vehicle and Mobility,Tsinghua University,Beijing  100084
  • Received:2022-04-19 Revised:2022-06-12 Online:2023-12-25 Published:2023-12-21
  • Contact: Enlin Zhou E-mail:tenrey18@163.com

Abstract:

The Busload rate of CAN bus is of vital importance to the security and delay bounds of Vehicle-Bus. Whereas, the traditional continuous Hopfield neural network (Continuous Hopfield Neural Network, CHNN) has the defects of poor penalty parameter robustness and the resulting solution that easy to be trapped in local optimality when solving such problems, based on the Metropolis thought in the simulated annealing algorithm, an improved continuous Hopfield neural network algorithm (SA-CHNN) applied to the CAN bus load rate optimization problem is proposed in this paper. Micro-electric vehicle’s ninety-nine communication signals are selected and tested as experimental data. The results show that the SA-CHNN algorithm successfully solves the problems of the traditional CHNN algorithm in solving the CAN bus load rate optimization, which has obvious advantages. Finally, based on the Simulink-Speedgoat CAN bus experimental platform, the real-time load rate simulation with the optimal signal distribution messages is conducted and the result reveals the accuracy of the SA-CHNN algorithm.

Key words: CAN bus, load rate, neural network, simulate annealing algorithm, Metropolis guidelines