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A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

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  • Xiaokai Guo

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Xianguo Yan

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Zhi Chen

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Zhiyu Meng

    (Shanxi Setan Defense Technology Co., Ltd., Taiyuan 030024, China)

Abstract

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

Suggested Citation

  • Xiaokai Guo & Xianguo Yan & Zhi Chen & Zhiyu Meng, 2021. "A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN," Energies, MDPI, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:21-:d:707786
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    References listed on IDEAS

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    1. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    2. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    3. Olatomiwa, Lanre & Mekhilef, Saad & Ismail, M.S. & Moghavvemi, M., 2016. "Energy management strategies in hybrid renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 821-835.
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    Cited by:

    1. Yashuo Li & Bo Zhao & Weipeng Zhang & Liguo Wei & Liming Zhou, 2022. "Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, December.

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