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A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model

Author

Listed:
  • Bin Ma

    (School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
    Beijing Laboratory for New Energy Vehicles, Beijing 100192, China)

  • Penghui Li

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xing Guo

    (School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Hongxue Zhao

    (Research Institute of Highway Ministry of Transport, Beijing 100088, China)

  • Yong Chen

    (School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
    Beijing Laboratory for New Energy Vehicles, Beijing 100192, China)

Abstract

The auto-regressive integrated moving average (ARIMA) model has shown promise in predicting vehicle velocity and road gradient (V–G) for the purpose of constructing power demands in predictive energy management strategies (PEMS) for electric vehicles (EVs). It offers flexibility, accuracy, and computational efficiency. However, the performance of a conventional ARIMA model with fixed structure parameters can be disappointing when the data fluctuate. To overcome this limitation, a novel and flexible-structure-based ARIMA (FS–ARIMA) is proposed in this paper to improve online prediction performance. First, the sliding window method was developed to produce fitting data in real time based on real local historical data, reducing the online computation time. Secondly, the influence of the sliding window sample size, differencing order, and lag in the model on the prediction accuracy was investigated. Based on this, an FS–ARIMA was proposed to improve the prediction accuracy, where an augmented Dickey–Fuller (ADF) test was developed to select the differencing order in real time and the Bayesian information criterion (BIC) was applied to update the model and determine its lag under an optimal sample size. Lastly, to validate the proposed FS–ARIMA, simulations were conducted using two typical driving cycles collected via experiments, as well as the following three typical driving cycles: the New European Driving Cycle (NEDC), the Urban Dynamometer Driving Schedule (UDDS), and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results demonstrated that FS–ARIMA improved prediction accuracy by approximately 41.63% and 42.19% for the velocity and gradient, respectively. The proposed FS–ARIMA prediction model has potential applications in predictive energy management strategies for EVs.

Suggested Citation

  • Bin Ma & Penghui Li & Xing Guo & Hongxue Zhao & Yong Chen, 2023. "A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model," Sustainability, MDPI, vol. 15(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15639-:d:1274594
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    References listed on IDEAS

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    1. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
    3. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Receding-Horizon Prediction of Vehicle Velocity Profile Using Deterministic and Stochastic Deep Neural Network Models," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
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