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<p>The driving conditions in mining areas are complex, and developing a suitable automatic shifting strategy for mining trucks is crucial. However, the development of automatic shifting strategies faces challenges, as it relies on experience and historical experimental data, which are the highest commercial secrets of manufacturers. In recent years, some shifting strategies based on artificial intelligence technologies have been implemented. However, many people shift gears based on the current state of the vehicle, ignoring the influence of historical data. There is a potential risk of mis-shift when unexpected sensor data is received, and continuously shifting gears in a short period of time can increase the likelihood of transmission damage, affecting the driving experience. To this end, this study proposes a novel gear shifting prediction method based on a multi-parameter Bi-directional Long Short-Term Memory(Bi-LSTM) network operating over continuous time periods. Real-time vehicle state data is collected via the CAN bus and 9 parameters that are positively correlated with gear shifting are selected through R/S analysis. By inputting values of those 9 parameters within continuous time periods into the machine learning model, gear shifting prediction is conducted. The experimental results show that our model predicts gear shifting with 96.85% accuracy while its average time cost is around 3.86&#xa0;ms, meeting the real-time processing requirement. The model balances prediction accuracy and time consumption, and it overcomes the impact of transient abnormal sensor data. Hence, it has the potential for wide application in predictive models based on data with temporal characteristics.</p>

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Intelligent gear shifting strategy of mining truck based on deep learning and real-time vehicle condition

  • 期刊论文

  • 发表日期：

    2025年1月13日

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  • Qinghua Su,
  • Xiaoyu Xu,
  • Liyong Wang,
  • Dingge Zhang,
  • Min Xie,
  • Pengbo Zhang

摘要

The driving conditions in mining areas are complex, and developing a suitable automatic shifting strategy for mining trucks is crucial. However, the development of automatic shifting strategies faces challenges, as it relies on experience and historical experimental data, which are the highest commercial secrets of manufacturers. In recent years, some shifting strategies based on artificial intelligence technologies have been implemented. However, many people shift gears based on the current state of the vehicle, ignoring the influence of historical data. There is a potential risk of mis-shift when unexpected sensor data is received, and continuously shifting gears in a short period of time can increase the likelihood of transmission damage, affecting the driving experience. To this end, this study proposes a novel gear shifting prediction method based on a multi-parameter Bi-directional Long Short-Term Memory(Bi-LSTM) network operating over continuous time periods. Real-time vehicle state data is collected via the CAN bus and 9 parameters that are positively correlated with gear shifting are selected through R/S analysis. By inputting values of those 9 parameters within continuous time periods into the machine learning model, gear shifting prediction is conducted. The experimental results show that our model predicts gear shifting with 96.85% accuracy while its average time cost is around 3.86 ms, meeting the real-time processing requirement. The model balances prediction accuracy and time consumption, and it overcomes the impact of transient abnormal sensor data. Hence, it has the potential for wide application in predictive models based on data with temporal characteristics.