Automatic gear shift strategy for manual transmission of mine truck based on Bi-LSTM network

As mine trucks face severe and changeable driving conditions in mining area, it is important to formulate a general automatic gear shift strategy for reducing the intensity and difficulty of the work. Therefore, gear shift strategy performs a significant role in automatic transmission studies. However, it is difficult to devise an automatic shift strategy due to the fact that it relies on tremendous historical experiment data which is the highest classified commercial secret of the automobile manufacture. Since the artificial neural network can build self-learning model by inputting multiple parameters, adjusting weights and automatically outputting results, many automatic gear shift strategy studies are reported based on it. However, the gear shift prediction accuracy by traditional neural network is limited due to the non-existence of feedback connection, lacking of associative memory function and missing implicit connection between continuous data. Hence, this paper proposes a novel method based on Bi-directional Long Short-Term Memory (Bi-LSTM) network, which controls gear shifting with nine parameters and updates the traditional mine truck transmission system from manual to automatic. Truck status data while manual driving are captured through the Controller Area Network-bus (CAN-bus) and converted to the proper sequence data format. Some sequence data are used to train the Bi-LSTM network for gear prediction, while the rest is used for verification. In order to evaluate the proposed machine learning model performance, it is compared with the Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) networks with two and nine parameters, respectively. The experimental results demonstrate that machine learning networks with nine parameters reach higher gear shifting prediction accuracy than those with dual-parameter, except BPNN. In addition, the highest prediction accuracy is 95.8 %, which is achieved by the Bi-LSTM network with nine parameters. This proves that Bi-LSTM network outperforms the traditional neural networks, such as BPNN with 81.5 %, and other recurrent neural networks, such as RNN and LSTM with 87 % and 91.5 %, respectively. As for the average time consumption, the Bi-LSTM network spends 1.86 ms per prediction calculation which is longer than LSTM and RNN respectively having 1.58 ms and 1.06 ms. Thus, it still fits the requirement of real-time processing on mine truck industry. In general, the Bi-LSTM network shows its advantage in processing sequential data within limited time, and has the potential to be widely applied in the field of prediction based on data with temporal characteristic.