Cabin Load Prediction Using Time Series Forecasting in Long-haul Trucks for Optimal Energy Management

Abstract

Predicting the electrical loads experienced by a battery pack during the 10 hour hotel period of a long haul class 8 mild hybrid truck with a sleeper cab, and using the information to achieve an optimal energy management strategy and controlling the State of Charge (SOC) of battery pack can help in improving it's freight efficiency. In this work, Machine Learning (ML) based algorithm has been proposed to predict the driver activity during the hotel period. Hence, the power load demanded from the auxiliaries can be predicted. A special kind of Recurrent Neural Network (RNN) called Long and Short Term Memory (LSTM) is used for the prediction task because of its ability to store recurrent information of a small and a large time horizon. To train the LSTM algorithm, the synthetic load profles are synthesized using rules and observations derived from the existing baseline electrical power load profile of the hotel period. This paper entails the whole process of data synthesis to training the neural network on the synthesized data and the prediction and validation of the power load. The input to the network is a time series of 600 time steps. Dynamic Time Warping (DTW) is used to manipulate the time axis and point wise euclidean distance between the forecast and the test data is used to quantify the accuracy of the model. Then by performing hyper-parameter optimization we find the best combination for number of hidden units and the number of training days for the algorithm.