Intelligent energy management strategy for hybrid electric vehicles using reinforcement learning

ABSTRACT

Predicting the performance of a Hybrid EV (Electric Vehicle) is significant to alleviate the complications of insufficient fuel and other battery-related problems that affect the driving capacity. Many energy management techniques with ML (Machine Learning) and DL (Deep Learning) methodologies have been deployed in predicting the operation of vehicles based on energy sources and their features. Still, it is being noticed that prediction errors have increased along with the increase or decrease of input data. To solve the challenges addressed in existing studies, the proposed approach intends to use both ML and DL to predict faults from battery features that considerably manage the energy. It is accomplished through efficient feature extraction employed by Bi-LSTM (Bidirectional Long Short Term Memory), which extracts the best energy features required for predicting the defects. From the extracted input features collected hourly, daily, and monthly from the dataset, classification is done using the Modified XGBoost Algorithm with Empirical Loss function. Normal and Abnormal functioning of the Hybrid EV is classified effectively. Followed by that, the prediction of improper functioning is made with the assistance of the vehicle Id to maximise the vehicle’s performance through consideration of battery constraints. Experimentation is performed with MATLAB simulation, and the performance of the proposed system is evaluated using error metrics like RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MSE (Mean Square Error), and accuracy. Efficiency is exhibited through the minimum loss obtained through the proposed work. From analysis of comparing LSTM method with proposed model, it is found that the LSTM method produced RMSE value of 1.055% which is 0.84% reduced in proposed model. On the other hand, the MAE of LSTM is 0.826% and the proposed model is decreased with 0.174%. Further, the MSE of LSTM is 1.113% and is 0.69% reduced in proposed model.

KEYWORDS:

• Hybrid Electric vehicle
• energy management system
• prediction
• convolutional neural network
• Bidirectional long short term memory
• Xgboost

Disclosure statement

No potential conflict of interest was reported by the author(s).