Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

The uncertainties and disturbances in the actual driving conditions of hybrid electric vehicles (HEVs) complicate the design of energy management strategy (EMS). To achieve better EMS performance for a battery-supercapacitor HEV, this paper proposes an improved and adaptive deep learning-based velocity prediction control EMS that can prolong the battery lifetime through efficient utilization of both the battery and supercapacitor. First, feature engineering techniques are used to extract and increase the key features from the historical driving cycle data of known driving conditions. With the extracted features, an improved long short-term memory (LSTM) velocity predictor was developed to predict future driving cycles for a real-time EMS under an unknown driving condition. Second, a real-time EMS based on the rule-based framework optimized with a neural network is proposed to optimize the power allocation online. Simulation results show that the proposed strategy smoothens battery peak power (i.e. prolongs battery life span) by approximately 26.85% on average and increases supercapacitor participation in the EMS, as evidenced by its increased energy throughput. Furthermore, compared with other EMS approaches, the proposed strategy improved the efficiency by significantly reducing total energy losses by approximately 22.25%. These results validate the reliability and robustness of the proposed strategy.

Automated summary

This paper proposes an improved and adaptive deep learning-based velocity prediction control EMS for battery-supercapacitor HEVs, which prolongs battery lifetime and increases energy utilization efficiency through feature engineering and optimized power allocation.