Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs

This paper focuses on optimal energy sharing between the two sources i.e., the internal combustion engine and the battery-powered electric motor in a hybrid electric vehicle (HEV). It is necessary that these sources operate in their efficient operating region while fulfilling the energy demanded by the vehicle to obtain the maximum fuel economy. As both of these sources have different operating characteristic and vehicle running conditions, the situation requires a smart controller to address this problem appropriately. In this work, fuzzy logic and Elman neural network-based adaptive energy management strategies (EMS) in an HEV are designed and implemented. The input parameters to these EMS are torque demand, battery state of charge, and regenerative braking. The proposed strategy aims to maximise the fuel economy while maintaining the battery health. A power-split HEV along with EMS is designed, modelled and simulated in MATLAB/Simulink first and then the whole system is validated in real-time using controller hardware in the loop testing platform (CHIL). The FPGA based MicroLabBox CHIL has been employed to test the system behaviour in real-time. The proposed EMS have been compared with conventional strategies and the comparison reveals that the Elman neural network-based method results in higher fuel economy, faster response, and minimal mismatch between desired and attained vehicle speeds. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper focuses on optimal energy sharing in a hybrid electric vehicle (HEV) between the internal combustion engine and battery-powered electric motor. Fuzzy logic and Elman neural network-based adaptive energy management strategies (EMS) were designed and implemented, leading to higher fuel economy, faster response, and improved vehicle speed control. The system behavior was validated using CHIL testing platform.