Department of Electrical and Electronics Engineering

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Subject: search.filters.subject.Hybrid Electric Vehicle (HEV).

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    Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series–parallel hybrid electric vehicle to improve fuel economy
    (Springer, 2020-01) Bansal, Hari Om; Singh, Dheerendra
    A hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds.
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    Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs
    (Elsevier, 2021-06) Bansal, Hari Om; Singh, Dheerendra
    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.