Department of Electrical and Electronics Engineering
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Item Feed forward modelling 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) Singh, Dheerendra; Bansal, Hari OmA 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.Item Development of an ANFIS based equivalent consumption minimization strategy to improve fuel economy in Hybrid Electric Vehicles(IET, 2021-03) Singh, Dheerendra; Bansal, Hari OmThe most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from each other and thus require an adaptive control strategy to achieve higher fuel economy along with smooth operation and better performance of the vehicle. An energy management control strategy is proposed for an HEV based on an adaptive network-based fuzzy inference system (ANFIS). The proposed adaptive equivalent consumption minimisation strategy decides the power to be drawn from ICE and EM based on input parameters such as the speed of the vehicle, the state of charge of the battery, the EM torque and the ICE torque. The whole system is simulated in an advanced vehicle simulator tool. The proposed non-linear controller has also been tested for real-time behaviour using a field-programmable gate array–based MicroLabBox hardware controller to compare its performance against existing controllers. The authors compared the fuel economy obtained using the proposed method with several other methods available in the literature. The comparison clearly reveals that the proposed ANFIS-based method results in better optimization of energy and hence offers better fuel economy. The urban dynamometer driving schedule has been employed for this analysis.Item The efficient operating parameter estimation for a simulated plug-in hybrid electric vehicle(Springer, 2021-10) Singh, Dheerendra; Bansal, Hari OmHybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) are indispensable tools in reducing greenhouse gas emissions to fight the twin evils of pollution and climate change. In these vehicles, battery replacement and fuel costs are the major recurring costs over a lifetime. Hence, there is a growing attempt to develop strategies that reduce the long-run expenditure in these vehicles without compromising on performance levels. Further, an increase in the fuel economy is also required for the effective penetration of these vehicles in society. Here, the authors attempt to identify the optimal operating values for battery state of charge (SoC), power ratings of motor, and fuel converter to increase the battery life and fuel economy without degrading the vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the future automotive systems technology simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). In this paper, firstly, the effect of important parameters like battery SoC, fuel converter power, and motor power on HEVs’ driving range, battery life, fuel economy, cost, and charge-depleting range has been analyzed. Based on this analysis, the optimal values of the parameters have been estimated. These parameters have resulted in improvements of driving range by 4.3% and battery life by 18% at a minute cost of a 1% decrease in the charge-sustaining battery life and a 0.4-s increase in the time the car takes to hit 60 mph from the rest. This paper presents a simple, effective, and new approach that explores the effect of altering the existing design parameters on vehicle performance, without manipulating, adding, or deleting any component or controller. This can further be extended to study the impact of various other parameters in the proposed work and opens a way to explore other parameters that exist in various other components of XEVs (where X can be H/PH//F). This study will help in achieving optimal cost reduction in these vehicles.Item A comprehensive review on hybrid electric vehicles: architectures and components(Springer, 2019-03) Bansal, Hari Om; Singh, DheerendraThe rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.Item Development of an adaptive neuro-fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles(IET, 2020) Bansal, Hari Om; Singh, DheerendraThe most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from each other and thus require an adaptive control strategy to achieve higher fuel economy along with smooth operation and better performance of the vehicle. An energy management control strategy is proposed for an HEV based on an adaptive network-based fuzzy inference system (ANFIS). The proposed adaptive equivalent consumption minimisation strategy decides the power to be drawn from ICE and EM based on input parameters such as the speed of the vehicle, the state of charge of the battery, the EM torque and the ICE torque. The whole system is simulated in an advanced vehicle simulator tool. The proposed non-linear controller has also been tested for real-time behaviour using a field-programmable gate array–based MicroLabBox hardware controller to compare its performance against existing controllers. The authors compared the fuel economy obtained using the proposed method with several other methods available in the literature. The comparison clearly reveals that the proposed ANFIS-based method results in better optimization of energy and hence offers better fuel economy. The urban dynamometer driving schedule has been employed for this analysis.