Department of Electrical and Electronics Engineering

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    Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop
    (Elsevier, 2016-05) Mishra, Puneet
    In this paper, a novel scheme is proposed for speed control of highly nonlinear hybrid electric vehicle (HEV) having an electronic throttle control system (ETCS) in the cascade control loop. Fractional order fuzzy PD (FOFPD) and fractional order fuzzy PI (FOFPI) nonlinear controllers are developed and used as primary and secondary controllers, respectively in the cascade control loop. These controllers are variable gain fuzzy controllers having an adaptive nature. Their corresponding integral counterpart, fuzzy PD and fuzzy PI controllers are realized by keeping integer order differentiator and integrator in FOFPD and FOFPI controllers. Further, fractional order PD (FOPD) and fractional order PI (FOPI) controllers are implemented by using non-integer order operators in conventional PD and PI controllers. Extensive simulations have been carried out using National Instruments software LabVIEW™ and its add-on tools such as control design and simulation toolkit to perform a comparative study for FOFPD, FPD, FOPD and PD as primary controller and FOFPI, FPI, FOPI and PI as secondary controller for setpoint tracking of speed of HEV. Multi objective genetic algorithm is used to optimize the gains of primary and secondary controllers for minimization of integral of absolute error (IAE), maximum overshoot and settling time. Performances of tuned controllers are further evaluated for tracking of speed profile, disturbance rejection and model uncertainty. It has been observed that combination of FOFPD and FOFPI controllers outperformed rest of the controllers in servo, regulatory and uncertain environment and demonstrate very robust behavior for speed control of HEV.
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    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 Om
    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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    Development of an ANFIS based equivalent consumption minimization strategy to improve fuel economy in Hybrid Electric Vehicles
    (IET, 2021-03) Singh, Dheerendra; Bansal, Hari Om
    The 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.
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    The efficient operating parameter estimation for a simulated plug-in hybrid electric vehicle
    (Springer, 2021-10) Singh, Dheerendra; Bansal, Hari Om
    Hybrid 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.
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    Green transportation in India: Need analysis and solution
    (IEEE, 2013) Bansal, Hari Om
    Modern transport sector heavily relies on internal combustion (IC) engine based vehicles and depends on fossil fuels as a source of energy to propel the vehicles. These vehicles add on environmental and human health issues due to the emitted toxic gases. Apace usage of fossil fuels results in rapid depletion of these resources and price inflammation; it stimulates to find alternatives in transportation technology. This brings out an innovative solution in terms of hybrid vehicle technology to fulfill world's need for a greener environment. This paper discusses various aspects such as sources of pollution, decreasing level of fossil fuels, dependence on oil energy and need of green vehicles in context to India. Need analysis of hybrid vehicle technology makes it possible to determine India's energy policies to focus on securing its energy resources. Also, it includes the challenges in adopting green vehicles, their remedies and Indian government initiatives to promote them on road.
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    Fuel efficiency optimization of input-split hybrid electric vehicle using DIRECT algorithm
    (IEEE, 2014) Bansal, Hari Om
    For cleaner and greener future, Hybrid vehicle has been accepted as best practical applications for transportation. The presence of two power sources, i.e. engine and battery in hybrid electric vehicles makes it necessary to intelligently split the power for lesser fuel consumption. An intelligent power management strategy is developed to fulfil on road power demand with good fuel economy. This article uses DIRECT method to control toggling between the engine and battery to reduce the overall liquid fuel consumption. The battery charge is utilized effectively without deteriorating its health. The control strategy is based on the optimization of vital parameters such as state of charge in the battery, engine idle speed, engine on duration and power demand. Numerous simulations are executed on the advanced vehicle simulator (ADVISOR) to authenticate the feasibility of the proposed controller
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    Optimal fuel control of series-parallel input split hybrid electric vehicle using genetic algorithm based control strategy
    (IEEE, 2015) Bansal, Hari Om
    The present transportation system heavily relies on of internal combustion engine (ICE) based vehicles. These vehicles emit toxic gases, which results in environmental pollution and create massive health problem. For energy security and greener tomorrow, the concept of hybrid vehicle came into existence. Hybrid vehicles consist of alternative energy storages like fuel-cell, super capacitor, battery or hybrid storage. The presence of two power sources, i.e., engine and battery, makes it necessary to intelligently split the power between them to minimize the fuel consumption. An intelligent controller should be used to split the on road power demand for optimum fuel economy. This article applies a genetic algorithm based controller to toggle between engine and battery. The optimization is based on the selection of vital parameters such as state of charge in the battery, engine on time and power demand. The authenticity and feasibility of proposed controller are verified extensively through numerous simulation results.
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    Energy management in hybrid electric vehicles using particle swarm optimization method
    (IEEE, 2016) Bansal, Hari Om
    Increasing level of environmental pollution, petroleum prices and depleting level of natural resources are major troubles caused by internal combustion engine based transportation system. Hybrid electric vehicles (HEVs) have presented the solution to these problems and are assumed to be future green and sustainable transport medium. HEVs utilizes engine and battery together to give power to the wheels. Since, presence of two sources causes the complexity at architectural level of vehicle, hence requires a judicious power split between them. To split power efficiently between engine and battery, an intelligent energy management scheme is required to be implemented. An efficient power split scheme may consequence in better fuel economy and performance of HEVs. Here, particle swarm optimization based intelligent energy management scheme is implemented and compared with genetic algorithm and dividing rectangle algorithms. Modified state of charge (SOC) estimation method and 1RC battery model are used for simulation purposes in advanced vehicle simulator (ADVISOR).
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    Modeling and Analysis of a V2G Scheme: A Concept in Smart Grid
    (IEEE, 2020) Bansal, Hari Om
    Vehicle-to-Grid (V2G) model has the potential for providing a distributed reserve to the power system developed for large scale implementation of Hybrid Electric Vehicle model. The authors proposed a modified V2G control model managing the various renewable energy sources, vehicles' idle time and power generation, simultaneously, according to the vehicle user's day schedule. Vehicle-to-Grid power is controlled with respect to the desirable battery State-of-Charge and the detected plug-in terminal. In this paper, the proposed control block is implemented in MATLAB/Simulink. Performances of the control structure, the interface, the communication, the system efficiency, and time responses are analyzed by various simulated plots.
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    A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle
    (Hindawi Publishing Corporation, 2014) Bansal, Hari Om
    Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.