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Design of a fuzzy controller for energy management of a parallel hybrid electric vehicle
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This thesis addresses the design of a control scheme based on Fuzzy Logic to minimize automobile fuel consumption and exhaust emissions while maximizing battery state of charge (SOC) for hybrid vehicles. The advantages the hybrid vehicle has over the conventional vehicle are very low emission of pollutants, and more efficient fuel consumption if controlled properly. The principal components of the drive train are an internal combustion engine and an electric motor. Since there are two devices, it becomes impossible for the driver to individually control both components while driving along, and it will be necessary to automate the use of these elements so that the vehicle is driven in the same way as a conventional vehicle. In the parallel configuration, both devices apply torque directly to the drive shaft for propelling the vehicle. Each component of the hybrid vehicle is modeled, and throttle angle, motor current and brake torque command are chosen as the control inputs. Another input considered is the driver behavior. This input is obtained from an Artificial Neural Network that classifies the behavior based on the pedal angle characteristics over a period of time. The problem in question is how to distribute the energy demands for each component of the hybrid vehicle so that the objectives, maximizing the battery SOC and minimizing fuel consumption and pollutant emissions, are met. Because these objectives depend on different components, we must decide how to demand energy from them to fulfill the driver request and at the same time meeting the objectives. A Fuzzy Logic Controller is designed to meet the driver demand so that the engine, motor and battery are as little exposed as possible to abrupt transitions. Smooth transitions are desired in the engine in order to decrease fuel consumption and emission of pollutants. Smooth transitions in the battery will lead to extended battery life. Simulation results verify that the controller achieves the design objectives. Because the design procedure is based on trial and error, optimality is not guaranteed. Also stability is hard to prove, since there is not much information on this particular issue of Fuzzy Logic.
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Estrada Gutierrez, Pedro Cuauhtemoc (1997). Design of a fuzzy controller for energy management of a parallel hybrid electric vehicle. Master's thesis, Texas A&M University. Available electronically from
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