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Optimal Speed Control of Hybrid Electric Vehicle Using GWO Based Fuzzy-PID Controller

机译:基于GWO的Fuzzy-PID控制器的混合动力电动汽车最优速度控制。

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The Hybrid electric vehicle (HEV) is getting more attention now a days due to limited conventional energy resources and environment issues. The limited range of battery power of electrical vehicle (EV) motivates the researchers to explore HEV which combines both electrical machines and Internal combustion (IC) engine to produce power. The objective of this research work is speed control Hybrid Electric vehicle (HEV) by controlling the throttle position of the motor. Moreover, the designed controller needs to give smooth throttle movement with minimized the steady state error. To fulfill the desired objective two control schemes has been employed. To optimize the parameters of the controller, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) technique are used. The comparative analysis of response using different control schemes has been carried out. The observed results demonstrate the superiority of proposed scheme in terms of lessen fuel utilization of the HEV queue and improving traffic smoothness.
机译:由于常规能源资源和环境问题的局限性,混合动力电动汽车(HEV)如今已引起越来越多的关注。电动汽车(EV)的电池功率范围有限,促使研究人员探索混合动力汽车,该汽车结合了电机和内燃机(IC)来产生动力。这项研究工作的目标是通过控制电动机的油门位置来控制混合动力汽车(HEV)的速度。此外,设计的控制器需要在最小化稳态误差的情况下提供平稳的油门运动。为了实现期望的目的,已经采用了两种控制方案。为了优化控制器的参数,使用了灰狼优化(GWO)和粒子群优化(PSO)技术。已经对使用不同控制方案的响应进行了比较分析。观察结果表明,该方案在减少混合动力汽车队列的燃料利用率和改善交通通畅性方面具有优势。

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