Stability analysis and speed control of brushless DC motor based on self-ameliorate soft switching control methods

In recent years, electric vehicles are the large-scale spread of the transportation field has led to the emergence of brushless direct current (DC) motors (BLDCM), which are mostly utilized in electrical vehicle systems. The speed control of a BLDCM is a subsystem, consisting of torque, flux hysteresis comparators, and appropriate switching logic of an inverter. Due to the sudden load torque variation and improper switching pulse, the speed of the BLDCM is not maintained properly. In recent research, the BLDC current control method gives a better way to control the speed of the motor. Also, the rotor position information should be the need for feedback control of the power electronic converters to varying the appropriate pulse width modulation (PWM) of the inverter. The proposed optimization work controls the switching device to manage the power supply BLDCM. In this proposed self-ameliorate soft switching (SASS) system is a simple and effective way for BLDC motor current control technology, a proposed control strategy is intended to stabilize the speed of the BLDCM at different load torque conditions. The proposed SASS system method is analyzing hall-based sensor values continuously. The suggested model is simulated using the MATLAB Simulink tool, and the results reveal that the maximum steady-state error value achieved is 4.2, as well as a speedy recovery of the BLDCM's speed

Hybrid Electric Vehicle Energy Management Strategy with Consideration of Battery Aging

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Finding the appropriate equivalence factor often required prior knowledge of the entire drive cycle. While using the appropriate equivalence factor might miss the opportunities for fuel savings under certain conditions. Therefore, an adaptive control law of equivalence factor called Catch Energy Saving Opportunity (CESO) has been introduced in this work to make the proposed aging ECMS real-time implementable. In order to better understand the impact of the developed optimal strategies on battery aging in HEVs, systematic analysis has been performed to find relations between fuel economy, battery aging and the optimization decisions when using ECMS. Therefore, the varies equivalence factors, state of charge constraints and battery temperatures are observed and analyzed under different Combined Drive-cycles (CDs). The CDs are formulated to test the energy management strategy and battery aging with weights on city and highway drive. In addition, rule-based control in charge-depletion mode aimed to improve battery aging has been simulated in a HEV truck. The simulation results show that, the fuel consumed and battery aging degradation during varied operation could be significantly improved by using a simple control rule in charge-depletion mode. This further indicates the benefits of implementing a battery aging term which impacts the control decision in charge-sustaining ECMS. Based on the analysis results, an aging ECMS has been developed by adding a battery aging term as a cost to the battery. The simulation results showed that this optimal energy management strategy improves battery aging significantly with little or no penalty in fuel economy. In addition, aging CESO ECMS, a real-time optimal strategy, has been developed based on the proposed aging ECMS. The simulation results show that aging CESO ECMS improvs upon the basic aging ECMS performance

Catch energy saving opportunity in charge-depletion mode, a real-time controller for plug-in hybrid electric vehicles

© 2018 IEEE. The energy management of plug-in hybrid electric vehicles (HEVs) is commonly divided into two modes: charge-depletion mode and charge-sustaining mode. This paper presents the optimal adaption law for any type of adaptive energy consumption minimization strategy (ECMS) in charge-depletion mode for plug-in HEVs. To present the optimal law, a particular adaptive ECMS is selected, known as catch energy saving opportunity (CESO). CESO has previously been introduced for series and parallel HEVs in charge-sustaining mode. Here, by introducing the optimal adaption law, CESO strategy is expanded to charge-depletion mode for plug-in HEVs