Transportation is the second-largest sector contributing to greenhouse gas emissions due to CO2 gas generation from the combustion of fossil fuels. Electric vehicles (EVs) are believed to be a great solution to overcome this issue. EVs can reduce CO2 emissions because the vehicles use an electric motor as a propeller instead of an internal combustion engine. Combined with sustainable energy resources, EVs may become zero-emission transportation. This paper presents an overview of the EV drive train types, including their architecture with the benefits and drawbacks of each type. The aim is to summarize the recent progress of EV technology that always continues to be updated. Furthermore, a comparative investigation on energy density and efficiency, specific energy and power, cost, and application is carried out for batteries as the main energy storage. This discussion provides an understanding of the current development of battery technology, especially the batteries used in EVs. Moreover, the electric motor efficiency, power density, fault tolerance, reliability, and cost are also presented, including the most effective electric motor to use in EVs. The challenges and opportunities of EV deployment in the future are then discussed comprehensively. The government regulation for EVs is still a major non-technical challenge, whereas the charging time and battery performance are the challenges for the technical aspect.
Purpose -The purpose of this paper is to investigate the feasibility of controlling a small-scale helicopter by using the model predictive control (MPC) method. Design/methodology/approach -The MPC control synthesis is employed by considering five linear models representing the flight of a small-scale helicopter from hover to high-speed cruise. The internal model principle is employed for the trajectory tracking design. Findings -It is found that the MPC handles well the transition problems between the models, yields satisfactory tracking control performance and produces a suitable control signal. The performance of the tracking control of the helicopter is considerably influenced by the parameter selection in the states and inputs weighting matrices of the MPC. Simulation results also showed that faster dynamics, coupling problems, input and output constraints and changing linearized multi-inputs multi-outputs dynamics models in the small-scale helicopter can be handled simultaneously by the MPC controller.Research limitations/implications -The present study is limited for the application of MPC for the control of small-scale helicopters with nonaggressive maneuvers. Practical implications -The result can be extended to design a full envelope controller for an autonomous small-scale helicopter without the need to resort to a conventional gain scheduling technique. Originality/value -Helicopter control system designs using MPC with a single either linear or non-linear model have been studied and reported in numerous literatures. The main contribution of the paper is in the application of MPC to handle the control problems of a small-scale helicopter defined as a mathematical model with several different modes during a flight mission.
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