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.

The source of electricity storage that is widely used in electric vehicles is batteries. The important parameters for batteries are State of Charge (SOC) and State of Health (SOH). These parameters are important to help protecting the battery, increase battery life and for the safety of the user's operation. Since SOC & SOH from the battery cannot be measured directly, the SOC & SOH parameters need to be estimated. In this paper, the battery is modeled using the polynomial model. The parameter constant is then predicted using Generalized Reduced Gradient (GRG) optimization method. In order to estimate the SOC and SOH simultaneously, Dual Extended Kalman Filter (DEKF) method will be carried out. The first EKF is used to estimate the SOC while the second EKF estimates the internal resistance and the actual capacity of the battery. Futhermore, the results from the second EKF is used to determine the SOH by means of the capacity fade method and resistancy method. The use of DEKF provides more accurate results because it can compensate noise measurements and models and without requiring initial SOC value. It also gives less computational effort and real time results. Lastly, the results of DEKF method are compared with the EKF method to see the performance of the estimator. Simulation results show that DEKF method successfully estimates the SOC, internal resistance and capacity of the battery. DEKF also gives better result in estimating the SOC of battery. The SOH of battery can also be known from the results of battery parameters estimation.

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 non‐aggressive 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.

The problem of stabilization of a model helicopter in a hover configuration subject to parametric uncertainty and external disturbances is addressed. Multiinput multioutput (MIMO) proportional-integral-derivative (PID) control law is reformulated into a full-state feedback control law to synthesize the controller by using robust H∞ control theory. In full-state feedback representation, PID control has implicit integral-backstepping structure. Therefore a new parameter, ρ, can be introduced that acts on the derivative of the control signal. The parameters of MIMO PID controller are then obtained with solving the algebraic Riccati equation with selecting the values of ρ and γ. Model helicopter simulation is carried out to verify the performance of the proposed controller to stabilize the uncertain helicopter model and to suppress external disturbances.

This paper proposes an application of max-pressure control for network-wide signal control at Bandung, Indonesia. The max-pressure approach is employed for a specific disturbed network system synthetic scenario, created with the aim to simulate spillback conditions which causing long congestion across road segments in real traffic conditions. The max-pressure controller is implemented for a network of six signalized intersections in PTV Vissim, a traffic micro-simulation platform. The validated model is generated before implementing in the Vissim traffic simulation. Three types of controller are studied: the currently implemented controller (fixed time controller), cycle-based max-pressure and slotted-based max-pressure. The simulation results show that max-pressure control is more powerful than the currently implemented technique in terms of the capability to avoid congestion by spreading vehicles to other road segments with respect to some events that can be seen as a disturbance.