Adnan Mohammed Saleem Mohammed Hassan Alamili

Dynamic energy recovery in electric and hybrid electric vehicles can assist in restoring kinetic energy during descending and braking operations. The recouped energy can be stored in various storage units, such as a flywheel energy storage (FES) units, ultra-capacitors (UCs) or batteries, thus conserving energy for later use during acceleration condition. The FES is used in a variety of modern applications; for instance, it tends to be associated with the motor shaft to assist in starting the vehicle from rest. Several different components have been utilized to execute the motoring and braking mechanism in a structured test rig. However, there is an urgent need to represent this application within the limits of the laboratory facilities while being able to simulate the natural system. Consequently, this paper will examine the effect of rotating inertia at a different rotating speed in the drive system on propelled energy. It will also evaluate the stored energy in the UC storage unit and the braking time in the regenerative braking (RB) process. Moreover, it will demonstrate the experimental probing and mathematical modeling of the motor/flywheel (lumped into an equivalent inertial mass), which is used in different drive cycle schemes to simulate real system behavior. The analysis is accomplished with experimental verification, highlights the advantage of the new approach for evaluating storage capacity. Also, it gives the static and dynamic (time dependency) equations to show an accurate system representation as a ready-made (black box) models, which will be used to determine the overall system performance.

Ultra-capacitor (UC) is a type of rechargeable energy storage unit used in different industrial applications. It has been utilised to transmit high current on acceleration and to accept regenerative braking energy on descending and braking in electric vehicles, and hybrid electric vehicle power applications. In this research, the UC has been tested in the laboratory and studied in the simulation with the aim to investigate a method for harvesting energy. Analysis of electricity generated from the braking process by rotating machinery has been carried out at a designed test rig with the prime objective of monitoring UC charging voltage at different states. The newly proposed modelling and algorithm consider the generator and the UC as two storage units having a capacitance and different voltage levels for dynamic charge transfer and the observation of voltage states for demanding applications. This method defines the generator variable charging behaviour, and the UC response shows an accurate simulation.

Robust control for linear motor (LM) is essential in a mechatronic system where rapid response and high accuracy are requested. This research elaborates the design of robust control approach employing a second-order terminal sliding mode control (2OTSMC) strategy integrated with an online perturbation estimation (PE) technique, namely 2OTSMCPE for an LM system. The proposed control architecture can provide high accuracy in terms of tracking LM performance in nominal and uncertain models. Moreover, adopting PE has facilitated control design and simplified its practical applications. Since there is no need for upper bound system perturbation in the 2OTSMCPE control design, no significant effort is required for control, which avoids the overestimation issue. Furthermore, for optimum performance, we have given a guideline to the parameter's selection of the proposed controller. Finally, simulations studies have been conducted to demonstrate the validity of the 2OTSMCPE controller in terms of time response and tracking performance across the non-singular terminal sliding mode controller (NTSMC).

The proposed robust sliding mode learning control (RSMLC) is a new control approach that uses immediate feedback from the closed-loop system to improve tracking performance. A recursive learning technique is integrated with the sliding mode controller to ensure that the tracking error and sliding variables asymptotically converge to zero, which can be guaranteed within the framework of the proposed control approach. Moreover, the proposed controller design does not require system uncertainty and its upper limits. Thus, these benefits can be significantly simplified and mitigated by the design and implementation of RSMLC for DC motor applications. In comparison with conventional sliding mode control (CSMC), the RSMLC structure does not contain an explicit switching element, so the chattering phenomenon will be eliminated. Meanwhile, it will preserve the CSMC’s durability feature. Based on Lyapunov criteria, the stability and convergence analysis of the proposed controller were rigorously proved. Additionally, CSMC and SMLC controllers have been shown to outperform proportional integral derivative (PID) controllers in systems with nonlinear dynamics, high-order systems, or uncertainties. Finally, simulation studies of the DC motor system were carried out under the proposed controller. In contrast, the CSMC simulation results are also presented for comparison purposes and to verify the validity and effectiveness of the proposed RSMLC via CSMC and PID controllers.

In this paper, a robust sliding mode-based learning control (RSMLC) scheme is developed for a micro-scale actuator (MSA) system. By exploiting the instantaneous information of the system closed-loop stability, a sliding mode controller based on the recursive learning technique is designed. The proposed control approach can govern the sliding variable and tracking error between the desired references and actual displacement of the MSA to asymptotically converge to zero. Unlike conventional sliding mode control (CSMC), no prior knowledge of MSA system uncertainties and parameters variation is required in the RSMLC design. Besides, there is no explicit switching element in the RSMLC structure, it can be considered as a chattering free control method. Meanwhile, the inherent high robustness property of the CSMC is fully preserved in the proposed controller. Where the learning algorithm continuously adjusts the closed-loop response based on the most recent history of closed-loop stability. Thus, the stability and convergence analysis of the RSMLC is proved rigorously. Simulation studies have been conducted for a piezoelectric actuator (PEA) system, as a prototype of MSA. For comparison reasons, these results are presented correspondingly with CSMC results to demonstrate the effectiveness of the proposed controller over the CSMC.

This PhD thesis presents a simulation of the dynamic energy recovery with an experimental-based model of the braking process. Accordingly, it describes the steps taken to develop a small-scale representation of the designed testing equipment, which was built within the laboratory boundaries to simulate the natural system. Moreover, it demonstrates the experimental probing and mathematical modelling of the system from an electronic perspective as well as in the mechanical perception as ready-made (black box) models. Meanwhile, the designed system shows an accurate representation in simulation, which was verified experimentally. In this study, energy recovery with two distinct storage units, especially, ultra-capacitors (UCs) and battery energy storage systems (ESS) was considered as an alternative energy source with propulsion strategies to assess their effect on storing and generating electricity from the braking process. A simulation was determined to signal the system behaviour for different operating scenarios. Consequently, the voltage generated by a permanent magnet brushless direct current (PMBLDC) motor of the test network, when used as a generator in the braking operation, was used to study the impact of the uncontrolled charging loads (batteries and UCs) on the system performance in the braking process.