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This paper presents Adaptive Second-Order Sliding Mode Control (SOSMC) for power control of Permanent Magnet Synchronous Generator (PMSG). The control objective is to force the PMSG to generate the desired power through regulating the winding current. Specifically, Super Twisting (ST) algorithm SOSMC is adopted in this paper to derive a robust and fast current control for PMSG-based Wind Energy Conversion System (WECS). ST algorithm is renowned for its robustness against parametric uncertainty and external disturbance, but it suffers from the chattering problem. The existing Adaptive Super Twisting (AST) algorithm can reduce the chattering effect, but often at the expense of degraded transient response. In this work, an alternative way is proposed to implement AST algorithm in order to achieve fast transient response, while at the same time attenuate the chattering problem. The controller performance is validated through an experimental setup consisting of a wind turbine emulator and a PMSG which is connected to the grid via back-to-back converter. The experimental results show better performance of the closed-loop system in terms of response time, steady-state error, and chattering despite the presence of parametric uncertainty. [ABSTRACT FROM AUTHOR]

A disturbance observer based control method for a grid-connected doubly fed induction generator is presented in this study. The proposed control method consists of a state-feedback controller and a disturbance observer (DO). The DO is used to compensate for model uncertainties with the aim of removing the steady-state error. The control objective consists of regulating the stator currents instead of the rotor currents in order to achieve direct control of the stator active and reactive powers. Such a control scheme removes the need for an exact knowledge of the machine parameters to achieve accurate control of the stator active and reactive powers. The main advantage of this control method is ensuring a good transient performance as per the controller design specifications, while guaranteeing zero steady-state error. Moreover, the proposed control method was experimentally validated on a small scale DFIG setup. [ABSTRACT FROM AUTHOR]

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

• Multiobjective optimization problem to optimally size hybrid-energy-source HEVs. • Battery, ultracapacitor, and fuel cell unit configurations as hybrid energy sources. • Storage size design to recover braking energy via UC/BU to reduce power loss. • Adaptive energy management strategy with quantum butterfly optimization algorithm. • Proposed strategy performs better than discrete wavelet transform power splitting. This paper proposes an optimal hybrid energy sources sizing methodology for hybrid electric vehicles comprising ultracapacitor (UC) and fuel cell (FC) with battery units (BU). For this purpose, a multi objective problem is formulated using dynamic-source models to evaluate the system's initial cost, weight, running cost, and cost associated with source degradation. Furthermore, a novel adaptive energy management strategy (AEMS) that focuses on dynamic-source characteristics and drive cycle power demand is proposed as an integral part of the optimization problem. Finally, to solve the hybrid energy source optimization problem, the butterfly optimization algorithm (BOA) is improved by employing the quantum wave concept to explore the search space more effectively. The performance of the proposed method is evaluated with different hybrid source configurations and various drive cycles using improved BOA, BOA and particle swarm optimization. The Matlab® simulation results show that battery rating can be downsized by approximately 40% upon the inclusion of UC and FC units using improved BOA. Furthermore, when the proposed AEMS is compared with a conventional discrete wavelet transform power-splitting approach used in the optimization process, the proposed AEMS performs better and could reduce the system relative cost and weight for BU-UC-FC configuration by 16% and 10% respectively. [ABSTRACT FROM AUTHOR]