Microgrids are industrial technologies that can provide energy resources for the Internet of Things (IoT) demands in smart grids. Hybrid microgrids supply quality power to the IoT devices and ensure high resiliency in supply and demand for PV-based grid-tied microgrids. In this system, the usage of predictive energy management systems (EMS) is essential to dispatch power from different resources, while the battery energy storage system (BESS) is feeding the loads. In this article, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS . The main role of the SIEMS is to maintain the SOC at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this article is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g., smart grid and microgrids, in order to produce perturbation for training data. To secure the microgrid’s SIEMS , we propose two Defence algorithms based on defensive distillation and adversarial training strategies for the first time in EMSs. We apply and evaluate these benchmark adversarial attack and Defence methods against the proposed machine learning models to increase the robustness of the models in the system against adversarial attacks.

The increase of electric vehicles (EVs), environmental concerns, energy preservation, battery selection, and characteristics have demonstrated the headway of EV development. It is known that the battery units require special considerations because of their nature of temperature sensitivity, aging effects, degradation, cost, and sustainability. Hence, EV advancement is currently concerned where batteries are the energy accumulating infers for EVs. This paper discusses recent trends and developments in battery deployment for EVs. Systematic reviews on explicit energy, state-of-charge, thermal efficiency, energy productivity, life cycle, battery size, market revenue, security, and commerciality are provided. The review includes battery-based energy storage advances and their development, characterizations, qualities of power transformation, and evaluation measures with advantages and burdens for EV applications. This study offers a guide for better battery selection based on exceptional performance proposed for traction applications (e.g., BEVs and HEVs), considering EV’s advancement subjected to sustainability issues, such as resource depletion and the release in the environment of ozone and carbon-damaging substances. This study also provides a case study on an aging assessment for the different types of batteries investigated. The case study targeted lithium-ion battery cells and how aging analysis can be influenced by factors such as ambient temperature, cell temperature, and charging and discharging currents. These parameters showed considerable impacts on life cycle numbers, as a capacity fading of 18.42%, between 25–65 °C was observed. Finally, future trends and demand of the lithium-ion batteries market could increase by 11% and 65%, between 2020–2025, for light-duty and heavy-duty EVs.

This paper studies a dual-level response surface methodology (DRSM) coupled with Booth's algorithm using a simulated annealing (BA-SA) method as a multiobjective technique for parametric modeling and machine design optimization for the first time. The aim of the research is for power maximization and cost of manufacture minimization resulting in a highly optimized wind generator to improve small power generation performance. The DRSM is employed to determine the best set of design parameters for power maximization in a surface-mounted permanent magnet synchronous generator with an exterior-rotor topology. Additionally, the BA-SA method is investigated to minimize material cost while keeping the volume constant. DRSM by different design functions including mixed resolution robust design, full factorial design, central composite design, and box-behnken design are applied to optimize the power performance resulting in very small errors. An analysis of the variance via multilevel RSM plots is used to check the adequacy of fit in the design region and determines the parameter settings to manufacture a high-quality wind generator. The analytical and numerical calculations have been experimentally verified and have successfully validated the theoretical and multiobjective optimization design methods presented.

Energy harvesting (EH), as an enabling technology of energy derivation from ambient sources, has attracted much research attention in wireless sensor network (WSN) context. The magnetic energy harvester (MEH) introduces ambient energy harvesters’ most promising technological development. This paper presents a review and analysis of MEH applications in WSN like vehicular systems and technologies. The successful approaches are introduced and classified based on technical characteristics and working principles. The fundamentals of their operation are discussed in detail, and the power points of each method are reviewed. To select the optimal energy harvester, in this work a case study is provided for feeding navigational sensors mounted on a rotating wheel of vehicles. Finally, the performance of the developed MEH model is evaluated and discussed for harvesting energy at the rotating wheels of a ground vehicle. To offer electromagnetic field analysis of the studied MEH, the simulations are performed in Ansys Maxwell software for further harvested power evaluation.

A robust design in electrified powertrains substantially helps to enhance the vehicle’s overall efficiency. Robustness analyses come with complexity and computational costs at the vehicle level. The use of sensitivity analysis (SA) methods in the design phase has gained popularity in recent years to improve the performance of road vehicles while optimizing the resources, reducing the costs, and shortening the development time. Designers have started to utilize the SA methods to explore: i) how the component and vehicle level design options affect the main outputs i.e. energy efficiency and energy consumption; ii) observing sub-dependent parameters, which might be influenced by the variation of the targeted controllable (i.e. magnet thickness) and uncontrollable (i.e. magnet temperature) variables, in nonlinear dynamic systems; and iii) evaluating the interactions, of both dependent, and sub-dependent controllable/uncontrollable variables, under transient conditions. Hence the aim of this study is to succinctly review recent utilization of SA methods in the design of AC electric machines (EM)s used in vehicle powertrains, to evaluate and discuss the findings presented in recent research papers while summarizing the current state of knowledge. By systematically reviewing the literature on applied SAs in electrified powertrains, we offer a bibliometric analysis of the trends of application-oriented SA studies in the last and next decades. Finally, a numerical-based case study on a third-generation TOYOTA Prius EM will be given, to verify the SA-related findings of this article, alongside future works recommendations.