Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

The escalating adoption of low-carbon energy technologies underscores the imperative to transition from conventional fossil fuel-dependent sources to sustainable alternatives. The expansion of Distributed Energy Resources (DERs) signifies an essential shift towards a more resilient and environmentally friendly energy landscape. However, integrating inverter-based DERs introduces challenges, particularly in system inertia and grid instability. This review delves into the critical area of inverter-based grid control strategies, focusing on the primary and secondary control mechanisms. Primary controls are investigated, including traditional droop control and low-voltage ride-through (LVRT) capability. The secondary control strategies, involving virtual impedance (VI) and load frequency control (LFC), are vital in maintaining grid stability and reliability are reviewed. The aim is to offer a comprehensive understanding of the principles, advancements, and challenges associated with inverter-based grid controls, contributing valuable insights for the seamless integration of DERs into modern power grids.

The demand for wind turbines has been ultimately increased over the last decades. Accordingly, the power converter controller plays the primary role in extracting energy out of the generator, using efficient and reliable techniques as Maximum Power Extraction (MPE) and delivering the power to the grid. This research pursues to present a Cascade-Forward Neural Network (CFNN) MPE that maintains the MPEs advantages besides providing the flexibility of limiting the output power at significantly lower complexity in the control loop. The proposed strategy uses the cascade-forward neural network to learn the wind turbines aerodynamic nonlinear dynamics and achieves accurate power tracking. Additionally, it reformulates the machine d-q axes voltages equations to operate the wind energy conversion systems (WECS) in optimal condition by considering the wind speed, air temperature, power demand, and disturbances. Furthermore, it does not require any tuning procedure. The power tracking performance of the recommended CFNN MPE controller is evaluated through several experimental and simulation tests in different situations, and all the results are matched with the manufacturers datasheets and another proven strategy to confirm its effectiveness.

In this paper, an energy efficiency improvement strategy is developed using a simplified current sensorless dynamic direct voltage maximum torque per ampere (MTPA) speed control method of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle applications. Tracking the MTPA angle using a unique voltage amplitude without current sensing at any electric vehicle's speed yields minimum current/power consumption and high energy efficiency. These aims are achieved by considering the dynamic model of the motor that improves the controller reaction and accuracy during the transient states as contrary to the existing literature. Moreover, the simplified current sensorless dynamic direct voltage control (DDVC) technology contains a couple of control gains versus three times that with the classical MTPA Field-oriented control (FOC) method. Moreover, a comparative validation of the simplified current sensorless dynamic direct voltage MTPA methodology and the FOC strategy is executed. Experimental results with energy consumption measurements and energy efficiency study prove that the proposed simplified DDVC MTPA strategy is a promising alternative to the existing MTPA technologies of IPMSMs' drives as it preserves high energy efficiency during transient.

This research presents a current sensorless dynamic direct voltage method based on the maximum torque per ampere (MTPA) technique to control the rotor speed of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) applications. It considers the dynamic model of the machine and generates a particular pair of voltage amplitude and its angle to track the speed profile of the driving cycle. The proposed control strategy contributes by eliminating the need for a current sensor, avoiding cascaded PI controllers, and overcoming the limitations of transient states. Consequently, the measurement noise and equipment failure do not affect the control loop, the tuning of a single PI controller becomes much simpler than two cascaded PI controllers, and the absence of MTPA tracking and current or voltage overshooting at transient states cannot limit the performance of the control technique. Additionally, the aforementioned modifications improve the response time of the proposed strategy and make it nimble enough for EV applications. A comparative analysis is carried out on the proposed current sensorless dynamic direct voltage MTPA control, simplified dynamic direct voltage control (S-DDVC), and the field oriented control (FOC) models. The outcomes confirm that the introduced model substantially reduces the inrush current and voltage. Experimental results with quantitative tracking and energy consumption assessments reveal that the proposed method demands 9.7% lower energy than the FOC counterpart in urban driving plans by operating IPMSM in high efficiency region.

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy.