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This paper proposes a novel 1-ϕ, Cuk based on-board electric vehicle (EV) charger with least power components. The proposed EV charger has a special feature to achieve power factor correction (PFC) at AC grid without requirement of the grid voltage and current sensors which cuts the cost and increases the power density of the EV charger along with robustness to noise. The automatic PFC at AC grid is accomplished by operating the output DC inductor in discontinuous conduction mode (DCM). The proposed EV charger necessitates a minimal number of power components for positive and negative half cycles of AC grid which improves the overall efficiency of the system. This is possible due to the combination of inverting and non-inverting Cuk converters are used for each half cycle of the AC grid. Further, the presence of output inductor in the EV charger reduces the ripples in the output current which is not common with all the existing chargers in the literature.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

The increasing electrification of vehicles raises system reliability concerns as the electrical and electronic components deteriorate faster after an event. In addition, the traditional method of scheduled maintenance is not efficient for managing a fleet of vehicles; because, the degradation processes are distinct in different vehicles. Therefore, integrating an online degradation forecasting and abatement module into a vehicle that is able to assess the vehicle status and predict the degradation process to take timely appropriate actions to reach satisfactory reliability and long-term goals, is valuable. Quantifying uncertainty is one of the main challenges of degradation forecasting; because, the degradation process of a vehicular system is distinct. This paper proposes an online degradation forecasting framework to predict the degradation processes to reallocate energy sources in the system, obtaining long-term goals while adhering to the reliability requirements.

The application of power electronic converters (PEC) in electric vehicles (EVs) has increased immensely as they provide enhanced controllability and flexibility to these vehicles. Accordingly, the interest in developing innovative and sustainable technologies to ensure safe and reliable operation of PECs has also risen. One of the most difficult challenges experienced during the development of reliable PECs is the design of proper thermal management systems for controlling the junction temperature and reducing the thermal cycling of power semiconductors. The addition of Active Thermal Control (ATC) can mitigate these concerns. Moreover, the performance of the thermal management system can be enhanced further by the integration of active cooling methods. An active cooling system consumes external energy for circulating cooling air or liquid within the PECs.

Electric vehicles (EVs) are becoming increasingly recognized as an effective solution in the battle against climate change and reducing greenhouse gas emissions. Lithium-ion batteries have become the standard for energy storage in the automobile industry, widely used in EVs due to their superior characteristics compared to other batteries. The growing popularity of the Vehicle-to-grid (V2G) concept can be attributed to its surplus energy storage capacity, positive environmental impact, and the reliability and stability of the power grid. However, the increased utilization of the battery through these integrations can result in faster degradation and the need for replacement. As batteries are one of the most expensive components of EVs, the decision to deploy an EV in V2G operations may be uncertain due to the concerns of battery degradation from the owner’s perspective.

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network.

This study proposes a multi-objective finite control set model predictive control (FCS-MPC) for traction motor drive systems in electric/hybrid-electric vehicles. The proposed method seeks to find the most optimal drive with respect to three objectives, i.e., electric power quality, inverter thermal cycling, and motor thermal cycling. Suitable lumped-parameter thermal models are used for the inverter and the motor based on validated methods in the literature to estimate temperatures. The estimated temperatures are integrated into the multi-objective control law to obtain the desired trade-off performances from the drive system. This paper shows that by adding inverter and motor thermal models into the FCS-MPC, thermal cycling can be reduced in the inverter and the motor while maintaining satisfying speed/torque requirements. The proposed methodology is tested via a standard driving schedule for an interior permanent magnet traction motor in a hybrid electric vehicle.