As the global transition toward electrification accelerates across the transportation and stationary energy storage sectors, the critical need for accurate end-of-life (EoL) prediction of lithium-ion batteries (LIBs) has become increasingly apparent. Current battery failures impose substantial costs on manufacturers through warranty claims, while creating significant safety risks that threaten both electric vehicle (EV) adoption and grid-scale energy storage deployment. This paper examines the modeling approaches to predict the EoL and the remaining useful life (RUL) of LIBs in EVs. The paper includes data-driven models, physics-based approaches, and hybrid frameworks. Through systematic analysis of recent advances, the paper identifies that hybrid models demonstrate superior performance compared to single-approach methods, effectively addressing the inherent limitations of individual methodologies across diverse operating conditions. Key challenges remain in Battery Management System (BMS) integration complexity, data quality constraints, and real-time computational requirements. The proposed review establishes that next-generation prediction systems and incorporates transfer learning, digital twin technologies, and second-life battery strategies to support sustainable EV adoption and circular economy principles.

This paper presents a multi-objective control strategy for a four-port solid-state transformer (SST) interfacing photovoltaic (PV) system, battery energy storage (BESS), utility grid, and a 48V data center load. The proposed control approach simultaneously optimizes multiple objectives, including power allocation and load voltage regulation. The main objectives of the proposed approach are to supply a constant 48V to the data center while maximizing renewable energy utilization, and balancing the demand and supply by dynamically injecting the extra available power into the grid or the battery and consuming it from the grid or the battery if there is no sufficient renewable energy availability. The proposed approach is experimentally implemented and its effectiveness is validated through a set of different case studies.

This paper proposes a DC plasma power supply topology with systematic design and robust voltage control approach. The proposed topology consists of a three-port solid-state transformer (SST), comprising one port for the input source, a second port to deliver high voltage (HV) required for plasma ignition, and a third port for supplying a constant low voltage (LV) for sustained plasma reactor operation. A comprehensive design methodology is presented to determine all passive elements, including the resonant tank components and transformer’s turns ratios. Moreover, a seamless transition algorithm between ignition and operation modes is presented. To achieve micro-scale dynamic response, an inverse model predictive control (IMPC) strategy is employed to identify the necessary switching frequency in feedforward fashion. In parallel, a PI controller is used to eliminate the deviation and steady state error of the output voltage and ensure asymptotic error convergence, even in the presence of parameter uncertainties. The proposed circuit, seamless transition algorithm, and the control technique are implemented on a lab-scale test-bed with voltage range between 0-1200 V.

The integration of distributed energy resources (DERs) via three-phase inverters plays a significant role in supporting the stability and reliability of AC power systems. This paper focuses on meeting grid code (GC) requirements concerning the integration of renewable energy sources (RES) and electric vehicles (EVs). A novel model predictive control (MPC) algorithm is proposed to provide the required grid support for parallel inverters during disturbances. The proposed strategy is validated across various fault scenarios, including symmetrical and asymmetrical faults. The considered scenarios focused on different severity levels of voltage sags, demonstrating the algorithm's robustness and adaptability in maintaining grid stability under adverse conditions. The results showcase the effectiveness of the topology in eliminating double-line frequency disturbances in active power during fault conditions. Moreover, it ensures accurate active and reactive power injections into the grid while adhering to operational standards and meeting the required GC.

This study introduces a novel Boost Packed E-Cell Thirteen-Level Inverter (BPEC13), a boost multilevel inverter (MLI) with promising voltage boosting capability. This novel design features reduced count components consisting of nine power switches, a single four-quadrant switch, and two DC capacitors. A key feature of the BPEC13 inverter is its ability to boost the voltage, achieving a maximum voltage level that is 1.5 times the input DC voltage. To harness the capabilities of the BPEC13 inverter, a finite-control set model predictive control (FCS-MPC) algorithm has been developed. This control strategy is carefully designed to ensure superior grid current control and maintain balanced DC-link capacitor voltages. The effectiveness of the FCS-MPC with the BPEC13 inverter is thoroughly evaluated through numerical simulations. The results confirm that the novel BPEC13 inverter, utilizing FCS-MPC, delivers superior grid current quality with total harmonic distortions of just 0.42. Additionally, it maintains DC-link capacitor voltage balance while providing significant voltage boosting capabilities.