Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICEs), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESSs) present a viable current solution to these issues. This review thoroughly explores the state of the art in the emerging field of multisource EVs that utilize HESSs, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

With the increasing integration of Distributed Energy Resources (DERs), effective methods for flexibility quantification and evaluation become necessary to ensure grid stability and reliability. This review systematically explores two complementary approaches to flexibility quantification in medium voltage (MV) and low voltage (LV) networks: metric-based (MB) and region-based (RB) methodologies. MB methods provide concise evaluations of system-wide flexibility, facilitating benchmarking and operational insights. However, they often fail to capture the spatiotemporal complexities and interdependencies inherent in modern grids. RB methods, on the other hand, leverage Feasible Operating Regions (FOR) to map flexibility spatially and temporally but are hindered by low observability characterizing distributions systems and limited uncertainty modelling. Compared to previous studies that analyze MB and RB approaches separately, this review uniquely synthesizes both methodologies and proposes their combination as a more comprehensive tool for dynamic flexibility management in modern distribution networks. By enhancing FOR with flexibility metrics, the approach bridges gaps in uncertainty and time modelling, addressing key challenges posed by the increased penetration of DERs. This integration offers a practical and scalable solution for system operators to achieve a more comprehensive and dynamic assessment of flexibility, paving the way for enhanced grid stability and operational efficiency.

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICE), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESS) present a viable current solution to these issues. This review thoroughly explores state-of-the-art in the emerging field of multisource EVs that utilize HESS, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning.