Automotive Science and Engineering

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The high penetration of renewable energy sources (RES) makes the power system unreliable due to its uncertain nature. In this paper, the quantifying impact of electric vehicles (EV) charging and discharging on power system reliability and relieving the congestion is analyzed. The proposed reliability assessment is formulated by considering generation and demand interruption costs for N-1 contingency criteria. The proposed algorithm manages the optimal scheduling of EV to mitigate the uncertainties associated with RES and relieving the congestion. The impact of EV charging and discharging on expected energy not supplied (EENS) and expected interruption cost (ECOST) for generating companies (GENCOs), transmission companies (TRANSCOs), customers, and entire power system are calculated. The charging station of EV is selected by the trade-o_ between investment cost of EV and percentage change in EENS and ECOST value for the entire power system, GENCOs, TRANSCOs, and customers. The effectiveness of the proposed approach is tested on the modified IEEE RTS 24 bus system. The impact of EV charging stations on system reliability has been evaluated by quantifying the EENS and the ECOST across all available EV capacities. The results clearly demonstrate the improvement of system reliability and minimize the objective function consisting of generator re-dispatch and load curtailment considering N-1 contingency in the face of uncertainties of wind and solar generation sources by considering EV. The results show that EV can improve the reliability by about 40%. The problem is modeled in GAMS environment and solved using CONOPT as a nonlinear programming (NLP) solver.
 

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The escalating proliferation of electric vehicles (EVs) as a pivotal solution to address energy consumption and air pollution challenges within the transportation sector necessitates a comprehensive understanding of the factors influencing their performance and driving range. Among these factors, driving patterns exert a direct and significant impact on energy consumption and battery state. This study aims to quantify the influence of diverse driving cycles on the performance of an electric vehicle, specifically the Audi e-tron 50.   Utilizing Simcenter Amesim software, a longitudinal vehicle dynamics model, coupled with an equivalent circuit model (ECM) for the lithium-ion battery, was developed for simulation purposes. The vehicle's performance was evaluated under five distinct driving cycles, including global standards (WLTC, NEDC, HWFET) and two real-world driving cycles recorded in Tehran (Route1, Route2). Key parameters such as state of charge (SoC), depth of discharge (DoD), battery temperature, and estimated driving range were analyzed. The results revealed a significant impact of driving cycles on all investigated parameters. Driving cycles characterized by higher speeds and accelerations (e.g., WLTC and HWFET) led to increased specific energy consumption, accelerated temperature rise, and a notable reduction in estimated driving range (with the lowest range observed in WLTC). Conversely, milder urban driving cycles (particularly Route1) resulted in improved energy efficiency, minimal thermal stress, and the highest estimated driving range. These findings underscore the critical importance of considering real-world and localized driving patterns for accurate performance evaluation, range estimation, and the development of optimized energy management strategies in electric vehicles.
 

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This study proposes a hierarchical nested cascade control framework to enhance voltage regulation and current management in fuel cell hybrid electric vehicles (FCHEVs). The architecture addresses limitations of conventional cascade control by reducing design complexity and improving resilience under dynamic and uncertain conditions. It integrates three coordinated layers: an outer control level (OCL) employing an adaptive proportional–integral controller for DC bus voltage regulation, and two internal layers, middle (MCL) and inner (ICL), implemented via backstepping controllers for precise current control of fuel cells, batteries, and supercapacitors. By combining nonlinear control with model reference adaptive control, the system dynamically tunes parameters to maintain voltage stability across variable load profiles. Simulations using the WLTC-Class 3 cycle show that the proposed strategy (Case 1) achieves superior battery sustainability, with a final SOC of 74.2%, compared to 71% and 72.5% in benchmark strategies (Cases 2 and 3). Under battery aging (20% increased resistance, 15% reduced capacity), DC bus voltage remains within ±3.5 V of the 380 V reference, with only 18% ripple increase and 0.8% additional SOC depletion. A resilience index of 96.5% confirms robustness, outperforming benchmarks (84.2%, 89.7%). To further validate performance under real-world urban conditions, date-specific driving cycles tailored for Shiraz city were employed. Results confirm the framework’s effectiveness in sustaining stability, efficiency, and scalability for next-generation FCHEV energy systems.