Automotive Science and Engineering

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Vehicular ad-hoc network (VANET) is an important component of intelligent transportation systems, in which vehicles are equipped with on-board computing and communication devices which enable vehicle-to-vehicle communication. Consequently, with regard to larger communication due to the greater number of vehicles, stability of connectivity would be a challenging problem. Clustering technique as one of the most important data mining techniques is a possible method that can improve the stability of connectivity in VANET. Stable communication within a VANET leads to enhanced driver safety and better traffic management. Therefore, this paper presented a novel clustering algorithm based on ant system-based algorithm called IASC in order to provide a fast clustering algorithm with high accuracy and improve the stability of VANET topology. A comparative study was proposed to analogize the results of the proposed algorithm with six VANET clustering algorithms in the literature which were taken as benchmarks. Results revealed improvement in stability and overhead on VANET.

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The new product development (NPD) is the process by which a new product idea is conceived, investigated, taken through the design process, manufactured, marketed and serviced. In Automotive Engineering these related to the product realization process (PRP) which consists of five phases: "Plan and Define Program", "Product Design and Development", "Process Design and Development", "Product and Process Validation", and "Production Launch, Feedback Assessment and Corrective Action". This paper proposes a process-based management concept focusing on controlling and measuring for their effective management including literature review of NPD performance measurement. Integrating the process-based management concept with the proper performance measure can initiate new knowledge which will contribute to the improvement of the automotive industry.

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With respect to the limitations of fossil energy resources, different types of electric vehicles (EVs) are developed as suitable alternatives. Lithium-ion (Li-ion) battery cells play an extremely important role in EVs due to their unique features. But they need a thermal management system (TMS) to maintain their surface temperature uniformity and avoid them from thermal runaways. In the current study a phase change material (PCM) based TMS is introduced and applied to provide a uniform temperature distribution on a Li-ion battery cell surface. This PCM based TMS declines the final maximum temperature difference to (1/5) and (2/3) at 1 C and 2 C discharge rate respectively.
 

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This paper investigates the effect of temperature on a hybrid energy storage system with various energy management systems. The hybrid energy storage system consists of a fuel cell, ultracapacitor and battery with associated DC/DC and DC/AC converters. The energy management strategies employed are the state machine control strategy, fuzzy frequency/logic decoupling strategy, minimization strategy of equivalent consumption (ECMS) and external energy maximization strategy (EEMS). Initially, a module of 3.3v 2.3Ah LiPo4 batteries consisting of 15 cells in series and 15 rows in parallel are studied without considering the temperature effect. In the next step, the studies are repeated considering the temperature variation effects. The current and SOC associated with the battery, the hydrogen consumption, and battery life are studied for each strategy. The results suggest that the errors associated with the battery life estimation, as well as the battery current are significant with and without considering the temperature effects with the values of 30% and 20%, respectively.

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As an effective means of displacing fossil fuel consumption and reducing greenhouse gas emissions, plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) have attracted more and more attentions. From the power grid perspective, PHEVs and PEVs equipped with batteries can also be used as energy storage facilities, due to the fact that, these vehicles are parked most of the time. Since, the temperature has a strong influence on the battery life-time and also the inherent characteristics of PHEV/PEV energy storage systems limit their use as appropriate resources for energy tuning, this paper, at first, presents a detailed model for energy storage systems of PEVs considering the cooling system and set temperature, and then, it proposes a reliable energy management method for scheduling of PEVs in the multi-microgrid (MMG) systems for both faulted and normal operations using parametric multi-objective function. The simulation results indicate that, considering proper energy management of energy storage systems of PEVs has significant influence on energy scheduling of MMG systems. For this investigation, all data analysis and simulations were done and implemented in MATLAB/Simulink environment.

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This study examines the impact of a fuzzy logic-based control strategy on managing peak power consumption in the auxiliary power unit (APU) of a hybrid electric bus. The APU comprises three components: an air compressor, a power steering system, and an air conditioning system (AC) connected to an electric motor. Initially, these components were simulated in MATLAB-SIMULINK software. While the first two were deemed dependent and independent of vehicle speed, respectively, the stochastic behavior of the steering was emulated using the Monte Carlo method. Subsequently, a fuzzy controller was designed and incorporated into the APU to prevent simultaneous operation of the three accessories as much as possible. The results of repeated simulations demonstrated that the designed fuzzy controller effectively distributed the operation of the accessories throughout the driving cycle, thereby reducing overlaps in auxiliary loads. Consequently, the APU's average and maximum power consumption exhibited significant reductions. Furthermore, through multiple simulations with an upgraded power system model integrating the new APU-controller package, it was established that the proposed strategy for managing auxiliary loads in the bus led to lower fuel consumption and emissions.

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A novel liquid cooling system for pouch-type lithium-ion batteries (LIBs) is proposed by focousing on uniform temperatue disturbution and effective heat dissipation. The system utilizes a michrochannel cold plate with an innovative coolant disturbution design. This study proposes a novel microchannel disturbution path design with each microchannel dimensioning 1 mm² and embeded in the battery's ciritical region to enhance the thermal contact among the LIB and the microchannels. This study aims to simulate and evaluate the performance of cooling system under varius Iranian environmental conditions (Tehran, Shiraz, Isfahan, and Bandar Abbas) and operational parametrs (channel pattern, flow rate) to achieve optimal battery temperature and reduce energy consumption.

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This paper presents an efficient dynamic programming method in order to examine the problem of optimal power management of hybrid electric vehicle (HEV) powertrains and compares its performance with a rule-based method. Since dynamic programming is a trajectory based optimization algorithm and provides a globally optimal solution, it can be used as a benchmark for assessment of other control strategies. However, a major limitation of this method is its extreme computational load which is known as the curse of dimensionality. The computation time and the memory requirements increase exponentially with the increase of states and inputs. In this paper, a novel approach is used to decrease the total computation load and shows how this improvement can provide more accurate results.
 

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Lithium-ion batteries hold great promise for addressing environmental and energy challenges, driving their increased adoption in electric vehicles. Their advantages include stability, high energy density, low self-discharge, and long lifespan. However, both high and low temperatures pose significant challenges. High temperatures can lead to thermal runaway and safety hazards such as short circuits and explosions, while low temperatures can promote the formation of lithium dendrites, resulting in degradation and performance issues. To mitigate these thermal challenges, phase change materials (PCMs) have emerged as a promising solution for battery thermal management systems (BTMS). This review provides a comprehensive overview of PCMs and their application in BTMS. We categorize PCMs used in BTMS based on their modified filler materials and functionalities, including carbon-based (carbon fiber-PCM composites, carbon nanotube-PCM composites, and expanded graphite-PCM composites), metal foam, metal mesh, and organic and inorganic materials. Both inorganic and carbon-based materials can serve as highly thermally conductive encapsulants and fillers for PCMs. Finally, we present a thorough review of recent research on the thermal properties of modified PCMs and their impact on BTMS performance, including a detailed discussion of PCM performance metrics and selection criteria.

 

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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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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.

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The rising demand for sustainable transportation has intensified research on Fuel Cell Hybrid Electric Vehicles (FCHEVs). Integrating fuel cells with lithium-ion batteries provides a pathway to enhance energy efficiency and driving performance, but ensuring the durability of both components under real operating conditions remains a critical challenge. This work proposes an integrated framework to improve FCHEV performance and lifetime through combined modeling, degradation analysis, and optimized energy management. Dynamic vehicle simulations were conducted using the ADVISOR platform under both the Urban Dynamometer Driving Schedule (UDDS) and a real-world cycle based on Tehran traffic data. Degradation models were implemented to capture platinum dissolution in the Proton Exchange Membrane Fuel Cell (PEMFC) and capacity loss in the lithium-ion battery, incorporating the effects of state of charge, temperature, and current rate. An energy management strategy was developed using a Fuzzy Logic Controller (FLC) for fuel cell–battery power distribution, which was further refined with a Genetic Algorithm (GA). The optimization objectives included reducing hydrogen consumption and extending component lifetimes. The GA-optimized FLC extended PEMFC lifetime by 50.6% Tehran and 12.9%  UDDS and reduced battery capacity fade by 10% and 4.9%, respectively. While direct hydrogen consumption increased in Tehran due to more aggressive regenerative-energy routing to the battery, the Equivalent Fuel Consumption (EFC) decreased from 971.32 → 937.21 g/100 km (Tehran) and 794.41 → 782.24 g/100 km (UDDS), reflecting a net efficiency gain once SOC restoration is accounted for.