Automotive Science and Engineering

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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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Hardware implementation of the Plug-in hybrid electric vehicles (PHEVs) control strategy is an important stage of the development of the vehicle electric control unit (ECU). This paper introduces Model-Based Design (MBD) approach for implementation of PHEV energy management. Based on this approach, implementation of the control algorithm on an electronic hardware is performed using automatic code generation. The advantages of the MBD in comparison with the traditional methods are the capability of eliminating the manual coding complexities as well as compiling problems and reducing the test duration. In this study, hardware implementation of a PHEV rule-based control strategy is accomplished using MBD method. Also, in order to increase the accuracy of the results of the implementation, the data packing method is used. In this method, by controlling the primer and end data of the data packet transferred between the electronic board and the computer system, the noisy data is prevented from entering. In addition, to verify the performance of the implemented control strategy, hardware-in-the-loop (HIL) simulation is used with the two frequency rates. The results show the effectiveness of the proposed approach in correct and rapid implantation procedure.

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Recently, number of Hybrid Electric Vehicles (HEV) is on the rise due to concerns over environmental issues. By combining fuel and electricity as two sources of power, this type of vehicle is capable of bettering fuel economy and lowering emission. In this work, fuel and electrical energy consumption of a parallel hybrid electric vehicle are investigated through TEH-CAR urban drive cycle. For this purpose, a forward looking model is developed in AVL CRUISE M. To ensure adequacy of the model and take engine gas path components’ dynamic interaction into account, a crank based model with individual cylinders is utilized. Furthermore, a throttle filter is presented to slow down engine’s response and also, allow the electric motor to have the larger share of delivering power in transients. Finally, genetic algorithm is used to find optimal values for throttle filter parameter and electric motor load ratio, in order to have minimal overall fuel and electrical energy consumption. The optimization results show 1.2% of fuel and 20.2% of total energy consumption reduction in comparison with conventional torque assist.

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The development and adoption of electric vehicles (EVs) appears to be an excellent way to mitigate environmental problems such as climate change and global warming exacerbated by the transportation sector. However, it faces numerous challenges, such as optimal locations for EV charging stations and underdeveloped EVCS infrastructure, among the major obstacles. The present study is based on the location planning of charging stations in real cases of central and densely populated districts of Tehran, the capital of Iran. In order to achieve this goal, this paper attempts to validate the results of a previous study in another country. Secondly, by employing preceding principals in accordance with relevant information collected from the car park and petrol stations in the regions of study, a five-integer linear program is proposed based on a weighted set coverage model considering EV users' convenience, daily life conditions, and investment costs, and finally optimally solved by genetic algorithm under various distribution conditions; normal, uniform, Poisson and exponential, to specify the location and number of EV charging stations in such a way that EV drivers can have access to chargers, within an acceptable driving range.

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The braking system has always been considered one of the most significant vehicle subsystems since it plays a key role in safety issues. To design such a complex system, modeling can be a helpful tool for designers to save time and costs. In this paper, the hydraulic braking system of a B-Class vehicle was modeled by simulating the relationship between brake components such as pedals, boosters, main cylinders, and wheel cylinders, with the vehicle dynamics by using the existing models of the tire and their dynamic relationships. The performed modeling was compared with the results of a concerning vehicle's direct movement. The results of this comparison showed that our modeling is very close to the experimental data. The braking distance parameter was selected to examine the effects of each braking component on the vehicle dynamics. The results of investigating the effect of different parameters of the braking system on the dynamic behavior of the vehicle indicated that the main cylinder diameter, the diameter of the front and rear wheels’ brake cylinders, the effective diameter of the front disk, and the diameter of the rear drum are the most effective design parameters in vehicle's braking system and optimal results are obtained by applying changes to these parameters.

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Fleet routing is one of the basic solutions to meet the good demand of customers in which decisions are made based on the limitations of product supply warehouses, time limits for sending orders, variety of products and the capacity of fleet vehicles. Although valuable efforts have been made so far in modeling and solving the fleet routing problem, there is still a need for new solutions to further make the model more realistic. In most research, the goal is to reach the shortest distance to supply the desired products. Time window restrictions are also applied with the aim of reducing product delivery time. In this paper, issues such as customers' need for multiple products, limited warehouses in terms of the type and number of products that can be offered, and also the uncertainty about handling a customer's request or the possibility of canceling a customer order are considered. We used the random model method to deal with the uncertainty of customer demand. A fuzzy clustering method was also proposed for customer grouping. The final model is an integer linear optimization model that is solved with the powerful tools of Mosek and Yalmip. Based on the simulation results, it was identified to what extent possible and accidental changes in customer behavior could affect shipping costs. It was also determined based on these results that the effective parameters in product distribution, such as vehicle speed, can be effective in the face of uncertainty in customer demand.

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Electric Power Steering (EPS) systems are increasingly being integrated into modern vehicles, offering enhanced fuel efficiency and improved maneuverability. However, these systems are often subject to noise and disturbances, which can significantly impact steering precision and driver comfort. Addressing these challenges requires the implementation of robust control strategies capable of mitigating noise and disturbances in EPS systems. This paper explores advanced methods for achieving robust control in Electric Power Steering systems by reducing noise interference and countering external disturbances. Key techniques involve adaptive control algorithms and robust filtering mechanisms that maintain system stability and performance even under variable operating conditions. Experimental results demonstrate that these robust control approaches effectively minimize noise levels and disturbance impacts, leading to smoother steering response and greater reliability. This study underscores the critical role of robust control in enhancing the functionality and safety of Electric Power Steering systems while highlighting the intricate dynamics between noise, disturbances, and control system robustness in automotive applications.

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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.

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This study investigates the dynamic response of a lithium‑ion battery pack subjected to environmental vibrations. Considering the widespread use of such packs in electric vehicles and energy storage systems, and the adverse effects of vibrations on their performance and safety, both numerical and experimental approaches are employed. In the numerical simulation phase, a detailed three-dimensional model of the battery pack, including all components and joints, is developed in Abaqus, and a full modal analysis is performed to extract the natural frequencies and mode shapes of the system. In the experimental phase, modal testing is conducted using an impact hammer and an accelerometer on a physical battery-pack sample under free‑free boundary conditions to validate the simulation results. A systematic comparison between the two approaches demonstrates a good agreement, with the maximum deviation in the primary natural frequencies being less than 10%. This level of consistency confirms the accuracy and reliability of the proposed model. The developed model can serve as an effective tool during the early design stages for mechanical optimization, dynamic behavior prediction, and mitigation of vibration‑induced failures in battery packs. The results of this study mark an important step toward improving the reliability and safety of battery packs in operational environments.