پارافیز کردن 4 پاراگراف

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شرح پروژه

سلام4  تا پاراگراف جدا جدا هستن×یعنی هیچ ربطی به هم ندارن میخام پارافیز شن  

This paper presents a numerical framework for automating the design of lithium-ion cells to maximize cell energy density while meeting specific power density requirements. The various processes in a battery cell are simulated using a physics-based electrochemistry model. The design is automatically performed by coupling the battery model with a gradient-based optimization algorithm. We demonstrate the potential for gradient-based optimization by applying this framework to optimize the design of a lithium-ion cell with spinel manganese dioxide cathode and meso-carbon micro beads (MCMB) anode for a range of power requirements. Results indicate that variations in the electrode thickness and porosity at optimal cell designs can be quantified via active mass ratios and it is found that the active mass ratios for optimal cell designs are independent of discharge rate.

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Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV’s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV’s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV’s design parameters are optimized such as in-wheel electric motors’ torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a realworld driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.-----

This study presents a multiobjective optimisation applied to the gear shifting fuzzy control of a vehicle equipped with an automated manual transmission (AMT), aiming to improve acceleration performance and reduce engine fuel consumption and emissions. An Adaptive-Weight Genetic Algorithm was employed to find optimum fuzzy membership functions, according to the input and output ranges, and also optimum control rules with their respective weights. The vehicle behaviour is represented by longitudinal dynamics simulations developed in Simulink/Matlab™ interface, associated with the ADVISOR™ fuel converter block, that provides the engine emissions and fuel consumption. These simulations were based on the FTP-75 emissions test procedure, that considers cold and hot phases of the driving cycle evaluating the engine transient operation as a function of the catalyst efficiency during the warm-up period. The optimum fuzzy control with the best trade-off among the optimisation criteria presented 19.72% fuel saving associated with 12.90% hydrocarbon, 29.20% carbon monoxide and 17.02% nitrogen oxides emissions reduction and an acceleration performance improvement when compared to a standard gear shifting procedure for a manual controlled gearbox. Moreover, the optimised fuzzy gear shifting control, improves the relationship between fuel consumption and emissions significantly, when compared to another optimum AMT control based on speed limits only

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The Ohio State University Buckeye Current is a student team formed in 2010 to provide practical experience in designing and building electric motorcycles, and to compete in national and international races. The mission of the team is to teach students about electric vehicles, promote electric vehicle events, and give students an engaging educational work environment through real-world experiences. This paper illustrates the design process for the racing motorcycle that participated to the 2016 Pikes Peak International Hill Climb, focusing on the design, optimization and system integration of the Lithium ion battery pack. Starting from a physics-based, electrochemical-thermal model for Li-ion cells, simulation studies were conducted to predict the temperature distribution across the cells during current cycling operations. The properties of the pack, such as battery chemistry, spatial cell arrangement, and electrical layout, were evaluated to optimize the electrical and cooling performance during race conditions. Using the simulation results, the new battery pack was designed and integrated into the electric motorcycle.

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