基于神经网络的驾驶行为控制系统原型模型节油指标评估

Q2 Engineering Archives of Transport Pub Date : 2022-09-30 DOI:10.5604/01.3001.0016.0019
S. Munahar, A. Triwiyatno, M. Munadi, J. Setiawan
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引用次数: 0

摘要

由于汽车使用量的增加和全球石油产量的减少,世界上有效的燃料消耗对汽车技术的发展至关重要。已经进行了几项研究,以增加燃料消耗节约,燃料电池(fc),替代能源汽车的应用和发动机控制单元(ECU)系统。燃料电池汽车不需要石油能源来驱动汽车,因此这项技术有望减少能源消耗和排放。然而,这项研究仍然存在一些问题。fc容易出现短路危险,而且拥有成本非常高。替代能源应用产生的功率更小,加速反应更慢,而且能源不足,无法进入大规模生产。ECU的应用仍然以达到化学计量值为导向,因此燃油效率的提高有可能得到改善。驾驶行为是一个与油耗效率关系密切的变量。然而,对驾驶行为的研究仅针对自动跟随汽车技术、安全系统、电动汽车充电需求特性、排放控制、车载信息系统显示图像等方面的实现。同时,将驾驶行为作为提高燃油效率的控制系统的研究尚未开展。为此,本研究提出将驾驶行为用于新设计的控制系统,以提高燃油效率。本研究中的控制系统是一个原型模型,将使用燃油节约指数(Fuel Saving Index, FSI)分析进行评估。使用人工神经网络来帮助识别驾驶行为。结果表明,新设计的控制系统符合FSI量表IV。在此尺度下,在生态方案驾驶行为下,发动机产生的功率是相当理想的。驾驶行为控制系统能显著提高汽车的油耗效率。空气燃料比(AFR)达到高于化学计量值
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Fuel Saving Indeks Assessment on Driving Behavior Control System Prototype Model Using Neural Network
Efficient fuel consumption in the world is essential in automotive technology development due to the increase in vehicle usage and the decrease in global oil production. Several studies have been conducted to increase fuel consumption savings, Fuel Cells (FCs), the application of alternative energy vehicles and the Engine Control Unit (ECU) system. FCs do not require oil energy to propel the vehicle, so this technology promises to reduce energy consumption and emissions. However, this research still leaves problems. FCs are susceptible to short circuit hazards, and ownership costs are very high. Alternative energy applications produce less power, less responsive acceleration, and insufficient energy sources to enter mass production. The ECU application still has an orientation toward achieving stoichiometry values, so the increase in fuel efficiency has the potential to be improved. Driving behavior is a variable that has a close relationship with fuel consumption efficiency. However, research on driving behavior is only studied for implementation in autonomous car-following technologies, safety systems, charging needs characteristic of electric vehicles, emission controls, and display images on in-vehicle information systems. Meanwhile, research on driving behavior as a control system to improve fuel efficiency has not been carried out. To that end, this study proposes the use of driving behavior for a newly designed control system to improve fuel efficiency. The control system in this research is a prototype model to be assessed using the Fuel Saving Index (FSI) analysis. An artificial neural network is used to help the recognition of driving behavior. The results showed that the newly designed control system was categorized on scale IV of FSI. On this scale, the power generated by the engine is quite optimal when it is in the eco-scheme driving behavior. The driving behavior control system can significantly improve the efficiency of fuel consumption. Air to Fuel Ratio (AFR) is achieved above the stoichiometric value
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来源期刊
Archives of Transport
Archives of Transport Engineering-Automotive Engineering
CiteScore
2.50
自引率
0.00%
发文量
26
审稿时长
24 weeks
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