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 หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Fuzzy Learning Control of Rail Pressure in Diesel-Dual-Fuel Premixed-Charge-Compression-Ignition Engine) ผู้เขียน: ดร.วิทิต ฉัตรรัตนกุลชัย, รองศาสตราจารย์ ,  Supparat Damyot, Dumrongsak Kijdech, กิตติพงษ์ เยาวาจา สื่อสิ่งพิมพ์:pdf AbstractCommon rail systems have added a new degree of freedom in controlling diesel engines. A dieseldual- fuel, premixed-charge-compression-ignition (DF-PCCI) engine was modified from a diesel engine by injecting compressed natural gas (CNG) into the intake ports as the main fuel and injecting a smaller amount of diesel directly into the cylinders. The diesel injection timing was advanced to early in the compression stroke creating a mixture of diesel, CNG and air before being ignited almost simultaneously in the combustion chamber. The DF-PCCI engine had several modes of fueling; only diesel was used during idling, both diesel and CNG were used at low load with cylinder skipping, and both diesel and CNG were used with various energy replacement ratios during medium and high loads. As a result, a rail pressure set point was required to vary over a wide range and with a more abrupt change than that of a diesel engine mainly to obtain appropriate diesel atomization and to avoid excessive combustion. The rail pressure set point was also used as a factor in choosing the appropriate injection timing and duration during calibrations; therefore, it was necessary to track the set point of the rail pressure even more accurately. A novel rail pressure control system was presented based on fuzzy logic. One standard fuzzy system, having the tracking error and its integral as inputs, produced a necessary variation of the common-rail duty cycle to minimize the tracking error. The other fuzzy learning system, connected in parallel with the first fuzzy system, having engine speed and load as inputs, received this variation and used it to adjust centers of output membership functions to produce an appropriate mean value of the common-rail duty cycle to the engine. The fuzzy learning system’s rule-base was initialized from scratch, that is, with output membership functions centered at zeros. The rule-base can also be pre-programmed with the best human experience obtained during steady-state engine calibrations. A DF-PCCI engine, modified from a Toyota 2KD-FTV diesel engine, was connected to an engine test-bed. A new European driving cycle test was performed. Substantial improvement of the common-rail pressure tracking was observed during subsequent urban cycles because the fuzzy learning system was able to learn from the earlier urban cycle. Transient tracking results were also improved. |
หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Knock Control in a Diesel-Dual-Fuel Premixed-Charge-Compression-Ignition (DF-PCCI) Engine Using a Fuzzy Supervisory System) ผู้เขียน:กิตติพงษ์ เยาวาจา, ดร.วิทิต ฉัตรรัตนกุลชัย, รองศาสตราจารย์ สื่อสิ่งพิมพ์:pdf AbstractTypical diesel-dual-fuel (DDF) engines have compressed natural gas (CNG) injected into the intake ports as the main fuel and diesel injected into the cylinders for ignition. Recently, a new DDF combustion technology, developed and patented by the PTT Public Company Limited, advanced the diesel injection timing to be early in the compression stroke, resulting in higher energy replacement by the CNG and lower emissions than those of typical DDF engines. In the so-called diesel-dual-fuel premixed-charge-compression-ignition (DF-PCCI) engine, the diesel, CNG and air were mixed together during the compression stroke. Auto-ignition could occur at many places in the mixture; therefore, the combustion process was more sudden and violent. The engine experienced heavy knocking at high loads due to the hotter combustion chamber as well as hard combustion during transient stages due to the high in-cylinder pressure gradient. Without knock control, the engine had to be operated conservatively away from the knock threshold and not at its optimum point. This paper presents a novel knock control algorithm using a fuzzy supervisory system. The work progressed with the following steps. First, the knock intensity was measured on-line in a time-domain for each cylinder with one knock sensor per engine. Second, a knock threshold was selected from correlating the knock intensity of the DF-PCCI engine with that of the diesel engine. Third, several factors that affected knock intensity were identified. Fourth, standard fuzzy controllers were used to adjust the set points of those factors to regulate the knock intensity at the knock threshold. Each cylinder was treated separately. Fifth, fuzzy supervisors were used to determine the amount of each factor to be applied at each operating point by adjusting the output gains of the standard fuzzy controllers. Sixth, a DF-PCCI engine, mounted on an engine dynamometer, ran the NEDC test with on-line emissions measurement. Seventh, performance (torque and drivability), efficiency (the amount of CNG that can replace diesel) and emissions (total hydrocarbon emissions, NOx, CO, and CH4) were compared with and without knock control. The results showed very good performance with improvements in drivability, efficiency and NOx emission when the proposed knock control algorithm was applied. |
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 Researcherดร. กิตติพงษ์ เยาวาจา, ผู้ช่วยศาสตราจารย์ที่ทำงาน:ภาควิชาวิศวกรรมเครื่องกล คณะวิศวกรรมศาสตร์ ศรีราชา สาขาที่สนใจ:การควบคุมอัตโนมัติขั้นสูง (Advanced Automatic Control), ระบบควบคุมเครื่องยนต์และยานยนต์ (Automotive Control), หุ่นยนต์และระบบอัตโนมัติ (Robotics and Automation) Resume |
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