基于神经网络和遗传算法的Atkinson循环发动机全负荷范围性能优化研究
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摘要
能源危机和环境保护己经成为全球性的问题，传统内燃机的发展面临巨大挑战，研究高效、节能和环保的发动机技术是传统内燃机产业可持续发展的必由之路。本文研究一种大几何压缩比的Atkinson循环发动机。和传统的Otto循环发动机相比，其可以实现更大膨胀比，更多的热能可以转化为机械能，因而具有更高的热效率。当Atkinson循环发动机用于混合动力轿车时，其主要工作于高燃油效率区域，从而大大降低了车辆的燃油耗。
和原型Otto循环发动机相比，Atkinson循环发动机采用更大的几何压缩比，爆燃倾向增加，需要通过较大地推迟进气门关闭正时（Late Intake Valve Closure，LIVC)来降低有效压缩比以避免爆震。几何压缩比越大则需要的LIVC操作也越大，而全负荷时LIVC操作会降低发动机的有效排量。在设计Atkinson循环发动机时，需要确定一个最佳的儿何压缩比，在改善Atkinson循环发动机燃油经济性的同时保证全负荷扭矩不能下降过多，以免过多地降低发动机的动力性能。此外，Atkinson循环发动机主要工作于中、高负荷工况。部分负荷时的燃油经济性改善对较少混合动力车辆的燃油耗才具有实际意义。
由于研究的Atkinson循环发动机的设计和操作参数较多，且这些参数间高度耦合、交互地影响发动机的性能，为了降低成本和提高设计、优化的效率，本文研究了基于神经网络和遗传算法进行Atkinson循环发动机全负荷范围性能优化的方法,并辅以热力循环分析作为理论指导。因此，本文的主要研究内容包括：
(1)为了研究Atkinson循环发动机的重要设计和操作参数对发动机循环热效率的影响特性，应用有限时间热力学方法分别建立了Atkinson、Otto和Miller循环热机的不可逆循环模型。数值计算结果表明，Atkinson循环热机的循环热效率存在最佳的压缩比和峰值缸压；在一定的范围内，随着膨胀比增加，峰值缸压增加，而排气温度下降，因而循环热效率改善；当量比增加，循环热效率降低；Miller循环热机和Atkinson循环发动机实际情况比较接近，而Atkinson循环热机的循环热效率是实际发动机的效率改善极限。
(2)研究的Atkinson循环发动机是在几何压缩比为10.6的1.8升Otto循环发动机的基础上进行设计的。本文首先建立了Otto循环发动机的外特性GT-Power仿真模型，然后利用实验数据对模型进行了精确的标定。标定后仿真的发动机扭矩和比油耗和相应实验值间的最大误差分别为2%和2.9%,同时调整爆震模型参数使爆震指数等于200时代表实际发动机发生轻微爆震。
(3)几何压缩比，进、排气门正时，点火角和空燃比交互地影响发动机的爆震强度和最终的动力性和燃油经济性，增加了确定最佳的几何压缩比和进行操作参数优化的难度。为了更加高效、准确地进行Atkinson循环发动机的设计和操作参数优化，用人工神经网络方法建立了目标Atkinson循环发动机的优化模型，神经网络的训练和测试数据均通过GT-Power计算采集。然后联合神经网络模型和遗传算法同时进行几何压缩比和操作参数优化。经过遗传算法优化后，确定最佳几何压缩比为12.5。Atkinson循环发动机样机的台架实验结果表明，4400转以下的转速范围内，神经网络模型优化得到的点火角等操作参数和实验结果非常接近，神经网络模型优化得到的外特性扭矩、比油耗和相应实验值间的最大误差分别不超过2.2%和2.53%。
(4)为了优化Atkinson循环发动机部分负荷时的操作参数以提高燃油经济性水平，建立了Atkinson循环发动机的万有特性GT-Power仿真模型，并利用Atkinson循环发动机样机的台架实验数据进行模型参数标定。实验结果表明，整个部分负荷区域内，GT-Power模型都可以获得较高的预测精度，最大比油耗预测误差为8.5%。然后，利用设计的基于MATLAB/GT-Power耦合的遗传算法优化方法优化发动机部分负荷操作变量以最大化燃油经济性。
(5)根据遗传算法整个负荷范围操作参数的优化结果，总结并提出了基于进气门关闭正时（Intake Valve Closure, IVC)和电子节气门（ETC)联合的发动机负荷控制策略。在中、高负荷区域采用LIVC+ETC的负荷控制策略，在低负荷区域利用ETC控制负荷，而提前IVC正时以改善循环热效率。
台架实验表明，经过优化并进行实验标定后，Atkinson循环发动机整个负荷范围的燃油经济性明显改善。全负荷时，在4400转以下转速范围内，和原Otto循环发动机相比，Atkinson循环发动机的最大扭矩下降不超过6%，2400转时的燃油经济性改善最大，为13%。在4400转以上转速范围内，为了保证足够的Atkinson循环发动机的额定功率，采用较小LIVC操作，同时推迟点火角和采用较浓混合气以避免爆震发生，因而燃油经济性降低，平均燃油耗增加3%。用遗传算法优化Atkinson发动机部分负荷操作参数后，部分负荷区域实验燃油经济性明显改善，最大改善为7.67%。和原Otto循环发动机相比，Atkinson循环发动机的低油耗操作区域面积明显增加，这对减少应用Atkinson循环发动机的混合动力车辆的燃油耗是非常有意义的。
The energy crisis and environment protection have become a global problem,and thus conventional internal combustion engine (ICE) face huge challenge. Studyon highly efficient, energy and environment-friendly engine technology is the onlyway for the sustainable development of the conventional ICE industry. This thesisresearches on an Atkinson cycle engine with bigger geometrical compression ratio(GCR) comparing to conventional Otto cycle engines. In this situation, the Atkinsoncycle engine can realize bigger expansion ratio, thus more heat energy is converted tomechanical energy and higher thermal efficiency is resulted. When this kind ofAtkinson cycle engine is used in a hybrid car, it can mainly work in the fuel efficientarea and greatly reduce the engine fuel consumption.
The knock tendency for the Atkinson cycle engine increases due to the big GCR.Thus late intake valve closure (LIVC) operation is necessary to decrease the effectivecompression ratio in order to avoid the knock. Bigger the GCR is, more LIVCoperation is needed. However, at WOT (widely open throttling) operating condition,the LIVC operation would reduce the engine effective displacement. When designingthe Atkinson cycle engine, we need to determine an optimum GCR to maximize thefuel economy while maintaining enough WOT torque. Furthermore, the Atkinsoncycle engine mainly works in the medium to high load operating area, and thus thefuel economy improvement for part loads is more useful for the fiiel consumptionreduction of the hybrid car.
The Atkinson cycle engine addressed in this thesis has many design andoperative parameters, and these parameters are highly interrelated with each other andinteractively influence the engine performances. In order to decrease cost and enhancedesign and optimization efficiency, this thesis investigates on the methodology thatoptimizes the engine performances in the entire load range of the Atkinson cycleengine based on artificial neural network (ANN) and genetic algorithm (GA).Additionally, thermodynamic cycle analysis is also conducted to provide theoreticalguidance for the design and optimization. Therefore, the main study aspects for thisthesis include:
(1) In order to research on the effect characteristics of the important design andoperating parameters on the cycle thermal efficiency of the Atkinson cycle engine, theifnite-time thermodynamics was used to establish the irreversible cycle model for theAtkinson, Otto and Miller cycle heat engine, respectively. The following conclusionsfrom the numerical computation results could be obtained: there is an optimumcompression ratio and peak cylinder pressure that maximizes the cycle efficiency ofthe Atkinson cycle heat engine; in some range, as the expansion ratio increases, thepeak cylinder pressure increases and the exhaust temperature decreases, thusimproving the cycle thermal efficiency; as equivalence ratio increases，the cyclethermal efficiency decreases; Miller cycle heat engine is closer to the real Atkinsoncycle engine while the cycle thermal efficiency of the Atkinson cycle heat engine isthe cycle efficiency improvement extreme of the real Atkinson cycle engine.
(2) The Atkinson cycle engine was developed based on a1.8L Otto cycle enginewith GCR of10.6. We first established the WOT GT-Power simulation model for thebaseline Otto cycle engine. Corresponding experimental data were measured andcollected to precisely calibrate the GT-Power model. The maximal error between thesimulated and experimental values for the WOT torque and BSFC in the whole enginespeed range is2%and2.9%, respectively. Calibrate the relevant parameters inGT-Power knock model to make the knock index equal to200represent the real slightknock.
(3) The GCR, intake and exhaust valve timing, spark angle and air-fuel ratiointeractively influence the knock intensity, and final engine performance of theAtkinson engine. This increases the difficulty for determining the optimum GCR andoptimizing the operative parameters. In order to more efficiently and preciselyconduct the optimization for the design and operative parameters, the artificial neuralnetwork (ANN) method was used to establish the optimization models for the targetAtkinson cycle engine. The ANN models were trained and tested using the datacollected from the GT-Power computations. Then, the GCR and the operatingparameters were optimized by combining the ANN models and genetic algorithm(GA). Atfer optimized by the GA, the optimum GCR was determined as12.5. Theexperimental results for the prototype Atkinson cycle engine indicate that, in thespeed range below4400rpm, the predicted operating parameters such as the sparkangle by the ANN models are very close to the experimental ones. Furthermore, themaximal error between the ANN prediction and the experimental for the WOT torqueand BSFC is only2.2%and2.53%, respectively.
(4) In order to optimize the part load operating parameters aimed at maximizingthe fuel economy, the GT-Power simulation models at a series of representativespeed-load points covering the entire speed-load operating range of the Atkinsoncycle engine were established. These GT-Power models were all precisely calibratedusing the experimental data of the prototype Atkinson cycle engine. Experimentalresults show that, in the entire part load area the GT-Power models have highprediction accuracy with the maximum BSFC prediction error of8.5%. Then, partload operating parameters for the Atkinson cycle engine were optimized by the GAthrough MATLAB/GT-Power coupling to maximize the fuel economy merit.
(5) According to the GA optimization results for the operative parameters in theentire load range, we proposed a novel torque-based load control strategy that baseson combination of the IVC (Intake Valve Closure, IVC) timing and ETC operation.This strategy could be described as: in the medium to high load range, LIVC+ETCoperation is used to control the engine output; and in the low load range, only theETC is adopted to control the engine load while advancing the IVC timing to improveengine cycle thermal efficiency.
The experiments in a test-bed indicate that, atfer optimized by the GA andexperimental calibrations, fuel economy in the entire load range for the Atkinsoncycle engine is obviously optimized. For the WOT operating conditions below4400rpm, the maximal torque reduction for the Atkinson cycle engine is less than6%comparing to the baseline Otto cycle engine while the maximal fuel economyimprovement for the Atkinson cycle engine is13%at2400rpm. For the WOToperating conditions above4400rpm, in order to maintain enough engine rate power,less LIVC operation is used, but the spark angle delaying and rich mixture have to beadopted to avoid the knock. Therefore, the fuel economy for the Atkinson cycleengine deteriorates with the average fuel consumption increasing as3%.
Atfer the part load operating parameters of the Atkinson cycle engine wereoptimized by GA, part load experimental fuel economy are obviously optimized withthe maximal improvement of7.67%. Comparing to the baseline Otto cycle engine, theoperating area for the lowest fuel consumption of the Atkinson cycle engine isobviously larger, which is very useful for reducing the fiiel consumption of the hybridcar using the Atkinson cycle engine.
和原型Otto循环发动机相比，Atkinson循环发动机采用更大的几何压缩比，爆燃倾向增加，需要通过较大地推迟进气门关闭正时（Late Intake Valve Closure，LIVC)来降低有效压缩比以避免爆震。几何压缩比越大则需要的LIVC操作也越大，而全负荷时LIVC操作会降低发动机的有效排量。在设计Atkinson循环发动机时，需要确定一个最佳的儿何压缩比，在改善Atkinson循环发动机燃油经济性的同时保证全负荷扭矩不能下降过多，以免过多地降低发动机的动力性能。此外，Atkinson循环发动机主要工作于中、高负荷工况。部分负荷时的燃油经济性改善对较少混合动力车辆的燃油耗才具有实际意义。
由于研究的Atkinson循环发动机的设计和操作参数较多，且这些参数间高度耦合、交互地影响发动机的性能，为了降低成本和提高设计、优化的效率，本文研究了基于神经网络和遗传算法进行Atkinson循环发动机全负荷范围性能优化的方法,并辅以热力循环分析作为理论指导。因此，本文的主要研究内容包括：
(1)为了研究Atkinson循环发动机的重要设计和操作参数对发动机循环热效率的影响特性，应用有限时间热力学方法分别建立了Atkinson、Otto和Miller循环热机的不可逆循环模型。数值计算结果表明，Atkinson循环热机的循环热效率存在最佳的压缩比和峰值缸压；在一定的范围内，随着膨胀比增加，峰值缸压增加，而排气温度下降，因而循环热效率改善；当量比增加，循环热效率降低；Miller循环热机和Atkinson循环发动机实际情况比较接近，而Atkinson循环热机的循环热效率是实际发动机的效率改善极限。
(2)研究的Atkinson循环发动机是在几何压缩比为10.6的1.8升Otto循环发动机的基础上进行设计的。本文首先建立了Otto循环发动机的外特性GT-Power仿真模型，然后利用实验数据对模型进行了精确的标定。标定后仿真的发动机扭矩和比油耗和相应实验值间的最大误差分别为2%和2.9%,同时调整爆震模型参数使爆震指数等于200时代表实际发动机发生轻微爆震。
(3)几何压缩比，进、排气门正时，点火角和空燃比交互地影响发动机的爆震强度和最终的动力性和燃油经济性，增加了确定最佳的几何压缩比和进行操作参数优化的难度。为了更加高效、准确地进行Atkinson循环发动机的设计和操作参数优化，用人工神经网络方法建立了目标Atkinson循环发动机的优化模型，神经网络的训练和测试数据均通过GT-Power计算采集。然后联合神经网络模型和遗传算法同时进行几何压缩比和操作参数优化。经过遗传算法优化后，确定最佳几何压缩比为12.5。Atkinson循环发动机样机的台架实验结果表明，4400转以下的转速范围内，神经网络模型优化得到的点火角等操作参数和实验结果非常接近，神经网络模型优化得到的外特性扭矩、比油耗和相应实验值间的最大误差分别不超过2.2%和2.53%。
(4)为了优化Atkinson循环发动机部分负荷时的操作参数以提高燃油经济性水平，建立了Atkinson循环发动机的万有特性GT-Power仿真模型，并利用Atkinson循环发动机样机的台架实验数据进行模型参数标定。实验结果表明，整个部分负荷区域内，GT-Power模型都可以获得较高的预测精度，最大比油耗预测误差为8.5%。然后，利用设计的基于MATLAB/GT-Power耦合的遗传算法优化方法优化发动机部分负荷操作变量以最大化燃油经济性。
(5)根据遗传算法整个负荷范围操作参数的优化结果，总结并提出了基于进气门关闭正时（Intake Valve Closure, IVC)和电子节气门（ETC)联合的发动机负荷控制策略。在中、高负荷区域采用LIVC+ETC的负荷控制策略，在低负荷区域利用ETC控制负荷，而提前IVC正时以改善循环热效率。
台架实验表明，经过优化并进行实验标定后，Atkinson循环发动机整个负荷范围的燃油经济性明显改善。全负荷时，在4400转以下转速范围内，和原Otto循环发动机相比，Atkinson循环发动机的最大扭矩下降不超过6%，2400转时的燃油经济性改善最大，为13%。在4400转以上转速范围内，为了保证足够的Atkinson循环发动机的额定功率，采用较小LIVC操作，同时推迟点火角和采用较浓混合气以避免爆震发生，因而燃油经济性降低，平均燃油耗增加3%。用遗传算法优化Atkinson发动机部分负荷操作参数后，部分负荷区域实验燃油经济性明显改善，最大改善为7.67%。和原Otto循环发动机相比，Atkinson循环发动机的低油耗操作区域面积明显增加，这对减少应用Atkinson循环发动机的混合动力车辆的燃油耗是非常有意义的。
The energy crisis and environment protection have become a global problem,and thus conventional internal combustion engine (ICE) face huge challenge. Studyon highly efficient, energy and environment-friendly engine technology is the onlyway for the sustainable development of the conventional ICE industry. This thesisresearches on an Atkinson cycle engine with bigger geometrical compression ratio(GCR) comparing to conventional Otto cycle engines. In this situation, the Atkinsoncycle engine can realize bigger expansion ratio, thus more heat energy is converted tomechanical energy and higher thermal efficiency is resulted. When this kind ofAtkinson cycle engine is used in a hybrid car, it can mainly work in the fuel efficientarea and greatly reduce the engine fuel consumption.
The knock tendency for the Atkinson cycle engine increases due to the big GCR.Thus late intake valve closure (LIVC) operation is necessary to decrease the effectivecompression ratio in order to avoid the knock. Bigger the GCR is, more LIVCoperation is needed. However, at WOT (widely open throttling) operating condition,the LIVC operation would reduce the engine effective displacement. When designingthe Atkinson cycle engine, we need to determine an optimum GCR to maximize thefuel economy while maintaining enough WOT torque. Furthermore, the Atkinsoncycle engine mainly works in the medium to high load operating area, and thus thefuel economy improvement for part loads is more useful for the fiiel consumptionreduction of the hybrid car.
The Atkinson cycle engine addressed in this thesis has many design andoperative parameters, and these parameters are highly interrelated with each other andinteractively influence the engine performances. In order to decrease cost and enhancedesign and optimization efficiency, this thesis investigates on the methodology thatoptimizes the engine performances in the entire load range of the Atkinson cycleengine based on artificial neural network (ANN) and genetic algorithm (GA).Additionally, thermodynamic cycle analysis is also conducted to provide theoreticalguidance for the design and optimization. Therefore, the main study aspects for thisthesis include:
(1) In order to research on the effect characteristics of the important design andoperating parameters on the cycle thermal efficiency of the Atkinson cycle engine, theifnite-time thermodynamics was used to establish the irreversible cycle model for theAtkinson, Otto and Miller cycle heat engine, respectively. The following conclusionsfrom the numerical computation results could be obtained: there is an optimumcompression ratio and peak cylinder pressure that maximizes the cycle efficiency ofthe Atkinson cycle heat engine; in some range, as the expansion ratio increases, thepeak cylinder pressure increases and the exhaust temperature decreases, thusimproving the cycle thermal efficiency; as equivalence ratio increases，the cyclethermal efficiency decreases; Miller cycle heat engine is closer to the real Atkinsoncycle engine while the cycle thermal efficiency of the Atkinson cycle heat engine isthe cycle efficiency improvement extreme of the real Atkinson cycle engine.
(2) The Atkinson cycle engine was developed based on a1.8L Otto cycle enginewith GCR of10.6. We first established the WOT GT-Power simulation model for thebaseline Otto cycle engine. Corresponding experimental data were measured andcollected to precisely calibrate the GT-Power model. The maximal error between thesimulated and experimental values for the WOT torque and BSFC in the whole enginespeed range is2%and2.9%, respectively. Calibrate the relevant parameters inGT-Power knock model to make the knock index equal to200represent the real slightknock.
(3) The GCR, intake and exhaust valve timing, spark angle and air-fuel ratiointeractively influence the knock intensity, and final engine performance of theAtkinson engine. This increases the difficulty for determining the optimum GCR andoptimizing the operative parameters. In order to more efficiently and preciselyconduct the optimization for the design and operative parameters, the artificial neuralnetwork (ANN) method was used to establish the optimization models for the targetAtkinson cycle engine. The ANN models were trained and tested using the datacollected from the GT-Power computations. Then, the GCR and the operatingparameters were optimized by combining the ANN models and genetic algorithm(GA). Atfer optimized by the GA, the optimum GCR was determined as12.5. Theexperimental results for the prototype Atkinson cycle engine indicate that, in thespeed range below4400rpm, the predicted operating parameters such as the sparkangle by the ANN models are very close to the experimental ones. Furthermore, themaximal error between the ANN prediction and the experimental for the WOT torqueand BSFC is only2.2%and2.53%, respectively.
(4) In order to optimize the part load operating parameters aimed at maximizingthe fuel economy, the GT-Power simulation models at a series of representativespeed-load points covering the entire speed-load operating range of the Atkinsoncycle engine were established. These GT-Power models were all precisely calibratedusing the experimental data of the prototype Atkinson cycle engine. Experimentalresults show that, in the entire part load area the GT-Power models have highprediction accuracy with the maximum BSFC prediction error of8.5%. Then, partload operating parameters for the Atkinson cycle engine were optimized by the GAthrough MATLAB/GT-Power coupling to maximize the fuel economy merit.
(5) According to the GA optimization results for the operative parameters in theentire load range, we proposed a novel torque-based load control strategy that baseson combination of the IVC (Intake Valve Closure, IVC) timing and ETC operation.This strategy could be described as: in the medium to high load range, LIVC+ETCoperation is used to control the engine output; and in the low load range, only theETC is adopted to control the engine load while advancing the IVC timing to improveengine cycle thermal efficiency.
The experiments in a test-bed indicate that, atfer optimized by the GA andexperimental calibrations, fuel economy in the entire load range for the Atkinsoncycle engine is obviously optimized. For the WOT operating conditions below4400rpm, the maximal torque reduction for the Atkinson cycle engine is less than6%comparing to the baseline Otto cycle engine while the maximal fuel economyimprovement for the Atkinson cycle engine is13%at2400rpm. For the WOToperating conditions above4400rpm, in order to maintain enough engine rate power,less LIVC operation is used, but the spark angle delaying and rich mixture have to beadopted to avoid the knock. Therefore, the fuel economy for the Atkinson cycleengine deteriorates with the average fuel consumption increasing as3%.
Atfer the part load operating parameters of the Atkinson cycle engine wereoptimized by GA, part load experimental fuel economy are obviously optimized withthe maximal improvement of7.67%. Comparing to the baseline Otto cycle engine, theoperating area for the lowest fuel consumption of the Atkinson cycle engine isobviously larger, which is very useful for reducing the fiiel consumption of the hybridcar using the Atkinson cycle engine.
引文
[1]Climate Change: Basic Information. United States Environmental Protection Agency.2006-12-14;"In common usage,'global warming' often refers to the warming that can occuras a result of increased emissions of greenhouse gases from human activities."2007-02-09.
[2]http://en.wikipedia.org/wiki/Hybrid_electric_vehicle.
[3] littp://en.wikipedia.org/wiki/Toyota Prius?
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