Prediction of emission and performance of internal combustion engine via regression deep learning approach

Samaneh Soltanalizadeh, Mohammad Reza Haeri Yazdi, Vahid Esfahanian, Mohammad Nejat
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Abstract

Strict environmental laws increase the importance of reducing emissions. While; improving three-way catalyst (TWC) technology can help reduce emission levels, attention should also be given to reducing exhaust gases before they enter the TWC. This can be achieved through the development of engine technology and calibration strategies. By doing so, low-cost TWC can be used, and emissions increase less after catalyst aging. In addition to reducing emissions at the engine-out stage before entering the TWC, it is important to reduce emissions after the TWC during the cold start and engine warm-up phase, known as the TWC light-off period. During this stage, the TWC does not reach optimal working efficiency, which can result in higher emissions. Therefore, to comply with environmental regulations, it is necessary to calculate the reduction of emissions while maintaining optimal engine and vehicle performance. Finding the optimal values of control parameters to reduce fuel consumption and emissions simultaneously makes engine calibration a complex multi-objective optimization problem. To meet calibration requirements, it is essential to accurately identify the nonlinear and multivariable behavior of engines. Thus, this study focuses on empirical engine modeling and developing an emissions model for internal combustion engines in both warm and cold engine conditions through an intelligent identification method. To enhance steady state engine modeling in warm conditions, this study proposes a hybrid MLP+CNN method based on the benefits of regression deep neural network. Additionally, the hybrid MLP+CNN+LSTM method adds a long short-term memory (LSTM) neural network, enabling the model to capture the dynamic behavior of emissions during cold start conditions and under the impact of oxygen storage and the temperature in TWC. The results demonstrate that these approaches significantly improve the accuracy of emission modeling when compared to conventional methods. The results demonstrate that using the deep learning approach and dividing the engine emission modeling into two parts, static in warm conditions and dynamic in cold conditions, significantly improve the accuracy of emission modeling compared to conventional methods. Developed models can be used in the model-based calibration due to their high accuracy in emission prediction as well as predicting Torque, BSFC, and other outputs. By coupling the developed model with optimization techniques, calibration of the engine map and cold start can be performed by considering the emissions, torque, etc., simultaneously.
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通过回归深度学习方法预测内燃机的排放和性能
严格的环保法律增加了减少排放的重要性。虽然改进三元催化器(TWC)技术有助于降低排放水平,但还应注意减少进入三元催化器之前的废气。这可以通过开发发动机技术和标定策略来实现。这样就可以使用低成本的 TWC,并减少催化剂老化后排放物的增加。除了在进入 TWC 之前的发动机熄火阶段减少排放外,在进入 TWC 之后的冷启动和发动机预热阶段(即 TWC 熄火期)减少排放也很重要。在这一阶段,TWC 无法达到最佳工作效率,从而导致排放增加。因此,为了符合环保法规,有必要在保持最佳发动机和车辆性能的同时计算排放量的减少。寻找控制参数的最佳值以同时降低油耗和排放,使得发动机标定成为一个复杂的多目标优化问题。为满足标定要求,必须准确识别发动机的非线性和多变量行为。因此,本研究重点关注发动机的经验建模,并通过智能识别方法建立内燃机在冷暖机工况下的排放模型。为了加强暖机工况下的稳态发动机建模,本研究基于回归深度神经网络的优点,提出了一种混合 MLP+CNN 方法。此外,混合 MLP+CNN+LSTM 方法还增加了一个长短期记忆(LSTM)神经网络,使模型能够捕捉到冷启动条件下以及在氧气储存和 TWC 温度影响下的排放动态行为。结果表明,与传统方法相比,这些方法大大提高了排放建模的准确性。结果表明,与传统方法相比,使用深度学习方法并将发动机排放建模分为两部分(温暖条件下的静态和寒冷条件下的动态),可显著提高排放建模的准确性。由于开发的模型在排放预测以及扭矩、BSFC 和其他输出预测方面具有很高的准确性,因此可用于基于模型的标定。通过将开发的模型与优化技术相结合,可以同时考虑排放、扭矩等因素,对发动机图谱和冷启动进行标定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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