Combustion process estimation in cylinders based on an integrated engine model of the wiebe function and a piston-crank mechanism

Katsuaki, Nagaosa; Miyagi, Naoki; Yamazaki, Tomoki; Iwase, Masami

doi:10.1109/sice.2015.7285560

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…System identification has become another important research direction. The parameters are identified by algebraic method, 16 unscented Kalman filter, 19 artificial neural network method. 12,20 In the model parameter identification, the least squares method has the characteristics of simple principle, fast convergence and local optimum.…”

Section: Introductionmentioning

confidence: 99%

Modeling of 0-D heat release rate of heavy-duty diesel engine based on classification algorithm

Liu

Yao

Chen

et al. 2022

International Journal of Engine Research

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With the shortage of fossil fuel and the strengthening of emission regulations, the control strategy using in-cylinder pressure signals has caused extensive concern. Modeling the combustion heat release rate is an indispensable work along with the in development of the diesel combustion control hardware-in-loop system. Based on the analysis of experimental data, four combustion modes are proposed in this paper. The Wiebe-based functions developed for each combustion mode are given. According to experimental launch data, the combustion mode is classified automatically using the multi-classification support vector machine algorithm. The LM-Chaos algorithm based on function parameter identification is proposed. The results show that HRR shape is mainly related to the rate of premixed combustion and diffusion combustion under different operating conditions, and the variation of HRR directly affects the structure of Wiebe-based function. Different combustion modes can be simulated by selecting the starting time of combustion, the duration of combustion phases and the co-existence time of combustion phases. This method can simulate the heat release curves of various working conditions and improve the fitting of heat release curves of long combustion time by adding error function.

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Section: Introductionmentioning

confidence: 99%

Modeling of 0-D heat release rate of heavy-duty diesel engine based on classification algorithm

Liu

Yao

Chen

et al. 2022

International Journal of Engine Research

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“…11–13 Furthermore, the pumping loss can be decreased by more opening of the throttle valve under low-load conditions. 14,15 However, practical realization of LP-EGR faces several challenges due to the highly nonlinear behavior and time delay from a long passage of the LP-EGR system. 16–19 These are especially critical issues for precise estimation of the LP-EGR rate with respect to sophisticated LP-EGR control.…”

Section: Introductionmentioning

confidence: 99%

In-cylinder pressure-based convolutional neural network for real-time estimation of low-pressure cooled exhaust gas recirculation in turbocharged gasoline direct injection engines

Jung

Hwang

et al. 2019

International Journal of Engine Research

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Low-pressure cooled exhaust gas recirculation is one of the most promising technologies for improving fuel efficiency of turbocharged gasoline direct injection engines. To realize the beneficial effects of the low-pressure cooled exhaust gas recirculation, the accurate estimation of the low-pressure cooled exhaust gas recirculation rate is essential for precise low-pressure cooled exhaust gas recirculation control. In this respect, previous studies have suggested in-cylinder pressure-based low-pressure cooled exhaust gas recirculation models to obtain the low-pressure cooled exhaust gas recirculation rate into the cylinders with fast response. However, these methods require considerable manual process of feature engineering to extract and analyze the combustion characteristics from the cylinder pressure traces. Furthermore, the performance of the entire model is limited solely to certain hand-crafted characteristics and their mathematical formulations. To resolve these limitations, we propose an in-cylinder pressure-based convolutional neural network for low-pressure cooled exhaust gas recirculation estimation. Because the convolutional neural network model automatically learns the complex function between the raw input of the high-dimensional cylinder pressure traces and the low-pressure cooled exhaust gas recirculation rate through an end-to-end deep learning framework, this convolutional neural network model provides a more effective and precise modeling process compared to the conventional combustion characteristics-based regression models. The proposed convolutional neural network model consists of the input layer with the previous consecutive cycles of the pressure traces to resolve the model uncertainty from cycle-to-cycle variations. This input layer is connected to one convolutional layer, two fully connected layers, and the final output layer that is the target low-pressure cooled exhaust gas recirculation rate. The proposed model was trained, validated, and tested using a total of 50,000 cycles of engine experimental data under various transient driving conditions. The remarkable accuracy of the proposed model was evaluated with R2 values over 0.99 and root mean square error values of less than 1.5% under the transient conditions. Moreover, the real-time performance and low memory requirement were also verified on the target embedded platform.

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State Estimation of Internal Combustion Engine Based on Mathematical Model

Yamazaki

Iwase

2017

Lecture Notes in Electrical Engineering

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Combustion process estimation in cylinders based on an integrated engine model of the wiebe function and a piston-crank mechanism

Cited by 5 publications

References 6 publications

Modeling of 0-D heat release rate of heavy-duty diesel engine based on classification algorithm

Modeling of 0-D heat release rate of heavy-duty diesel engine based on classification algorithm

In-cylinder pressure-based convolutional neural network for real-time estimation of low-pressure cooled exhaust gas recirculation in turbocharged gasoline direct injection engines

State Estimation of Internal Combustion Engine Based on Mathematical Model

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