An indicated torque estimation method based on the Elman neural network for a turbocharged diesel engine

Ge, Yanwu; Huang, Ying; Hao, Donghao; Li, Gang; Li, Huan

doi:10.1177/0954407015606271

Cited by 9 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improved Elman Neural Network. Elman neural network (Elman NN) [24][25][26][27] is a typical dynamic neural network with recurrent feedback layer based on BP neural network. The Elman NN stores the internal state through the context layer, which has the performance of mapping dynamic characteristics.…”

Section: Background Theorymentioning

confidence: 99%

Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

Hai-bo

Zhang

2018

Journal of Sensors

View full text Add to dashboard Cite

In order to solve the problems of strong coupling, nonlinearity, and complex mechanism in real-world engineering process, building soft sensor with excellent performance and robustness has become the core issue in industrial processes. In this paper, we propose a new soft sensor model based on improved Elman neural network (Elman NN) and introduce variable data preprocessing method to the soft sensor model. The improved Elman NN employs local feedback and feedforward network mechanism through context layer to accurately reflect the dynamic characteristics of the soft sensor model, which has the superiority to approximate delay systems and adaption of time-varying characteristics. The proposed variable data preprocessing method adopts combining Isometric Mapping (ISOMAP) with local linear embedding (LLE), which effectively maintains the neighborhood structure and the global mutual distance of dataset to remove the noises and data redundancy. The soft sensor model based on improved Elman NN with variable data preprocessing method by combining ISOMAP and LLE is applied in practical sintering and pelletizing to estimate the temperature in the rotary kiln calcining process. Comparing several conventional soft sensor model methods, the results indicate that the proposed method has more excellent generalization performance and robustness. Its model prediction accuracy and anti-interference ability have been improved, which provide an effective and promising method for the industrial process application.

show abstract

Section: Background Theorymentioning

confidence: 99%

Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

Hai-bo

Zhang

2018

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…The IVL is varied between 50% and 120% of the original valve lift, and the engine speed is varied between 1500 r/min and 6000 r/min (Figure 8). The homographic function in equation (20) was then used in order to estimate these data samples. As can be seen in Figure 9, the homographic function is easily capable of predicting the above-mentioned data, meaning that the linear form in equation (19) is appropriate.…”

Section: Second Submodel: Including the Engine Speedmentioning

confidence: 99%

“…10,19 While having a reasonable accuracy, neural networks usually need much fewer experimental data. Therefore, they have been widespread in various fields of engine modelling such as performance and emissions, [20][21][22][23] cylinder pressure prediction 24,25 and volumetric efficiency. 10,19,26 Some miscellaneous black-box approaches have also been utilized by researchers.…”

Section: Introductionmentioning

confidence: 99%

A novel volumetric efficiency model for spark ignition engines equipped with variable valve timing and variable valve lift Part 1: model development

Kakaee

Mashadi

Ghajar

2016

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Estimation of the air charge and the volumetric efficiency is one of the most challenging tasks in the control of internal-combustion engines owing to the intrinsic complexity and the non-linearity of the gas flow phenomena. In particular, with emerging new technologies such as systems with variable valve timing and variable valve lift, the number of effective parameters increases greatly, making the estimation task more complicated. On the other hand, using a three-way catalyst converter needs strict control of the air-to-fuel ratio to around the stoichiometric ratio, and hence more accurate models are required for estimation of the air charge. Therefore, various models have been proposed in the literature for estimation of the volumetric efficiency and the air charge. However, they are either strictly based on physical first principles, making them impractical for conventional applications, or nearly fully empirical and need many experimental data for calibration. In this paper, using a novel approach, a new semiempirical model is proposed for estimation of the volumetric efficiency, which is calibrated with very few experimental data and can be used easily for real-time applications. In addition to the valve timings, the engine speed and the intake manifold pressure, the inlet valve lift is also considered as the model input. The generalizability of the model is proved by applying it to estimate the volumetric efficiency of six different engines. Furthermore, a systematic approach is taken to simplify the proposed model and to strengthen its prediction capability. The result is a simple, practical and generalizable model which can be used for various spark ignition engines, can be trained with very few data and can be utilized for estimating accurately the volumetric efficiency in real-time applications.

show abstract

“…As a typical representative of data-driven modeling method, ANN can approximate any complex nonlinear relationship and is widely used in engine dynamic modeling [28,29]. Accuracy of the ANN engine model can be guaranteed because such model avoids specific details of engine intake and combustion processes [30]. However, traditional ANN engine dynamics models (AEDMs) mostly focus on the normal operating dynamic characteristics of engines regardless of the engine starting and stopping processes occurring frequently in HEVs under real driving conditions, which are crucial for the generation of vehicle jerks [31].…”

Section: Introductionmentioning

confidence: 99%

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

et al. 2018

View full text Add to dashboard Cite

Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

show abstract

An indicated torque estimation method based on the Elman neural network for a turbocharged diesel engine

Cited by 9 publications

References 21 publications

Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

A novel volumetric efficiency model for spark ignition engines equipped with variable valve timing and variable valve lift Part 1: model development

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Contact Info

Product

Resources

About