Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 2—Implementation of Adaptive Strategies

Brusa, Alessandro; Cavina, Nicolò; Rojo, Nahuel; Mecagni, Jacopo; Corti, Enrico; Moro, Davide; Cucchi, Matteo; Silvestri, Nicola

doi:10.3390/en14175342

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…To comply with this goal, one of the important steps is to have solutions that can accurately predict and control the engine parameters in Real-Time (RT). Having an accurate combustion model helps in more precise control of the various actuations available to control the engine thus improving the efficiency [2,3]. Today the state-of-the-art for the combustion models is composed by the following categories:…”

Section: Introductionmentioning

confidence: 99%

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Advanced, Guided Procedure for the Calibration and Generalization of Neural Network-Based Models of Combustion and Knock Indexes

Brusa,

Shethia,

Mecagni

et al. 2023

SAE Int. J. Engines

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<div>In the last few years, the artificial neural networks have been widely used in the field of engine modeling. Some of the main reasons for this are, their compatibility with the real-time systems, higher accuracy, and flexibility if compared to other data-driven approaches. One of the main difficulties of using this approach is the calibration of the network itself. It is very difficult to find in the literature procedures that guide the user to completely define a network. Typically, the very last steps (like the choice of the number of neurons) must be selected by the user on the base of his sensitivity to the problem.</div> <div>This work proposes an automatic calibration procedure for the artificial neural networks, considering all the main hyper-parameters of the network such as the training algorithms, the activation functions, the number of the neurons, the number of epochs, and the number of hidden layers, for modeling various combustion indexes in a modern internal combustion engine. However, the proposed procedure can be applied to the training of any neural network-based model.</div> <div>The automatic calibration procedure outputs a configuration of the network, giving the optimal combination in terms of hyper-parameters. The decision of the optimal configuration of the neural network is based on a self-developed formula, which gives a rank of all the possible hyper-parameter combinations using some statistical parameters obtained comparing the simulated and the experimental values. In the end, the lowest rank is selected as the optimal one as it represents the combination having the lowest error. Following the definition of this rank, high accuracy on the results has been achieved in terms of the root mean square error index, for example, on the combustion phase model, the error is 0.139°CA under steady-state conditions. On the maximum in-cylinder pressure model, the error is 1.682 bar, while the knock model has an error of 0.457 bar for the same test that covers the whole engine operating field.</div>

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Section: Introductionmentioning

confidence: 99%

“…The optimization algorithm proposed is based on the definition of a novel accuracy index, in this work named "Rank", which evaluates the weighted sum of certain quantities as mentioned in the Equations ( 2) and (3).…”

mentioning

confidence: 99%

Advanced, Guided Procedure for the Calibration and Generalization of Neural Network-Based Models of Combustion and Knock Indexes

Brusa,

Shethia,

Mecagni

et al. 2023

SAE Int. J. Engines

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An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx

Giovannardi,

Brusa,

Petrone

et al. 2023

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)

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Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

et al. 2022

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In this paper, a methodology based on data-driven models is developed to predict the NOx emissions of an internal combustion engine using, as inputs, a set of ECU channels representing the main engine actuations. Several regressors derived from the machine learning and deep learning algorithms are tested and compared in terms of prediction accuracy and computational efficiency to assess the most suitable for the aim of this work. Six Real Driving Emission (RDE) cycles performed at the roll bench were used for the model training, while another two RDE cycles and a steady-state map of NOx emissions were used to test the model under dynamic and stationary conditions, respectively. The models considered include Polynomial Regressor (PR), Support Vector Regressor (SVR), Random Forest Regressor (RF), Light Gradient Boosting Regressor (LightGBR) and Feed-Forward Neural Network (ANN). Ensemble methods such as Random Forest and LightGBR proved to have similar performances in terms of prediction accuracy, with LightGBR requiring a much lower training time. Afterwards, LightGBR predictions are compared with experimental NOx measurements in steady-state conditions and during two RDE cycles. Coefficient of determination (R2), normalized root mean squared error (nRMSE) and mean average percentage error (MAPE) are the main metrics used. The NOx emissions predicted by the LightGBR show good coherence with the experimental test set, both with the steady-state NOx map (R2 = 0.91 and MAPE = 6.42%) and with the RDE cycles (R2 = 0.95 and nRMSE = 0.04).

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Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 2—Implementation of Adaptive Strategies

Cited by 7 publications

References 18 publications

Advanced, Guided Procedure for the Calibration and Generalization of Neural Network-Based Models of Combustion and Knock Indexes

Advanced, Guided Procedure for the Calibration and Generalization of Neural Network-Based Models of Combustion and Knock Indexes

An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx

Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

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