Study on Design Method of Combustion Reference Values for Model-Based Control of Advanced Diesel Engine

Author(s):  
Jihoon Kim ◽  
Yudai Yamasaki

Abstract Model-based control systems are drawing attention in relation to implementing next-generation combustion technologies with high thermal efficiency and low emissions, such as homogeneous charge compression ignition (HCCI) and premixed charge compression ignition (PCCI) combustion, which have low robustness. A model-based control system derives control inputs according to reference values and operating conditions during every cycle and has potential to replace the conventional control map, which requires a large number of experiments. However, model-based control for engines requires reference values for combustion, such as heat release peak timing and heat release peak value; such values represent the combustion state. Therefore, the reference for the transient condition is important for utilizing the benefit of model-based control systems, given that such systems derive control outputs cycle by cycle. In this study, a design method for the combustion reference values for the transient operating condition is described for advanced diesel combustion, which uses premixed compression ignition combustion by multiple fuel injections. Specifically, a statistical method and a method based on model prediction considering the driving characteristics are proposed and compared in engine control experiments. These proposed methods were evaluated under defined simple transient operation conditions and worldwide harmonized light vehicles test cycles (WLTC) mode considering real road conditions. Results showed that designing the combustion reference values for transient operation by model prediction is effective, and such method has the potential to reflect the driving characteristics.

Author(s):  
Jihoon Kim ◽  
Yudai Yamasaki

Abstract Model-based control systems are drawing attention in relation to implementing next-generation combustion technologies with high thermal efficiency and low emissions, such as homogeneous charge compression ignition (HCCI) and premixed charge compression ignition (PCCI) combustion, which have low robustness. A model-based control system derives control inputs according to reference values and operating conditions during every cycle, and has potential to replace the conventional control map, which requires a large number of experiments. However, model-based control for engines requires reference values for combustion, such as heat release rate peak timing and heat release rate peak value; such values represent the combustion state. Therefore, the reference for the transient condition is important for utilizing the benefit of model-based control systems, given that such systems derive control outputs cycle by cycle. In this study, design method for the combustion reference values for the transient operating condition is described for advanced diesel combustion, which uses premixed compression ignition combustion shows multiple heat releases. Specifically, a method utilizing future operating conditions in consideration of the driving characteristics is proposed and compared in engine control experiments. The proposed method was evaluated under certain part of worldwide harmonized light vehicles test cycles (WLTC) mode considering real road conditions. Results showed that designing the combustion reference values for transient operation by considering future operating conditions is effective to ensure advanced combustion, and such method has the potential to consider the driving characteristics.


Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.


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