Error propagation and accumulation is a common problem for system level engine modeling at which individually modeled components are connected to form a complete engine model. Engines with exhaust gas recirculation (EGR) and turbocharging have components connected in a feedback configuration (the exhaust conditions affect the intake and the intake, consequently, affects the exhaust), thus they have a challenging model tuning process. This paper presents a systematic procedure for effective tuning of an engine air-charge path model to improve accuracy at the system level as well as reducing the computational complexity of tuning a large set of components. Based on using sensitivity analysis, the presented procedure is used to inspect which component influences more a set of selected outputs in a model with high degree of freedom caused by many parameters of different components. After selecting the influential component, which is the turbocharger in this study, further tuning is applied to parameters in the component to increase the overall accuracy of the complete engine model. The corrections applied to the air-charge path model of a 6 cylinder 13L heavy duty diesel engine with EGR and twin-scroll turbocharger was shown to effectively improve the model accuracy.
- Dynamic Systems and Control Division
Effective Component Tuning in a Diesel Engine Model Using Sensitivity Analysis
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Salehi, R, & Stefanopoulou, A. "Effective Component Tuning in a Diesel Engine Model Using Sensitivity Analysis." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T12A003. ASME. https://doi.org/10.1115/DSCC2015-9729
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