Mehran Bidarvatan

Integrated HCCI Engine Control Based on a Performance Index

Integrated control of HCCI combustion phasing, load, and exhaust aftertreatment system is essential for realizing high efficiency HCCI engines, while maintaining low HC and CO emissions. This paper introduces a new approach for integrated HCCI engine control by defining a novel performance index to characterize different HCCI operating regions. The experimental data from a single cylinder engine at 214 operating conditions is used to determine the performance index for a blended fuel HCCI engine. The new performance index is then used to design an optimum reference trajectory for a multi-input multi-output HCCI controller. The optimum trajectory is designed for control of combustion phasing and IMEP, while meeting catalyst light-off requirements for the exhaust aftertreatment system. The designed controller is tested on a previously validated physical HCCI engine model. The simulation results illustrate the successful application of the new approach for controller design of HCCI engines.Copyright

Grey-box modeling and control of HCCI engine emissions

Real-time model based control of Homogeneous Charge Compression Ignition (HCCI) engines faces a critical challenge of maintaining a perfect balance between model accuracy and computational load. In particular, currently available HCCI emissions models in the literature are highly computationally expensive for control applications. This paper develops a computationally efficient grey-box HCCI engine model for predicting Total Hydrocarbon (THC), Carbon Monoxide (CO), and Nitrogen Oxides (NOx). The grey-box model consists of a feed forward Artificial Neural Networks (ANN) model in combination with physical models for estimating combustion phasing and Indicated Mean Effective Pressure (IMEP). The emission model is experimentally validated over a large range of HCCI engine operation including 208 steady state test conditions. The validation results show that the grey-box model is able to predict NOx, CO, and THC with average relative errors less than 10%. Using a Genetic Algorithm optimization method along with the developed emission grey-box model, an optimum CA50 trajectory is obtained for every given load trajectory in order to minimize THC and CO emissions. A model-based controller is designed and tested on the grey-box virtual engine model for tracking IMEP and the optimum CA50 trajectories, while indirectly minimizing the engine emissions. Control results show that the developed grey-box model is of utility for real time HCCI control applications.

Grey-Box Modeling for HCCI Engine Control

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.Copyright

Integrated cycle-to-cycle control of exhaust gas temperature, load, and combustion phasing in an HCCI engine

Precise and integrated cycle-to-cycle control of exhaust gas temperature (Texh), load, and combustion phasing is essential for realizing high efficiency Homogeneous Charge Compression Ignition (HCCI) engines with low exhaust emissions. In this paper a model-based control framework is developed for an integrated control of Texh, Indicated Mean Effective Pressure (IMEP), and combustion phasing in an HCCI engine. A discrete Control Oriented Model (COM) is developed to predict the HCCI outputs on a cycle-to-cycle basis and validated against steady-state and transient experimental data from a single cylinder Ricardo engine. The COM provides sufficient accuracy with an average uncertainty of 7 °C, 0.3 bar, and 1.6 CAD for predicting Texh, IMEP and combustion phasing, respectively. In addition, the COM is computationally efficient for real-time HCCI control. A three-input three-output controller is designed using a Discrete Sliding Mode Control (DSMC) method to control Texh, IMEP, and combustion phasing by adjusting the intake manifold pressure, fuel mass flow rate, and ratio of two Primary Reference Fuels (PRFs), respectively. The results indicate the DSMC is capable of maintaining the stability of the engine operation and tracking the desirable HCCI engine outputs, while also rejecting internal disturbances.

Energy Management Control of a Hybrid Electric Vehicle by Incorporating Powertrain Dynamics

Energy management strategies in parallel Hybrid Electric Vehicles (HEVs) usually ignore effects of Internal Combustion Engine (ICE) dynamics and rely on static maps for required engine torque-fuel efficiency data. It is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper addresses this shortcoming by investigating effects of some major Spark Ignition (SI) engine dynamics and clutch dynamics on torque split management in a parallel HEV. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE and clutch dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by 8.7% for city and highway driving conditions in a combined common North American drive cycle. This fuel penalty is often overlooked in conventional HEV energy management strategies. A Model Predictive Control (MPC) of torque split is developed by incorporating effects of the studied influencing dynamics. Results show that the integrated energy management strategy can improve the total energy consumption of HEV by more than 6% for combined Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET)drive cycles.Copyright

Modeling, design and implementation of a closed-loop combustion controller for an RCCI engine

Reactivity Controlled Compression Ignition (RCCI) is a promising low temperature combustion strategy with high thermal efficiency and effective means to control combustion phasing and heat release rate. Being a dual-fuel stratified combustion process, RCCI requires precise control over the injection timing of direct injected fuel and in-cylinder mixture reactivity gradient. This paper focuses on developing a real-time, model-based controller for adjusting combustion phasing of an RCCI engine. Optimum combustion phasing is achieved by controlling mixture reactivity and injection timing of higher reactive fuel. A control-oriented model (COM) is developed using physics-based equations to predict combustion phasing during steady-state and transient operating conditions. The experimental validation results show that the COM is able to predict combustion phasing with less than 2 crank angle degrees (CAD). The COM is used to design a Proportional-Integral (PI) controller to track the desired combustion phasing by adjusting dual-fuel mixing ratio and injection timing. The PI controller is experimentally implemented on an RCCI engine test bench. The experimental results show that the designed controller can follow the desired combustion phasing with an average tracking error of 2 CAD and rise time of three engine cycles.

Novel Exergy-wise predictive control of Internal Combustion Engines

Exergy is an effective metric to evaluate the performance of energy systems. Exergy analysis has been extensively used to study and understand loss mechanisms of Internal Combustion Engines (ICEs). However knowledge from exergy analysis has not been used for control of ICEs. This paper presents the first application of exergy-based control to ICEs. In this paper, an exergy model is developed for an advanced ICE with low temperature combustion mode that has higher efficiency compared to conventional diesel and spark ignition engines. The exergy model is based on quantification of the Second Law of Thermodynamic (SLT) and irreversibilities which are not identified in commonly used First Law of Thermodynamics (FLT) analysis. An optimal control method is developed based on minimizing irreversibilities and exergy losses. The new controller finds the optimum combustion phasing at every given engine load to minimize exergy destruction/loss. Application of the new developed control algorithm is demonstrated for a Combined Heat and Power (CHP) case study. The results show that by using the exergy-based optimal control strategy, the engine output power and exhaust exergies are maximized.

Impact of Engine Dynamics on Torque Split Management of a Hybrid Electric Vehicle

Energy management strategies in a parallel Hybrid Electric Vehicle (HEV) greatly depend on the accuracy of internal combustion engine (ICE) data. It is a common practice to rely on static maps for required engine torque-fuel efficiency data. The engine dynamics are ignored in these static maps and it is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper presents the impact of ICE dynamics on the performance of the torque split management strategy. A parallel HEV torque split strategy is developed using a method of model predictive control. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by more than 20% for city driving conditions in a common North American drive cycle. This also leads to substantial fuel penalty which is often overlooked in conventional HEV energy management strategies.Copyright