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Turbocharged diesel engines often suffer significant response delay due to so-called turbo-lag, especially engines with large displacement. For this reason, many technologies have been developed to reduce turbo-lag. This paper develops a coordinated control strategy for a diesel engine equipped with an eBoost (electrical compressor) system to significantly reduce turbo-lag. A multiple-input and multiple-output (MIMO) Linear Quadratic Tracking with Integral (LQTI) control strategy, along with its scheduling logic, is developed for the Ford 6.7 L 8-cylinder diesel engine equipped with a variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), and added eBoost along with a bypass valve. Note that the existing production engine does not have an eBoost and bypass valve. Multiple model-based LQTI controllers were designed at different engine operational conditions based on the associated linearized models, and the control outputs are scheduled based upon the engine load condition and bypass valve position. The developed control strategy is validated in both simulation and experimental studies, and the test results show a reduction in engine response time by up to 81.36% in terms of reaching target intake manifold (boost) pressure following a load step-up, compared with the production configuration (without eBoost and bypass valve) with no significant impact on NOx emissions. [ABSTRACT FROM AUTHOR]

Spark ignition engines are often desired to be operated close to their knock borderline when MBT (maximum brake torque) cannot be achieved for optimizing combustion efficiency. Under this circumstance, a calibrated baseline spark timing, along with other control parameters such as intake and exhaust valve timings, is found for the engine control system to maximize fuel economy, and a stochastic scheme can be used for the control based on a large number of history data. However, cycle-to-cycle combustion variations still exist, resulting in a relatively conservative baseline control. To reduce cycle-to-cycle combustion variations, a real-time cycle-wised knock compensation is required. The correlation between exhaust temperature at the current cycle and knock intensity at the next cycle was found in our earlier research. In this paper, a cycle-to-cycle spark timing compensation scheme is developed based on the measured exhaust temperature when the engine is operated close to its knock borderline. To make model-based control possible, q -Markov COVER (COVariance Equivalent Realization) system identification was used to obtain a linearized engine exhaust system model from incremental spark timing to associated exhaust temperature and knock intensity. Accordingly, a Linear–Quadratic–Gaussian (LQG) controller is designed, based on the identified model, to minimize the knock intensity fluctuations based on incremental exhaust temperature variation. The LQG control strategy was integrated with the existing entire knock control architecture, where the baseline spark timing is generated based on the offline machine training with an online updating scheme developed earlier, and demonstrated experimentally. Note that the cycle-based compensation only adds incremental spark timing to the baseline control so that knock combustion variations can be reduced. Three test scenarios are used to demonstrate the effectiveness of the proposed cycle-to-cycle compensation scheme when the engine is knock-limited. With the help of cycle-to-cycle based compensation, it was demonstrated that engine spark timing can be further advanced about one crank degree while maintaining the same knock intensity up-limit due to reduced knock combustion variations. Note that this is corresponding to 0.5%–1.0% fuel economy for this engine when it is operated under knock condition. [ABSTRACT FROM AUTHOR]

Spark ignition engines are often desired to be operated close to its knock borderline limit, when MBT (maximum brake torque) cannot be achieved, to optimize engine efficiency. Traditionally, engine knock is closed-loop controlled using a dual-rate PID scheme with the help of feedforward control based on off-line calibrated borderline knock limit along with associated corrections. Note that the feedforward control is often not accurate and very conservative. This paper proposed a data-driven knock baseline control architecture consisting of two-key components: (a) offline training knock borderline Surrogate models under the worst and lightest knock conditions and (b) online updating the composite Surrogate model for adaptation. A Bayesian-based multi-objectives optimization algorithm is used to obtain optimal knock control parameters with significantly improved calibration efficiency, where the offline trained models are obtained using spark and intake valve timings as control parameters, and indicated specific fuel consumption (ISFC) and knock intensity (KI) as two competing performance measures to be minimized. Based on our early results, two Kriging surrogate models with two corresponding Pareto fronts (most and least advanced timing) can be obtained, and the purpose of this paper is to generate a composite real-time Kriging model for compensating engine aging and operational environmental changes such as fuel type, temperature, humidity, etc. In order to reduce the number of control parameters used in online updating, principal component analysis is conducted to find dominated control parameter, and in this case, the spark timing is the most sensitive factor of Pareto front. During the online updating process, a likelihood ratio controller with short- and long-term buffers was proposed to update the surrogate model in real-time and to adapt to fast and slow environmental and engine variations so that the optimal borderline knock limit can be found based on the intersection of mean and variance of Pareto front from the real-time updated composite Kriging model; and accordingly, the optimal control parameters can be located in the design space using surrogate model. Both simulation and test results indicate that the proposed online updating scheme is able to update the machine-learned stochastic surrogate models and adjust the feedforward borderline knock control parameters adapted to engine aging and operational environment. [ABSTRACT FROM AUTHOR]