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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]

Engine calibration is a process of optimizing its control variables, which is a crucial step to achieve the desired performance from the engine system. However, its complexity has increased dramatically due to the constant evolution of the engine architecture with multiple control variables interacting with each other, making it difficult to calibrate its control variables with significantly increased experimental costs. System complexity also affects the physics-based modeling process as it makes the system model highly non-linear and difficult to capture system dynamics under all engine operating conditions. To overcome these challenges, the current work proposes a machine learning-based framework to reduce the required calibration effort. The work implements a stochastic surrogate assisted optimization approach on an actual engine experimental test setup, which efficiently looks for the global optimal solution based on only the input-output data set. This work performs a steady-state calibration of a turbocharged diesel engine with an additional external electric-assisted boosting system (eBoost). Three control parameters are considered for this study: Variable Geometry Turbocharger (VGT) vane position, Exhaust Gas Recirculation (EGR) valve position, and eBoost speed to identify the optimal trade-off between engine efficiency (Brake Specific Fuel Consumption) and emissions (NO x ). The addition of eBoost makes the calibration problem difficult because the two control parameters, VGT and EGR, also depend on eBoost speed. Using the proposed data-driven calibration approach, the experimental results showed enhanced engine performance in terms of efficiency and emissions using the eBoost compared with the case without eBoost. At the same time, the experimental cost was reduced significantly due to the reduced number of testing points required to achieve the optimum. [ABSTRACT FROM AUTHOR]

Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible. [ABSTRACT FROM AUTHOR]