Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

Novel vision based estimation techniques for the analysis of cavitation bubbles

Visualization and analysis of micro/nano structures throughout multiphase ow have received signi cant attention in recent years due to remarkable advances in micro imaging technologies. In this context, monitoring bubbles and describing their structural and motion characteristics are crucial for hydrodynamic cavitation in biomedical applications. In this thesis, novel vision based estimation techniques are developed for the analysis of cavitation bubbles. Cone angle of multiphase bubbly ow and distributions of scattered bubbles around main ow are important quantities in positioning the ori ce of cavitation generator towards the target and controlling the destructive cavitation e ect. To estimate the cone angle of the ow, a Kalman lter which utilizes 3D Gaussian modeling of multiphase ow and edge pixels of the cross-section is implemented. Scattered bubble swarm distributions around main ow are assumed to be Gaussian and geometric properties of the covariance matrix of the bubble position data are exploited. Moreover, a new method is developed to track evolution of single, double and triple rising bubbles during hydrodynamic cavitation. Proposed tracker fuses shape and motion features of the individually detected bubbles and employs the well-known Bhattacharyya distance. Furthermore, contours of the tracked bubbles are modeled using elliptic Fourier descriptors (EFD) to extract invariant properties of single rising bubbles throughout the motion. To verify the proposed techniques, hydrodynamic cavitating bubbles are generated under 10 to 120 bars inlet pressures and monitored via Particle Shadow Sizing (PSS) technique. Experimental results are quite promising

Robust hovering control of a quadrotor using acceleration feedback

This paper presents a novel acceleration feedback control method for robust hovering of a quadrotor subject to aerodynamic disturbances. An acceleration based disturbance observer (ABDOB) is designed to reject disturbances acting on the positional dynamics of the quadrotor. In order to provide high stiffness against disturbances acting on the attitude dynamics, a nested position, velocity and inner acceleration feedback control structure that utilizes PID and PI type controllers is developed. To obtain reliable angular acceleration information, a cascaded estimation technique based on an extended Kalman filter (EKF) and a classical Kalman filter (KF) is proposed. EKF estimates the Euler angles and gyro biases by fusing the data from gyroscope, accelerometer and magnetometer. Compensated gyro data are then fed into a Kalman filter whose process model is derived from Taylor series expansion of angular velocities and accelerations where angular jerks are considered as stochastic inputs. The well-known kinematic relation between Euler angular rates and angular velocities is employed to estimate reliable Euler accelerations. Estimated Euler angles, rates and accelerations are then used as feedback signals in the nested attitude control structure. Performance of the proposed method is assessed by a high fidelity simulation model where uncertainties in the sensor measurements, e.g. sensor bias and noise, are also considered. Developed controllers that utilize estimated acceleration feedback provide extremely robust hovering results when the quadrotor is subject to wind gusts generated by Dryden wind model. Simulation results show that utilization of acceleration feedback in hovering control significantly reduces the deviations in the x-y position of the quadrotor

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively

Driver evaluation in heavy duty vehicles based on acceleration and braking behaviors

In this paper, we present a real-time driver evalua-tion system for heavy-duty vehicles by focusing on the classifica-tion of risky acceleration and braking behaviors. We utilize animproved version of our previous Long Short Memory (LSTM)based acceleration behavior model [10] to evaluate varyingacceleration behaviors of a truck driver in small time periods.This model continuously classifies a driver as one of six driverclasses with specified longitudinal-lateral aggression levels, usingdriving signals as time-series inputs. The driver gets accelerationscore updates based on assigned classes and the geometry ofdriven road sections. To evaluate the braking behaviors of atruck driver, we propose a braking behavior model, which usesa novel approach to analyze deceleration patterns formed duringbrake operations. The braking score of a driver is updated foreach brake event based on the pattern, magnitude, and frequencyevaluations. The proposed driver evaluation system has achievedsignificant results in both the classification and evaluation ofacceleration and braking behaviors

Estimating soot emission in diesel engines using gated recurrent unit networks

In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles

Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors

n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness

Robust trajectory control of an unmanned aerial vehicle using acceleration feedback

In this work, acceleration feedback is utilised in a hierarchical control structure for robust trajectory control of a quadrotor helicopter subject to external disturbances where reference attitude angles are determined through a nonlinear optimisation algorithm. Furthermore, an acceleration-based disturbance observer (AbDOB) is designed to estimate disturbances acting on the positional dynamics of the quadrotor. For the attitude control, nested position, velocity, and inner acceleration feedback loops consisting of PID and PI type controllers are developed to provide high stiffness against external disturbances. Reliable angular acceleration is estimated through a cascaded filter structure. Simulation results show that the proposed controllers provide robust trajectory tracking performance when the aerial vehicle is subject to wind gusts generated by the Dryden wind model along with the uncertainties and measurement noise. Results also demonstrate that the reference attitude angles calculated through nonlinear optimisation are smooth and within the desired bounds

Robust trajectory control of an unmanned aerial vehicle using acceleration feedback

In this work, acceleration feedback is utilised in a hierarchical control structure for robust trajectory control of a quadrotor helicopter subject to external disturbances where reference attitude angles are determined through a nonlinear optimisation algorithm. Furthermore, an acceleration-based disturbance observer (AbDOB) is designed to estimate disturbances acting on the positional dynamics of the quadrotor. For the attitude control, nested position, velocity, and inner acceleration feedback loops consisting of PID and PI type controllers are developed to provide high stiffness against external disturbances. Reliable angular acceleration is estimated through a cascaded filter structure. Simulation results show that the proposed controllers provide robust trajectory tracking performance when the aerial vehicle is subject to wind gusts generated by the Dryden wind model along with the uncertainties and measurement noise. Results also demonstrate that the reference attitude angles calculated through nonlinear optimisation are smooth and within the desired bounds