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Future government emission regulations have lead to the development and implementation of advanced aftertreatment systems to meet stringent emission standards for both on-road and off-road vehicles. These aftertreatment systems require sophisticated control and diagnostic strategies to ensure proper system functionality while minimizing tailpipe NOx and PM emissions across all engine operating conditions. In this paper, an integrated algorithm design approach with controls and diagnostics for an aftertreatment system consisting of a fuel doser, fuel reformer, LNT, DPF, and SCR is discussed.

This paper addresses Hardware-In-the-Loop modeling and simulation for Diesel aftertreatment controls system development. Lean NOx Trap (LNT) based aftertreatment system is an efficient way to reduce NOx emission from diesel engines. From control system perspective, the main challenge in aftertreatment system is to predict temperature at various locations and estimate the stored NOx in LNT. Accurate estimation of temperatures and NOx stored in the LNT will result in an efficient system control with less fuel penalty while still maintaining the emission requirements. The optimization of the controls will prolong the lifespan of the system by avoiding overheating the catalysts, and slow the progressive process of component aging. Under real world conditions, it is quite difficult and costly to test the performance of a such complex controller by using only vehicle tests and engine cells.

In recent years, due to more and more stringent government regulations on diesel emissions, diesel aftertreatment systems have attracted great deal of attention from both academia and diesel engine industries. Many different devices and approaches, such as Urea SCR, LNT, engine control related EGR and in-cylinder post injection, have been developed and applied to reduce nitrogen oxides (NOx) emissions. Among those solutions, Lean NOx Trap (LNT)-based emission reduction control system is one of the common approaches. The NOx storage capacity of an LNT depends on many different factors and operating conditions. Accurate and real-time estimation of NOx storage is quite important for efficient system controls, particularly for enhancing system lifespan and reducing overall fuel consumption. A more precise modeling of NOx storage has more significant impact for overall system performance.

This paper addresses feedforward tracking control for electronic throttles. A robust and accurate tracking control scheme based on the training of a Neural Network model and feedback term (PID) is developed. The Neural Network based term can be trained off-line. This feedfoward term serves as a mathematical model capable of describing Electronic Throttle dynamics over a wide range. We have shown that by adding the Neural Network based feedforward control to a common feedback control method, such as the gain-scheduled PID used in many ETC production controllers, that the tracking control performance criteria such as transient errors, steady state errors, response time and overshoot, are greatly improved. Experiments conducted on a production Electronic Throttle Body with a Motorola H-brigde driver IC have shown good results utilizing this approach.

In this paper, we discuss the mathematical modeling of suspension and rollover characteristics. With dynamics analysis, a simple active suspension control is designed with rollover prevention. Also studied is the framework of how to integrate active suspension and steering in the emergency situations besides regular ride and handling suspension control. Simulation results are presented.

ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.

In this paper, a sensor fusion approach is introduced to estimate lane departure. The proposed algorithm combines the camera, inertial navigation sensor, and GPS data with the vehicle dynamics to estimate the vehicle path and the lane departure time. The lane path and vehicle path are estimated by using Kalman filters. This algorithm can be used to provide early warning for lane departure in order to increase driving safety. By integrating inertial navigation sensor and GPS data, the inertial sensor biases can be estimated and the vehicle path can be estimated where the GPS data is not available or is poor. Additionally, the algorithm can be used to reduce the latency of information embedded in the controls, so that the vehicle lateral control performance can be significantly improved during lane keeping in Advanced Driver Assistance Systems (ADAS) or autonomous vehicles. Furthermore, it improves lane detection reliability in situations when camera fails to detect lanes.

Hardware-in-the-loop (HIL) testing is part of automotive V-design which is commonly used in automotive industries for the development of Electronic Control Unit (ECU). HIL test platform provides the capacity to test the ECU in a controlled environment even with scenarios that would be too dangerous or impractical to test on real situation, also the ECU can be tested even before the actual plant under building. This paper presents a HIL test platform for the validation of a seat ECU. The HIL platform can also be used for control and diagnostics algorithm development. The HIL test platform consists of three parts: a real time target machine (dSPACE SCALEXIO AutoBox), an ECU (Magna Seating M12 Module), and a signal conditioning unit (Load Box). The ECU produces the control commands to the real-time target machine through load box. The real time target machine hosts the plant model of the power seat which includes the kinematics and dynamics of the seat movements.