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In the age of 5G, the cloud constitutes a massive computational resource. Such capability is greatly underutilized, especially for the purpose of vehicle diagnostics and prognostics. Diagnostics and prognostics run mostly in the limited and cost sensitive electronic module of the vehicle. Utilizing vehicle connectivity, along with the massive capability of the cloud would allow the deployment of smarter algorithms that provide improved vehicle performance and operation management. In this paper, a streamlined process to develop and deploy off-board diagnostics is presented. The process included developing multiphysics digital twins and running the diagnostics off-board. It was demonstrated on a fleet of virtual Hybrid Electric Vehicles (HEV). The Digital Twin replica was created using Simulink® and Simscape®. The microcontroller used to demonstrate the diagnostic is a Raspberry Pi hardware running in real time.

The interest for NOx estimators (also known as virtual sensors or inferential sensors) has increased over the recent years due to benefits attributed to cost and performance. NOx estimators are typically installed to improve On-Board Diagnostics (OBD) monitors or to lower bill of material costs by replacing physical NOx sensors. This paper presents initial development results of a virtual engine-out NOx estimator planned for the implementation on an ECM. The presented estimator consists of an airpath observer and a NOx combustion model. The role of the airpath observer is to provide input values for the NOx combustion model such as the states of the gas at the intake and exhaust manifolds. It contains a nonlinear mean-value model of the airpath suitably transformed for an efficient and robust implementation on an ECM. The airpath model uses available sensory information in the vehicle to correct predictions of the gas states.

In this paper we consider the issues facing the design of a practical multivariable controller for a diesel engine with dual exhaust gas recirculation (EGR) loops. This engine architecture requires the control of two EGR valves (high pressure and low pressure), an exhaust throttle (ET) and a variable geometry turbocharger (VGT). A systematic approach suitable for production-intent air handling control using Model Predictive Control (MPC) for diesel engines is proposed. Furthermore, the tuning process of the proposed design is outlined. Experimental results for the performance of the proposed design are implemented on a 2.8L light duty diesel engine. Transient data over an LA-4 cycle for the closed loop performance of the controller are included to prove the effectiveness of the proposed design process.