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In order to improve the vehicle’s fuel economy while in cruise, the Model Predictive Control (MPC) technology has been adopted utilizing the road grade preview information and allowance of the vehicle speed variation. In this paper, a focus is on robustness study of delivered fuel economy benefit of Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier in the literature to several noise factors, e.g. vehicle weight, fuel type etc. Further, the vehicle position is obtained via GPS with finite precision and source of road grade preview might be inaccurate. The effect of inaccurate information of the road grade preview on the fuel economy benefits is studied and a remedy to it is established.

Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved.

The Exhaust Gas Recirculation (EGR) rate is a critical parameter of turbocharged diesel engines because it determines the trade-off between NOx and particulate matter (PM) emissions. On some heavy duty engines the EGR mass flow is directly measured with a Venturibased sensor and a closed loop control system maintains EGR flow. However, on most light duty diesel engines the EGR mass flow must be estimated. This paper compares two methods for estimating EGR mass flow. The first method, referred to as the Speed Density method, serves as a baseline for comparison and uses sensors for engine speed, intake manifold pressure and temperature, as well as fresh air flow (MAF). The new, second method adds turbo speed to this sensor set, and includes additional engine modelling equations, such as the EGR valve equation and the turbine equation. Special measures are taken to allow the additional equations to execute without issue on production ECMs (Electronics Controls Modules).

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc.

On downsized turbocharged engines, turbo speed is correlated with maximum engine airflow and therefore with maximum engine power. To ensure safe operation in the field, auto makers introduce significant engineering margins to the turbocharger maximum speed limit. Physical turbo speed sensors provide one way to reduce this engineering margin, but are not appropriate for some applications. An accurate mathematical estimation of turbocharger speed using virtual sensor can help reduce these margins, therefore increasing available power. This paper examines the best turbo speed estimation accuracy that can be achieved using a given set of production engine sensors. “Best” is defined in a minimax sense as the smallest turbo speed error interval achievable assuming the worst case combination of sensor and actuator errors and plant parameter mismatch.