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This paper uses the brake control allocation method for Electric Vehicles (EVs) based on system-level vehicle Reference Point (RP) motion feedback. The RP motion control is an alternative to the standard brake torque allocation methods and results in improved vehicle stability in both longitudinal and lateral directions without requiring additional measurements beyond what is available in EVs with ABS and ESP. The proposed control law simplifies the brake torque allocation algorithm, reduces overall development time and effort, and merges most of the braking systems into one. Additionally, the measured or estimated signals required are reduced compared to the standard approach. The system-level RP measurements and references are transformed into individual wheel coordinate systems, where tracking is ensured by actuating both friction torques and electric motor regenerative torques using a proposed brake torque blending mechanism.

A supervisory Model Predictive Control (MPC) approach is developed for an air path system for multi-mode operation in a diesel engine. MPC is a control method based on a predictive dynamic model of system and determines actuator control positions through the optimization of various factors such as tracking performances of target setpoints, moving speed of actuators, limits, etc. Previously, linear MPC has been successfully applied on the air path control problem of a diesel engine, however, most of these applications were developed for a single operation mode which has only one set of control target setpoint values. In reality, a single operation mode cannot cover all requirements of current diesel engines and this complicates practical implementations of linear MPC. The high priority targets for the development of diesel engines are low emissions, high thermal efficiency and robustness.

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.

The paper provides an overview of a developed methodology and a toolchain for modeling and control of a complex aftertreatment system for passenger cars. The primary objective of this work is to show how the use of this methodology allows to streamline the development process and to reduce the development time thanks to a model based semi-automatic control design methodology combined with piece-wise optimal control. Major improvements in passenger car tailpipe NOx removal need to be achieved to fulfil the upcoming post EURO 6 norms and Real Driving Emissions (RDE) limits. Multi-brick systems employing combinations of multiple Selective Catalytic Reduction (SCR) catalysts with an Ammonia Oxidation Catalysts, known also as Ammonia Clean-Up Catalyst (CUC), are proposed to cover operation over a wide temperature range. However, control of multi-brick systems is complex due to lack of available sensors in the production configurations.

Many control approaches for selective catalytic reduction (SCR) systems require knowledge of ammonia storage (NH3 storage) to dose urea accurately. Currently there are no technologies to directly measure internal NH3 storage in a vehicle, so it can only be inferred from hardware sensors located upstream, downstream, or in the catalyst. This paper describes an application of extended Kalman filter (EKF) state estimator used as a virtual sensor for urea injection control of a multi-brick aftertreatment system. The proposed estimator combines mean-value physics-based models of combined SCR and diesel particulate filter (SCR/DPF), SCR and clean-up catalyst (CUC). It uses hardware sensors at the inlet and outlet of the aftertreatment system, and includes no sensors between the catalysts. Performance of the proposed estimator was validated in simulations against a high-fidelity model of the aftertreatment system.

Increasingly strict CO2 and emissions norms are pushing the automotive industry towards increasing adoption of Hybrid Electric Vehicle (HEV) technology. HEVs are complex hardware systems which are often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of different subsystems of HEV is an attractive proposition. In this paper, Model Predictive Control (MPC) and Equivalent Consumption Minimization Strategy (ECMS) based supervisory controllers have been developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A dynamical physics based HEV model has been developed for simulation of the system behavior. A cost function has been formulated to improve fuel economy and battery life.

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.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

The amount of ammonia stored on the walls of the catalyst (or ammonia storage) is a parameter with significant impact on NOx reduction efficiency and undesired ammonia slip of Selective Catalytic Reduction catalysts. This makes the ammonia storage interesting for utilization in urea injection control. However, ammonia storage is not directly measurable onboard vehicles, it can only be estimated. Model-based online estimation requires models that are capable of capturing the main phenomena of the SCR and at the same time can be computed onboard vehicle. While the modeling of SCR and model-based control is well present in the literature, it is apparent that few attempts of implementing the models on production ECUs were published. This paper reviews literature on ammonia storage, outlet NH3 and NOx concentration estimation in SCR and SCR/DPF systems-including the estimation of NOx sensor cross-sensitive to NH3-in order to present the state of the art.

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.

This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration.

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.