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The paper presents the main results of a research activity focused on the analysis, development, and real time implementation of a closed-loop, individual cylinder combustion control system, based on ion sensing technology. The innovative features of the proposed control system consist of extracting combustion quality related information from the ion current signal, and of using such information, together with pre-defined look-up-tables, for feedback control of the spark advance throughout the entire engine operating range. In particular, the ion current signal processing algorithm that is carried out in real-time, initially determines whether knocking is affecting or not the actual combustion process.

This paper analyzes the control of the series-parallel powersplit used in the 2001 Michigan Tech FutureTruck. An electronic throttle controller is implemented and a new control algorithm is proposed and tested. A vehicle simulation has been created in MATLAB and the control algorithm implemented within the simulation. A program written in C has also been created that implements the control algorithm in the test vehicle. The results from both the simulation and test vehicle are presented and discussed and show a 15% increase in fuel economy. With the increase in fuel economy, and through the use of the original exhaust after treatment, lower emissions are also expected.

A study, for future applications, of a model-based Idle Speed Control (ISC) system for the L535 Lamborghini 6.2L-48 valve V12 gasoline engine is presented in this paper. Main features of the controller are: Real-time auto-adaptation; Synchronization of Throttle Angle (TA) opening with Spark Advance (SA) timing, through model-based Drive-by-Wire (DBW) control strategies; Auto-adaptive management of the absolute pressure levels in the two, completely separated, intake manifolds; Feed-forward compensation for known loads; Integrated Air-to-Fuel Ratio (AFR) control at idle. Design targets are: Idle speed error from the nominal value imperceptible by the driver, considering that this study is for a high performance engine; Emissions reduction; Minimization of the engine speed undershoot (overshoot) when applying (removing) unknown loads.

Today's advanced technology engines have a high content of electronic actuation requiring sophisticated real-time embedded software sensing and control. To enable research on such engines, a system with a flexible engine control unit (ECU) that can be rapidly configured and programmed is desired. Such a system is being used in the Advanced Internal Combustion Engine (AICE) Laboratories at Michigan Tech University (MTU) for research on a multi-cylinder spark-ignited gasoline, a high pressure common rail diesel and a single cylinder alternative fuels research engine. The system combines a production ECU with a software development system utilizing Mathworks Simulink/Stateflow © modeling tools. The interface in the Simulink modeling environment includes a library of modeling and interface blocks to the production Operating System (OS), Low Level Drivers (LLD) and CAN-based calibration tool.

Individual cylinder Air-to-Fuel Ratio (AFR) control has been proposed by many authors in recent years as a technique of controlling the AFR of the various cylinders individually, based on a single lambda measurement for each engine bank. Most of such works describe theoretical and experimental efforts to develop and identify an observer, able to estimate the AFR of each cylinder separately. In this paper, a simple individual cylinder AFR controller is described, based on the observation that any type of AFR disparity between the various cylinders is reflected in a specific harmonic content of the AFR signal spectrum. In particular, any type of AFR disparity will be reflected on a limited number of frequencies, or harmonics, multiple of the engine cycle frequency.

Dual Mass Flywheel (DMF) systems are today widely adopted in compression ignition automotive powertrains, due to the well-known positive effects on vehicle drivability and fuel consumption. This work deals with the analysis of undesirable effects that the installation of a DMF may cause to engine and transmission dynamics, with the objective of understanding the causes and of determining possible solutions to be adopted. The main results of an experimental and simulation analysis, focused on the rotational dynamics of a powertrain equipped with a DMF system, are presented in the paper. A mathematical model of the physical system has been developed, validated, and used to investigate, in a simulation environment, the anomalous behavior of the powertrain that had been experimentally observed under specific conditions. Particular attention has been devoted to two aspects that are considered critical: engine cranking phase; interactions between powertrain dynamics and idle speed control.

New engine control strategies, designed for drive-by-wire systems, will require the measurement (or the estimation) of several operative engine parameters in order to control emissions and efficiency, while satisfying the driver demand in terms of driveability and performance. Both load and indicated torque (i.e. the torque due to the gas pressure acting on the pistons) will play an essential role in this context, since the driver pedal command may be appropriately interpreted by the Electronic Control Unit (ECU) in terms of an engine (or load) torque request. In fact, the accelerator pedal variation forces the vehicle to reach a final steady-state condition, corresponding to a new level of engine and load torques, thus making it possible to assume the existence of a direct link between the pedal position and the “desired” final engine (or load) torque.

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.