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This year, in the third year of FutureTruck competition, the Ohio State University team has taken the challenge to convert a 2002 Ford Explorer into a more fuel efficient and environmentally friendly SUV. This goal was achieved by use of a post-transmission, charge sustaining, parallel hybrid diesel-electric drivetrain. The main power source is a 2.5-liter, 103 kW advanced CIDI engine manufactured by VM Motori. A 55 kW Ecostar AC induction electric motor provides the supplemental power. The powertrain is managed by a state of the art supervisory control system which optimizes powertrain characteristics using advanced energy management and emission control algorithms. A unique driver interface implementing advanced telematics, and an interior designed specifically to reduce weight and be more environmentally friendly add to the utility of the vehicle as well as the consumer appeal.

This paper presents the development of a model-based air/fuel ratio controller for a high performance engine that uses, in addition to other usual signals, the throttle angle to enable predictive air mass flow rate estimation. The objective of the paper is to evaluate the possibility to achieve a finer air/fuel ratio control during transients that involve sudden variations in the physical conditions inside the intake manifold, due, for example, to fast throttle opening or closing actions. The air mass flow rate toward the engine cylinders undertakes strong variation in such transients, and its correct estimation becomes critical mainly because of the time lag between its evaluation and the instant when the air actually enters the cylinders.

Electronic Throttle Control systems substitute the driver in commanding throttle position, with the driver acting on a potentiometer connected to the accelerator pedal. Such strategies allow precise control of air-fuel ratio and of other parameters, e.g. engine efficiency or vehicle driveability, but require detailed information about the engine operating conditions, in order to be implemented inside the Electronic Control Unit (ECU). In order to determine throttle position, an interpretation of the driver desire (revealed by the accelerator pedal position) is performed by the ECU. In our approach, such interpretation is carried out in terms of a torque request that can be appropriately addressed knowing the actual engine-vehicle operating conditions, which depend on the acting torques. Estimates of the torque due to in-cylinder pressure (indicated torque), as well as the torque required by the vehicle (load torque), must then be available to the control module.

Electronic throttle control is increasingly being considered as a viable alternative to conventional air management systems in modern spark-ignition engines. In such a scheme, driver throttle commands are interpreted by the powertrain control module together with many other inputs; rather than directly commanding throttle position, the driver is now simply requesting torque - a request that needs to be appropriately interpreted by the control module. Engine management under these conditions will require optimal control of the engine torque required by the various vehicle subsystems, ranging from HVAC, to electrical and hydraulic accessories, to the vehicle itself. In this context, the real-time estimation of engine and load torque can play a very important role, especially if this estimation can be performed using the same signals already available to the powertrain control module.

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

Over the years numerous researchers have suggested that the ionization current signal carries within it combustion relevant information. The possibility of using this signal for diagnostics and control provides motivation for continued research in this area. To be able to use the ion current signal for feedback control a reliable estimate of some combustion related parameter is necessary and therein lies the difficulty. Given the nature of the ion current signal this is not a trivial task. Fei An et al. [1] employed PCA for feature extraction and then used these feature vectors to design a neural network based classifier for the estimation of air to fuel ratio (AFR). Although the classifier predicted AFR with sufficient reliability, a major draw back was that the ion current signals used for prediction were averaged signals thus precluding a cycle to cycle estimate of AFR.