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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.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

There is increasing interest in the use of alternative fuels for transportation, due to the increasing cost of petroleum based fuels. One possible alternative to the use of petroleum for transportation is to use electric grid power. This paper explores a possible design solution based on a plug-in fuel cell hybrid. A scaled down version of FC-HEV that is applicable to this concept, has been implemented as a proof of concept with fast prototyping toolkits, including a 32 bit micro processor, Matlab/Simulink software and an embedded system development kit. The resulting prototype vehicle demonstrated a high gasoline equivalent MPG as well as a successful functionality of micro grid power generation.

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.

This paper presents a sensitivity analysis-based study aimed at robustly calibrating the parameters of an adaptive energy management strategy designed for a Plugin Hybrid Electric Vehicle (PHEV). The supervisory control is developed from the Pontryagin's Minimum Principle (PMP) approach and applied to a model of a GM Chevrolet Volt vehicle. The proposed controller aims at minimizing the fuel consumption of the vehicle over a given driving mission, by achieving a blended discharge strategy over the entire cycle. The calibration study is conducted over a wide set of driving conditions and it generates a look-up table and two constant values for the three controller parameters to be used in the in-vehicle implementation. Finally, the calibrated adaptive control strategy is validated against real driving cycles showing the effectiveness of the calibration approach.

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.

New vehicle technologies open up a vast number of new options for the designer, removing traditional constraints. Though hybrid powertrains have thus far been implemented chiefly to improve the fuel economy of already economical passenger cars, hybrid technology may have even more to offer in a performance vehicle. In the year when the C6 Corvette and two large GM hybrid projects have been unveiled, a new case study looks to combine these ideas and explore the performance limits for the next generation high performance sports car. Through an innovative transmission concept and thoughtful packaging, the next generation Corvette could enhance a 600 HP spark-ignited V-8 (supercharged LS2) with 1200 HP from electric machines, and still meet current emission standards. Such immense tractive power, however, would be useless without an intelligent means of delivering this power to the wheels.

The optimization of a high performance engine in order to achieve maximum power at full load and high speed can cause an unstable behavior when the engine is running at different conditions, thus making a robust combustion diagnosis for on board diagnostic EOBD/OBD II purposes (misfiring detection) particularly challenging. In fact, when a misfire occurs, its detection can be critical because of the high background noise due to high indicated mean effective pressure (IMEP) cyclic variability. A partial reduction of the high IMEP variability had been achieved by optimizing control parameters of a new prototype high performance V8/4.2 l engine. Spark advance and VVT phasing maps had in fact been re-designed based on in-cylinder pressure variability (cycle by cycle and cylinder by cylinder) analysis.

This paper discusses an improved design of a vehicle-based mobile off-road terrain profile measurement system. The proposed system includes an apparatus of sensors and on-board data acquisition hardware, equipped on a platform vehicle used to measure and record the relevant data while the vehicle travels through the off-road or terrain surface to be surveyed. A unique post-processing algorithm is then used to derive the elevation profile based on the collected data. The derived elevation profile data could be used to characterize the roughness of an off-road testing course or perform a general geographical survey or mapping. The major technical issue addressed in this system is to eliminate the effect of platform vehicle vibration on sensor measurement which if left unaddressed will result in large measurement error due to high amplitude pitch and roll movements of the platform vehicle.

For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

Compression-ignited engines are still considered the most efficient and reliable technology for automotive applications. However, current and future emission regulations, which severely limit the production of NOx, particulate matter and CO2, hinder the use of diesel-like fuels. As a matter of fact, the spontaneous ignition of directly-injected Diesel leads to a combustion process that is heterogeneous by nature, therefore characterized by the simultaneous production of particulate matter and NOx. In this scenario, several innovative combustion techniques have been investigated over the past years, the goal being to benefit from the high thermal efficiency of compression-ignited engines, which results primarily from high Compression Ratio and lean and unthrottled operation, while simultaneously mitigating the amount of pollutant emissions.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

The widely used Extended Kalman Filter (EKF) is applied to a planar model of an articulated vehicle to predict jackknifing events. The states of hitch angle and hitch angle rate are estimated using a vehicle model and the available or “measured” states of lateral acceleration and yaw rate from the prime mover. Tuning, performance, and compromises for the EKF in this application are discussed. This application of the EKF is effective in predicting the onset of instability for an articulated vehicle under low-μ and low-load conditions. These conditions have been shown to be most likely to render heavy articulated vehicles vulnerable to jackknife instability. Options for model refinements are also presented.

This paper presents several applications of a precise, moderate sampling rate measurement of the crankshaft angular position of a reciprocating IC engine. It is shown that the measurement can be made with a relatively inexpensive noncontacting sensor. Given sufficient precision and sampling rate, the various applications include: crankshaft reference position measurements for ignition timing (gasoline fueled engines), or injector timing (for electronically controlled diesel engines); crankshaft angular speed and acceleration measurements for estimating instantaneous indicated torque, and for diagnosing engine malfunctions. The torque estimate is potentially useful for engine control, to improve engine performance with respect to reducing cycle to cycle and cylinder to cylinder nonuniformity, and with respect to fuel economy.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

Multi-jet injection strategies open significant opportunities for the combustion management of the modern diesel engine. Splitting up the injection process into 5 steps facilitates the proper design of the combustion phase in order to obtain the desired torque level, whilst attempting a reduction in emissions, particularly in terms of NOx. Complex 3-D models are needed in the design stage, where components such as the injector or combustion chamber shape have to be determined. Alternatively, zero-dimensional approaches are more useful when fast interpretation of experimental data is needed and an optimization of the combustion process should be obtained based on actual data. For example, zero-dimensional models allow a quick choice of optimum control settings for each engine operating condition, avoiding the need to test all the possible combinations of engine control parameters.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.

This paper introduces a high accuracy method of measuring crankshaft angular position of an I-C engine. The method uses a sensor which couples magnetically to the starter ring gear. There are many automotive applications of this measurement of crankshaft angular position including ignition timing reference, engine performance measurement and certain diagnostic functions. The present paper disusses only the ignition timing application. Engine performance measurements are reported in refs. (1,2,3). The diagnostic application is discussed in refs. (4-5). The passage of a starter ring gear tooth past the sensor axis causes a pulse to be generated in the sensor output. The waveform of this sensor voltage is independent of engine angular speed (including zero speed). However, this waveform is a function of gear tooth profile and is consequently influenced by gear wear. The present method uses a finite state machine to process the sensor output signal.

The paper presents the development of a model study whose results enable to evaluate the torque production non-uniformity between the cylinders of an Internal Combustion Engine (ICE). This non-uniformity can be due, for example, to different air breathing into the cylinders, to different injector characteristics, or to pathological operating conditions such as misfires or misfuels, as well as to other abnormal operating conditions. Between the nominal torque production and the one corresponding to the absence of combustion there exist, in fact, a series of possible intermediate conditions, each of them corresponding to a value of produced torque between the nominal value and the one corresponding to the lack of combustion (due for example to statistical dispersion in manufacturing, or aging in the injection system). The diagnosis of this type of non-uniformity is a very important issue in today's engine control strategies design.