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With the inception of model-based design and automatic code generation, many organizations are developing controls and diagnostics algorithms in model-based development tools to meet customer and regulatory requirements. Advances in model-based design have made it easier to generate C code from models and help software engineers streamline their workflow. Typically, after the software has been developed, the models are handed over to a calibration team responsible for calibrating the features to meet specified customer and regulatory requirements. However, once the models are handed over to the calibration team, the calibration engineers are unaware of how to calibrate the features because documentation is not available. Typically, model documentation trails behind the software process because it is created manually, most of this time is spent on formatting. As a result, lack of model documentation or up-to date documentation causes a lot of pain for OEM’s and Tier 1 suppliers.

This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software, with a focus on a closed-loop vehicle drive-train model incorporating a detailed automatic transmission plant dynamics model developed for certain applications. Specifically, this paper presents the closed-loop integration of a 6-speed automatic transmission model developed for our HIL transmission controller and algorithm test bench (Opal-RT TestDrive based). The model validation, integration and its application in an HIL test environment are described in details.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

Traffic engineers use time-of-day travel time databases to characterize normal travel times on roads. This information is used by traffic management centers together with information from sensors in the highway to identify problems and to make alternate route recommendations. In this paper, the travel time database concept is extended to a vehicle-to-vehicle communications network for traffic and safety information, wherein the travel time database is generated and stored by vehicles in the network, and used by the vehicles to identify abnormal traffic conditions. This infrastructure-free approach is attractive due to the potential to eliminate highway sensor and sensor maintenance costs, which are major factors that limit the growth of traffic information beyond major roadways in urban regions. Initial work indicates that database storage requirements in the vehicle should be manageable.

During the production controller and software development process, one critical step is the controller and software verification. There are various ways to perform this verification. One of the commonly used methods is to utilize an HIL (hardware-in-the-loop) test bench to emulate powertrain hardware for development and validation of powertrain controllers and software. A key piece of an HIL bench is the plant dynamics model used to emulate the external environment of a modern controller, such as engine (ECM), transmission (TCM) or powertrain controller (PCM), so that the algorithms and their software implementation can be exercised to confirm the desired results. This paper presents a 6-speed automatic transmission plant dynamics model development for hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The modeling method, model validation, and application in an HIL test environment are described in details.

A key quantity for use in engine control is the exhaust manifold pressure. For production applications it is an important component in the calculation of the engine volumetric efficiency, as well as EGR flow and residual fraction. For cost reasons, however, it is preferable to not have to measure the exhaust manifold pressure for production applications. For that reason, it is advantageous to develop a model for estimating the exhaust manifold pressure in production application software that is small, accurate, and simple to calibrate. In this paper, a mean-value model for calculating the exhaust manifold pressure is derived from the compressible flow equation, treating the exhaust system as a fixed-geometry restriction between the exhaust manifold and the outlet of the tailpipe. Validation data from production applications is presented.

Production software validation is critical during software development, allowing potential quality issues that could occur in the field to be minimized. By developing automated and repeatable software test methods, test cases can be created to validate targeted areas of the control software for confirmation of the expected results from software release to release. This is especially important when algorithm/software development timing is aggressive and the management of development activities in a global work environment requires high quality, and timely test results. This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The powertrain model used within the HIL consists of an engine model and a detailed automatic transmission dynamics model. The model runs in an OPAL-RT TestDrive based HIL system.

The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.

As automotive electronic control systems continue to increase in usage and complexity, the challenges for developing automotive diagnostics also increase. Reduced development cycle times, the increased significance of diagnostics for safety critical systems, and the integration of vehicle systems across multiple control systems all add to the tasks of developing diagnostics for the automobiles of today and tomorrow. Addressing automotive diagnostics now requires the Tier 1 supplier to utilize a formal diagnostic development methodology. There are also opportunities for Tier 1 suppliers to add value by developing vehicle-level supervisory diagnostic strategies, in addition to subsystem and system-level diagnostic strategies. There is also a prospect to provide strategies and tools to enhance service at the vehicle level. This paper proposes an approach for Tier 1 suppliers to address diagnostic and service issues at the component, system, and vehicle level.

Implementation of engine turnoff at idle is desirable to gain improvements in vehicle fuel economy. There are a number of alternatives for implementation of the restarting function, including the existing cranking motor, a 12V or 36V belt-starter, a crankshaft integrated-starter-generator (ISG), and other, more complex hybrid powertrain architectures. Of these options, the 12V belt-alternator-starter (BAS) offers strong potential for fast, quiet starting at a lower system cost and complexity than higher-power 36V alternatives. Two challenges are 1) the need to accelerate a large engine to idle speed quickly, and 2) dynamic torque control during the start for smoothness. In the absence of a higher power electrical machine to accomplish these tasks, combustion-assisted starting has been studied as a potential method of aiding a 12V accessory drive belt-alternator-starter in the starting process on larger engines.

In this paper, a comparative study of the production applications of hybrid electric powertrains is presented. Vehicles studied include the Toyota Prius, Honda Insight, Toyota Estima, Toyota Crown, Honda Civic Hybrid, and Nissan Tino. The upcoming Ford Escape Hybrid and General Motors Parallel Hybrid Truck (PHT) will also be included, although advance information is limited. The goal of this paper is to look at what hybrid drivetrain architectures have actually been selected for production and what are the underlying details of these drivetrains. Since hybridizing a powertrain involves significant changes, the powertrain architectures are presented in diagram form, with analysis as to the similarities and advantages represented in these architectures. The specific hybrid functions used to save fuel are discussed. Peak power-to-weight ratio and degree of hybridization are plotted for the vehicles. System voltage versus electric power level are also plotted and analyzed.