Search Results

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy. Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model.

The classical methods of measuring acoustic absorption coefficient using an impedance tube and a reverberation chamber are well established [1, 2]. However, these methods are not suitable for in-situ applications. The two in-situ methods; single channel microphone (P- probe) and dual channel acoustic pressure and particle velocity (Pu-probe) methods based on measurement of impulse response functions of the material surface under test, provide considerable advantage in data acquisition, signal processing, ease and mobility of measurement setup. This paper evaluates the measurement techniques of these two in-situ methods and provides results of acoustic absorption coefficient of a commercial artificial Astroturf, a Dow quash material, and a grass surface.

The Time Variant Discrete Fourier Transform was implemented as a real-time order tracking method using developed software and commercially available hardware. The time variant discrete Fourier transform (TVDFT) with the application of the orthogonality compensation matrix allows multiple tachometers to be tracked with close and/or crossing orders to be separated in real-time. Signal generators were used to create controlled experimental data sets to simulate tachometers and response channels. Computation timing was evaluated for the data collection procedure and each of the data processing steps to determine how each part of the process affects overall performance. Many difficulties are associated with a real-time data collection and analysis tool and it becomes apparent that an understanding of each component in the system is required to determine where time consuming computation is located.

Increasingly, the exchanges of data in complex ECM (Electronic Control Module) systems rely on multiple communication networks across various physical and network layers. This has greatly increased system flexibility and provided an excellent medium to create well-defined exchangeable interfaces between components; however this added flexibility comes with increased network complexity. A system-level approach allows for the optimization of data exchange and network configuration as well as the development of a comprehensive network failure strategy. Many current ECM systems utilize complex multi-network communication strategies to exchange and control data to components. Recently, Caterpillar implemented an HIL (Hardware-In-the-Loop) test system that provides an approach for developing and testing a comprehensive ECM network strategy.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

The effect of flow direction towards the spark plug electrodes on ignition parameters is analyzed using an innovative spark aerodynamics fixture that enables adjustment of the spark plug gap orientation and plug axis tilt angle with respect to the incoming flow. The ignition was supplied by a long discharge high energy 110 mJ coil. The flow was supplied by compressed air and the spark was discharged into the flow at varying positions relative to the flow. The secondary ignition voltage and current were measured using a high speed (10MHz) data acquisition system, and the ignition-related metrics were calculated accordingly. Six different electrode designs were tested. These designs feature different positions of the electrode gap with respect to the flow and different shapes of the ground electrodes. The resulting ignition metrics were compared with respect to the spark plug ground strap orientation and plug axis tilt angle about the flow direction.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques.

Large channel count data acquisition systems have seen increasing use in the acquisition and analysis of rotating machinery, these systems have the ability to generate very large amounts of data for analysis. The most common operating measurement made on powertrains or automobiles on the road or on dynamometers has become the order track measurement. Order tracking analysis can generate a very large amount of information that must be analyzed, both due to the number of channels and orders tracked. Analysis methods to efficiently analyze large numbers of Frequency Response Function (FRF) measurements have been developed and used over the last 20 years in many troubleshooting applications. This paper develops applications for several FRF based analysis methods as applied for efficient analysis of large amounts of order track data.

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.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

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.