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This paper describes a systematic approach to identify the best sensor combination by performing sensor placement analysis to detect and isolate clutch stuck-off faults in Automatic Transmissions (AT) based on structural analysis. When an engaged clutch in the AT loses pressure during operation, it is classified as a clutch stuck-off fault. AT can enter in neutral state because of these faults; causing loss of power at wheels. Identifying the sensors to detect and isolate these faults is important in the early stage of the AT development. A universal approach to develop a structural model of an AT is presented based on the kinematic relationships of the planetary gear set elements. Sensor placement analysis is then performed to determine the sensor locations to detect and isolate the clutch stuck-off faults using speed sensors and clutch pressure sensors. The proposed approach is then applied to a 10-Speed AT to demonstrate its effectiveness.

Engine start-up (cranking) can be an important source of particle emissions from vehicles. With the penetration of GDI vehicles in the global vehicle fleet, it is important to analyze and understand the contribution of start-up particle emissions from GDI vehicles, and the potential effects of fuel properties on that process. In this work, chassis dynamometer based investigation on the effect of several gasoline fuels (commercial and blended) on both, naturally aspirated and turbocharged GDI vehicles were conducted to understand the importance of engine start up, in particular, cranking. 10 commercially available gasoline fuels were tested on a naturally aspirated 2010 model year GDI vehicle, 3 among these commercially available fuels were tested on another 2009 model year turbocharged GDI vehicle, and 8 blended gasoline fuels were tested on 12 other GDI vehicles (7 turbocharged and 5 naturally aspirated) ranging in model years 2011-2015.

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.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

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.

Electrical connectors and terminals are widely used in the automotive industry. It is desirable to mate the electrical connections using materials or coatings with low friction force to improve the ergonomics of the assembly process while maintaining good electrical conduction over the lifetime of the vehicle. We have previously shown that plasma-enhanced chemical vapor deposition (PECVD) of graphene on gold (Au) and silver (Ag) terminals can significantly reduce the insertion force (friction force during the terminal insertion process). However, the cost of this deposition method is rather high, and its high temperature process (> 400 oC) makes it impractical for materials with low melting temperatures. For example, tin (Sn) coating with a melting temperature of 232 oC is commonly used in electrical connectors, which cannot sustain the high temperature process. In this study, reduced graphene oxide was prepared using a low-cost solution process and applied onto metallic terminals.

The long-term weathering behavior of three different 3D printable, non-stabilized, UV cure resin formulations (A and B with thiol-ene base, and C with acrylate chemistry) was studied using tensile testing, nano-indentation, and photoacoustic infrared (FTIR-PAS) spectroscopy. To this end, type I tensile bars were printed from each resin system using a 3D printer, and were post UV-cured under a broad spectrum source. Systems A and C showed a similar trend after weathering. They first experienced an increase in modulus and tensile strength, due to additional crosslinking of the residual unreacted species. This increase in mechanical properties was followed by a drop in modulus, tensile strength, and percent elongation, due to the over-crosslinking and consequent embrittlement. System B, however, showed remarkable retention of the mechanical properties before/after weathering.

This paper analyzes the use of an ammonia sensor for feedback control in diesel exhaust systems. We build our case around the specific example of the heavy duty transient cycle, and an exhaust system with an SCR catalyst, a single urea injector and an upstream and downstream NOx sensor. A key component in our analysis is the inclusion of the tolerance of the ammonia sensor. We show that with the current understanding of the sensor tolerance, the ammonia sensor has limited benefit for controls.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

This program involved the detailed evaluation of a novel laser-based in-exhaust ammonia sensor using a diesel fuel-based burner platform integrated with an ammonia injection system. Test matrix included both steady-state modes and transient operation of the burner platform. Steady-state performance evaluation included tests that examined impact of exhaust gas temperature, gas velocity and ammonia levels on sensor response. Furthermore, cross sensitivity of the sensor was examined at different levels of NOX and water vapor. Transient tests included simulation of the FTP test cycles at different ammonia and NOX levels. A Fourier transform infrared (FTIR) spectrometer as well as NIST traceable ammonia gas bottles (introduced into the exhaust stream via a calibrated flow controller) served as references for ammonia measurement.

The layout of a vehicle steering system has to resolve a compromise. While it is important for lateral vehicle control to feel steering torque feedback of lateral tire to ground interaction, disturbing forces shall not be present in the feedback steering torque. These disturbing forces result from road irregularities, wheel rotor imbalance, suspension asymmetry caused by production tolerances, wear or impacts, and additional vehicle internal forces, e.g. the steered wheels also driven by the engine or braked. In general these disturbances are reduced by an optimization of the suspension geometry to decrease the impact of the unintended forces on the steering system. The remaining disturbance is controlled to an acceptable level via force feedback sensitivity calibration of the steering system, what in return influences the intended driver sensitivity to feel lateral tire forces.

Government regulations for fuel economy and emission standards have driven the development of technologies that improve engine performance and efficiency. These technologies are enabled by an increased number of actuators and increasingly sophisticated control algorithms. As a consequence, engine control calibration time, which entails sweeping all actuators at each speed-load point to determine the actuator combination that meets constraints and delivers ideal performance, has increased significantly. In this work we present two adaptive optimization methods, both based on an indirect adaptive control framework, which improve calibration efficiency by searching for the optimal process inputs without visiting all input combinations explicitly. The difference between the methods is implementation of the algorithm in steady-state vs dynamic operating conditions.

A novel laser-absorption gas sensing apparaOn-vehicle Testing at VERtus capable of measuring NO directly within vehicle exhaust was developed and tested. The sensor design was enabled by key advances in the construction of optical probes that are sufficiently compact for deployment in real-world exhaust systems and can survive the harsh, high-temperature, and strongly vibrating environment typical of exhaust streams. Prototype test campaigns were conducted at high-temperature flow facilities intended to simulate exhaust gas conditions and within the exhaust of vehicles mounted on a chassis dynamometer. Results from these tests demonstrated that the sensor prototype is fundamentally free of cross-interference with competing species in the exhaust stream, can achieve a 1 ppmv NO detection limit, and can be operated across the full range of thermodynamic conditions expected for typical vehicle exhausts.

As electric vehicles are becoming increasingly popular and necessary for the future mobility needs of civilization, further effort is continually made to improve the efficiency, cost, and safety of the lithium-ion battery packs that power these vehicles. To facilitate these goals, this paper introduces a data driven model to predict a distribution of surface temperatures for a lithium-ion battery pack: a long short-term memory (LSTM) neural network. The LSTM model is trained and validated with lithium-ion cells electrically connected to form a battery pack. Voltage, current, state of charge (SOC), and cell surface temperature from two arrays are used as inputs from a wide range of high and low temperature drive cycles. Additionally, ambient temperature is added as an input to the LSTM model.

SmartDeviceLink (SDL) is open-source software that connects the vehicle’s infotainment system to mobile applications. SDL includes an open-source software development kit (SDK) that enables a smart-device to connect to the vehicle’s human-machine interface (HMI), read vehicle data, and control vehicle sub-systems such as the audio and climate systems. It is extensible, so other convenience subsystems or brought-in aftermarket modules can be added. Consequently, it provides a platform for cyber-physical systems that can integrate wearables, consumer sensors and cloud data into an intelligent vehicle control system. As an Open Innovation Platform, new features can be rapidly developed and deployed to the market, bypassing the longer vehicle development cycles. This facilitates a channel for rapid prototyping and innovation that is not constrained by the traditional process of automotive parts development, but is rather on the timeline of software development.

Improving passenger safety inside vehicle cabins requires continuously monitoring vehicle seat occupancy statuses. Monitoring a vehicle seat’s occupancy status includes detecting if the seat is occupied and classifying the seat’s occupancy type. This paper introduces an innovative non-intrusive technique that employs capacitive sensing and an occupancy classifier to monitor a vehicle seat’s occupancy status. Capacitive sensing is facilitated by a meticulously constructed capacitance-sensing mat that easily integrates with any vehicle seat. When a passenger or an inanimate object occupies a vehicle seat equipped with the mat, they will induce variations in the mat’s internal capacitances. The variations are, in turn, represented pictorially as grayscale capacitance-sensing images (CSI), which yield the feature vectors the classifier requires to classify the seat’s occupancy type.