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The use of electric motors to independently control the torque of two or four wheels of a vehicle has the potential to significantly improve safety and handling. One virtue of electric motors is that their output torque can be accurately estimated. Using this known output torque, longitudinal tire force and coefficient of friction can be estimated via a controller output observer. This observer works by constructing a model of wheel dynamics, with longitudinal tire force as an unknown input quantity. A known wheel torque is input to the physical and modeled system and the resulting measured and predicted wheel speeds are compared. The error between the measured and predicted wheel speed is driven towards zero by a robust feedback controller. This controller modulates an estimate of longitudinal tire force used as an input by the wheel dynamics model. The resulting estimate of longitudinal tire force quickly converges towards the actual value with minimal computational expense.

A new control strategy for wheel slip control, considering the complete dynamics of the electro-hydraulic brake (EHB) system, is developed and experimentally validated in Cranfield University's HiL system. The control system is based on closed loop shaping Youla-parameterization method. The plant model is linearized about the nominal operating point, a Youla parameter is defined for all stabilizing feedback controller and control performance is achieved by employing closed loop shaping technique. The stability and performance of the controller are investigated in frequency and time domain, and verified by experiments using real EHB smart actuator fitted into the HiL system with driver in the loop.

A unique concept for a multi-body test rig enabling the simulation of longitudinal, steering and vertical dynamics was developed at the Institute for Mechatronic Systems (IMS) at TU Darmstadt. A prototype of this IMS test rig is currently being built. In conjunction with the IMS test rig, the Vehicle Terrain Performance Laboratory (VTPL) at Virginia Tech further developed a full car, seven degree of freedom (7 DOF) simulation model capable of accurately reproducing measured displacement, pitch, and roll of the vehicle body due to terrain excitation. The results of the 7 DOF car model were used as the reference input to the multi-body IMS test rig model. The goal of the IMS/VTPL joint effort was to determine whether or not a controller for the IMS test rig vertical actuator could accurately reproduce wheel displacements due to different measured terrain constraints.

This paper presents the use of a second life battery pack in a smart grid-tied photovoltaic battery energy system. The system was developed for a single family household integrating a PV array, second life battery pack, grid back feeding, and plug-in hybrid electric vehicle charging station. The battery pack was assembled using retired vehicle traction batteries. The pack is configured with 9 cells in each parallel bank, 15 banks in series featuring 48V nominal and a 12kWh nominal capacity. Limited by the weakest bank in the pack, the second life battery pack has an accessible capacity of 10kWh, or 58% of its original condition. A battery management was developed to handle the bank-to-bank imbalance and ensure the safe operation of the battery pack. An energy management algorithm was established to optimize the energy harvest from PV while minimizing the grid dependence.

A new vacuum-oven sublimation method (PVOS) for measuring the hydrocarbon fraction of diesel particulates has been developed to overcome problems with the standard vacuum-oven sublimation technique (VOS). VOS gave extremely low (negative at 15:1 A/F) results for diesel particulate samples taken at medium to high load (25:1 or lower A/F). The low results are due to the adsorption by the samples of trace amounts of continuously regenerated (from test samples and oven seals) heavy-hydrocarbon oven contaminants. PVOS corrects the problem by using a nitrogen gas purge to sweep hydrocarbons from the oven during testing. Comparisons among PVOS, VOS and methylene chloride extraction (MCE) over a range of A/F and engine speeds showed that PVOS results correlate more closely with MCE results than do results determined by the standard VOS technique.

Emissions, fuel economy, and performance are determined over a light and a heavy driving cycle designed to represent the vehicles in-use driving patterns. The vehicles are 2010 class 8 Freightliner tractor trucks equipped with Cummins engines with Selective Catalytic Reduction and Diesel Particulate Filter emission control systems. The hybrid has lower carbon dioxide emissions, better fuel economy, and nitrogen oxide emissions statistically the same as the conventional. The CO emissions are well below the standards for both vehicles, but they are higher from the hybrid. The higher CO emissions for the hybrid are primarily related to the cooling of the Diesel Oxidation Catalyst (DOC) during the standard 20 minute key-off soak between repeats of the driving cycles. With a 1 minute key-off soak the CO emissions from the hybrid are negative.

Previous work has demonstrated the capabilities of gasoline compression ignition to achieve engine loads as high as 19.5 bar BMEP with a production multi-cylinder diesel engine using gasoline with an anti-knock index (AKI) of 87. In the current study, the low load limit of the engine was investigated using the same engine hardware configurations and 87 AKI fuel that was used to achieve 19.5 bar BMEP. Single injection, “minimum fueling” style injection timing and injection pressure sweeps (where fuel injection quantity was reduced at each engine operating condition until the coefficient of variance of indicated mean effective pressure rose to 3%) found that the 87 AKI test fuel could run under stable combustion conditions down to a load of 1.5 bar BMEP at an injection timing of −30 degrees after top dead center (°aTDC) with reduced injection pressure, but still without the use of intake air heating or uncooled EGR.

The vertical force generated from terrain-tire interaction has long been of interest for vehicle dynamic simulations and chassis development. To improve simulation efficiency while still providing reliable load prediction, a terrain pre-filtering technique using a constraint mode tire model is developed. The wheel is assumed to convey one quarter of the vehicle load constantly. At each location along the tire's path, the wheel center height is adjusted until the spindle load reaches the pre-designated load. The resultant vertical trajectory of the wheel center can be used as an equivalent terrain profile input to a simplified tire model. During iterative simulations, the filtered terrain profile, coupled with a simple point follower tire model is used to predict the spindle force. The same vehicle dynamic simulation system coupled with constraint mode tire model is built to generate reference forces.

Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components.

The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed. A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations.