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The significance and the number of vehicle safety features enabled via connectivity continue to increase. OnStar, with its automatic airbag notification, was one of the first vehicle safety features that demonstrate the enhanced safety benefits of connectivity. Vehicle connectivity benefits have grown to include remote software updates, data analytics to aid with preventative maintenance and even to theft prevention and recovery. All of these services require available and reliable connectivity. However, except for the airbag notification, none have strict latency requirements. For example, software updates can generally be postponed till reliable connectivity is available. Data required for prognostic use cases can be stored and transmitted at a later time. A new set of use cases are emerging that do demand continuous, reliable and low latency connectivity. For example, remote control of autonomous vehicles may be required in unique situations.

The performance of five different skyhook control methods is studied experimentally, using a quarter-car rig. The control methods that are analyzed include: skyhook control, groundhook control, hybrid control, displacement skyhook, and relative displacement skyhook. Upon evaluating the performance of each method in frequency domain for various control conditions, they are compared with each other as well as with passive damping. The results indicate that no one control method outperforms other control methods at both the sprung and unsprung mass natural frequencies. Each method can perform better than the other control methods in some respect. Hybrid control, however, comes close to providing the best compromise between different dynamic demands on a primary suspension. The results indicate that hybrid control can offer benefits to both the sprung and unsprung mass with control gain settings that provide equal contributions from skyhook control and groundhook control.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2008 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Laboratory (ANL) and sponsored by General Motors (GM) and the U.S. Department of Energy (DoE). Following GM's vehicle development process, HEVT established goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in, range-extended hybrid electric vehicle. The challenge involves designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use and well-to-wheels (WTW) greenhouse gas (GHG) emissions. In order to interface with and control the vehicle, the team added a National Instruments (NI) CompactRIO (cRIO) to act as a hybrid vehicle supervisory controller (HVSC).

Improving fuel economy and overall vehicle emissions are very important in today's society with strict new regulations throughout the world. To help in the education process for the next generation of design engineers, this paper seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. The 1 Hz model described within this work aims to allow energy to be simply and understandably traced through a hybrid powertrain. Through the use of a “backwards” energy tracking method, demand for a drive cycle is found, and, after tracing the energy demand through each powertrain component, the resulting fuel to meet vehicle demand and associated powertrain losses is found.

Vehicle analytical models are often favorable due to describing the physical phenomena associated with vehicle operation following from the principles of physics, with explainable mathematical trends and with extendable modeling to other types of vehicle. However, no experimentally validated analytical model has been developed as yet of diesel engine fuel consumption rate. The present paper demonstrates and validates for trucks and light commercial vehicles an analytical model of supercharged diesel engine fuel consumption rate. The study points out with 99.6% coefficient of determination that the average percentage of deviation of the steady speed-based simulated results from the corresponding field data is 3.7% for all Freeway cycles. The paper also shows with 98% coefficient of determination that the average percentage of deviation of the acceleration-based simulated results from the corresponding field data under negative acceleration is 0.12 %.

Heavy-vehicle event data recorders (HVEDRs) provide a source of temporal vehicle data just prior to, during, and for a short period after, an event. In the 1990s, heavy-vehicle (HV) engine manufacturers expanded the capabilities of engine control units (ECU) and engine control modules (ECM) to include the ability to record and store small amounts of parametric vehicle data. This advanced capability has had a significant impact on vehicle safety by helping law enforcement, engineers, and researchers reconstruct events of a vehicle crash and understand the details surrounding that vehicle crash. Today, EDR technologies have been incorporated into a wide range of heavy vehicle (HV) safety systems (e.g., crash mitigation systems, air bag control systems, and behavioral monitoring systems). However, the adoption of EDR technologies has not been uniform across all classes of HVs or their associated vehicle systems.

This study undertakes an investigation of the effect of battery charge balance in hybrid electric vehicles (HEVs) on EPA fuel economy label values. EPA's updated method was fully implemented in 2011 and uses equations which weight the contributions of fuel consumption results from multiple dynamometer tests to synthesize city and highway estimates that reflect average U.S. driving patterns. For the US06 and UDDS cycles, the test results used in the computation come from individual phases within the overall certification driving cycles. This methodology causes additional complexities for hybrid vehicles, because although they are required to be charge-balanced over the course of a full drive cycle, they may have net charge or discharge within the individual phases. As a result, the fuel consumption value used in the label value calculation can be skewed.

This paper covers the development of a closed loop transaxle synchronization algorithm which was a key deliverable in the control system design for the L3 Enigma, a Battery Dominant Hybrid Electric Vehicle. Background information is provided to help the reader understand the history that lead to this unique solution of the input and output shaft synchronizing that typically takes place in a manual vehicle transmission or transaxle when shifting into a gear from another or into a gear from neutral when at speed. The algorithm stability is discussed as it applies to system stability and how stability impacts the speed at which a shift can take place. Results are simulated in The MathWorks Simulink programming environment and show how traction motor technology can be used to efficiently solve what is often a machine design issue. The vehicle test bed to which this research is applied is a parallel biodiesel hybrid electric vehicle called the Enigma.

The more electric aircraft (MEA) concept has gained popularity in recent years. As the main building blocks of advanced aircraft power systems, multi-converter power electronic systems have advantages in reliability, efficiency and weight reduction. The pulsed power load has been increasingly adopted--especially in military applications--and has demonstrated highly nonlinear characteristics. Consequently, more design effort needs to be placed on power conversion units and energy storage systems dealing with this challenging mission profile: when the load is on, a large amount of power is fed from the power supply system, and this is followed by periods of low power consumption, during which time the energy storage devices get charged. Thus, in order to maintain the weight advantage of MEA and to keep the normal functionality of the aircraft power system in the presence of a high-peak pulsed power load, this paper proposes a novel multidisciplinary weight optimization technique.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2012-2014 EcoCAR 2: Plugging in to the Future Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM) and the U.S. Department of Energy (DOE). The goals of the competition are to reduce well-to-wheel (WTW) petroleum energy consumption (PEU), WTW greenhouse gas (GHG) and criteria emissions while maintaining vehicle performance, consumer acceptability and safety. Following the EcoCAR 2 Vehicle Development Process (VDP), HEVT is designing, building, and refining an advanced technology vehicle over the course of the three year competition using a 2013 Chevrolet Malibu donated by GM as a base vehicle.

An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). Following GM's Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in extended-range hybrid electric vehicle. The competition requires participating teams to improve and redesign a stock Vue XE donated by GM. The result of this design process is an Extended-Range Electric Vehicle (E-REV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design is predicted to achieve an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an estimated all electric range of 69 km (43 miles) [1].

1 Most traditional methods and equations for estimating the structural and nonstructural weights and aerodynamics used at the aircraft conceptual design phase are empirical relations developed for conventional tube-and-wing aircraft. In a computation-heavy design process, such as Multidisciplinary Design and Optimization (MDO) simplicity of calculation is paramount, and for conventional configurations the aforementioned approaches work well enough for conceptual design. But, for non-traditional designs such as strut-braced winged aircraft, empirical data is generally not available and the usual methods can no longer apply. One solution to this is a movement toward generalized physics-based methods that can apply equally well to conventional or non-traditional configurations.

In an age of growing complexity with regards to vehicle control systems, verification and validation of control algorithms is a rigorous and time consuming process. With the help of rapid control prototyping techniques, designers and developers have cost effective ways of validating controls under a quicker time frame. These techniques involve developments of plant models that replicate the systems that a control algorithm will interface with. These developments help to reduce costs associated with construction of prototypes. In standard design cycles, iterations were needed on prototypes in order to finalize systems. These iterations could result in code changes, new interfacing, and reconstruction, among other issues. The time and resources required to complete these were far beyond desired. With the help of simulated interfaces, many of these issues can be recognized prior to physical integration.

The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.

EcoRouting refers to determining a route that minimizes vehicle energy consumption compared to traditional routing methods, which usually attempt to minimize travel time. EcoRoutes typically increase travel time and in some cases this increase may have to be constrained for the route to be viable. While significant research on EcoRouting exists for conventional vehicles, incorporating the novel aspects of plug-in hybrids opens up new areas to be explored. A prototype EcoRouting system has been developed that takes in map information and converts it to a graph of nodes containing route information such as speed and grade. The route with the minimum energy consumption is selected as the EcoRoute unless there is more than an 8% difference between the minimum time route and the EcoRoute.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2012 - 2014 EcoCAR 2: Plugging in to the Future Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). The goals of the competition are to reduce well-to-wheel (WTW) petroleum energy consumption, WTW greenhouse gas and criteria emissions while maintaining vehicle performance, consumer acceptability and safety. Following the EcoCAR 2 Vehicle Development Process (VDP), HEVT will design, build, and refine an advanced technology vehicle over the course of the three year competition using a 2013 Chevrolet Malibu donated by GM as a base vehicle. In year 1 of the competition, HEVT has designed a powertrain to meet and exceed the goals of the competition.

A vehicle's response is predominately defined by the tire characteristics as they constitute the only contact between the vehicle and the road; and the surface friction condition is the primary attribute that determines these characteristics. The friction coefficient is not directly measurable through any sensor attachments in production-line vehicles. Therefore, current chassis control systems make use of various estimation methods to approximate a value. However a significant challenge is that these schemes require a certain level of perturbation (i.e. excitation by means of braking or traction) from the initial conditions to converge to the expected values; which might not be the case all the time during a regular drive.

Ambient temperature plays an important role in the operational behavior of a vehicle. Temperature variances from 20 F to 72 F to 95 F produce different operation from different HEVs, as prescribed by their respective energy management strategies. The extra variable of Climate Control causes these behaviors to change again. There have been studies conducted on the differences in operational behavior of conventional vehicles as against HEVs, with and without climate control. Lohse-Bush et al conclude that operational behavior of conventional vehicles is much more robust as compared to HEVs and that the effect of ambient temperature is felt more prominently in HEVs (1).