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In July 2007, GM announced that it would produce the Chevy Volt, the first high-production volume electric vehicle with extended range capability, by 2010. In January 2009, General Motors announced that the Chevrolet Volt's lithium ion Battery Pack, capable of propelling the Chevy Volt on battery-supplied electric power for up to 40 miles, would be designed and assembled in-house. The T-shaped battery, a subset of the Voltec propulsion system, comprises 288 cells, weighs 190 kg, and is capable of supplying over 16 kWh of energy. Many technical challenges presented themselves to the team, including the liquid thermal management of the battery, the fast battery pack development timeline, and validation of an unproven high-speed assembly process. This paper will first present a general overview of the approach General Motors utilized to bring the various engineering organizations together to design, develop, and manufacture the Volt battery.

Mid 2006 a study group at General Motors developed the concept for the electric vehicle with extended range (EREV),. The electric propulsion system should receive the electrical energy from a rechargeable energy storage system (RESS) and/or an auxiliary power unit (APU) which could either be a hydrogen fuel cell or an internal combustion engine (ICE) driven generator. The study result was the Chevrolet VOLT concept car in the North American Auto Show in Detroit in 2007. The paper describes the requirements, concepts, development and the performance of the battery used as RESS for the ICE type VOLTEC propulsion system version of the Chevrolet Volt. The key requirement for the RESS is to provide energy to drive an electric vehicle with “no compromised performance” for 40 miles. Extended Range Mode allows for this experience to continue beyond 40 miles.

This paper presents Telematics Battery Monitoring (TBM). TBM is a multi-faceted approach of collecting and analyzing electric power and vehicle data used to ultimately determine battery state of charge (SOC) and battery state of health (SOH) in both pre- and post-sale environments. Traditional methods of battery SOC analysis include labor intensive processes such as going out to the site of individual vehicle(s), gaining access to the vehicle battery, and then after the vehicle electrical system obtains its quiescent current level, performing a battery voltage check. This time-consuming manual method can practically only cover a small percentage of the vehicle population. In using the vehicle communication capabilities of Telematics, electric power and vehicle data are downloaded, compiled, and post-processed using decision-making software tools.

In this paper, a low-cost means to improve fuel economy in conventional vehicles by employing ultracapacitor based Active Energy Recovery Buffer (AERB) scheme will be presented. The kinetic energy of the vehicle during the coast down events is utilized to charge the ultracapacitor either directly or through a dc-dc converter, allowing the voltage to increase up to the maximum permissible level. When the vehicle starts after a Stop event, the energy stored in the capacitor is discharged to power the accessory loads until the capacitor voltage falls below a minimum threshold. The use of stored capacitor energy to power the accessory loads relieves the generator torque load on the engine resulting in reduced fuel consumption. Two different topologies are considered for implementing the AERB system. The first topology, which is a simple add-on to the conventional vehicle electrical system, comprises of the ultracapacitor bank and the dc-dc converter connected across the dc bus.

Recently, an increased emphasis has been seen for improving the cooling uniformity and efficiency of HEV battery pack in an effort to increase the battery performance and life. This study examined the effects of geometry changes in cooling systems of battery packs on thermal behavior of battery cells and pressure drop across the battery pack. Initially, a multi-physics battery thermal model was correlated to physical test data. An analytical design of experiments (DOE) approach using Latin-hypercube technique was then developed by integrating the correlated battery thermal model with a commercial optimization code, iSIGHT, and a morphing code, DEP Morpher. The design concepts of battery pack cooling systems were finally identified by performing analytical DOE/optimization studies to estimate the effects of cooling flow and geometries of cooling ducts on the battery temperature variation and pressure drop across the battery pack.

Simscape is a physical modeling language developed by Mathworks Inc. The language uses equation statements instead of assignment statements to describe physical systems. The paper focuses on the Simscape language itself instead of using components in the Simscape libraries. The language will be introduced from a perspective different from the Mathworks' Physical Network point of view. Our perspective focuses on two types of variables at the connectors. In additional, internal variables are not separated into through and across variables. The alternative perspective is more general and easier to understand. The paper also illustrates how to develop components in a powertrain library following the proposed new perspective.

The Chevrolet Volt is an electric vehicle (EV) that operates exclusively on battery power as long as useful energy is available in the battery pack under normal conditions. After the battery is depleted of available energy, extended-range (ER) driving uses fuel energy in an internal combustion engine (ICE), an on-board generator, and a large electric driving motor. This extended-range electric vehicle (EREV) utilizes electric energy in an automobile more effectively than a plug-in hybrid electric vehicle (PHEV), which characteristically blends electric and engine power together during driving. A specialized EREV powertrain, called the "Voltec," drives the Volt through its entire range of speed and acceleration with battery power alone, within the limit of battery energy, thereby displacing more fuel with electricity, emitting less CO₂, and producing less cold-start emissions than a PHEV operating in real-world conditions.

This work is to propose a new Iterative Learning Observer (ILO)-based strategy for State Of Charge (SOC) estimation. The ILO is able to estimate the SOC in real time while identifying modeling errors and/or disturbances at the same time. An Electrical-Circuit Model (ECM) is adopted to characterize the Lithium-ion battery behavior. The ILO is designed based on this ECM and the stability is proved. Several experiments are conducted and the collected data is used to extract ECM parameters. The effectiveness of the estimated SOCs via ILO is verified by the experimental results. This implies that the ILO-based SOC determination scheme is effective to identify the SOC in real time.

Great work has been done already in developing robust engineering techniques to improve ideal functions for systems and sub systems. Characterizing an ideal function as a dynamic response type is most preferred way to build quality into a product over a range of input signal values. However, when it is difficult to measure ideal functions, symptomatic outputs such as oil leaks, vibrations, and squeaks, are selected and treated as “Smaller-the-Better” response in non-dynamic response manner. A better approach is to reduce the symptomatic responses over the entire usage range. In order to accomplish this goal, engineers often switch output response and signal axes and apply dynamic response formulation for making the design robust. In this paper, a new and better formulation is proposed and compared with the other formulation. These two formulations were applied on a real automotive case study of decklid bobble and inaccuracies associated with the other formulation were discussed.

This paper presents ‘Virtual Shaker Table’: a CAE method that enables random frequency structural response and random vibration fatigue analyses of a battery system. The Virtual Shaker Table method is a practical and systematic procedure that effectively assesses battery system vibration performance prior to final design, build and testing. A random structural frequency response analysis identifies the critical frequencies and modes at which the battery system is excited by random inputs. Fatigue life may be predicted after PSD stresses have been ascertained. This method enables frequency response analysis techniques to be applied quickly and accurately, thereby allowing assessment of multiple design alternatives. Virtual Shaker Table facilitates an elegant solution to some of the significant challenges inherent to complex battery system design and integration.

The development and validation of an electric vehicle presents numerous issues that are not normally encountered during the development of a traditional internal combustion powered vehicle. Many of the issues that are encountered involve components that are common to both electric and internal combustion vehicles but are utilized in new or unique ways that may present challenges during the development process. The integration of the electric motors, power supply, batteries, and associated content into a traditional vehicle can bring new and challenging issues to light. This paper discusses the solution for an issue that arose during the testing and development of the chassis and powertrain hardware of an electric vehicle. In particular, the large rotational inertia of the electric drive motor presented significant challenges when it was accelerated by forces that were external to the drive unit.

With the introduction of plug-in electric vehicles (PEVs), the conventional mindset of “fill-up time” will be challenged as customers top off their battery packs. For example, using a standard 120VAC outlet, it may take over 10hrs to achieve 40-50 miles of EV range-making range anxiety a daunting reality for EV owners. As customers adapt to this new mindset of charge time, it is critical that automotive OEMs supply the consumer with accurate charge time estimates. Charge time accuracy relies on a variety of parameters: battery pack size, power source, electric vehicle supply equipment (EVSE), on-board charging equipment, ancillary controller loads, battery temperature, and ambient temperature. Furthermore, as the charging events may take hours, the initial conditions may vary throughout a plug-in charge (PIC). The goal of this paper is to characterize charging system sensitivities and promote best practices for charge time estimations.

As the auto industry becomes more dependent upon Electric Vehicles (Plug-In Hybrid Electric Vehicles, Battery Electric Vehicles, and Extended Range Electric Vehicles), the Plug-In Charging Feature will become an integral part of the driver's daily routine. The Plug-In Charging feature enables off-board electrical power grid (grid based) power to be used immediately or at a later time by on-vehicle functions. The primary use of this grid power is to charge the vehicle's High Voltage (HV) battery, but other uses also do exist. These functions will mainly be active when the vehicle is off.

This paper details the design and operating attributes of a triple input clutch, layshaft automatic transmission (TCT) with a torque converter in a rear wheel drive passenger vehicle. The objectives of the TCT design are to reduce fuel consumption while increasing acceleration performance through the design of the gearing arrangement, shift actuation system and selection of gear ratios and progression. A systematic comparison of an 8-speed TCT design is made against a hypothetical 8-speed planetary automatic transmission (AT) with torque converter using an energy analysis model based upon empirical data and first principles of vehicle-powertrain systems. It was found that the 8-speed TCT design has the potential to provide an approximate 3% reduction in fuel consumption, a 3% decrease in 0-100 kph time and 30% reduction in energy loss relative to a comparable 8-speed planetary AT with an idealized logarithmic ratio progression.

In designing vehicles with significant electric driving range, optimizing vehicle energy efficiency is a key requirement to maximize the limited energy capacity of the onboard electrochemical energy storage system. A critical factor in vehicle energy efficiency is the vehicle mass. Optimizing mass allows for the possibility of either increasing electric driving range with a constant level of electrochemical energy storage or holding the range constant while reducing the level of energy storage, thus reducing storage cost. In this paper, a methodology is outlined to study the tradeoff between the battery cost savings achieved by vehicle mass reduction for a constant electric driving range and the cost associated with lightweighting a vehicle. This methodology enables informed business decisions about the available engineering options for lightweighting early in the vehicle development process. The methodology was applied to a compact extended-range electric vehicle (EREV) concept.

Among the several enhancements in AUTOSAR Release 4.0 is the addition of an End-to-End (E2E) Communication Protection Library. This library defines several E2E profiles, each of which implements a combination of End-to-End protection mechanisms such as sequence counters, data IDs and CRCs. Two of these profiles, Profiles 1 and 2, are intended to protect inter-ECU communication via databus systems like FlexRay or CAN, and are designed to address various communication faults. Although the AUTOSAR specification includes detailed descriptions of the profiles, it provides only limited insight about the fault coverage that can be obtained when using these profiles to detect communication faults. This paper focuses on the fault detection capabilities that profiles 1 and 2 offer with respect to message corruptions.

The On Board Jump Start feature utilized on GM's Belt Alternator Starter (BAS) hybrids takes advantage of the High Voltage (HV) battery and other HV propulsion systems to supplement a low 12V battery during starting events. This feature has the potential to eliminate the need for an off vehicle 12V power source, such as a 2nd vehicle, to start the vehicle when the 12V battery is low.

Fuel economy and stringent emissions requirements have steered the automotive industry to invest in advanced propulsion hybrids, including Plug-in hybrid vehicles (PHEV) and Fuel cell vehicles. The choice of battery technology, its power and thermal management and the overall vehicle energy optimization during different conditions are crucial design considerations for PHEVs and battery electric vehicles (BEV). Current industry focus is on Li-Ion batteries due to their high energy density. However, extreme operating temperatures may impact battery life and performance. Different cooling strategies have been proposed for efficient thermal management of battery systems. This paper discusses the modeling and analysis strategy for a thermally managed Lithium Ion (Li-Ion) battery pack, with coolant as the conditioning medium.

In this paper we discuss the use of a formal approach to the problem of describing, evaluating, and specifying human-machine interaction. The statecharts language, originally conceived by David Harel [1], is used to describe the behavior of the machine (i.e., its states and transitions), interface indications (e.g., light indicators on switches), and user interaction (selecting applications, switching modes, entering parameters, etc.). We illustrate how the statecharts language can be used to describe driver interaction with a climate control system, and show how it is possible to systematically evaluate user interaction. The paper concludes with several observations about the utility of formal language for generating sound design specification of human-machine systems.

Key fobs, also known as remote keys or remote transmitters, have become a common piece of equipment in today's vehicle, being ubiquitous in every market segment. Once limited to remote locking and unlocking operations, today's key fobs can be used to control many comfort and security features beyond locking and unlocking, such as alarm system operation, vehicle locate, approach lighting, memory seat recall, and remote starting systems. Key fobs are designed to be easy to use as well as easy to carry and transport in personal containers, such as purses, pockets, wallets, and the like. Accordingly, as with other personal effects, key fobs and other portable remote devices can be lost or misplaced or can be otherwise difficult to find. Even with careful tracking of a remote device, children and pets, among other factors, can make location difficult. Moreover, multiple remote devices are often distributed with each vehicle.