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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.

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.

General Motors' (GM) eAssist powertrain builds upon the knowledge and experience gained from GM's first generation 36Volt Belt-Alternator-Starter (BAS) system introduced on the Saturn VUE Green Line in 2006. Extensive architectural trade studies were conducted to define the eAssist system. The resulting architecture delivers approximately three times the peak electric boost and regenerative braking capability of 36V BAS. Key elements include a water-cooled induction motor/generator (MG), an accessory drive with a coupled dual tensioner system, air cooled power electronics integrated with a 115V lithium-ion battery pack, a direct-injection 2.4 liter 4-cylinder gasoline engine, and a modified 6-speed automatic transmission. The torque-based control system of the eAssist powertrain was designed to be fully integrated with GM's corporate common electrical and controls architectures, enabling the potential for broad application across GM's global product portfolio.

Conventional HVAC systems adjust the position of a temperature door, to achieve a required air temperature discharged into the passenger compartment. Such systems are based upon the fact that a conventional (non-hybrid) vehicle's engine coolant temperature is controlled to a somewhat constant temperature, using an engine thermostat. Coolant flow rate through the cabin heater core varies as the engine speed changes. EREVs (Extended Range Electric Vehicles) & PHEVs (Plug-In Hybrid Electric Vehicles) have two key vehicle requirements: maximize EV (Electric Vehicle) range and maximize fuel economy when the engine is operating. In EV mode, there is no engine heat rejection and battery pack energy is consumed in order to provide heat to the passenger compartment, for windshield defrost/defog and occupant comfort. Energy consumption for cabin heating must be optimized, if one is to optimize vehicle EV range.

The battery system in the Chevrolet Volt is very complex and must balance a variety of performance criteria, including the safety of vehicle occupants and other users. In order to assure a thorough approach to battery system safety, a system safety engineering process was applied and found to provide a useful framework. This methodical approach began with the preliminary hazard analysis and continued through requirements definition, design development and, finally, validation. Potentially hazardous conditions related directly to functional safety (for example, charge control) and primary physical safety (for example, short circuit conditions) can all be addressed in this manner. Typical battery abuse testing, as well as newly defined limit testing, supported the effort. Extensive documentation, traceability and peer reviews helped to verify that all issues were addressed.

Current manufacture of alternative energy sources for automobiles, such as fuel cells and lithium-ion batteries, uses repeating energy modules to achieve targeted balances of power and weight for varying types of vehicles. Specifically for lithium-ion batteries, tens to hundreds of identical plastic parts are assembled in a repeating fashion; this assembly of parts requires complex dimensional planning and high degrees of quality control. This paper will address the aspects of dimensional quality for repeated, injection molded thermoplastic battery components and will include the following: First, dimensional variation associated with thermoplastic components is considered. Sources of variation include the injection molding process, tooling or mold, lot-to-lot material differences, and varying types of environmental exposure. Second, mold tuning and cavity matching between molds for multi-cavity production will be analyzed.

Rechargeable energy storage systems with Lithium-ion pouch cells are subject to various ambient temperature conditions and go through thousands of charge-discharge cycles during the life time of operation. The cells may change their thickness with internal heat generation, cycling and any other mechanisms. The stacked prismatic cells thus experience face pressure and this could impact the pack electrical performance. The pack consists of stiff end plates keeping the pack in tact using bolts, cooling fins to maintain cell temperature and foam padding in between cells. The pack level thermal requirements limit the amount of temperature increase during normal operating conditions. Similarly, the structural requirements state that the stresses and the deflection in the end plates should be minimal. Uncertainties in cell, foam mechanical and thermal properties might add variation to the pack performance.

Hybrid high voltage battery pack is not only heavy mass but also large in dimension. It interacts with the vehicle through the battery tray. Thus the battery tray is a critical element of the battery pack that interfaces between the battery and the vehicle, including the performances of safety/crash, NVH (modal), and durability. The tray is the largest and strongest structure in the battery pack holding the battery sections and other components including the battery disconnect unit (BDU) and other units that are not negligible in mass. This paper describes the mass optimization work done on one of the hybrid batteries using CAE simulation. This was a multidisciplinary optimization project, in which modal performance and fatigue damage were accessed through CAE analysis at both the battery pack level, and at the vehicle level.

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.

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.

Plug in hybrid electric vehicles (PHEV) and electric vehicles (EV) are using large lithium ion battery packs to store energy for powering electric traction motors. These batteries, or Rechargeable Energy Storage Systems (RESS), have a narrow temperature operating range and require thermal management systems to properly condition the batteries for use in automotive applications. This paper will focus on energy optimization of a RESS cooling system. The battery thermal management system for the General Motors Chevrolet Volt has three distinct modes for battery cooling: active cooling, passive cooling, and bypass. Testing was conducted on each individual thermal cooling mode to optimize, through control models, the energy efficiency of the system with the goal of maximizing electric vehicle range.

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.

An Extended Range Electric vehicle brings a wealth of new features since it is capable of driving on battery alone, has a range extending engine, and has a high voltage battery pack that can be recharged by plugging into wall power. The customer is able to interact with the vehicle's plug-in charging system through mobile applications. Along with all these new features is the challenge of designing a driver interface to provide important information to the customer. This paper will describe the unique customer interface features added to the vehicle, and will include some additional specifics related to the hardware used to provide the information.

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.

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.

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.

As advanced battery systems become a standard choice for mainstream production vehicle portfolios, comprehensive battery system validation plans are essential to ensure that the battery performance, reliability, and durability targets are met prior to vehicle integration. (Note: Safety and Abuse testing are outside of the scope of this paper.) The validation plan for the Chevrolet Volt Rechargeable_Energy Storage System (RESS), the first lithium-ion battery pack designed and manufactured by General Motors (GM), was developed using a functional silo approach based on the battery design requirements documentation. While the Chevrolet Volt was the lead program at General Motors to use this validation plan development approach, other GM programs with different battery system mounting locations and cooling techniques are now using this method.

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.

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.

Electrification of the transportation industry is increasing rapidly with batteries currently the technology of choice. At the end of life, the battery chemistry used to electrify the vehicle may not be easily identifiable. A simple, common identifier is required to allow consumers, service and waste management personnel to direct unknown battery types to appropriate recyclers or secondary use markets. Recyclers also benefit from this identifier as it allows them to sort, screen for potential contamination to existing process streams, and identify the manufacturer so they may contact them to find detailed information about the battery to ensure proper and safe recycling. The SAE Battery Recycling Committee has recommended that batteries be identified by battery system, miscellaneous hazards and date of manufacture be identified as part of chemistry identification code. For the lithium-ion chemistry it is further recommended that cathode and anode be specified.