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Considerable improvements can be obtained in battery performance for hybrid electric vehicles (HEVs) by employing an electrochemistry-transport model based on a multi-physics modeling framework and ultrafast numerical algorithms. One important advantage of this approach over the lumped equivalent circuit (or look-up table) approach is the ability of the former to adapt to changes in design and control. In this work, we present mathematical and numerical details of our approach, and demonstrate the robustness of this battery model in simulation of short-pulse charge/discharge characteristic of HEV driving cycles under room and low temperatures.

The University of Wisconsin-Madison Snowmobile Team designed and constructed a hybrid-electric snowmobile for the 2005 Society of Automotive Engineers' Clean Snowmobile Challenge. Built on a 2003 cross-country touring chassis, this machine features a 784 cc fuel-injected four-stroke engine in parallel with a 48 V electric golf cart motor. The 12 kg electric motor increases powertrain torque up to 25% during acceleration and recharges the snowmobile's battery pack during steady-state operation. Air pollution from the gasoline engine is reduced to levels far below current best available technology in the snowmobile industry. The four-stroke engine's closed-loop EFI system maintains stoichiometric combustion while dual three-way catalysts reduce NOx, HC and CO emissions by up to 94% from stock. In addition to the use of three way catalysts, the fuel injection strategy has been modified to further reduce engine emissions from the levels measured in the CSC 2004 competition.

The University of Wisconsin - Madison Hybrid Vehicle Team has designed, fabricated, tested and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2006 Challenge X competition. This multi-year project is based on a 2005 Chevrolet Equinox platform. Trade-offs in fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were weighed to establish Wisconsin's Vehicle Technical Specifications (VTS). Wisconsin's Equinox, nicknamed the Moovada, utilizes a General Motors (GM) 110 kW 1.9 L CIDI engine coupled to GM's 6-speed F40 transmission. The rear axle is powered by a 65 kW Ballard induction motor/gearbox powered from a 44-module (317 volts nominal) Johnson Controls Inc., nickel-metal hydride hybrid battery pack. It includes a newly developed proprietary battery management algorithm which broadcasts the battery's state of charge onto the CAN network.

The University of Wisconsin Hybrid Vehicle Team has implemented and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2008 ChallengeX competition. This four year project is based on a 2005 Chevrolet Equinox platform. Fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were appropriately weighted to determine powertrain component selections. Wisconsin's Equinox, nicknamed the Moovada, is a split-parallel hybrid utilizing a General Motors (GM) 110 kW 1.9L CDTi (common rail diesel turbo injection) engine coupled to an F40 6-speed manual transmission. The rear axle is powered by a SiemensVDO induction motor/gearbox power-limited to 65 kW by a 40-module (288 volts nominal) Johnson Controls Inc, nickel-metal hydride battery pack.

Today's fuel cell powered vehicles typically utilize compressed hydrogen storage systems with a nominal working pressure of either 35MPa or 70Mpa. This coexistence of working pressures has, in a large part, developed in isolation, in that automakers have primarily considered vehicle side issues when choosing the storage system pressure. This study looks at hydrogen fueling from a holistic perspective by considering both vehicle side and station side issues with the goal to determine an optimum hydrogen working pressure. The approach utilized is to first conduct a data driven study of vehicle fueling at different working pressures and ambient temperatures to determine the vehicle and thermodynamic considerations of hydrogen fueling. This data is then contrasted with the hydrogen station hardware required to perform fueling at these temperatures and pressures.

This paper identifies important engine, alternator and battery characteristics needed for determining an appropriate engine control strategy for a series hybrid electric vehicle Examination of these characteristics indicates that a load-leveling strategy applied to the small engine will provide better fuel economy than a power-tracking scheme An automatic energy management strategy is devised whereby a computer controller determines the engine-alternator turn-on and turn-off conditions and controls the engine-alternator autonomously Battery state of charge is determined from battery voltage and current measurements Experimental results of the system's performance in a test vehicle during city driving are presented

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

Vehicle-to-Grid (V2G) technology is expected to play a role in addressing the imbalance between periods of peak demand and peak supply on the electricity grid. V2G technology enables two-way power flow between the grid and the high-power, high-capacity propulsion batteries in an electrified vehicle. That is, V2G allows the vehicle to store electricity during peak supply periods, and then discharge it back into the grid during peak demand periods. The authors have performed an architectural design and a modeling and simulation study for a bi-directional wireless charging system for V2G applications. This research activity aims to adapt an existing SAE J2954 compatible uni-directional system design to enable bi-directional wireless power transfer with minimum impact to system cost, while maintaining full compatibility with the requirements of SAE J2954.

The University of Wisconsin - Madison Clean Snowmobile team has designed, constructed and now refined an electric snowmobile with 40 km (24 mi) range and acceleration comparable to a 75 kW (100 hp) internal-combustion-powered snowmobile. Starting with a Polaris IQ Fusion chassis, a direct-drive chain-case was engineered to couple a General Motors EV1 copper-bar rotor AC induction electric motor to the track drive shaft. The battery pack uses 104 28 V, 2.8 A-hr Lithium-Ion battery modules supplied by Milwaukee Tool to store 8.2 kW-hr of energy at a nominal voltage of 364 V. Power is transmitted to the electric motor via an Azure Dynamics DMOC445LLC motor controller. All of the components fit within the original sled envelope, leading to a vehicle with conventional appearance and a total mass of 313 kg (690 lb). The vehicle, dubbed the BuckEV, accelerates to 150 m (500 ft) in 6.9 seconds and has a top speed of 122 km/hr (76 mph) with a pass-by sound level of 55 dB.

The temperature monitoring of the lithium-ion battery is crucial for the advanced battery thermal management systems (BTMS) to improve performance and ensure operational safety and reliability of the battery system. In real applications, the core temperature of the battery is unfortunately unmeasurable due to the impracticality of placing a sensor inside the core, and has to be estimated online in real-time. Meanwhile, only the measurement of battery surface temperature can not meet the need for advanced BTMS due to the impact of the large temperature gradient between the surface and internal in high power applications. The battery core temperature estimation will become challenging when encountering sensor bias and noise.