Halo Project

Displaying the breadth of automotive engineering at Mississippi State University

The term "halo car" often applied to an automaker's top-of-the-line vehicle which demonstrates the newest technology and represents the direction that a brand is going. Similarly, MSU's Halo Project unites MSU experts on automotive engineering to collaborate on a technological showpiece. The MSU Halo Vehicle is an off-road, all-electric SUV with advanced autonomous driving features. The project utilizes CAVS materials science, advanced powertrains, computational fluid dynamics, vehicle integration, and autonomous systems expertise.

The vehicle's innovative PowerDriveLine is custom designed from the ground up to combine the functions of the traditional powertrain, driveline, chassis and suspension subsystems. By designing these systems as a single unit, the complete assembly can be optimized, which reduces both mass and complexity.

Optimizing multiple subsystems simultaneously requires extreme computational power, such as that available to CAVS experts through the High Performance Computing Collaboratory. The Halo PowerDriveLine was simulated using a model of over 5 million elements, demonstrating a new paradigm of design optimization as one of the most complex FEA models ever developed by CAVS and simulated at HPCC. It combines materials such as aluminum, titanium, and high-strength steel.

close-up look of material design/optimization properties
of vehicle

A close-up view of the vehicle's materials design and optimization properties

"This vehicle makes an impactful statement about our contribution to the future of autonomy and off-road mobility."

- Clay Walden

CAVS Executive Director

The vehicle's all-electric powertrain must be lightweight, powerful, and provide sufficient range. To satisfy these requirements, cutting-edge nickel-manganese- cobalt (NMC) lithium-ion batteries are used. The vehicle's 90 kWh battery pack provides 230 miles of range. Each wheel is powered independently using four 100 kW, 400 Nm electric motors along with a 7:1 gear reduction, resulting in over 10,000 Nm of wheel torque. Several novel technologies are employed in the electrified powertrain, including prototype thermal interface materials, distributed "supermodule" battery packs with independent contractors and unique heat exchanger designs.

Digital renderings of Halo Powertrain Design

Active air-cooled thermal management is employed in one of the vehicle's supermodule battery packs. A computational fluid dynamics model simulates the heat transfer inside the pack. The simulation, using a tooolchain including Pointwise and ANSYS Fluent, includes 12.6 million elements and both isothermal and adiabatic boundary conditions. The results of the simulation are used to improve air flow within the pack and therefore improve thermal performance.

Image of vehicle powertrain and battery packs

The vehicle was built at CAVS using a variety of internal resources such as machining, welding, x-ray CT inspection, and materials characterization. The PowerDriveLine structure alone uses 181 custom-machined parts and 24 OEM or aftermarket parts. Some suspension components are made from custom corrorsion-resistant, high-strength steel produceed in the CAVS small-scale steel mill. These steel componenets are transformed from raw carbon and other elements into finished components, all within CAVS.

Corrosion-resistant coatings are applied to each part in the assembly. Each part is coated with an application-specific coating (i.e. thermally conductive , abrasion resistant, etc.)

High-strength steel produced at CAVS for the Halo Project

Designed to investigate autonomous mobility in unstructured or off-road environments, the vehicle uses advanced sensors such as lidar, cameras, GPS and inertial measurement units (IMUs). Sensor data is processed using artificial intelligence algorithms on an onboard GPU-based computer. The vehicle is fully drive-by-wire, with computer control of ignition status, shifting, throttle, braking, steering, horn, power windows, and all exterior lighting. Advances currently being tested allow machine learning algorithms to identify candidate paths in off-road environments using cameras and classify objects using lidar. The Halo Project autonomous research contributes to the development of MAVS and uses MAVS extensively for development and testing.

Halo Project Partners