Maximizing Autonomous Mobility and Energy Efficiency on-the-go through Exteroceptive and Proprioceptive Self-Learning-and-Improvement

Project began mid-2020 and was completed in Q3-2022.

In recent research work [13], a new velocity-based mobility performance index was proposed to characterize the influence of power split between the front and rear wheels on vehicle mobility performance. This index was used as the objective function for the wheel power distribution optimization in a preliminary study involving a JLTV[13]; computer simulations showed that the optimization allows for increasing mobility performance.

It was established that the energy efficiency indices, which were derived from the tire slip power loss (i.e., velocity-based loss of power), should not be used to estimate mobility of vehicles [13]. Instead, a velocity-based mobility performance index should be utilized. This index compares the actual velocity of a vehicle having any advanced power split between the driving wheels to the theoretical velocity of the base vehicle configuration (with a mechanical driveline system that is designed with the same and constant gear ratios from the transfer case to the front and rear wheels).

This finding has led to a new challenge in vehicle design: mechanical driveline systems need to be flexible enough to provide various power splits to the driving wheels for the purpose of either energy efficiency or mobility. With individual electric drives, new analytical fundamentals need to be developed to coordinate the power delivering to the wheels, which do not have mechanical connections in fully electric vehicles. To overcome these challenges, this project proposes new vehicle dynamics fundamentals needed to mathematically formulate and solve the problem of the wheel power distribution and to establish conditions for maximum terrain mobility.

The differentiation of optimal wheel power splits required for mobility and energy efficiency opens up technical opportunities to develop fundamentals for intelligent management of mobility and energy efficiency on the basis of Artificial Intelligence (AI) advances. By correlating new analytical accomplishments and other features in mobility and energy efficiency management with distinctive features and requirements of autonomous vehicle models, research directions have been identified and formulated for developing AI-based fundamentals to benefit the autonomous mobility and energy efficiency management.

Reference:

[13] Vantsevich, V., Gorsich, D., Paldan, J., and Letherwood, M., “A Virtual Driveline Concept to Maximize Mobility Performance of Autonomous Electric Vehicles,” SAE Technical Paper 2020-01-0746, 2020.

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