Powertrain Control via Model Predictive Rollout Scheme Universitaet Kassel

Format:

Book

Conference/Event

Author/Creator:

Arend, Jannik, author.

Contributor:

Ayeb, Mohamed

Veller, Sergej

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Multi-motor powertrain topologies are playing an increasingly important role in the development of heavy duty battery electric trucks due to the changing driving requirements of these vehicles. The use of multiple motors and/or transmissions in a powertrain provides additional degrees of freedom for the energy management. The energy management system (EMS) consist of the gear selection strategy and torque split between the drive motors. The aim of the EMS is thereby to achieve high energy efficiency in motor and regenerative operation, while reducing the number of gear changes to ensure driving comfort. Ongoing research focuses on the energy management system of hybrid electric trucks, where the aim is to optimize the torque split between the combustion engine and the electric motor.In this paper, the EMS for an electric truck is described as a mixed-integer nonlinear control problem. This type of optimal control problem is notoriously difficult to solve. Current control strategies mainly use techniques such as dynamic programming or Pontryagin's minimum principle, which lead to optimal solutions but also to high computational complexity.The approach presented in this paper uses a model predictive rollout scheme to determine the shift strategy and torque split between the motors in terms of energy efficiency for a multi-motor-transmission design. The vehicle environment is assumed to be known. In addition, the performance of the proposed scheme is compared with the optimal solution under a representative driving scenario. The proposed approach requires less computation, but shows close to optimal results

Notes:

Vendor supplied data

Publisher Number:

2024-01-2141

Access Restriction:

Restricted for use by site license