Torque distribution strategies for energy-efficient electric vehicles with multiple drivetrains

Lenzo, B, De Filippis, G, DIzqah, A M, Sorniotti, A, Gruber, P, Fallah, S and De Nijs, W (2017) Torque distribution strategies for energy-efficient electric vehicles with multiple drivetrains. Journal of Dynamic Systems, Measurement and Control, 139 (12). ISSN 0022-0434

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Abstract

The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to driving/braking and cornering are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of an analytically derived algorithm are contrasted with those from two other control allocation strategies, based on the offline numerical solution of more detailed formulations of the control allocation problem (i.e., a multiparametric nonlinear programming (mp-NLP) problem). The control allocation algorithms are experimentally validated with an electric vehicle with four identical drivetrains along multiple driving cycles and in steady-state cornering. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Dynamics, Control and Vehicle Research Group
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA0174 Engineering design
T Technology > TJ Mechanical engineering and machinery > TJ0212 Control engineering systems. Automatic machinery (General)
Depositing User: Arash Moradinegade Dizqah
Date Deposited: 21 Mar 2019 11:21
Last Modified: 21 Mar 2019 11:22
URI: http://srodev.sussex.ac.uk/id/eprint/82682

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