Fail-operational powertrain systems can increase the availability of automated transportation systems like robocabs and shuttles. New degrees of freedom, arising from redundancy in fail-operational powertrain topologies, require new strategies for the control of such systems in normal and failure case operation. Adaptivity to possible system and component failures as well as short fault reaction times are required. Additional degrees of freedom for the control arise due to the fact that automated driving systems have the knowledge about the destination and the planned driving trajectory. The use of predictive and optimal control can increase the overall powertrain efficiency and reduce aging of the components. The work presented in this paper introduces a novel approach for the design and implementation of predictive and adaptive control strategies based on dynamic optimization of torque distribution between redundant motors for the entire trip to the destination. Simulation results using a MATLAB/Simulink model show the advantages and the real-time capability of the presented control strategy.


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    Title :

    Optimal, Adaptive and Predictive Real-Time Control of Fail-Operational Powertrain for Automated Electric Vehicles


    Contributors:


    Publication date :

    2018-06-01


    Size :

    302289 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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