Global Sensitivity Analysis on the Torque Accuracy of the Powertrain in Electric Vehicles

Braband, Matthias; Adams, M.J.; Wilhelmi, Andreas; Scherer, Matthias

doi:10.1016/j.ifacol.2020.12.939

Cited by 1 publication

(4 citation statements)

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“…The proposed extension by x 0 , t and u(t) leads also to state-and time-dependent sensitivity indices. As already outlined in [27], the dependency of x 0 and u(t) can be ensured by covering all the relevant operating points of the physical system. In this article, this is ensured by defining representative drive cycles, as outlined in Section 3.4.…”

Section: Time and State Dependency Of Technical Processesmentioning

confidence: 99%

“…Nevertheless, this work is based on a simple longitudinal motion model only considering efficiency rates in the electric drivetrain. More detailed sensitivity analyses on specific drivetrain components and not at the vehicle level have been conducted in [25][26][27][28]. In [25], a qualitative Morris screening was performed to analyze a basic equivalent circuit model of a battery system.…”

Section: Introductionmentioning

confidence: 99%

“…A Morris screening-based investigation considering the reaction kinematic models of a battery has been detailed in [26]. Parameter dependencies on the torque accuracy of an electric drive system using Sobol indices have been outlined in [27]. Furthermore, a good review regarding sensitivity analysis for electrical drives can be found in [28].…”

Section: Introductionmentioning

confidence: 99%

“…The special case of equal weights and uniform time steps has been suggested in [36] for time-dependent processes, which is used throughout this article. The indices are calculated using the estimators described in [34] and convergence of this estimators is ensured as proposed in [27].…”

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confidence: 99%

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Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential of model predictive control (MPC) methods to reduce energy consumption. However, this results in a complex control system with many parameters that affect the energy efficiency. Thus, an important question remains: how do these partially uncertain (system or controller) parameters influence the energy efficiency? In this article, a global variance-based sensitivity analysis method is used to answer this question. Therefore, a detailed powertrain model controlled by a longitudinal nonlinear MPC (NMPC) is developed and parameterized. Afterwards, a qualitative Morris screening is performed on this model, in order to reduce the parameter set. Subsequently, the remaining parameters are quantified using Generalized Sobol Indices, in order to take the time dependence of physical processes into account. This analysis reveals that the variations in vehicle mass, battery temperature, rolling resistance and auxiliary consumers have the greatest influence on the energy consumption. In contrast, the parameters of the NMPC only account for a maximum of 5% of the output variance.

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Section: Time and State Dependency Of Technical Processesmentioning

confidence: 99%