Abstract
A recursive least squares (RLS) based observer for simultaneous estimation of vehicle mass and road grade, using longitudinal vehicle dynamics, is presented. In order to achieve robustness to unknown disturbances and varying parameters, depth is chosen in a sufficient way. This is done with a sensitivity analysis, identifying parameters with significant influence on the estimation result. The identification of vehicle parameters is presented in detail. The method is validated with an all-electric vehicle (AEV) using natural driving cycles. The results show little deviation between estimation and reference, as well as good convergence in urban areas, providing sufficient excitation. However, on highway roads, environmental influences like wind and slipstream of trucks, worsen the results, especially in combination with little excitation for the observer.
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Karoshi, P., Ager, M., Schabauer, M., Lex, C. (2018). Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. In: Zachäus, C., Müller, B., Meyer, G. (eds) Advanced Microsystems for Automotive Applications 2017. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-66972-4_8
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DOI: https://doi.org/10.1007/978-3-319-66972-4_8
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