Abstract
The range is currently one of main drawbacks of e-mobility, as energy storage capacity is limited. On the other hand, various information and computational resources in the cloud can be used. The control scheme uses model based predictive controllers with hierarchy of prediction horizons with various lengths. A detailed range estimation model of a Doblo e-vehicle is basis with the main subsystems: vehicle 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. Due to the system substantial nonlinearity, a broad grid of linearized model is selected to rebuild a piecewise linear model. Trajectory velocity profile, designed by cloud control layer serves as input. Resulting controllers are merged using gain scheduling approach.
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Acknowledgments
This research has been realized using the support of EU FP 7 Project No. 608756, Integration and Management of Performance and Road Efficiency of Electric Vehicle Electronics and using the support of The Ministry of Education, Youth and Sports program NPU I (LO), project # LO1311 Development of Vehicle Centre of Sustainable Mobility. This support is gratefully acknowledged.
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Steinbauer, P., Pasteur, F., Macek, J., Šika, Z., Husák, J. (2017). E-vehicle Predictive Control for Range Extension. In: Jabłoński, R., Szewczyk, R. (eds) Recent Global Research and Education: Technological Challenges. Advances in Intelligent Systems and Computing, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-319-46490-9_38
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DOI: https://doi.org/10.1007/978-3-319-46490-9_38
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