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2017 | OriginalPaper | Chapter

12. Predictive Real-Time Energy Management

Authors : Thomas J. Böhme, Benjamin Frank

Published in: Hybrid Systems, Optimal Control and Hybrid Vehicles

Publisher: Springer International Publishing

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Abstract

This chapter discusses predictive control strategies for minimizing tank-to-wheel/tank-to-meters energy losses. An eco-driving management for battery electric vehicles known as predictive trip management is proposed and is implemented using a dynamic programming algorithm to calculate the recommended maximal vehicle speed to safely reach the target destination. This strategy is implemented on a rapid- prototyping hardware on PC level and is demonstrated on a subcompact BEV vehicle. Two different predictive real-time energy managements for (Plug-) HEVs are proposed and both use an indirect shooting method to solve a (switched) optimal control problem. The control strategies are implemented on a rapid prototype hardware on ECU level as (event-triggered) nonlinear model-predictive control. The PHEV strategy can be configured for the operation modes: charge-sustaining and charge-blending. The latter is used when the target destination provides a charging facility and the total driving distance exceeds the electrical range for the current state of charge. In this case the entire electrical energy can be depleted but the internal combustion engine has to be started several times, to prevent the high-voltage battery from falling below its minimum value before the target destination is reached.

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Metadata
Title
Predictive Real-Time Energy Management
Authors
Thomas J. Böhme
Benjamin Frank
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-51317-1_12