Overview
Hybrid energy systems, which consist of a load powered by a source and a form of energy storage, find applications in many systems, e.g., the electric grid and electric vehicles. A key problem for hybrid energy systems is the reduction of peak power consumption to ensure cost-efficient operation as peak power draws require additional resources and adversely affect the system reliability and lifetime. Furthermore, in some cases such as electric vehicles, the load dynamics are fast, not perfectly known in advance and the on-board computation power is often limited, making the implementation of traditional optimal control difficult. We aim to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts. We propose a scheme with two control levels and provide a sufficient condition for control of the different energy storage/generation components to meet the instantaneous load while satisfying a peak power threshold. The scheme provides performance comparable to Model Predictive Control, while requiring less computation power and only coarse-grained load predictions. For a case study, we implement the scheme for a battery-supercapacitor-powered electric vehicle with real world drive cycles to demonstrate the low execution time and effective reduction of the battery power (hence temperature), which is crucial to the lifetime of the battery.
Publications
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Peak Power Reduction in Hybrid Energy Systems with Limited Load Forecasts
Pant, Nghiem, Mangharam
American Control Conference (ACC), 2014. -
Final Report: Protodrive: Simulation of Electric Vehicle Powertrains
Price, Jain, Pant, Mangharam
World Embedded Software Competition (third place finish), 2013.
Contributors
Pant, Yash V. and Nghiem, Truong X. and Mangharam, Rahul
Citation
@INPROCEEDINGS{6859145,
author={Pant, Yash V. and Nghiem, Truong X. and Mangharam, Rahul},
booktitle={2014 American Control Conference},
title={Peak power reduction in hybrid energy systems with limited load forecasts},
year={2014},
volume={},
number={},
pages={4212-4217},
keywords={Batteries;Optimization;System-on-chip;Load forecasting;Prediction algorithms;Predictive control for linear systems;Optimization;Automotive},
doi={10.1109/ACC.2014.6859145}}
