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Optimal charging of valve-regulated lead-acid batteries based on model predictive control

Author

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  • Kujundžić, Goran
  • Ileš, Šandor
  • Matuško, Jadranko
  • Vašak, Mario

Abstract

In this paper an algorithm for optimal charging of a valve-regulated lead-acid (VRLA) battery stack based on model predictive control (MPC) is proposed. The main objective of the proposed algorithm is to charge the battery stack as fast as possible without violating the constraints on the charge current, the battery voltage and the battery temperature. In addition, a constraint on the maximum allowed voltage of every battery in the battery stack is added in order to minimize degradation of the individual batteries during charging. The convexity of the VRLA battery charging optimization problem is proven, which makes the control algorithm suitable for efficient on-line implementation via solving a quadratically constrained quadratic program (QCQP). The recursive feasibility and stability of the proposed control strategy is ensured. The proposed algorithm is validated both through simulation tests and on the experimental setup.

Suggested Citation

  • Kujundžić, Goran & Ileš, Šandor & Matuško, Jadranko & Vašak, Mario, 2017. "Optimal charging of valve-regulated lead-acid batteries based on model predictive control," Applied Energy, Elsevier, vol. 187(C), pages 189-202.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:189-202
    DOI: 10.1016/j.apenergy.2016.10.139
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    References listed on IDEAS

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    1. Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu & Zhibin Song, 2011. "Model Predictive Control-Based Fast Charging for Vehicular Batteries," Energies, MDPI, vol. 4(8), pages 1-19, August.
    2. Petrollese, Mario & Valverde, Luis & Cocco, Daniele & Cau, Giorgio & Guerra, José, 2016. "Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid," Applied Energy, Elsevier, vol. 166(C), pages 96-106.
    3. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    4. Wang, Xiaonan & Palazoglu, Ahmet & El-Farra, Nael H., 2015. "Operational optimization and demand response of hybrid renewable energy systems," Applied Energy, Elsevier, vol. 143(C), pages 324-335.
    5. Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Bernal-Agustín, José L., 2014. "Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems," Applied Energy, Elsevier, vol. 115(C), pages 242-253.
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    Cited by:

    1. Guan-Jhu Chen & Yi-Hua Liu & Yu-Shan Cheng & Hung-Yu Pai, 2021. "A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant & Andrés Aristizabal, 2018. "A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems," Energies, MDPI, vol. 11(4), pages 1-15, April.
    3. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    4. Adrian Chmielewski & Jakub Możaryn & Piotr Piórkowski & Krzysztof Bogdziński, 2018. "Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle," Energies, MDPI, vol. 11(11), pages 1-28, November.

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