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Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study

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  • Laird, Cary
  • Kang, Ziliang
  • James, Kai A.
  • Alleyne, Andrew G.

Abstract

Advances in power density, energy storage technology, and thermal management are crucial to increased electrification of vehicles, especially those with high ramp rate loads. To meet these demands, a systems-minded design approach is needed, capable of simultaneously optimizing multi-domain system dynamics including control. This work provides a framework for simultaneous plant and control design leveraging a graph-based modeling tool for multi-domain dynamics. The graph-based modeling tool captures system-level dynamics spanning multiple energy domains. This tool facilitates control design by providing a state-space-like set of dynamic equations that describe the system's behavior and are computationally inexpensive to simulate. Modular, scalable graph-based models enable design optimization for both plant and controller design. To demonstrate an application of the framework, a hybrid electro-thermal energy storage system is described to provide a power-dense energy storage solution for classes of future electrified vehicles with high ramp rate power demands. Heuristic controllers protect energy storage elements while meeting reference signal commands. Sizing and control parameters of the energy storage system are optimized using the graph-based optimization framework. Simultaneous optimization of both components and control parameters demonstrate significant reductions in size while retaining a high level of performance, leading to improvements in power density.

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  • Laird, Cary & Kang, Ziliang & James, Kai A. & Alleyne, Andrew G., 2022. "Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study," Energy, Elsevier, vol. 258(C).
  • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017583
    DOI: 10.1016/j.energy.2022.124855
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    References listed on IDEAS

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    1. Jiajun Liu & Huachao Dong & Tianxu Jin & Li Liu & Babak Manouchehrinia & Zuomin Dong, 2018. "Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation," Energies, MDPI, vol. 11(10), pages 1-25, October.
    2. Candanedo, J.A. & Dehkordi, V.R. & Stylianou, M., 2013. "Model-based predictive control of an ice storage device in a building cooling system," Applied Energy, Elsevier, vol. 111(C), pages 1032-1045.
    3. Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    4. Li, Tianyu & Liu, Huiying & Ding, Daolin, 2018. "Predictive energy management of fuel cell supercapacitor hybrid construction equipment," Energy, Elsevier, vol. 149(C), pages 718-729.
    5. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    6. Silva, Pedro D. & Gonçalves, L. C. & Pires, L., 2002. "Transient behaviour of a latent-heat thermal-energy store: numerical and experimental studies," Applied Energy, Elsevier, vol. 73(1), pages 83-98, September.
    7. Kuperman, Alon & Aharon, Ilan, 2011. "Battery-ultracapacitor hybrids for pulsed current loads: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 981-992, February.
    8. Hemmati, Reza & Saboori, Hedayat, 2016. "Emergence of hybrid energy storage systems in renewable energy and transport applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 11-23.
    9. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    10. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
    11. Wen, Shuli & Lan, Hai & Yu, David. C. & Fu, Qiang & Hong, Ying-Yi & Yu, Lijun & Yang, Ruirui, 2017. "Optimal sizing of hybrid energy storage sub-systems in PV/diesel ship power system using frequency analysis," Energy, Elsevier, vol. 140(P1), pages 198-208.
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