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Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules

Author

Listed:
  • Yunfeng Jiang

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

  • Louis J. Shrinkle

    (Pacific Battery Management Systems, Encinitas, CA 92024, USA)

  • Raymond A. de Callafon

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

Abstract

This paper presents the algorithms, hardware overview and testing results for controlling discharge currents from mixed battery modules placed in a parallel configuration. Battery modules with different open-circuit voltage (OCV), internal impedance or even state of charge (SOC) between modules are usually used to form a battery pack. Parallel placed mixed battery modules are typically seen in second-life, repurposed or exchangeable battery systems to increase power and energy storage capacity of a battery pack in mobile, electric vehicle (EV) and stationary energy storage application. This paper addresses battery module heterogeneity by taking advantage of buck regulators on each battery module and formulating scheduling algorithms to dispatch the buck regulators to balance the current out of each battery module. In this way, mixed battery modules can be combined and coordinated to provide a balanced power flow and guarantee safety of the total battery pack. Both open-loop and closed-loop scheduling of buck regulated battery modules are analyzed in this paper. In the open-loop algorithm, buck regulator dispatch commands are computed based on full knowledge of the OCV and impedance of each battery module, while monitoring the load impedance. In the closed-loop algorithm, dispatch commands are generated automatically by a digital proportional-integral-derivative (PID) feedback controller for which battery module current reference signals are computed recursively while monitoring the load impedance. The closed-loop scheduling method is also validated through experimental work that simulates a battery pack with several parallel placed buck regulated battery modules. The experimental results illustrate that the current from each battery module can be rated based on the SOC of each module and that the current remains balanced, despite discrepancies between OCV and internal impedance between modules. The experimental results show that the closed-loop algorithm allows scheduling of buck regulated battery modules, even in the absence of knowledge on the variations of OCV and impedance between battery modules.

Suggested Citation

  • Yunfeng Jiang & Louis J. Shrinkle & Raymond A. de Callafon, 2019. "Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules," Energies, MDPI, vol. 12(11), pages 1-20, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2095-:d:236245
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    References listed on IDEAS

    as
    1. Deng, Yelin & Li, Jianyang & Li, Tonghui & Zhang, Jingyi & Yang, Fan & Yuan, Chris, 2017. "Life cycle assessment of high capacity molybdenum disulfide lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 123(C), pages 77-88.
    2. Bouchhima, Nejmeddine & Schnierle, Marc & Schulte, Sascha & Birke, Kai Peter, 2017. "Optimal energy management strategy for self-reconfigurable batteries," Energy, Elsevier, vol. 122(C), pages 560-569.
    3. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2016. "Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios," Applied Energy, Elsevier, vol. 177(C), pages 366-383.
    4. Cai, Y. & Ouyang, M.G. & Yang, F., 2017. "Impact of power split configurations on fuel consumption and battery degradation in plug-in hybrid electric city buses," Applied Energy, Elsevier, vol. 188(C), pages 257-269.
    5. Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
    6. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    7. Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    8. Xia, Bing & Zhao, Xin & de Callafon, Raymond & Garnier, Hugues & Nguyen, Truong & Mi, Chris, 2016. "Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods," Applied Energy, Elsevier, vol. 179(C), pages 426-436.
    9. Tong, Shi Jie & Same, Adam & Kootstra, Mark A. & Park, Jae Wan, 2013. "Off-grid photovoltaic vehicle charge using second life lithium batteries: An experimental and numerical investigation," Applied Energy, Elsevier, vol. 104(C), pages 740-750.
    10. Wang, Yubo & Shi, Wenbo & Wang, Bin & Chu, Chi-Cheng & Gadh, Rajit, 2017. "Optimal operation of stationary and mobile batteries in distribution grids," Applied Energy, Elsevier, vol. 190(C), pages 1289-1301.
    11. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.
    12. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    13. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    14. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.
    15. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    16. Wang, Yujie & Chen, Zonghai & Zhang, Chenbin, 2017. "On-line remaining energy prediction: A case study in embedded battery management system," Applied Energy, Elsevier, vol. 194(C), pages 688-695.
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