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| State of health based battery reconfifiguration for improved energy effificiency |
| LeYiWang1,GeorgeYin2,YiDing3,CaipingZhang4 |
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| (1Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA;2Department of Mathematics, University of Connecticut, Storrs, CT 06269, USA;3US Army CCDC Ground Vehicle Systems Center in Warren, Warren, MI 48397, USA;4School of Electrical Engineering, Beijing Jiaotong University, Beijing, 100044, China) |
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| DOI：https://doi.org/10.1007/s11768-022-00123-w |
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| 基金项目:This work was supported in part by the Army Research Office (W911NF-19-1-0176). |
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| State of health based battery reconfiguration for improved energy efficiency |
| Le Yi Wang1,George Yin2,Yi Ding3,Caiping Zhang4 |
| (1Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA;2Department of Mathematics, University of Connecticut, Storrs, CT 06269, USA;3US Army CCDC Ground Vehicle Systems Center in Warren, Warren, MI 48397, USA;4School of Electrical Engineering, Beijing Jiaotong University, Beijing, 100044, China) |
| Abstract: |
| This paper analyzes the system-level state of health (SOH) and its dependence on the SOHs of its component battery modules.
Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth, operating
temperature, and environment conditions, capacities of battery modules decay unevenly and randomly. Based on estimated
SOHs of battery modules during battery operation, we analyze how the SOH of the entire system deteriorates when battery
modules age and become increasingly diverse in their capacities. A rigorous mathematical analysis of system-level capacity
utilization is conducted. It is shown that for large battery strings with uniformly distributed capacities, the average string
capacity approaches the minimum, implying an asymptotically near worst-case capacity utility without reorganization. It is
demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges
by using battery reconfiguration. An optimal regrouping algorithm is introduced. Analysis methods, simulation examples,
and a case study using real-world battery data are presented. |
| Key words: Battery system · State of health · Battery aging · Capacity utilization · Energy efficiency · Battery regrouping |
