期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 15, 期 1, 页码 127-138出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2794997
关键词
Capacity estimation; diagnostics; Gaussian process (GP) regression; incremental capacity (IC) analysis; lithium-ion battery
类别
资金
- RCUK Engineering and Physical Sciences Research Council [EP/K002252/1, TII-17-1314]
- EPSRC [EP/P510737/1, EP/K002252/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K002252/1, EP/P510737/1] Funding Source: researchfish
Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%-3% root-mean-squared error (RMSE).
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