Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
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DOI：
10.1109/tii.2018.2794997
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发表时间：
2019-01-01
影响因子：
12.3
通讯作者：
Howey, David A.
中科院分区：
文献类型：
--
作者：
Richardson, Robert R.;Birkl, Christoph R.;Howey, David A.
在锂离子电池应用中，准确的板上容量估计是至关重要的。电池充电/放电通常发生在恒定电流负载下，因此在这种条件下的电压与时间的测量在实践中可以是可访问的。本文提出了一种数据驱动的诊断技术，高斯过程回归原位容量估计（GP-ICE），估计电池容量使用电压测量在短时间内恒电流操作。与以前的作品，GP-ICE不依赖于解释的电压-时间数据增量容量（IC）或差分电压（DV）曲线。这克服了区分电压-时间数据的需要（放大测量噪声的过程），以及电压测量范围包含IC/DV曲线中的峰值的要求。GP-ICE应用于两个数据集，分别由8和20个细胞组成。在每种情况下，在一定的电压范围内，短短10 s的恒电流操作使容量估计具有约2%-3%的均方根误差（RMSE）。
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).