Denoising algorithms enhance self-discharge current measurements on Li-Ion cells

2020-01-13 | 4 min read

In a previous post “Keysight Solutions for Measuring Self-Discharge of Lithium Ion Cells Achieves Revolutionary Reduction in Test Time” (click on text to access) I gave an overview of the potentiostatic method of measurement that directly measures self-discharge current of a group of Li-Ion cells in as little as several minutes. This compares very favorably over the more traditional method of measuring the cells’ open circuit voltage (OCV) loss typically over 1-2 weeks, to determine the self-discharge rate of the cells. Keysight’s SDM solutions, shown in Figure 1, utilize the potentiostatic method to directly measure the self-discharge current of Li-Ion cells.

One tradeoff of characterizing the self-discharge of Li-Ion cells in dramatically less time by directly measuring self-discharge current is the data can have greater noise relative to the desired measurement. This complicates interpretation of the measurement. For direct self-discharge current measurements noise typically includes external electrical disturbances as well as short-term signal drifts induced by temperature changes. The Li-Ion cell’s temperature coefficient of voltage (TCV) can be a significant source of temperature-induced noise.

Noise removal, or denoising, algorithms applied to Li-Ion cell self-discharge current measurements can be extremely effective in removing unwanted noise and greatly improve measurement interpretation. Keysight now has a new application note available that discusses the following three different denoising algorithms as they apply to self-discharge current measurements:

Median subtraction method

Median fit method

Principal component analysis (PCA) method

The first two denoising algorithms listed are also now incorporated into the Keysight BT2152B Self-Discharge Analyzer, that processes the denoising algorithm in real-time while the measurement data is being acquired.

Using different algorithms provides a greater range of utility for different test situations. For example, the median-based methods are relatively simple, fast, robust, and require very little computational resources. They work well when there is predominantly one common noise signal present to be removed. In comparison, the PCA method is more effective when multiple noise sources are present but requires much greater computational resources. An example of denoising by using the median fit method is shown in Figure 2.

Figure 2: Denoising an SDM data set using median fit method

Keysight’s new application note goes into greater detail on these different denoising methods, including:

How they work.

How they compare to each other.

How they are applied for denoising self-discharge current measurements on Li-Ion cells.

To learn more about using denoising algorithms to enhance self-discharge current measurements on Lithium-Ion cells click on the following link “Removing Noise in Lithium-Ion Battery Cell Self-Discharge Data Sets” to download this new application note. You will be glad you did!

## Ed Brorein

## Application Specialist

Benchtop

## Denoising algorithms enhance self-discharge current measurements on Li-Ion cells

2020-01-13 | 4 min read

In a previous post “Keysight Solutions for Measuring Self-Discharge of Lithium Ion Cells Achieves Revolutionary Reduction in Test Time” (click on text to access) I gave an overview of the potentiostatic method of measurement that directly measures self-discharge current of a group of Li-Ion cells in as little as several minutes. This compares very favorably over the more traditional method of measuring the cells’ open circuit voltage (OCV) loss typically over 1-2 weeks, to determine the self-discharge rate of the cells. Keysight’s SDM solutions, shown in Figure 1, utilize the potentiostatic method to directly measure the self-discharge current of Li-Ion cells.

Figure 1: Keysight SDM solutions: BT2152B Self-Discharge Analyzer (left) and BT2155A Self-Discharge Analysis Software (right)

One tradeoff of characterizing the self-discharge of Li-Ion cells in dramatically less time by directly measuring self-discharge current is the data can have greater noise relative to the desired measurement. This complicates interpretation of the measurement. For direct self-discharge current measurements noise typically includes external electrical disturbances as well as short-term signal drifts induced by temperature changes. The Li-Ion cell’s temperature coefficient of voltage (TCV) can be a significant source of temperature-induced noise.

Noise removal, or denoising, algorithms applied to Li-Ion cell self-discharge current measurements can be extremely effective in removing unwanted noise and greatly improve measurement interpretation. Keysight now has a new application note available that discusses the following three different denoising algorithms as they apply to self-discharge current measurements:

The first two denoising algorithms listed are also now incorporated into the Keysight BT2152B Self-Discharge Analyzer, that processes the denoising algorithm in real-time while the measurement data is being acquired.

Using different algorithms provides a greater range of utility for different test situations. For example, the median-based methods are relatively simple, fast, robust, and require very little computational resources. They work well when there is predominantly one common noise signal present to be removed. In comparison, the PCA method is more effective when multiple noise sources are present but requires much greater computational resources. An example of denoising by using the median fit method is shown in Figure 2.

Figure 2: Denoising an SDM data set using median fit method

Keysight’s new application note goes into greater detail on these different denoising methods, including:

To learn more about using denoising algorithms to enhance self-discharge current measurements on Lithium-Ion cells click on the following link “Removing Noise in Lithium-Ion Battery Cell Self-Discharge Data Sets” to download this new application note. You will be glad you did!

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