BT2152B Self-Discharge Analyzer, BT2155A Self-Discharge Analysis Software

• Revolutionary reduction in the time required to discern good vs. bad cell self-discharge

performance in manufacturing

• Gain dramatic reductions in work-in-process, working capital, and facility costs

• Eliminate days or weeks of cell storage time

The Challenge in Evaluating Self-Discharge

• Li-Ion cell market growing fast

• Self-discharge evaluation takes a long time

• Huge impact on manufacturing inventory

The Li-Ion cell market is experiencing explosive growth, and this growth creates stress on cell manufacturing operations, with pressures on process costs, inventories, and deliveries. It’s a challenge for Li-Ion cell manufacturers to quickly discern whether newly formed cells exhibit acceptable self-discharge behavior.

Traditionally, self-discharge isn’t a complicated measurement – it’s relatively straightforward to measure how the open circuit voltage (OCV) of cells changes over time. The issue is how long it takes for that OCV to change enough to reliably tell whether the self-discharge of your cells is within acceptable limits. Cell manufacturers keep far greater numbers of cells in work-in-process inventory than they would like because of the time required to measure the change in cell OCV. That negatively impacts work-in process inventory metrics, and it consumes expensive floor space to hold that inventory in temperature controlled environments.

What is self-discharge current?

Most Li-Ion cells will gradually discharge even if they’re not connected to anything. This loss of stored energy leads to lower-than-desired available capacity from the cell. When cells are assembled into multiple-cell battery packs, differing rates of cell self-discharge leads to cell imbalances within the battery. Typical battery management systems will discharge all the cells to the level of the lowest cell, decreasing effective battery life. A simple model of self-discharge in Li-Ion cells is modelled in Figure 1.

This problem is worse for larger capacity cells, where a lot of the market growth is these days. Those

larger-capacity cells are higher-value inventory, and present more risk while sitting in inventory. Larger[1]capacity cells have longer settling times than smaller cells, which causes longer measurement times.

How Large Is This Financial Issue?

The time required to evaluate self-discharge impacts:

• Manufacturing inventory

• Working capital costs

• Facility cost and usage

• The delay in the feedback loop from test results to manufacturing process adjustments

Cell manufacturers are looking at every opportunity to reduce cell costs. Industry research shows that while materials are over half of the cost of a cell, process costs also represent a significant opportunity for improvement. Research indicates that the formation, aging, and test portion of the manufacturing process typically represents 10 – 14% of cell costs. And the aging process alone is 5 – 8% of the total cost.

What Drives the Aging Time?

Today, most of the total aging time is often caused by the time required to determine if the cells’ self discharge behavior is within acceptable limits. This large time period is driven by how long it takes for the change-in-OCV (ΔOCV) measurement. Reducing the amount of time cells spend in the aging step as work-in-process inventory provides savings that flow directly to the bottom line.

A Better Way to Evaluate Li-Ion Cell Self-Discharge

 Precision potentiostatic measurement directly measures self-discharge current.

To measure self-discharge performance, you would like to directly measure the self-discharge current of the cell. This would tell you whether the cell was good or bad much sooner than waiting for the cell’s open circuit voltage to change enough to reliably indicate good vs. bad behavior. A high-performance potentiostatic analyzer can hold the cell voltage constant and stable. However, the cell will continue to self-discharge. In the model in Figure 2, self-discharge current flows through the parallel resistance RSD, which acts to decrease the voltage on the effective capacitance of the cell. But the cell voltage is being held constant by the potentiostatic analyzer, because the analyzer supplies current to the cell equal to the cell’s self-discharge current. The analyzer accurately measures the current being supplied.

Process Improvements and Cost Savings in Self-Discharge Testing

Potential improvements in the aging process and resulting cost savings from directly measuring self discharge current can be seen by examining two models representing common aging processes in cell manufacturing. With each model, the traditional ΔOCV method is compared to directly measuring selfdischarge current for all or some of the cells.

Cost Saving Models for Working Capital and Facilities Costs

The savings from reducing the time that cells are kept in the aging process will depend on many things, including:

• The types of cells manufactured

• Cell defect rates & “suspect” rates

• Cell aging periods (at both room and elevated temperatures) required to discern good vs. bad cells

• Cell manufacturing volumes

• Cell manufacturing cost

• The target rate of return on working capital

• Floor space consumed by cell work-in-process inventory (aging areas)

• The cost of floor space

Estimating the actual cost savings from more quickly evaluating self-discharge performance has many aspects, such as:

• Reduced working capital costs for work-in-process

• Reduced facility costs (cost of space, cost of temperature control of that space)

• Reduced defects and scrap from being able to detect process deviations more quickly than waiting the traditional time required for open-circuit voltage measurements.

A simple model can compare estimated working capital and facilities cost savings resulting from reducing aging time by direct self-discharge current measurement. This allows you to rapidly discern good vs. bad self-discharge performance of cells. This is shown below for both the “Straight” Process and the “Suspect Cell” Process. The comparisons estimate the cost savings resulting from reduced aging periods by using direct self-discharge current measurement vs. the traditional ΔOCV method.

As you can see, there can be very significant working capital and facilities costs savings resulting from being able to reduce the time needed to discern good vs. bad self-discharge performance of cells. And these estimated costs are only part of the cost savings to be realized. Additional gains include:

• Reduced HVAC costs associated with reduced floor space required for aging and storage.

• Getting test results for self-discharge much more quickly creates a dramatic reduction in the time to detect manufacturing process variations from the time when those variations started. That allows you to make process corrections earlier, potentially saving significant cell scrap since you have less WIP.

Every cell manufacturer is trying to capture as much of the rapid growth of the Li-Ion cell market as possible. That places a lot of pressure on manufacturing operations to reduce their total cost envelope, and to shorten delivery times. Every manufacturer is looking to make breakthrough reductions in process costs and to reduce delivery times.

The time required to realize a payback on the investment in direct self-discharge measurement will depend on many factors that depend on the specifics of each installation. Considering that both the ΔOCV and SDM methods have roughly similar costs for fixturing, material handling, and software, the primary investment cost difference between the two methods is in the cost of the measuring equipment (precision DMM + multiplexers vs. self-discharge analyzers). The investment cost of self-discharge analyzers, combined with achievable SDM test execution times, indicates that the payback for SDM can be within 2 years, but will be highly dependent on the specifics of your process and installation. Keysight is ready to work with you on how self-discharge measurement can improve your cell manufacturing process.