Lithium-ion batteries have become staples of modern electrical and electronic products; in fact, they are now used in a wide range of applications from smartphones and power tools to medical equipment and electric vehicles. Well-made Li-ion cells can easily meet the needs of smartphone, laptop and power tool applications because of their high energy density, long calendar/cycle life, and relatively low cost in comparison with other rechargeable battery technologies, such as nickel-metal hydride. However, developing cells capable of cycling with minimal capacity and energy loss for ten years as required by automotive applications or even longer for grid energy storage is far from trivial.
Designing such high quality cells can be complicated, as can the process of evaluating their performance. Cell manufacturers constantly make small changes to the cell design that can impact the cell chemistry as part of their efforts to extend the cycle life or reduce the cost without sacrificing performance. However, testing all of these different experimental cell chemistries under real-world conditions would be impractical. Consider, for example, a battery for an electric vehicle that is only cycled once per day over its ten-year lifetime; obviously, it would take far too long to determine whether the experimental changes made were beneficial and therefore should be implemented in commercial cells. No one can afford to wait years to complete the R&D feedback loop. Cell chemistry researchers need tools and techniques that allow them to perform reliable accelerated lifetime testing to determine if proposed changes extend cell lifetimes.
Accelerated lifetime testing
The most common form of accelerated lifetime testing involves high rate cycling; cells are charged and discharged at rates up to one cycle per hour (up to around 20 cycles per day, many more than would occur in actual use) in order to acquire data for hundreds or thousands of cycles over an experiment of reasonable length (several months). Over these cycles, the experimental cell's loss of capacity and energy are measured and compared with those of a control cell or a cell already in production to decide whether the experimental chemistry offers any benefits.
The degradation of Li-ion cells is not only cycle-dependent but has a strong time dependency. Smith et al. [Ref. 1] showed that cells being cycled at different low rates (either one cycle per two, four or eight days) all failed after the same amount of time, despite differing in the number of cycles completed by factors of two and four. This means that if a cell is measured to only lose 10% of its initial capacity after 1,000 cycles in two months, it does not mean that the same cell would only lose only 10% of its capacity after 1,000 cycles over three years.
Work in Dr. Jeffery Dahn’s research group in the Department of Physics and Atmospheric Sciences at Dalhousie University has suggested a new method for distinguishing between the lifetimes of different experimental cells within just a few weeks: High Precision Coulometry. Given that the side reactions within cells (that is, solid electrolyte interphase or SEI growth, electrolyte oxidation, transition metal dissolution, etc.) all involve transferring charge that is not associated with the intercalation/deintercalation of lithium from the electrodes, those reactions can be detected coulometrically. If all of the lithium stored in the negative electrode on charge was returned during the subsequent discharge, then the charge (QC) and discharge (QD) capacity would be equal, so the coulombic efficiency (CE = QD/QC) would be exactly unity and the cell should be able to cycle indefinitely. However, due to these parasitic reactions occurring within a cell, the coulombic efficiency is less than the ideal value of 1.0000 and the cell degrades. This causes a typical voltage versus capacity curve to “slip” to high absolute capacities with subsequent cycles because the discharge capacity is always less than the previous charge capacity.
Figure 1. A typical voltage vs. capacity (V-Q) plot for 13 cycles. The insets show the top of charge and bottom of discharge endpoints shifting to the right with continual cycling.
Figure 1 is a typical V-Q curve showing this type of behaviour, with the insets showing the top of charge and bottom of discharge endpoints to better illustrate the rate at which the curve slips to the right. Because the coulombic efficiency is defined as the discharge divided by previous charge capacity, it is directly related to the rate of motion of the bottom of the discharge endpoint, referred to as discharge endpoint slippage - ΔD (CE = QD/QC = 1 – ΔD/QC). The top of charge endpoint slips to higher capacity with subsequent cycling as well, referred to as charge endpoint slippage. This can be measured independently of the coulombic efficiency because it primarily relates to reactions that occur at the positive electrode [Ref. 2]. Given that parasitic reactions cause the voltage curve to slip to the right (decreasing coulombic efficiencies and increasing charge endpoint slippage), then cells with higher coulombic efficiencies and lower charge endpoint slippage rates must have lower rates of parasitic reactions and therefore should have longer cycle lives. This idea is the underlying premise for using High Precision Coulometry as a way to compare cell lifetimes in short-term experiments.