Measuring Parasitic Power in Lithium-Ion Batteries Using a Battery Cycler Microcalorimeter

Cycling batteries to failure is time-consuming and hinders data analysis for developing novel battery chemistries. One ongoing problem is detecting parasitic activity, which can substantially impact the performance and durability of lithium-ion batteries.

In situ electrochemical calorimetry is the primary approach for studying these parasitic processes. The Battery Cycler Microcalorimeter solution combines sensitive isothermal microcalorimetry and electrochemical analysis.

This article outlines how the parasitic power of a Panasonic NCR18650GA cell is measured. The findings can be used to assess cell quality, aid in active material formulation, research the impact of additives, analyze the development and growth of the solid electrolyte interphase, and provide input for cycle and calendar life prediction models.

Introduction

The reversibility of the electrochemical reactions during charging and discharging significantly impacts the cycle life, efficiency, and overall quality of a lithium-ion battery (LIB).¹

Although calculating cycle life is very simple from an analytical standpoint, it represents a significant bottleneck in the testing procedure.²

Cycling a cell to failure might take months, slowing down research and delaying vital quality control information. Emerging research trends are centered on identifying diagnostic characteristics that can accurately predict long-term behavior.^2,3

A prime example is the study of parasitic processes, which have been linked to increased capacity fade, decreased coulombic efficiency, and early cell failure.^1,2,4,5

A parasitic reaction refers to any side reaction, chemical or electrochemical, that occurs within a battery. This can involve solvent breakdown, lithium plating, SEI development and breakdown, and self-discharge.⁵

The standard technique for evaluating how much energy is lost in a battery cycle is to calculate the Coulombic efficiency, with parasitic processes considered to be the source of the losses (Equation 1).

Although calculating Coulombic efficiency is useful, it accounts only for energy loss due to electrochemical side reactions. Because of the complexity and variety of parasitic reactions in a lithium-ion battery, the Coulombic efficiency does not capture the full range of chemical and electrochemical process behavior.^2,6

To completely capture parasitic reaction activity under cycle settings, a secondary analytical technique must be used with in situ electrochemistry. The primary aim for this work is to combine high-resolution isothermal microcalorimetry with existing electrochemical techniques.^1,2,4,5,7

Electrochemical calorimetry is a technique for determining the heat flow activity of a battery during active cycling. While it is a strong approach, the complexity and labor-intensive data processing have limited its practical application.

This method typically involves modifying the hardware to support the experiments, synchronizing the settings and experiment start times across two instruments with different software interfaces, combining the data files, and performing the appropriate computations before viewing the first plot.

TA Instruments' Battery Cycler Microcalorimeter solution streamlines this procedure by integrating the calorimeter and potentiostat at both the hardware and software levels.

The Battery Cycler Microcalorimeter detects real-time heat flow activity in batteries using conventional cell formats such as coin, 18650, and bag cells. Measurements can be taken under a variety of temperature and cycle circumstances.

The calorimeter and potentiostat data are timestamped to ensure that heat events and electrochemical stimulation are precisely correlated.

TAM Assistant, TA Instruments' data-gathering and analysis software, isolates the thermal contribution of parasitic processes (parasitic power) from the total heat-flow signal through a series of automated calculations.

This article describes the use of the TA Instruments Battery Cycler Microcalorimeter solution to evaluate the battery efficiency and parasitic responses of a Panasonic NCR18650GA cell.

Experimental

The Battery Cycler Microcalorimeter solution combines the BioLogic VSP-300 high-precision potentiostat with the TA Instruments TAM IV isothermal microcalorimeter.

Figure 1 shows how TAM Assistant automatically correlates heat flow events with electrochemistry in the results files by controlling both the calorimeter and the potentiostat.

Figure 1. The Battery Cycler Microcalorimeter Solution seamlessly integrates TA Instruments’ TAM IV isothermal microcalorimeter with BioLogic’s VSP-300 Potentiostat. Image Credit: TA Instruments

The TAM IV's pre-wired lifters provide electrical contact between the potentiostat and the battery while also reducing noise from ambient heat variations in the environment. Spring clips at the top (+) and bottom (-) terminals ensure a secure connection between the battery and lifter, eliminating the need for soldering or additional electrical isolation.

The four-wire connection to the battery uses a two-electrode design, with two wires for current and two for voltage sense (+/-).

The four wires connect to a keyed connector, where a cable (cut to length) will interface with the potentiostat leads. Figure 2 shows the different design components of the Macrocalorimeter lifter for 18650 battery cells.

Figure 2. Pre-wired 18650 battery lifter for the Macrocalorimeter. The cable that interfaces with the VSP 300 Potentiostat through the keyed connection is provided but not shown. Image Credit: TA Instruments

The system is calibrated using an external battery-shaped calibration heater that is specific to each lifter type. These calibration heaters have the physical dimensions of a genuine cell and use a high-accuracy 1000 Ω resistor to emit a consistent amount of heat.

TAM Assistant has a number of experiment wizards, including a calibration wizard that guides the user through the technique.

To begin calibration, place the calibration heater in the lifter and load the calorimeter according to the normal operating procedure outlined in the ‘Battery Cycler Microcalorimeter Getting Started Guide’.

Allow the calibration heater to reach thermal equilibrium with the bath temperature, then allow the baseline to stabilize. When prompted, the potentiostat will pulse the current, causing the calibration battery to emit a defined amount of heat. Figure 3 shows the results file for a gain calibration, with the voltage and heat-flow signals plotted over time.

Figure 3. Gain calibration with external battery-shaped calibration heater. Image Credit: TA Instruments

This calibration technique returns data for both gain and offset. Before shipping, each empty calorimeter undergoes temperature-dependent calibration at a TA Instruments laboratory. Any changes to the calorimeter arrangement, including the insertion of the lifter, cause a variation from the calibrated value.

The gain factor compensates for these variances, resulting in a gain constant specific to the user's setting. The offset is the deviation of the baseline signal from zero, and it is automatically adjusted after calibration.

Determination of Parasitic Heat on an 18650 LIB

The TAM IV bath was set to 40 °C and stabilized for 24 hours. A 3400 mAh Panasonic NCR18650GA LIB cell was placed in the battery lifter and inserted into the calorimeter, following the normal procedure outlined in the provided guide.

The battery was precycled between 3 and 4.2 V at 250 mA for 10 cycles to condition it for testing (10 to 20 cycles are suggested).

Following that, there was a 24-hour rest to achieve thermal equilibrium and settle the battery chemistry. To get the best results, monitor parasitic heat at low charge-cycle rates (C-rates).

This cell was cycled at 172 mA (C/20) for five cycles ranging from 3.0 to 4.2 V, with a one-hour rest interval between each charge and discharge. The Battery Cycler Wizard in TAM Assistant was used to program and run the experiment.

Theoretical Background

Equation 2 describes the heat flow signal during battery cycling.^1,5,7

Where:

• Q_Total is the total heat flow
• Q_Par is the parasitic power
• Q_Imp is the impedance power
• Q_Ent is the entropic power

The most important signal is parasitic power (Q_Par), the total thermal energy produced by nonreversible side reactions in a battery. To separate this signal from the overall heat flow, the impedance power (Q_Imp) and entropic power (Q_Ent) must be subtracted.

Entropic power represents the heat flow caused by reversible entropy changes. As seen in Figure 4, it often accounts for the majority of the overall heat flow during a charging or discharging cycle.

The entropic power is principally created by lithium intercalation/deintercalation and the resulting structural changes in the active materials, such as graphite layer expansion. Because these processes are reversible, the resulting heat flows are as well.

As a result, the entropic power during charging should have the same magnitude but the opposite sign to that during discharging.⁵ Equation 3 describes the sum of the integrated total heat flows over one full cycle, by removing QEnt's contribution from QTotal, leaving only Q_Par and Q_Imp.

Q is the heat-flow signal, t is time, and the subscripts d and c denote discharging and charging processes, respectively.

Figure 4. The deconvoluted heat-flow signals are shown relative to the total heat-flow signal. Image Credit: TA Instruments

Impedance power is the waste heat produced by flowing current through a resistive medium, commonly known as Joule heating. It can be estimated using Equation 4.

I is the applied current, while η is the overpotential.

The overpotential in this equation denotes the difference between the open-circuit voltage and the voltage when under load. The applied current will remain constant, but the overpotential will change according to the state of charge.

It can be measured directly by sending an open-circuit pulse at regular intervals, or by plotting voltage versus capacity and calculating the hysteresis of the charge and discharge curves. Equation 5 calculates the average impedance power throughout a whole cycle.

I is the applied current, and V is the voltage during the charging or discharging process.

This signal is always exothermic, but its contribution can be reduced by cycling at low C-rates. Once the average impedance and entropic power values have been established, Equation 6 may be used to calculate the average parasitic power per cycle.

By definition, Q_Ent,cycle will equal zero.

Results and Discussion

Figure 5 shows the unprocessed signals for heat flow and voltage. All raw signals are timestamped, allowing the electrochemical and calorimeter data to be correctly connected.

During an active experiment, raw voltage, current, and heat-flow signals can be examined; however, computed values can only be acquired after the experiment is complete.

Figure 5. TAM Assistant results file showing the time-correlated voltage and heat flow signal of a 3400 mAh Panasonic NCR18650GA LIB cell at C/20. Image Credit: TA Instruments

TAM Assistant will automatically calculate key values from this raw data and present them in a table or plot. Figure 6 depicts the plotting window, which has various x-axis options on the right and y-axis options near the top.

The software allows you to combine calculated and raw signals, overlay different cycles, and separate charge and discharge. These tools were created to optimize flexibility, speed, and convenience, allowing operators to more efficiently identify relevant trends or features in the data.

Figure 6 plots the parasitic power of the charging branch across four cycles against the relative state of charge (rSOC). The spikes at the upper and lower rSOC boundaries are artifacts arising from the inherent asymmetry of the voltage and heat-flow curves.

These are known as edge effects.⁵ A closer look at the overlaid curves reveals that the parasitic power decreases with each cycle, which is consistent with the traditional behavior of a passivation layer, such as the SEI.⁸

Figure 6. Parasitic power vs. relative state of charge with four cycles overlaid using the TAM Assistant plotting options. Image Credit: TA Instruments

The trends in the data can be observed throughout multiple cycles. Figure 7 displays the average parasitic power and Coulombic efficiency throughout four cycles. According to prior research,⁵ when parasitic power diminishes, Coulombic efficiency increases.

This corresponds to theoretical predictions because they are measuring the same event from opposite perspectives.

The Coulombic efficiency measures electrochemical efficiency, whereas parasitic power measures inefficiency, which includes both chemical and electrochemical side reactions. It is good practice to monitor the Coulombic efficiency since it can be used to validate thermal data, as shown in Figure 7.

Figure 7. The average parasitic power (top) and Coulombic efficiency (bottom) vs. cycle number. Values used to make this plot were automatically calculated by TAM Assistant and presented in a table. Image Credit: TA Instruments

Conclusions

Quantifying parasitic activity is crucial for assessing the efficiency, quality, and underlying chemistry of lithium-ion batteries. A TA Instruments Battery Cycler Microcalorimeter Solution was used to analyze parasitic power in a Panasonic NCR18650GA cell.

TAM Assistant simplifies the integration and control of the thermostat, calorimeters, and potentiostat, increasing the practical utility and usage of battery calorimetry. Over numerous cycles, patterns such as rising Coulombic efficiency and decreased parasitic power are observed.

This data can help researchers develop new active material formulations, demonstrate the effect of additives on parasitic activity reduction, examine the SEI, and screen out cells with greater than usual parasitic activity during quality control.

References and Further Reading

1. Krause, L.J., Jensen, L.D. and Dahn, J.R. (2012). Measurement of Parasitic Reactions in Li Ion Cells by Electrochemical Calorimetry. Journal of The Electrochemical Society, 159(7), pp.A937-A943. DOI:10.1149/2.021207jes. https://iopscience.iop.org/article/10.1149/2.021207jes/meta.
2. Burns, J.C., et al. (2013). Predicting and Extending the Lifetime of Li-Ion Batteries. Journal of The Electrochemical Society, 160(9), pp.A1451-A1456. DOI:10.1149/2.060309jes. https://iopscience.iop.org/article/10.1149/2.060309jes/meta.
3. Xiong, W., et al. (2023). Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves. Journal of Energy Storage, 62, p.106790. DOI:10.1016/j.est.2023.106790. https://www.sciencedirect.com/science/article/pii/S2352152X23001871.
4. Downie, L.E., Hyatt, S.R. and Dahn, J.R. (2015). The Impact of Electrolyte Composition on Parasitic Reactions in Lithium Ion Cells Charged to 4.7 V Determined Using Isothermal Microcalorimetry. Journal of The Electrochemical Society, 163(2), pp.A35-A42. DOI:10.1149/2.0081602jes. https://iopscience.iop.org/article/10.1149/2.0081602jes/meta.
5. Krause, L.J., Jensen, L.D. and Chevrier, V.L. (2017). Measurement of Li-Ion Battery Electrolyte Stability by Electrochemical Calorimetry. Journal of The Electrochemical Society, 164(4), pp.A889-A896. DOI:10.1149/2.1651704jes. https://iopscience.iop.org/article/10.1149/2.1651704jes/meta.
6. An, S.J., et al. (2016). The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling. Carbon, 105, pp.52–76. DOI:10.1016/j.carbon.2016.04.008. https://www.sciencedirect.com/science/article/pii/S0008622316302676.
7. Kunz, A., et al. (2022). Time-Resolved Electrochemical Heat Flow Calorimetry for the Analysis of Highly Dynamic Processes in Lithium-Ion Batteries. Journal of The Electrochemical Society, 169(8), p.080513. DOI:10.1149/1945-7111/ac8625. https://iopscience.iop.org/article/10.1149/1945-7111/ac8625/meta.
8. Luchkin, S.Y., et al. (2020). Solid-electrolyte interphase nucleation and growth on carbonaceous negative electrodes for Li-ion batteries visualized with in situ atomic force microscopy. Scientific reports, DOI:10.1038/s41598-020-65552-6. https://www.nature.com/articles/s41598-020-65552-6.

Acknowledgments

Produced using materials originally authored by Jeremy May, PhD, from TA Instruments.

This information has been sourced, reviewed and adapted from materials provided by TA Instruments.

For more information on this source, please visit TA Instruments.