Modified Chronoamperometry as an Evaluation Method for Li-Ion Batteries

Abstract

In the framework of new materials and next-generation batteries, the acceleration and quantificationof the improvements that these advances pose, have become a priority in the field. In thisregard, rate performance sits in an interesting spot. It is gaining more and more importance dueto the electrification of transportation but its quantitative analysis is usually overlooked.In this regard, recently, new methodologies have been developed to allow the quantification ofthe rate performance. [1] It has been shown that the parameters used for this quantification, canbe linked to intrinsic properties of the electrodes and materials, becoming a tool to further improvetheir understanding and optimization.[2]However, the usefulness of this methodology is damped by the slow acquisition of the ratedata by galvanostatic cycling, which at very low rates can take even weeks. This limitation leadsto reduced data density hampering the building of solid statistics to feed models.To mitigate this problem, chronoamperometry has been proposed as an accelerated methodto acquire rate-performance data.[3] It has been proved that the low current/rate section of thechronoamperometry indeed matches the data of the galvanostatic equivalent, and it has beenused as a way to characterize materials and electrodes.[4, 5] However, the high current/ratesection has proved much more challenging, showing inconsistent results across references anda not well-understood dependence on materials that manifest as another contribution that difficultthe data interpretation. [6]In this work, we focus on the high-rate behavior of the chronoamperometric response of theelectrodes. We show how this abnormal contribution can distort the fitting of the capacity-ratecurves highly affecting the quantification of the rate performance parameters. To avoid this distortion,we have investigated the origin of the contribution by electrochemical impedance spectroscopyand simulations, and developed a methodology to isolate and mitigate it. This leads tomore reliable datasets that permit very accurate fittings that match the galvanostatic data andworks for all the common LIBs materials tested.[7] These advances will surely boost the usefulnessand reliability of the technique as a way to characterize batteries.(1) Heubner, C.; L¨ammel, C.; Nickol, A.; Liebmann, T.; Schneider, M.; Michaelis, A. Journal ofPower Sources 2018, 397, Publisher: Elsevier, 11–15.(2) Tian, R.; Park, S.-H. H.; King, P. J.; Cunningham, G.; Coelho, J.; Nicolosi, V.; Coleman, J. N.Nature Communications 2019, 10, Publisher: Nature Publishing Group, 1933.(3) Heubner, C.; Seeba, J.; Liebmann, T.; Nickol, A.; B¨ orner, S.; Fritsch, M.; Nikolowski, K.;Wolter, M.; Schneider, M.; Michaelis, A. Journal of Power Sources 2018, 380, 83–91.(4) Pinilla, S.; Ryan, S.; McKeon, L.; Lian, M.; Vaesen, S.; Roy, A.; Schmitt, W.; Coleman, J. N.;Nicolosi, V. Advanced Energy Materials 2023, 2203747.(5) Tian, R.; Alcala, N.; O’Neill, S. J.; Horvath, D.; Coelho, J.; Griffin, A.; Zhang, Y.; Nicolosi, V.;O’Dwyer, C.; Coleman, J. N. ACS Applied Energy Materials 2020, DOI: 10.1021/acsaem.0c00034.(6) Tian, R.; King, P. J.; Coelho, J.; Park, S. H.; Horvath, D. V.; Nicolosi, V.; O’Dwyer, C.; Coleman,J. N. Journal of Power Sources 2020, 468, DOI: 10.1016/j.jpowsour.2020.228220.(7) Manuscript under preparation.