Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm

Abstract

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ∼6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%–2%) is required to dramatically improve the wetting (5–20 times) compared to an unstructured electrode.

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

“…So, Afzal (2021) [28] explored a unique approach, combining GAs with an in-house finite volume method (FVM) code to optimize key parameters of lithium-ion battery cells in EVs: the average Nusselt number, friction coefficient, and maximum temperature. Usseglio-Viretta et al (2023) [29] introduced a genetic algorithm to determine optimal patterns, utilizing a cost-effective proxy distance-based model. It examines wetting in coin-cell and pouchcell configurations, each with its electrolyte infiltration method.…”

The Impact of Structural Pattern Types on the Electrochemical Performance of Ultra-Thick NMC 622 Electrodes for Lithium-Ion Batteries

“…In addition, an enhanced capacity retention at high discharge/charge rates and a reduced charge transfer resistance were observed in cells containing laser-patterned Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 (NMC 532) [21], Li(Ni 0.6 Mn 0.2 Co 0.2 )O 2 (NMC 622) [22][23][24], LiCoO 2 (LCO) [25], Li(Ni 0.8 Mn 0.1 Co 0.1 )O 2 (NMC 811) [26,27], LiFePO 4 (LFP) [28], graphite [23,25,29], and Si/graphite (see reference [30] and those cited therein) using line patterns with varying pitches. In addition, alternative patterns such as throughholes in LFP cathodes [31][32][33], blind holes in graphite [34][35][36][37][38][39], and grid patterns in Si/graphite anodes [40] have also been reported. In general, the discharge capacity is enhanced for cells with laser-structured electrodes in contrast to those with unstructured electrodes when they are discharged for less than 2 h. However, the electrode material, mass loading, and pattern type influence the capacity increase.…”

Benefits of Femtosecond Laser 40 MHz Burst Mode for Li-Ion Battery Electrode Structuring

“…The impact of the pattern on the resulting performances is still ambiguous. Depending on the electrochemical modeling approach, it was shown, on the one hand, that the capability of these patterns is roughly similar [22] and, on the other hand, that hexagonal patterns are more efficient for fast charging [23]. In addition, these patterns do not present the same wetting properties and lead to different takt times [16,23].…”

“…Depending on the electrochemical modeling approach, it was shown, on the one hand, that the capability of these patterns is roughly similar [22] and, on the other hand, that hexagonal patterns are more efficient for fast charging [23]. In addition, these patterns do not present the same wetting properties and lead to different takt times [16,23]. For example, grooving parallel lines is less dependent on beam positioning precision than drilling.…”