The closed-loop optimisation (CLO) architecture:
Testing a single battery to failure under our fast-charging conditions requires approximately 40 days, meaning that when 48 experiments are performed in parallel, assessing all 224 charging protocols with triplicate measurements takes approximately 560 days.
The authors assume a constraint that only 48 experiments can run in parallel. Because of this, they need the Bayesian optimisation component in CLO to make the search faster. We actually may not have this limitation for charging profile testing, because we have lots of chambers for cycling(?) However, we will have this limitation for manufacturing, because only a very small number of cell material experiments can run in parallel. See ‣.
The authors constraint the fast-charging protocol design space in the following way: They only decide distinct C-rates for 0–20%, 20–40%, 40–60% SoC ranges (called CC1, CC2, CC3 respectively). The CC4 rate in 60–80% SoC range is determined by CC1, CC2, CC3, and the constraint that the protocol should take exactly 10 minutes for charging from 0% to 80%. The CC rate for SoC > 80% is fixed to 1C, followed by CV phase after 3.6V reached.
Most fast-charging protocols proposed in the battery literature suggest that current steps decreasing monotonically as a function of SOC are optimal to avoid Lithium plating on graphite, a well-accepted degradation mode during fast charging. In contrast, the protocols identified as optimal by CLO are generally similar to single-step constant-current charging (that is, CC1 ≈ CC2 ≈ CC3 ≈ CC4).
We hypothesize that solid-electrolyte interphase (SEI) growth, exacerbated by high temperatures, is the dominant degradation mode during these extreme operating conditions:
Battery surface temperature ("Can tempereature") vs. capacity during rate testing of the cells used in this study under charge (c) and discharge (d).
- Resistive (Ohmic) heat generation is governed by $I^2R$. We determined that the resistance is both Ohmic (linear overpotential vs. current relationship) and largely independent of SoC, particularly for minimally-cycled cells. For a constant R, $I^2$ is minimized via constant-current charging.
Discharge resistance does strongly depend on SoC: rises at low SoC (see Cell internal resistance).
The proportion of cell energy lost to Joule heating is roughly IR/V, i. e. it a linear, not a quadratic dependency on the current.
I also want to note that the authors predict cell's life from 100 fast charging cycles done in just 4 days. The discharge step (at 4C rate, i. e. 15 minutes to full discharge) follows the fast charging step with just 5 seconds of rest in between. Then the next charging loop starts after just another 5 seconds of rest! This non-stop fast charging and discharging puts the cell at the equilibrium temperature of probably at least 40 °C (the authors didn't measure the temperature during their main experiment; the above charts are from a separate lab test).
In such an experiment, the charging protocol that minimises the cell's equilibrium temperature may perform the best because accelerated parasitic reactions (such as the growth of the SEI layer on anode) due to the temperature of about 40 °C might contribute more to cell's degradation than Lithium plating.
But I wouldn't say this result refutes the "conventional battery wisdom" that Lithium plating is the leading degradation factor to look at in the design of real-life fast-charging protocols. The temperature increase from charging at, say, 6C (on the chart above; 30→36°C) doesn't look dramatic to me, assuming that the cell will have time cool down when it is discharged slowly with intermittent rest (for example, during city driving) for many hours after the fast charging. Let alone there will likely be an active cooling system in the battery, trying to keep cells' temperatures at about 30 °C (?). So the high C-rates during fast-charging shouldn't noticeably affect the long-term average temperature of the cells.
Different dot shapes indicate different charging protocols
We attribute the source of this systematic bias to additional calendar ageing, which lowers the initial discharge capacities and thus leads to larger predictions. A similar effect was presented in Supplementary Figures 6 and 7 and Supplementary Note 2 of Severson et al.
This is a strange statement because actually Severson et al. [2] observed the opposite effect: "...the calendar life effect reduces the predicted lifetime in the full model." (Supplimentary Note 2.) And here is their predictions plotted against the reality:
Each dot - one cell. Blue circle - Train, red square - Primary test, orange triangle - Secondary test (1 year later)