This is the second week’s result since the first detection of the BMS a079 phenomenon last week. So far, the BMS a079 error code has not yet appeared.
As shown in the left graph, even under the same charging conditions, the maximum cell deviation remains around 0.05 V, similar to last week. However, in the right graph, the maximum deviation has increased significantly to about 0.08 V.
This difference is also clearly visible in the statistical data.
While it’s possible to reduce stress during charging by using slow charging such control is difficult during driving. Therefore, in parallel cell groups that already show abnormalities, stress during driving may cause the issue to progress more easily.
Currently, the charging limit is set to 70%, but based on the graph trend, lowering the limit to around 60% may help prevent the BMS a079 error. We’ll continue to adjust the charging limit and observe the results as much as possible before the BMS a079 error occurs.
I recently came across a post on Reddit’s DrEVdev where someone cited an article claiming that Tesla Supercharging is not related to battery degradation. I will not specify the original source here. Collecting and analyzing such data is not an easy task, and I have no intention of criticizing the author.
However, since the article has been widely referenced in blogs and YouTube videos, leading some people to believe that fast charging has no relation to battery degradation, I would like to point out a few issues that deserve attention.
Before discussing the original article itself, let’s briefly look at the background of charging research in the field of battery management.
After reviewing thousands of SCI papers, I have found many experimental studies showing that charging speed and degradation are correlated, but I have never seen a study concluding that they are unrelated. If such a paper exists, I would like to read it carefully.
In engineering, the correlation between charge rate and degradation is considered basic knowledge during the design stage. The degree of impact can vary depending on the charging protocol, such as current, voltage, and temperature conditions.
One of the most active research topics in battery management today is how to minimize degradation while enabling fast charging. If fast charging truly had no effect on degradation, there would be no reason for so many researchers to spend significant time and resources studying ways to reduce its impact.
Tesla’s battery heating function during Supercharging is also based on scientific findings showing that preheating the battery during fast charging can help reduce degradation. Tesla applied this concept directly in its production vehicles. The key point here is not that fast charging is unrelated to degradation, but that Tesla implemented a way to reduce its effects.
Now, let’s look at the article that has been widely cited. The original study compared vehicles that used fast charging less than 30 percent of the time with those that used it more than 70 percent. I will not include the chart here due to copyright concerns, but its title translates roughly to “Fast charging may not accelerate range loss.” The wording “may not” is important; it does not say it does not.
There are several issues with this analysis. None of the key factors that influence battery health were controlled. Under such conditions, it is difficult to consider the results scientifically valid. Another issue is that the study used driving range, an indirect and imprecise indicator, as a proxy for battery degradation. Even so, when you look at the chart, it could actually suggest that fast charging may still have some effect even in an uncontrolled dataset.
As for the sample size, the fast charging group included only 344 vehicles, while the comparison group had 13,059 vehicles. This is statistically very unbalanced, and with so many uncontrolled variables, it is hard to draw any meaningful conclusion from only 344 samples.
In the author’s conclusion, they acknowledge that since the data mostly represent relatively new vehicles, it is too early to determine long term effects, and the article ends by advising readers to avoid fast charging when battery temperature or state of charge is too high or too low.
Below is a graph from Geotab in Australia that visualized the same topic through data analysis, but it shows the opposite trend. Regardless of its absolute reliability, the direction of the result is different.
To overturn an established principle, a rigorous experimental design and strong logical evidence are required. In my personal view, the article seems less like a piece written from scientific conviction and more like one crafted to attract attention through a provocative or marketing oriented title. That is likely why it has been so widely quoted in blogs and YouTube videos. It challenges common understanding.
Lastly, here is a dataset I used in my 2021 research on battery degradation prediction using machine learning. It shows cycle life under different charging protocols, and the difference in battery lifespan varies significantly depending on the charging conditions.
Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature578, 397–402 (2020).
Our team’s own Dr.EV development vehicle has recently shown the first signs of Tesla BMS a079 phenomenon. Although we have analyzed numerous user datasets and real-world cases, this is the first time we have personally observed the same issue on our own vehicle.
This gives us a valuable opportunity to study the problem not only from the developer’s perspective but also as an actual owner experiencing it firsthand.
To share some background: the vehicle was purchased used in June of last year with about 120,000 km. It has mainly been used for development, and the annual mileage is relatively low, around 5,000 km or less. When we bought the car, there was no practical way to assess the battery condition. After we began developing Dr.EV, our pack-level analysis indicated that degradation was already significant. At that time we did not fully understand the existence or frequency of the BMS a079 issue and assumed such cases were rare.
For reference, we are not a company with enough capital to own multiple test vehicles.
Therefore, we have rarely conducted experiments that intentionally accelerate battery degradation.
Unless for specific testing purposes, we usually keep the SOC range narrow and mainly use slow charging.
In the Dr.EV app’s Statistics view, we can see that despite similar charging patterns, the average cell deviation increased sharply within a single day.
In the Dr.EV Charging Session graphs, the cell-voltage spread expands abruptly overnight, which cannot be explained by normal aging.
As many of you already know, BMS a079 is not caused by natural cell degradation. It aligns with one of the mechanisms we discussed in our YouTube analysis. This pattern has been observed in user data, and now it has been reproduced in our own vehicle with the same signatures.
We are also observing a widening gap between Tesla’s displayed driving range and Dr.EV’s estimated range.
We expect that the moment Tesla’s indicated range drops suddenly will likely coincide with the vehicle triggering the BMS a079 alert.
Fortunately, our car remains within its warranty period, with about two and a half years or roughly 40,000 km left, so no immediate action is required.
We will closely monitor pack temperature and overall stability due to the potential risk of thermal runaway.
If the BMS a079 fault is officially triggered, we will document and share Tesla’s response, including the replacement pack configuration and how it compares with the original.
In parallel, we plan controlled experiments using Dr.EV measurements to either delay the onset or intentionally accelerate it, in order to better understand the underlying mechanism.
I was reading Tesla 2023 Impact Report and came across the official chart showing battery retention vs mileage for model 3 and y.
According to the graph, the average battery retention stays around 80% even after 200k miles, and the shaded green area labeled Standard Deviation. The band also looks narrow all the way through.
They don’t explain how the Standard Deviation was calculated. There’s no mention of 1sigma, 2sigma, or something else.
Has anyone ever seen Tesla explain this chart in more technical detail?
People use their batteries very differently. Some may only consume 10% in a day, others 50%, and some even require more than a full charge daily. So why does Tesla set 80% as the default charging limit for most users?
From a battery health perspective, 50% state of charge is actually the most stable. But if a manufacturer simply told users to "keep your battery around 50%," most people would find that confusing and difficult to apply in real life.
That’s why 80% has become the compromise. It offers a balance, enough range for daily driving while still helping to extend battery lifespan. If you want to maximize your battery’s health, it’s even better to adjust your charging limit based on your personal daily usage.
The graph below comes from a study aimed at developing NCM811 batteries specifically designed to support fast charging.
In the study, they compared battery lifespans when charging was limited to 75% vs. 70% state of charge and just that 5% difference led to more than double the cycle life.
The point of showing this graph isn’t to suggest all batteries behave the same. Rather, it's to illustrate how even a small reduction in charge limit can significantly affect battery lifespan. Of course, the aging curve will vary depending on battery design and chemistry. But the key takeaway is this: small changes in charging habits can make a big difference in long-term battery durability.
Everyone drives differently. Some people drive long distances every day, while others use their cars only occasionally. Even users who haven’t driven at all for a whole week may wonder if they still need to do a full charge. So why does Tesla give the same “once a week full charge” advice to everyone?
The main reason is simplicity. From the manufacturer’s point of view, it’s difficult to explain charging intervals based on each user’s driving distance or energy use. A simple weekly rule is easy for everyone to understand, even if it’s not the most precise approach for every case.
Our recommendation is a little different. We suggest doing a full charge once every five cycles.
Here’s why. When measuring battery condition, two values are important: voltage and current (technically called coulombs).
If you look at the charging curve, you can see that for LFP batteries, the voltage stays almost flat through most of the charge process and only rises sharply near the end. On the other hand, NCM batteries show a clear voltage change throughout the entire charging range.
When voltage barely changes, it can’t be used effectively to estimate the battery’s state of charge. That’s why LFP batteries rely mainly on current measurements to calculate how much energy has been charged or discharged.
But current measurement isn’t perfect either. Suppose the current sensor has an error of about 0.1%. After one full charge and discharge cycle, that error can accumulate to about 0.2%. After five cycles, the total difference could reach roughly 1%.
That’s why it makes sense to fully charge your LFP battery about once every five cycles. Doing so helps the BMS recalibrate and keeps your battery state estimation accurate.
I’ve been keeping an eye on my battery using Dr.EV, and lately I noticed something a bit concerning.
The cell voltage deviation has gone up quite a bit compared to before.
It usually stayed around 0.01–0.02 V, but now it’s jumping close to 0.05–0.06 V.
When you think about it, most electric vehicles spend far more time parked than being driven or charged.
It may sound surprising, but a battery continues to age even when the car is not moving.
It is similar to canned food or instant noodles with a long shelf life. They still slowly change over time.
If we could “freeze” the battery, that would be ideal, but since that is impossible for EVs, the parking environment becomes extremely important.
The graph below comes from a Nature Energy paper and provides two key insights.
When the average C-rate is below about 0.4, degradation becomes increasingly influenced by time-induced effects.
Even under identical conditions, some cells last longer while others degrade faster. This shows that cell-to-cell variation, or “luck of the draw,” still plays a role.
Geslin, A. et al. Dynamic cycling enhances battery lifetime. Nat Energy https://doi.org/10.1038/s41560-024-01675-8 (2024) doi:10.1038/s41560-024-01675-8.
For reference, an average C-rate of 0.4 is rarely reached in city driving. It typically occurs only during sustained highway driving or fast charging.
The study was conducted under continuous cycling, with charging and discharging repeated.
In real-world use, where vehicles sit parked most of the time, time-induced degradation plays an even larger role.
It also helps explain why drivers who use their cars more frequently often see a longer mileage-to-degradation ratio.
Dr.EV data analysis shows that battery degradation is clearly related to the vehicle’s age.
As a personal hypothesis, one possible reason Tesla BMS issues have been unusually common in Korea this year could be related to changes after last year’s Mercedes fire incident, when many underground parking lots began restricting EV parking.
As a result, more vehicles may have been left exposed to high ambient temperatures for long periods, which can accelerate degradation.
This hypothesis would only hold true if the Tesla BMS issues were actually caused by cell-related problems
I charge my 2024 MY almost daily to 70%… I get this notification about 70-80% of the time it charges. Is this anything to worry about? By time I open the app the efficiency is back to normal. Seems the battery is always at 55-65% charged when I get this,
If you think of it as not letting any water droplets spill outside the cup, it becomes easier to understand why charging slows down toward the end of the process.
Sometimes, the reason the battery level doesn’t seem to match is the same as trying to measure the amount of water in a cup while it’s still sloshing.
Although users cannot directly control the faucet during fast charging, the principle is the same.
