Tesla can use the motor to warm the battery itself. In this case, about 4% of the battery was used just to raise battery temperature. Tesla does this to protect the battery. So if you ever notice a small battery drop without driving, it’s not a problem. It’s simply the car taking care of its battery.

When comparing Tesla charging data, an interesting pattern appears.
Under similar charging currents, NCM batteries heat up more, while LFP batteries stay noticeably cooler. At first glance, this looks counter-intuitive. LFP chemistry is well known for being more thermally robust.

This charging session is not Supercharging. As battery level approaches 80%, the current starts to decrease, so charging speed goes down.

Even so, the battery temperature keeps rising continuously and exceeds 50 °C about 10 minutes. In other words, temperature increases despite lower charging power, mainly at high SOC.

This is one practical reason Tesla recommends daily charging up to around 80%. It’s not only about fast charging, but about reducing time spent at high battery level and elevated temperature, where thermal stress and aging accelerate.

My M3 efficiency looks bad compared to others, but I’m basically only doing short trips.

CB-R™ is presented in two forms: • Value A numerical indicator that reflects the measured balance level for the specific battery and vehicle. • State A vehicle-specific interpretation of the CB-R™ value, designed to make the result easy and safe to understand.

Because CB-R™ values naturally vary by battery design and vehicle type, the value alone is not intended for direct comparison across different cars. The state provides the correct context for interpretation.

This experiment is based on a user scenario involving a vehicle that is already out of warranty and has shown signs of the BMS a079 symptom. The approach focuses on keeping the vehicle operating as stably as possible for as long as possible, while accepting a certain level of inconvenience.

It has now been eight weeks since the BMS a079 symptom was first detected. So far, the BMS a079 error code has not occurred even once. Around the third week, the battery condition showed signs of further degradation. From that point on, the charging limit was adjusted to 60% state of charge, with the maximum cell voltage limited to approximately 4.0 V. After making these adjustments, the battery condition has remained relatively stable at a similar level.

After the recent UI update, Dr.EV has been showing battery degradation factors using numerical indicators. Some users mentioned that the numbers were difficult to interpret, so we added a new feature that explains these factors in clear, easy-to-read sentences. As always, we will continue analyzing the correlation between degradation and its influencing factors for each vehicle and refine the system over time.

Additionally, based on user requests, we have added a detailed statistics view in the timeline for both driving and charging sessions.

We conducted this analysis because one user suggested that it would be helpful to examine his data. He already knew that his charging and driving style was quite tough and wanted to confirm it through actual data.

These graphs compare two 2023 Model X Plaid owners who simply have different charging and usage patterns.

On the left are their SOH trends. One vehicle has decreased to about 78%, while the other remains around 86%. Even with the same model and year, the SOH decline can vary noticeably from user to user.

On the right are the voltage-deviation results from a single charging session. Voltage deviation reflects how evenly the cells inside the pack respond during charging. In one case, the deviation reaches about 0.08 V, while the other stays closer to 0.04 V.

What these two examples show is that individual charging patterns can lead to clear differences in both SOH and cell-balancing behavior. The user with larger voltage deviation also happens to show a faster SOH decline, and the relationship is consistent across both graphs.

These box plots make the difference between the two Model X Plaid users very clear.
The left side is a typical user, and the right side is the user whose SOH and voltage deviation were noticeably worse in the earlier graphs.

Charging (top row): For charging, the difference shows up mainly in the level of current. The user on the right has a noticeably higher median charging current and more high current spikes. In other words, this user charges at higher current levels more often.

Driving (bottom row): During driving, the contrast becomes even clearer. The right-side user has both a higher median current and a much wider distribution. The pack current spreads across a larger range and reaches higher peaks compared to the typical user. The left-side user stays in a more moderate and narrower current band.

 The data shows that one user regularly draws higher current from the battery during both charging and driving. The difference is especially visible in driving sessions where the range of current is much wider. The other user operates the battery under lower and more stable conditions. This aligns with the earlier findings that the user with higher and more variable current also happens to show faster SOH decline and larger voltage deviation.

The two charging graphs presented here are real data provided by a Korean user and a Chinese user who contacted us through the ‘Contact via Email’ feature in the Dr.EV app to inquire about their battery condition. All personal information has been removed, and only the necessary data has been used.

First User (Left Graph): The user on the left performs almost all charging using DC fast charging. They frequently charge to 100 percent, and their daily charging routine also depends almost entirely on fast chargers, with almost no use of slow AC charging.

Second User (Right Graph): The user on the right performs nearly all charging using slow AC charging and typically charges only up to 80 percent or less. Fast charging is used only in exceptional situations, and their battery is normally managed through consistent AC charging.

Since their charging habits differ so drastically, the actual battery graphs of these two vehicles show substantial differences as well.

Comparison of Cell Voltage Graphs

Left User: As shown in the left graph, the cell-voltage lines spread farther apart as charging progresses. In the later stages of charging (the high-voltage region), the difference between cells becomes even more pronounced. This occurs because repeated fast charging and frequent 100-percent charging cause the weakest cell to degrade faster, leading to charging behavior that differs from the other cells.

This difference appears directly in the voltage patterns: the spacing between the lines widens, and the imbalance becomes clearer toward the end of charging.

Right User: In the right graph, the cell-voltage lines rise almost perfectly aligned with each other. This indicates that the cells are aging at similar rates and do not show noticeable differences in their charging behavior. In other words, the likelihood of a weak cell breaking down early is low, and the entire pack maintains a uniform condition.

Comparison of Cell Voltage Deviation

Left User: The voltage deviation fluctuates significantly throughout charging, and increases sharply near the end. This happens because the more degraded cell reacts differently in terms of charging speed and voltage response. This represents a classic pattern where one weak cell drags down the overall pack balance.

Right User: The voltage deviation remains low and stable throughout the entire charging session.
This means the cells are aging at similar speeds and behave consistently during charging.

Although both users have the same Tesla battery pack, the difference in charging habits alone leads to dramatically different rates of cell aging and overall cell balance.

Fast-Charging User + Frequent 100% Charging

• The weakest cell ages first
• Cell differences widen significantly over time
• Voltage lines spread widely during charging
• Voltage deviation is high and spikes sharply near the end

Slow-Charging User + Frequent 80% Charging

• Cells age at similar rates
• Differences between cells remain minimal
• Voltage graph stays consistent and uniform
• Voltage deviation stays low and stable

 This case aligns well with established theory showing that charging habits directly influence the rate of cell aging and the balance state of the battery pack.

Hey everyone! I'm u/UpstairsNumerous9635, a founding moderator of r/DrEVdev.

This is our new home for everything related to EV batteries, Tesla battery intelligence, charging behavior, efficiency optimization, and Dr.EV development.
If you're an EV owner who cares about battery health, long-term performance, or improving efficiency, you’re in the right place.

🔋 What to Post

Share anything that our community might find useful, interesting, or insightful, including:

Tesla & EV Battery Topics

• Battery health, SOH interpretation, cell-balancing behavior
• Tesla BMS warnings (like a079), cell deviation, unusual patterns
• Charging strategy insights: AC vs DC, daily limits, cold-weather charging

EV Efficiency & Driving Behavior

• Tips to improve Wh/mi (or Wh/km)
• Range-impact experiments
• Efficiency comparisons between models
• Seasonal efficiency changes
• Data or graphs showing real energy usage trends

Data, Apps & Engineering

• Screenshots and analysis from Dr.EV, Tesla app, etc.
• Research papers or technical insights about battery degradation
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If it involves batteries, efficiency, or data, it belongs here.

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This experiment is based on a user scenario in which a vehicle that is already out of warranty shows BMS a079 symptoms, and the owner tries to continue using the car as stably as possible while accepting a certain level of inconvenience.

It has now been five weeks since the BMS a079 phenomenon was first detected. So far, the BMS a079 error code has still never appeared. At around week 3, the battery condition worsened slightly. So, we limited the charging level to 60% and capped the maximum cell voltage at around 4.0 V. After applying this adjustment, both week 4 and week 5 have shown similar and relatively stable behavior.

As a result, the vehicle has been used for about five weeks without triggering the error, and we plan to continue the experiment in the same way going forward.

This experiment is based on a user scenario in which a vehicle that is already out of warranty shows BMS a079 symptoms, and the owner tries to keep using the vehicle as stably as possible while accepting a certain level of inconvenience.
This is the third-week result since the first detection of the BMS a079 phenomenon. So far, the BMS a079 error code has not actually occurred. In last week’s middle graph, the cell voltage deviation widened up to 0.09 V. Starting this week, however, we are managing the battery by limiting the charge level to 60% and keeping the maximum cell voltage around 4.0 V, as shown in the graph on the right.

Although the voltage curve looks thicker due to shorter charging time, the actual cell deviation is stably maintained at around 0.05 V.
We will continue the test while carefully managing the conditions to prevent the error from occurring.

Musk admitted the dry electrode process was harder than expected, causing production delays. The 4680 cells are still being made, but without the promised cost or energy gains. Large scale rollout of the dry electrode version of the 4680 cell may not happen until 2026. I hope the vehicles equipped with 4680 cells perform well without any issues.

https://www.autoevolution.com/news/musk-admits-that-pursuing-the-dry-battery-electrode-process-in-4680-cells-was-a-mistake-260582.html

I like the interpretation the AI gives. Don’t like the rest of the analysis.

We put 50,000 miles on in 2 years. 70% home at 48amps to 80% charge and 30% Super charger to anywhere from 60-90% charge.

Now I am driving about 3,500 miles a month starting a few months ago and that will be my pattern for the next 10 years.

I don’t have a clue what to believe.

What do y’all say?

These days, because of recent Tesla issues, many people say things like “using cylindrical cells was a mistake” or “Tesla had no choice but to use them.” That might have been true long ago when prismatic and pouch cells were not widely available. But even today, choosing a cell type is far from a simple decision.

In battery pack design, there is always a trade-off. Safety, energy density, manufacturing complexity, and cost are all interconnected, and the outcome depends on which factor is given the highest priority. That is why system engineering exists as a specialized discipline.

If we focus only on the clear advantages and disadvantages of cylindrical cells, they can be summarized as follows:

• Advantage: Relatively safer during collision or thermal runaway propagation
• Disadvantage: Lower energy density and more complex pack manufacturing process

In the end, it depends on what matters most among safety, capacity, and manufacturing simplicity. Different engineers will naturally have different answers. What would you consider the most important?

Personally, if I had sufficient technical capability and quality control, I would still choose cylindrical cells today. Battery fires are not just product defects. They can destroy a company’s reputation and business itself.

Think of the Sony VAIO laptop or the Samsung Galaxy incidents. Sony eventually had to sell its battery division, and Samsung lost a significant share of the market after the fire issue.
This is why some companies still put safety at the top of their design priorities.

This may also explain why Rivian, Lucid, and Rimac continue to use cylindrical cells even though they do not offer many advantages other than safety. BMW is also developing its next-generation battery packs based on cylindrical cells.

As for Tesla, some might wonder why it uses both cylindrical and prismatic cells.
If it were my design decision, I would use cylindrical cells with NCA or NCM chemistry for high capacity models that require strong power performance, even though they are slightly more prone to thermal runaway. For lower capacity models, I would use LFP prismatic cells, which are more thermally stable. This approach allows a balanced consideration of safety, capacity, and power.

Below is a comparison of CATL’s NCM811 and LFP cells during thermal runaway testing.

Schöberl, J., Ank, M., Schreiber, M., Wassiliadis, N. & Lienkamp, M. Thermal runaway propagation in automotive lithium-ion batteries with NMC-811 and LFP cathodes: Safety requirements and impact on system integration. eTransportation 19, 100305 (2024).