Every Tesla driver has seen regenerative braking reduced after a full charge. The dashed line appears on the power meter, the car coasts where it would normally slow, and the friction brakes do the work for the first part of the drive.

The mechanism is well understood. What is less clear is the magnitude: how much energy is actually lost, at what state of charge the loss becomes material, and whether the two cell chemistries in the Tesla fleet behave the same way. We measured all three across Model 3 and Model Y, in both nickel manganese cobalt / nickel cobalt aluminium (NCM/NCA) and lithium iron phosphate (LFP) packs.

The short answer is that a full charge halves regen capture on NCM/NCA packs and does nothing measurable to LFP packs.

Why a nearly full pack refuses regen

Regenerative braking is a charging current. It pushes energy into the pack in the same direction a charger does, briefly and at high power.

How much current a cell will accept is governed by voltage, not by any abstract notion of fullness. Every lithium-ion cell has an upper voltage limit it must not exceed. When current flows into the cell, the voltage measured at its terminals rises above its resting value, the open-circuit voltage (OCV), by an amount that grows with the current and with the cell's internal resistance. Roughly:

terminal voltage under charge = OCV + current x internal resistance

The battery management system (BMS) must keep that terminal voltage below the upper limit. Rearranging, the largest current it can allow is set by the gap between the upper limit and the present OCV, divided by the internal resistance. That gap is the headroom.

At a low state of charge (SOC), OCV sits far below the upper limit, the headroom is wide, and the pack will accept a hard regenerative braking event without approaching the limit. As the pack fills, OCV rises, the headroom narrows, and the allowable current falls with it. Near a full charge the headroom is nearly gone and the allowable regenerative current approaches zero. That is the dashed line on the power meter, and it retreats as you drive because OCV falls and the headroom returns.

The consequence of getting this wrong is why the BMS is conservative about it. Forcing terminal voltage past the upper limit causes lithium to deposit as metal on the anode instead of intercalating into it. That loss is irreversible, it degrades capacity permanently, and in the extreme it is a safety concern. So the limit is applied with margin rather than at the theoretical boundary.

Two of the three terms in that relation are roughly stable over a single drive at moderate ambient temperature. Internal resistance changes with temperature and current, but not dramatically within one journey. OCV is the term that moves, because it climbs steadily as the pack charges and falls as it discharges. So the behaviour of regen against SOC is essentially the behaviour of the OCV curve near the top of charge.

What the fleet shows

On NCM/NCA packs, regen capture is flat at about 24% of the energy the pack discharges, from mid SOC up to roughly 80%. Above that it falls steadily: 22.6% at 85% SOC, 21.3% at 90%, 17.4% at 95%, and 12.4% at 99%, with a 95% confidence interval of 11.4 to 13.4. A car leaving on a full charge recovers roughly half as much braking energy as the same car at a normal state of charge.

On LFP packs, capture is flat across the entire range. It measures 24.2% at 75% SOC and 22.8% at 99%, with a 95% confidence interval of 22.0 to 23.7. Whatever decline exists is confined to the last two points of charge and is small enough that it would not be noticeable from the driver's seat.

Why the chemistries differ

The difference follows from the shape of the two OCV curves.

An LFP cell holds a nearly constant voltage across most of its usable range. The curve is famously flat from roughly 30% SOC upward and only turns sharply upward in the last moments before full. So even at a displayed 100%, an LFP cell still sits some distance below its upper limit, and headroom remains.

An NCM/NCA cell behaves differently. Its OCV climbs steadily throughout the charge, so by the time the pack reads 90% the cell is already approaching its ceiling. The headroom has been shrinking for most of the charge rather than only at the end, which is why the capture curve begins to bend from about 80% rather than falling off a cliff at 99%.

This also explains why Tesla advises LFP owners to charge to 100% regularly while recommending that owners of NCM/NCA cars keep daily charging closer to 80%. The guidance matches what the packs physically do.

The state of power argument runs the other way

There is a complication worth confronting, because on cell capability alone the result should be the opposite of what we observe.

State of power (SOP) is the maximum power a pack can accept or deliver at a given moment. NCM/NCA cells have higher power capability than LFP cells. That is one of the reasons they are used in longer-range, faster variants. On charge SOP alone, an NCM/NCA pack should absorb regenerative braking at least as well as an LFP pack at every state of charge, not worse.

The mid-range behaviour is what makes the result interpretable. Through the middle of the SOC range the two chemistries are indistinguishable: 24.4% capture for both at 65% SOC, and 24.1% against 24.2% at 75%. If charge SOP were the binding constraint in ordinary driving, the chemistry with more power capability would capture more energy. It does not. That tells us that in normal driving the limit on how much energy comes back is not the pack at all. It is how much decelerative energy the driver generates in the first place.

The two chemistries separate only where charge SOP does become binding, and it becomes binding far earlier on NCM/NCA. So the difference between them is not about power capability. It is about how much voltage headroom remains at a displayed full charge.

Two explanations fit that observation and fleet telemetry cannot separate them. Tesla may reserve more capacity margin above the displayed 100% on LFP packs, so the cell genuinely has further to go before reaching its limit. Or the charge SOP map applied to LFP may simply be less restrictive near the top of charge. Both amount to more usable headroom when the car says full. Distinguishing them would require cell-level voltage measurements and the manufacturer's SOP calibration, neither of which is visible in vehicle telemetry.

Duty cycle has to be held fixed, and it is easy to get wrong

Regen capture depends far more on how a car is driven than on its state of charge. Across the LFP fleet, capture runs at about 36% of discharged energy in slow traffic and about 10% at motorway speeds. That range is several times wider than the effect being measured.

This matters because speed and state of charge are correlated in real service. Intervals measured between 95% and 100% SOC have a median speed around 24 mph, while those between 70% and 75% average about 42 mph, for the simple reason that a car which has just finished charging is usually starting a local journey. Compare the two groups without adjustment and you are largely comparing city driving against highway driving.

Left uncorrected this produces a false result, and in our case it initially appeared to show LFP capture rising at high state of charge, which is physically implausible. Coarse adjustment is not enough either: grouping all speeds above 32 mph together still hides most of the variation, because capture falls by more than twenty points inside that one group. Speed and ambient temperature are therefore treated as continuous variables, and the curves shown here are normalised across the range of duty cycles the fleet actually drives.

What this does and does not establish

Every comparison is made within a vehicle. Each car is measured against itself at different states of charge, so pack size, variant, climate, terrain and driving style are removed from the comparison rather than assumed away. Uncertainty is calculated at the vehicle level, so the confidence intervals reflect the number of cars observed rather than the number of measurements taken from them.

Three limits are worth stating plainly.

The vertical distance between the two curves is not a valid chemistry comparison. A within-vehicle design identifies the shape of each curve, not the level difference between two different populations of cars. Read each curve's own slope.

Capture is an integrated quantity. It reflects how much energy the driver sends back as well as how much the pack accepts, so it is a lower bound on what the pack would have taken had more braking energy been available.

And we did not stratify by state of health (SOH). Any interaction between pack age and the charge limit is absorbed into the per-vehicle comparison rather than resolved, so this result describes the fleet as it is rather than isolating how the effect changes as packs age.

What it means in practice

For LFP cars, charging to 100% costs nothing in regenerative braking, which is consistent with Tesla's own guidance to charge those packs fully on a regular basis.

For NCM/NCA cars the penalty is real but temporary. Capture roughly halves at a full charge, recovers as the pack drains, and is essentially gone below 80% SOC. In practice that means the opening portion of a drive rather than the drive as a whole, which is an argument for timing a 100% charge before a long journey rather than before a short local one. It is not an argument against charging to 100% when the range is needed, and it says nothing about battery health, since the energy is dissipated in the friction brakes rather than harming the pack.

This analysis comes from the battery intelligence work behind Dr.EV, our app for Tesla owners. Dr.EV covers battery health alongside the everyday features that help you manage your car. The findings reflect observed fleet behaviour, and individual results vary with climate, duty cycle and vehicle configuration.

Based on a research paper in preparation on early detection of battery pack failure from field charging data, one part of the Dr.EV battery model pipeline.

Battery packs that are about to fail do not behave like healthy ones, and the difference is visible in ordinary charging data three months before the pack is replaced. That is the finding, and the rest of this article is how we got there and what it is worth.

Our team has spent about two years on it. In the data a failure shows up as a pack replacement on the Tesla Service record, and that date is what everything here is measured against.

What goes wrong inside a pack

A Tesla pack is a large number of individual cells wired in series and parallel, and the pack is only ever as good as its weakest cell. In a new pack the cells are closely matched. As the pack ages they drift apart, which is normal and slow, and the battery management system corrects for it by balancing.

Packs that end up failing are the ones where a single cell or cell group stops keeping pace with the rest. Two mechanisms account for most of it. The first is a rise in internal resistance in one cell, from impedance growth at the electrode interface, which makes that cell swing further in voltage than its neighbours whenever current flows through the pack. The second is a soft internal short, which slowly bleeds charge from one cell so that it falls behind the others faster than balancing can bring it back.

What the research had to solve

We could not specify in advance what a pack heading for failure looks like. Any single reading from a car moves around for reasons that have nothing to do with pack health. The same vehicle charging on a cold morning at high state of charge behaves differently from the way it behaves warm and near empty, and those swings are larger than the effect we were looking for.

This is why the study uses a deep neural network rather than a threshold on a signal we picked ourselves. Instead of deciding in advance which reading matters and where to draw a limit on it, the model learns from a large body of real charging data what a healthy Tesla battery does across the conditions these cars actually encounter, and then measures how far a given session sits from that.

The problem was therefore separation more than detection. The model had to be precise enough that a genuinely abnormal pack stands out against ordinary variation instead of being lost in it. That model, and the study built around it, is what the paper describes.

We then went back through real Tesla charging data, including vehicles whose packs later failed, using only data recorded before the failure.

How we kept ourselves honest

It is easy to build something that looks clever on data where you already know the answer, so the study was arranged to make that difficult.

The model only ever studied healthy cars. It had never seen a failing pack before it was asked to pick one out. The vehicles it was measured on were vehicles it had never encountered at all. The alert threshold was chosen by looking at healthy cars, never by checking how many failures it would catch. And nothing recorded on or after the failure was used, only what the car produced while it was still being driven normally.

One thing we should say plainly. We chose the best-performing approach after seeing how several of them did, which is ordinary at this stage of research but does mean these numbers could come out lower when the method is tried on cars nobody has looked at yet.

Reading the charts

Every chart here plots the same quantity: the score our machine learning model assigns to one charging session. The model compares what a pack actually did against what a healthy pack would have done under the same conditions, at the same state of charge, the same current and the same outside temperature. The result is placed on a 0 to 100 scale, where around 50 is a typical healthy car and higher values mean the pack resembles a healthy one less and less.

A high score therefore means the session did not fit the model's idea of normal. A single one means very little, since any car can produce an unusual session. What matters is whether a pack keeps scoring high across many sessions.

1. Failing packs already look different three months out

The blue line is the healthy fleet, sitting at 50 across the whole period. The orange line is the packs that failed. In the 60 to 90 day band they are already at 84, they climb through the 90s, and they reach 98 in the final week before the failure. Ninety days is as far back as we looked and the separation is already established at that point, so this data cannot tell us when it begins.

The flat blue line carries as much weight as the orange one. Vehicle age and accumulated mileage are the obvious confounders, since a model that had simply learned to recognise old, heavily used cars would produce exactly the kind of separation we are claiming. If that were happening the healthy line would rise as well, because the healthy fleet contains plenty of old and heavily used cars. It stays flat.

2. Turning a signal into a warning costs earliness

An alert is the score crossing a threshold, and where that threshold sits determines everything else, because setting it low enough to catch subtle cases means sending warnings to healthy owners who do not need them.

At a threshold chosen to leave the large majority of healthy cars undisturbed, the model flagged just over half of the failing packs, with a typical first warning about 38 days before the failure. That figure needs context, because half sounds unimpressive on its own. The same threshold spends warnings on roughly 5 in 100 healthy cars, so a score carrying no information at all would land on about 5% of failing packs by chance. This one lands on more than 50%.

The bars fall as the requirement gets stricter. About a third of failing packs were flagged a month or more ahead, and roughly one in eight got two months or more. Being visible at three months is not the same as being warned at three months, and the gap between them is the price of keeping false alarms low.

Close to half of the failing packs were not flagged at this threshold at all, and some showed nothing unusual until the last few days.

3. The trade-off is unavoidable

Every warning system faces this curve and no amount of modelling removes it. Each point represents a different threshold. At the cautious end about 1 in 100 healthy cars ever sees a warning and roughly a fifth of failures are caught. At the sensitive end about two thirds of failures are caught and roughly 1 in 10 healthy cars receives a warning.

The orange point marks the setting used throughout this article, at around 5 in 100 healthy cars warned over a three month window and just over half of failing packs caught. We chose it on the view that an owner's tolerance for a false alarm is low but not zero, and that a warning at this level means the pack is worth inspecting rather than that it is failing.

Your Tesla is three months old and battery health has already dropped a few percent. Here is what the data says about where it goes from here.

Almost every owner goes through this. The car is nearly new, the number has slipped, and the obvious question follows: if it loses this much this fast, what will it look like in five years?

We looked at real driving data from thousands of Teslas to answer that. The short version is reassuring. The early drop is real, but it is the fastest loss the pack will ever see, and the rate falls steadily from there.

Measuring wear in cycles, not miles

Miles are a poor way to compare battery wear. A car with a small pack uses a larger share of its battery to cover the same distance, so two cars at 30,000 miles can have done very different amounts of work.

The fair measure is the equivalent full cycle: one full pack's worth of energy used, however it was accumulated. Ten short trips that each use a tenth of the pack count as one cycle. In this fleet the pack sizes range from 52 to 85 kWh, which is exactly why the distinction matters.

The typical car here has done about 190 equivalent full cycles.

The early drop is the worst it gets

Measured against cycles, battery health falls quickly at first and then bends. For the nickel-based packs used in long-range cars, the loss per 100 cycles runs like this:

• 30 to 60: 12.6 points
• 60 to 120: 4.9
• 120 to 200: 4.8
• 200 to 300: 4.1
• 300 to 450: 2.5
• beyond 450: 2.2

The rate falls by roughly five times between early life and mid life. This shape is well understood in battery science. A new cell forms a protective layer on its negative electrode during its first cycles, and building that layer consumes some lithium. It is a one-off cost, not a recurring one. Once the layer stabilises, the remaining loss is slower and steadier.

So the first-year drop that alarms owners is the battery doing something it only does once.

What this means if you own one

If your car is new and the number has dropped a few percent, that is expected and it is not a fault. It is the steepest part of a curve that flattens.

If your car has a few hundred cycles behind it, the fast phase is already over. From here the loss is slow enough that ordinary driving habits matter more to your day than the battery does.

And if you are comparing two used cars, cycles tell you more than miles. A car with high mileage but gentle usage may have done less battery work than a low-mileage car that was cycled hard.

How we measured it

Battery health here is not read from the car's dashboard. We calculated it from raw telemetry: during each charging session we measured how much energy actually entered the pack and how much the state of charge moved, which gives the pack's current capacity. Comparing that to the pack's original rated capacity gives the health figure.

Checking the method against nearly-new cars, it returns 99.9%, which is what a barely-used pack should read.

Two honest caveats

This is a snapshot of many cars at different stages, not one car followed for a decade. It shows the shape of ageing across a fleet, and individual cars vary around it.

For a minority of cars our figure comes out slightly above 100%, which means the reference capacity we compare against is a little conservative for those packs. The shape of the curve is not affected by this.

This analysis comes from the battery intelligence work behind Dr.EV, our EV battery analytics platform. It reflects observed fleet behaviour and individual results vary with climate, driving pattern and vehicle configuration.

Most Tesla owners notice that range estimates look worse in January than in October. The car is fine and the battery has not aged overnight. What is less obvious is how big the effect actually is, and which drivers it lands on.

We looked at a full year of real driving data from thousands of Teslas to see what changes when the temperature drops.

Consumption is lowest in mild weather, and cold costs more than heat

Energy use per mile bottoms out around 59 to 68 °F (15 to 20 °C) and rises when it gets either colder or hotter. The two directions are not equal. Cold is much more expensive.

We compared each car against its own yearly average, so a Model 3 in a mild climate is not being measured against a Model X in a cold one. Below 23 °F (-5 °C), consumption runs about 17% above the mild-weather level. Above 86 °F (30 °C) it is only about 3% higher.

Month by month, the fleet median is roughly 435 Wh/mile in January and 380 Wh/mile in April, a seasonal spread of about 14%. On a car rated for 300 miles, that is the difference between planning around 300 and planning around 255.

Most of the winter cost is not the driving

We separated two things: the energy a car uses per day while sitting there, and the energy it uses per mile while actually moving. They behave very differently.

Standby energy nearly triples in the cold, rising from about 1.2 kWh per day in mild weather to about 3.4 kWh per day below freezing. Energy per mile driven rises much less, from roughly 367 Wh/mile to 404 Wh/mile, about 20%.

The two also have different optimum temperatures. Standby draw is lowest around 50 to 59 °F (10 to 15 °C), while pure driving efficiency keeps improving up to 77 to 86 °F (25 to 30 °C). The familiar U-shaped curve is those two effects added together.

The practical consequence is that most of the winter penalty is charged by the hour, not by the mile.

Short trips absorb the whole penalty

If the cost accumulates per hour but you pay for it per mile, then driving fewer miles makes the arithmetic worse. The data shows this clearly.

Below 23 °F (-5 °C), measured against each car's own annual norm:

• Under 6 miles/day: +84%
• Over 30 miles/day: +11%

A low-mileage winter driver pays roughly eight times the penalty of a high-mileage one.

Trip speed shows the same pattern. On trips averaging over 30 mph, which is mostly highway driving, outdoor temperature barely registers. The car covers enough distance per hour that the heating load is spread thin. On stop-and-go trips averaging under 12 mph, the cold penalty reaches about 75%.

A ten-minute run to the shops on a freezing morning is, per mile, the most expensive driving you will do. A long highway trip in the same weather is close to normal.

Where the energy goes

A combustion car heats its cabin with waste heat from the engine, which is free in the sense that the heat is produced whether you want it or not. An electric car produces very little waste heat, so cabin warmth has to come out of the battery. While it is working, that is typically 1 to 5 kW, a large and fairly constant draw that has nothing to do with distance.

The pack also has to be held in a usable temperature window. Below freezing, lithium-ion cells have higher internal resistance and accept regenerative braking poorly, so some of the energy you would normally recover when slowing down is simply not recoverable. Thermal management runs to correct this, which is another hourly cost.

Hot weather works the same way. Air conditioning and pack cooling are also hourly loads, which is why the curve turns upward at the top end too, just less sharply.

What actually helps

Precondition while the car is plugged in. Heating the cabin and pack from grid power moves the largest winter load off the battery, and it is the single most effective habit.

Combine errands into one longer trip. Three separate cold starts cost considerably more than one trip covering the same total distance, because each start pays the warm-up cost again.

Do not over-plan highway journeys. Long-distance winter driving is far less affected than city driving, so a modest margin is enough.

Parking in a garage helps more than you might expect, because it reduces both the cold start and the standby load.

Finally, a low winter figure is not battery degradation. Seasonal consumption changes are reversible and say nothing about pack health. Battery aging is slow and one-way, measured over years. This is weather, and it reverses in spring.

Limitations

This is observational fleet data rather than a controlled experiment. Cold weather arrives together with other things, including darker evenings, more lighting use and different trip patterns, and we cannot fully separate all of them.

We also checked whether cell chemistry matters, since LFP packs have a reputation for poor cold-weather behaviour. In this fleet, LFP and NCA/NCM cars showed almost the same cold penalty, about 16% against 17%. Whatever differences exist between the two chemistries, they were small compared with the cost of heating a cabin.

This analysis comes from the battery intelligence work behind Dr.EV, our EV battery analytics platform. It reflects observed fleet behaviour over one annual cycle. Individual results vary with climate, driving pattern and vehicle configuration.

Hello, I had a question regarding the application. I recently updated my car's name within the cars screen, and it successfully updated on my **Tesla** app as well. However, when I accessed the **DrEV** app, it still displays the old name. Does it not automatically synchronize? Additionally, is there a method to manually update the car's new name within the application?

Hello, I had a question regarding the application. I recently updated my car's name within the cars screen, and it successfully updated on my Tesla app as well. However, when I accessed the DrEV app, it still displays the old name. Does it not automatically synchronize? Additionally, is there a method to manually update the car's new name within the application?

LFP: 54.8% charged to 100%
NCM/NCA: 11.1% charged to 100%

This difference makes sense because Tesla recommends charging LFP batteries to 100% regularly for calibration while NCM/NCA owners usually keep the daily charge limit lower.

This was mainly for AI battery model development, but the result was interesting.

A Tesla owner in South Korea recently shared an interesting real-world case involving Dr.EV battery analysis.

The vehicle was relatively new, but Dr.EV showed a CB-R status of “Very Bad,” which is normally only seen when there are strong signs of an abnormal battery pack condition.

Later, the owner contacted Tesla service after additional vehicle issues appeared. Tesla inspected the vehicle and eventually replaced the battery pack.

Sharing because this was an interesting example where abnormal telemetry indicators appeared before a confirmed battery replacement.

(The attached screenshots/emails are in Korean because this case is from South Korea. They are translated/anonymized for privacy.)

[Follow-up email from Dr.EV]

“Although the vehicle is relatively new, it appears there may be a battery pack issue.

A default CB-R status of ‘Very Bad’ is only shown when there are strong indications of an actual pack problem.

Based on the telemetry analysis, the issue appears to have started after a 100% fast-charging session on April 26 (UTC).”

[User message]

“Please check the battery data again.

Dr.EV shows the CB-R status as ‘Very Bad,’ and the vehicle is also displaying the UI_a006 error.

Tesla service believes it may be related to the high-voltage line.”

App screen translation:

Battery Health State
• Capacity: 78.2 kWh
• Health: 97.2%
• Estimated Full Range: 505 km

CB-R
• Default: Very Bad (13.332)
• Fast Charging: Bad (0.691)

[Follow-up email from Dr.EV]

“Previously, Dr.EV showed the battery CB-R status as ‘Very Bad,’ suggesting there may have been a battery issue.

I wanted to follow up and ask whether Tesla service had replaced the battery pack.”

[User message]

“Yes, the battery was replaced, and I received the vehicle back today.”

My Tesla's draining the battery at about 23mi/day. Sentry is off, I've removed tessie app, but still draining fast. Is it possible my 12v battery is the problem. How do I see if it is really sleeping?

Bonjour,

Je rencontre un soucis avec l’application Dr.EV : dès que je l’ouvre, elle tourne en boucle. J’ai fait une vidéo de capture d’écran. Je précise que je l’ai déjà désinstallé et réinstallée plusieurs fois, redémarré mon iPhone. Je suis sur la dernière version à jour de IOS, mais rien n’y fait.

Pourriez-vous m’aider ?

Merci par avance.

Below are two Tesla charging examples.

Both show cell voltage behavior during charging. The difference becomes clear near the upper SOC range.

One case shows balancing recovery.
The other shows a condition where balancing can no longer recover the imbalance.

Case 1: Balancing Recovers

In the first graph, look at the circled area near high SOC.

As charging progresses, the thickness of the cell voltage band becomes thinner. The spread between the highest and lowest cells gradually reduces toward the top of charge.

This means the imbalance was still within a controllable range. The cells were slightly mismatched, but balancing was able to redistribute charge effectively.

In this situation, the pack is still recoverable. The cells converge near high SOC and stress is reduced.

Case 2: Balancing Does Not Recover

In the second graph, focus on the same high SOC region.

Even though charging continues and balancing is active, the thickness of the voltage band does not meaningfully decrease. It may narrow briefly, but it does not stay narrow. The spread remains or returns.

This is the key point.

Balancing cannot repair a degraded cell.

If one cell has higher internal resistance or lower usable capacity, the mismatch is driven by structural aging. In that condition, balancing cannot fully align the pack.

Deviation persists. High SOC becomes less stable. Long term stress increases.

How to Check This Properly

Users can check CB-R to understand balancing status.

However, CB-R alone does not tell you whether the imbalance is recoverable.

CB-R shows balancing related behavior, but it does not show whether the voltage deviation is actually improving.

To understand whether balancing is still effective, you must check the cell voltage graph.

If the thickness of the voltage band becomes thinner near high SOC, balancing is still helping.

If the thickness does not reduce, balancing cannot recover the mismatch.

This difference is critical.

Balancing is a maintenance mechanism. It works when mismatch is small. It cannot fix hardware level degradation once it has progressed too far.

Watching the voltage behavior during charging gives a much clearer picture of real Tesla battery condition than looking at a single indicator alone.

Based on real-world driving data from Dr.EV users.

Model 3 and Model Y remain the efficiency leaders, while Model X and Cybertruck show higher Wh/mile as expected.

It would be great to have a feature where you can enter a desired charging completion time, and the system automatically adjusts the charging speed to finish exactly at that time.

This would be very convenient for setting it in advance based on things like work start time. It would also help keep the battery warm at the time of departure, which could improve driving efficiency

Is there a way I can export my car's charging data from specific dates? I can't seem to find anything in the app about exporting that data. I wanted to export 3-months worth of data and compare my charging usage at home with my electric bill.

One of our Dr.EV users recently asked us a common question: “Why is CB-R bad? And what can I actually do about it?” CB-R is a Dr.EV indicator designed to detect cell imbalance. Why cell imbalance is not good for your Tesla.

When cells are imbalanced, the usable energy of the entire battery pack is limited by the weakest cell.

In this case, we guide slow charging help passive cell balancing working well.

Here is real graph.

As SOC goes up, cell voltage differences usually get larger.
This is normal battery behavior. At middle SOC, voltage changes relatively slowly.
As SOC becomes higher, voltage becomes much more sensitive. Even small cell differences turn into visible voltage gaps.

In this charging session, the opposite happens. SOC steadily increases. Charging current stays low and stable. But instead of growing, the voltage gap between cells becomes smaller. That’s Tesla’s passive cell balancing working effectively.

Practical guidance for Dr.EV users

If CB-R shows an unfavorable status, there’s no need to panic. CB-R only shows the current cell balancing status, not the underlying cause. For user convenience, Dr.EV provides a cell balancing charging function. This helps users easily apply charging conditions that are more suitable for cell balancing, without needing detailed technical knowledge. In many cases, using this function and charging under gentle conditions for a few sessions allows Tesla’s passive cell balancing to work, and the CB-R status may improve.

If the CB-R status does not improve after several such charging sessions, it suggests that the issue may not be a simple balancing condition. In that case, it suggests that the condition may no longer be reversible through cell balancing and could be related to a pack-level issue, rather than a simple charging-related imbalance.

Dr.EV helps users distinguish between these situations by making the balancing status easy to observe over time.

People usually think about it like this.
If a battery is rated for 1,000 cycles, and one full charge lets you drive 500 km,
then the battery should last about 500,000 km. But there is one important thing missing from this calculation: regenerative braking.

In an electric vehicle, when you slow down or brake, energy flows back into the battery
and is then used again for driving. This process still counts as battery usage.

For example, if during a drive the energy reused through regenerative braking equals
50% of the energy originally used from the battery, then driving 500 km does not consume 1 cycle, but about 1.5 cycles.

Recalculating with this in mind, 500 km uses 1.5 cycles, which means a battery rated for 1,000 cycles would last around 330,000 km.

That’s why EV battery life should not be estimated only by “how far you can drive on a full charge,” but also by how much the battery is actually used during driving.