R2 Highway Range at 70MPH = ~250 Miles (Modeled)

[tivoboy]

This is a longwinded post but the short version is I figured it was pretty straightforward to build a decent model to predict highway range. So I got together with Claude and Gemini and we did. I did this for another reason but then ran R2 through it and thought you folks would find it useful. The result is this: an AWD Launch Edition with 21" road tires should deliver ~250 miles of highway range at 70mph sea level in good weather (note Out of Spec at higher altitude add ~20 miles). This is pretty competitive with Model Y (Motortrend just benchmarked it at 252 which is consistent with the model).

I've summarized the model details below and attached the full writeup in pdf. It is sensitive to input data so that as always is the key caution.

EDIT - Updated the EPA estimate to 335 from 330 (no impact on model)

Here is the modeled 2026 Model Y for comparison (AWD 20" wheels)

The Calculation
1 Mechanical Power Requirements
The mechanical power required at the wheels to sustain a constant velocity is the sum of aerodynamic drag power and rolling resistance power.
Aerodynamic drag power, which overcomes air resistance, scales with the cube of velocity:
P_aero = ½ × ρ × v³ × Cd × A
Rolling resistance power, which overcomes tire deformation and surface friction, scales linearly with velocity:
P_rr = m × g × Crr × v
Where ρ is air density (kg/m³), v is velocity (m/s), Cd is the drag coefficient, A is the dynamic frontal area (m²), m is total vehicle mass including payload (kg), g is gravitational acceleration (9.81 m/s²), and Crr is the rolling resistance coefficient.

2 Electrical Power Draw
The total draw from the battery pack adjusts the mechanical power requirement for drivetrain efficiency and adds the constant auxiliary power demand:
P_total = (P_aero + P_rr) / η + P_aux
Where η is the hardware-specific drivetrain efficiency and P_aux is the baseline auxiliary power draw for thermal management, computing, and HVAC systems.

3 Range Calculation
The consumption rate in Wh/mi is derived by dividing total power by velocity and converting units. The theoretical range is the usable battery capacity divided by this consumption rate, multiplied by the 0.97 real-ideal adjustment factor:
Consumption (Wh/mi) = (P_total / v) × 0.44704
Range (mi) = (E_bat × 1000 / Consumption) × 0.97
The 0.97 adjustment factor (3% haircut) was derived empirically from calibration against multiple benchmark vehicles and accounts for BMS management overhead, parasitic losses in power distribution, and minor regenerative braking losses during speed maintenance that are not captured in the steady-state power equation.

The Validation
The model was validated against two independent benchmark datasets: Arena EV (controlled-speed road tests at 56 and 81 mph in Europe) and Out of Spec Reviews (70 mph highway range tests in the United States). The steady-state physics model described in this paper provides a reliable, transparent, and validated method for predicting highway range of battery electric vehicles at constant speeds of 70–81 mph. The model achieves ±3–5% accuracy against independently measured benchmark data when environmental conditions (altitude, temperature) are accounted for.

The Critical Input Variables
The precision of the model depends entirely on the rigorous extraction of its physical constants. Generic baselines are avoided in favor of platform-specific hardware tagging and dynamic geometric extraction.
1 Aerodynamic Variables (Cd and A)
The drag area (CdA) is the primary determinant of high-speed range and requires the most critical refinement.
Drag Coefficient (Cd) is sourced from published manufacturer wind-tunnel data where available. Where manufacturers do not publish Cd (notably Rivian), values are estimated from independent CFD simulations and cross-referenced against range test results. The sensitivity of range predictions to Cd uncertainty is documented for each such vehicle.
Dynamic Frontal Area (A) is calculated from published vehicle dimensions rather than static bounding-box estimation. The formula subtracts the ground clearance void from the gross bounding box, then applies a vehicle-class fill factor:

A = [(Width × Height) − GC × (Width − 2 × TireWidth)] × FillFactor​

The ground clearance void calculation excludes the area beneath the chassis where air passes freely, while retaining the tire footprint in the blocking profile. The fill factor accounts for greenhouse taper, fascia radius, and body sculpting that reduce the actual air-blocking area below the rectangular envelope.

A key finding of this research is that the fill factor for electric vehicles collapses to two categories rather than the three or four typically used for ICE vehicles. Because every EV designer optimizes the frontal profile for efficiency (CdA equals range), even vehicles that appear boxy from the side — including the Rivian R1S, Tesla Cybertruck, and Chevrolet Silverado EV — have heavily sculpted frontal profiles. The “boxy SUV” fill factor category that exists in ICE vehicle analysis does not exist in EV analysis.

2 Drivetrain Efficiency (η)
Drivetrain efficiency is tagged according to the specific electrical architecture of the vehicle, reflecting the combined efficiency of inverter, motor, and gearbox at steady-state highway operation. Generic industry averages are rejected in favor of architecture-specific multipliers.

Examples

800V SiC + Permanent Magnet
0.92 – 0.94
BMW Neue Klasse, Lucid, Porsche PPE, Hyundai E-GMP

Optimized 400V SiC
0.90 – 0.91
Tesla Model 3/Y/S/X, CT, Rivian R2

Standard 400V Silicon IGBT
0.88 – 0.89
Rivian R1, legacy GM/Ford platforms

3.3 Rolling Resistance (Crr)
Rolling resistance is tagged according to the physical tread pattern and compound of the specific tire fitted to the vehicle, not a generic vehicle-class assumption.

Examples

Optimized EV Road Tire (19–20")
0.0080 – 0.0085
Michelin LTX, Pirelli Scorpion Zero, Tempest

Performance / Sport Summer (21"+)
0.0090 – 0.0100
Eagle F1 Asymmetric, Pilot Sport EV

Aggressive All-Terrain
0.0120+
Pirelli AT Plus, BFGoodrich Trail-Terrain

3.4 Auxiliary Power (P_aux)
Auxiliary power represents the baseline thermal, computational, and HVAC load at steady-state highway conditions in the normalized 15°C environment. Two tiers are used: 1.2 kW for highly integrated heat pump systems (Tesla Octovalve architecture), and 1.5 kW for standard EV thermal management platforms.

3.5 Battery Capacity (E_bat)
The model uses BMS-accessible usable capacity, not gross pack size. Where discrepancies exist between manufacturer specifications and measured data (e.g., from OBD scans or drive-to-zero tests), the measured value is preferred. The Out of Spec Reviews dataset provides independently measured usable capacities for many vehicles and is used as the primary reference where available.

This is a longwinded post but the short version is I figured it was pretty straightforward to build a decent model to predict highway range. So I got together with Claude and Gemini and we did. I did this for another reason but then ran R2 through it and thought you folks would find it useful. The result is this: an AWD Launch Edition with 21" road tires should deliver ~250 miles of highway range at 70mph sea level in good weather (note Out of Spec at higher altitude add ~20 miles). This is pretty competitive with Model Y (Motortrend just benchmarked it at 252 which is consistent with the model).

I've summarized the model details below and attached the full writeup in pdf. It is sensitive to input data so that as always is the key caution.

EDIT - Updated the EPA estimate to 335 from 330 (no impact on model)

Here is the modeled 2026 Model Y for comparison (AWD 20" wheels)

The Calculation
1 Mechanical Power Requirements
The mechanical power required at the wheels to sustain a constant velocity is the sum of aerodynamic drag power and rolling resistance power.
Aerodynamic drag power, which overcomes air resistance, scales with the cube of velocity:
P_aero = ½ × ρ × v³ × Cd × A
Rolling resistance power, which overcomes tire deformation and surface friction, scales linearly with velocity:
P_rr = m × g × Crr × v
Where ρ is air density (kg/m³), v is velocity (m/s), Cd is the drag coefficient, A is the dynamic frontal area (m²), m is total vehicle mass including payload (kg), g is gravitational acceleration (9.81 m/s²), and Crr is the rolling resistance coefficient.

2 Electrical Power Draw
The total draw from the battery pack adjusts the mechanical power requirement for drivetrain efficiency and adds the constant auxiliary power demand:
P_total = (P_aero + P_rr) / η + P_aux
Where η is the hardware-specific drivetrain efficiency and P_aux is the baseline auxiliary power draw for thermal management, computing, and HVAC systems.

3 Range Calculation
The consumption rate in Wh/mi is derived by dividing total power by velocity and converting units. The theoretical range is the usable battery capacity divided by this consumption rate, multiplied by the 0.97 real-ideal adjustment factor:
Consumption (Wh/mi) = (P_total / v) × 0.44704
Range (mi) = (E_bat × 1000 / Consumption) × 0.97
The 0.97 adjustment factor (3% haircut) was derived empirically from calibration against multiple benchmark vehicles and accounts for BMS management overhead, parasitic losses in power distribution, and minor regenerative braking losses during speed maintenance that are not captured in the steady-state power equation.

The Validation
The model was validated against two independent benchmark datasets: Arena EV (controlled-speed road tests at 56 and 81 mph in Europe) and Out of Spec Reviews (70 mph highway range tests in the United States). The steady-state physics model described in this paper provides a reliable, transparent, and validated method for predicting highway range of battery electric vehicles at constant speeds of 70–81 mph. The model achieves ±3–5% accuracy against independently measured benchmark data when environmental conditions (altitude, temperature) are accounted for.

The Critical Input Variables
The precision of the model depends entirely on the rigorous extraction of its physical constants. Generic baselines are avoided in favor of platform-specific hardware tagging and dynamic geometric extraction.
1 Aerodynamic Variables (Cd and A)
The drag area (CdA) is the primary determinant of high-speed range and requires the most critical refinement.
Drag Coefficient (Cd) is sourced from published manufacturer wind-tunnel data where available. Where manufacturers do not publish Cd (notably Rivian), values are estimated from independent CFD simulations and cross-referenced against range test results. The sensitivity of range predictions to Cd uncertainty is documented for each such vehicle.
Dynamic Frontal Area (A) is calculated from published vehicle dimensions rather than static bounding-box estimation. The formula subtracts the ground clearance void from the gross bounding box, then applies a vehicle-class fill factor:

A = [(Width × Height) − GC × (Width − 2 × TireWidth)] × FillFactor​

The ground clearance void calculation excludes the area beneath the chassis where air passes freely, while retaining the tire footprint in the blocking profile. The fill factor accounts for greenhouse taper, fascia radius, and body sculpting that reduce the actual air-blocking area below the rectangular envelope.

A key finding of this research is that the fill factor for electric vehicles collapses to two categories rather than the three or four typically used for ICE vehicles. Because every EV designer optimizes the frontal profile for efficiency (CdA equals range), even vehicles that appear boxy from the side — including the Rivian R1S, Tesla Cybertruck, and Chevrolet Silverado EV — have heavily sculpted frontal profiles. The “boxy SUV” fill factor category that exists in ICE vehicle analysis does not exist in EV analysis.

2 Drivetrain Efficiency (η)
Drivetrain efficiency is tagged according to the specific electrical architecture of the vehicle, reflecting the combined efficiency of inverter, motor, and gearbox at steady-state highway operation. Generic industry averages are rejected in favor of architecture-specific multipliers.

Examples

800V SiC + Permanent Magnet
0.92 – 0.94
BMW Neue Klasse, Lucid, Porsche PPE, Hyundai E-GMP

Optimized 400V SiC
0.90 – 0.91
Tesla Model 3/Y/S/X, CT, Rivian R2

Standard 400V Silicon IGBT
0.88 – 0.89
Rivian R1, legacy GM/Ford platforms

3.3 Rolling Resistance (Crr)
Rolling resistance is tagged according to the physical tread pattern and compound of the specific tire fitted to the vehicle, not a generic vehicle-class assumption.

Examples

Optimized EV Road Tire (19–20")
0.0080 – 0.0085
Michelin LTX, Pirelli Scorpion Zero, Tempest

Performance / Sport Summer (21"+)
0.0090 – 0.0100
Eagle F1 Asymmetric, Pilot Sport EV

Aggressive All-Terrain
0.0120+
Pirelli AT Plus, BFGoodrich Trail-Terrain

3.4 Auxiliary Power (P_aux)
Auxiliary power represents the baseline thermal, computational, and HVAC load at steady-state highway conditions in the normalized 15°C environment. Two tiers are used: 1.2 kW for highly integrated heat pump systems (Tesla Octovalve architecture), and 1.5 kW for standard EV thermal management platforms.

3.5 Battery Capacity (E_bat)
The model uses BMS-accessible usable capacity, not gross pack size. Where discrepancies exist between manufacturer specifications and measured data (e.g., from OBD scans or drive-to-zero tests), the measured value is preferred. The Out of Spec Reviews dataset provides independently measured usable capacities for many vehicles and is used as the primary reference where available.

You should’ve just left out the line about Claude and Gemini and posted this and people would’ve thought you were aerospace engineer or PhD in spatial and fluid dynamics!

Question though. Isn’t there an additional element in the R2 from an engineering standpoint where it either switches off one of the motors or adjusts efficiency at the motor or dare I say mechanical cluth to optimize efficiency even further at highway speeds? I remember seeing something in the initial launch video throughout the engineering piece, but I can’t recall exactly what the mechanical or electrical engineering bit was.

And I’m not sure if that type of implementation is the same in the Tesla model Y. Certainly there’s no second speed or transmission in the model Y. But lots of OEMs are moving to a highway speed, mechanical or electrical shift to optimize that highway speeds for as much efficiency as possible, where force against the frontal area is the demon.

Sponsored

 

[Alan in Tempe]

It is always *faster* to drive faster and burn more electricity and stop more often. The a fast charging session is adding electrons at a rate of 250 - 550 miles per hour. You will never save *time* by driving slower. You will definitely save a lot of energy and go father before stopping.

Well, not always. If you can make it from A to B on one charge (and charge again at the end of the trip at B) if staying under speed S, but will need to stop and charge before getting to B when driving faster than S, driving slower can get you there faster. See real example below.

...And btw, the energy shifts significantly with temperature (warmer is better) and altitude (higher is better) as those things matter quite a lot at highway speeds where it is mostly about pushing air. Except in the rain and then on a wet road suddenly rolling resistance jumps significantly and can whack your range by 10-15% even while dropping speeds.

Another BIG factor is change in altitude.

I drive from Phoenix to Patagonia, AZ 4 to 8 times/year. It is 175 miles and 3000' altitude gain, all but 15 miles freeway. In my 290 mile range Mach-e (hopefully to be replaced with an R2!), following the 75 MPH limit, I typically arrive with 10-30 mile reserve, wind and temps being the main variables, and high head winds killing range. At 73 MPH, I typically have well over 40 miles left. At 80 MPH, I would have to stop for a 10 minute charge in Tucson (add another 5 min. on/off the freeway to the charger). On the opposite trip from Patagonia to Phoenix, I can do 80 MPH the whole way and arrive with typically 80-100 mile reserve. I do have an L2 EVSE at each end!

 

[TexasBob]

Question though. Isn’t there an additional element in the R2 from an engineering standpoint where it either switches off one of the motors or adjusts efficiency at the motor or dare I say mechanical cluth to optimize efficiency even further at highway speeds? I remember seeing something in the initial launch video throughout the engineering piece, but I can’t recall exactly what the mechanical or electrical engineering bit was.

And I’m not sure if that type of implementation is the same in the Tesla model Y. Certainly there’s no second speed or transmission in the model Y. But lots of OEMs are moving to a highway speed, mechanical or electrical shift to optimize that highway speeds for as much efficiency as possible, where force against the frontal area is the demon.

Yes, you are correct — Rivian does use a physical motor disconnect clutch, and it's specifically a highway efficiency mechanism. Both R1 and R2 use it. This is buried in variable 2 - Drivetrain efficiency η. I have categorized each of the drive train efficiencies - which incorporate all steps from battery energy to power at the wheels - into the categories below.

You will note that I have placed the R2 in the middle efficiency category. What actually bumps the R2 into a higher efficiency category than the R1 is the new Maximus drive unit — specifically its hybrid Si/SiC inverter modules from Infineon, moving away from the R1's pure silicon IGBT. It's not full SiC, which is why it sits alongside Tesla in the middle tier rather than at the top with e.g. Lucid. The vehicle loses some efficiency with its 400V architecture, so we are assuming Tesla level efficiency not Lucid level. But even if it bumped to the top category, it would only move the needle by around 1% - 2% at most.

800V SiC + Permanent Magnet
0.92 – 0.94
BMW Neue Klasse, Lucid, Porsche PPE, Hyundai E-GMP

Optimized 400V SiC
0.90 – 0.91
Tesla Model 3/Y/S/X, CT, Rivian R2

Standard 400V Silicon IGBT
0.88 – 0.89
Rivian R1, legacy GM/Ford platforms

 

[mkg3]

...where it either switches off one of the motors...

And I’m not sure if that type of implementation is the same in the Tesla model Y....ighway speeds for as much efficiency as possible, where force against the frontal area is the demon.

R2 becomes RWD (unlike R1, which becomes FWD) when one of the motor is disengaged.

The thing is, in the OP's plot, it starts at 55mph cruising. The R2 drivetrain is already in a single motor configuration by then so it is, I believe, apples-to-apples comparison.

There is so much more to the actual efficiency though. Tires rotational inertia, rolling resistance, vehicle mass total unsprung mass, ground clearance all contribute towards the overall efficiency, in addition to SW power management, all things being equal.

So, physics is physics, regardless of EV or ICE, and you already know that maintaining a constant speed requires much less energy than getting up to the speed (hence highway MPG is much higher than city, where acceleration is more prone). In EVs, the current required to maintain a speed is lower too.

Model Y AWD uses both motors full time and Tesla manages the energy consumption via SW and not HW disconnect. They have said 2 speed transmission makes no sense since the electric motor can provide necessary toque and manageable power consumption. It only adds more things to go wrong and drive the cost up.

Taycan and e-Tron GT are the only two that I am aware of 2 speed tranny and both have had fair share of warrantee claims on the tranny from what I've read (I been read a quite a bit about Taycan since I am interested in picking up 2025+ CPO later this year when leases come back - these are nearly half price now). If Macan or the new Cayenne EV has them, helps with long term reliability improvements.

 

[Fmc]

Good..my bladder is good for about 200 miles anyways.

 

[McLovin]

Good..my bladder is good for about 200 miles anyways.

That’s about 50 miles more than my back. Seriously…2 hours tops and I’m having to stop & stretch. Might as well charge & pee while I’m at it…

 

[mkhuffman]

Well, not always. If you can make it from A to B on one charge (and charge again at the end of the trip at B) if staying under speed S, but will need to stop and charge before getting to B when driving faster than S, driving slower can get you there faster. See real example below.

Another BIG factor is change in altitude.

I drive from Phoenix to Patagonia, AZ 4 to 8 times/year. It is 175 miles and 3000' altitude gain, all but 15 miles freeway. In my 290 mile range Mach-e (hopefully to be replaced with an R2!), following the 75 MPH limit, I typically arrive with 10-30 mile reserve, wind and temps being the main variables, and high head winds killing range. At 73 MPH, I typically have well over 40 miles left. At 80 MPH, I would have to stop for a 10 minute charge in Tucson (add another 5 min. on/off the freeway to the charger). On the opposite trip from Patagonia to Phoenix, I can do 80 MPH the whole way and arrive with typically 80-100 mile reserve. I do have an L2 EVSE at each end!

He is making the point that a DCFC adds kWh faster than you can burn it at 80 mph. That is true.

 

[mkhuffman]

Thanks, nice work indeed. So I guess the EPA testing takes lower speed in town type driving more into account then a lot of highway driving.

The EPA test cycle is clearly defined. It includes city and highway driving. The average speed of the test is much slower than most people drive on the highway. I believe the EPA updated the test cycle recently so it is more realistic, but it still is not the same as driving for 2 hours at 80 mph.

The 80% factor is a pretty good one to use for most electric vehicles. There are some notable exceptions such as Porsche and maybe BMW. They tend to report conservative EPA range numbers.

 

[DCFC]

I didn't dig into the calculations the AI did, but I think something is amiss. I plotted out the numbers and the curve has the wrong inflection. As speed increases, the range should drop exponentially. As noted, the power requirement goes up to the 3rd power with velocity. I drew in a representative curve shape, ignore the position. Be careful about trusting AI.

Edit: I was wrong about the inflection of the curve

 

[Billyk24]

Interesting "report". Now we need real world data to compare. Another two months?

 

[Alan in Tempe]

He is making the point that a DCFC adds kWh faster than you can burn it at 80 mph. That is true.

What you said and what he meant is true, but that is not exactly what he said. I just pointed out an exception to what was actually said. It is a valid exception that should be considered when applicable.

 

[TexasBob]

I didn't dig into the calculations the AI did, but I think something is amiss. I plotted out the numbers and the curve has the wrong inflection. As speed increases, the range should drop exponentially. As noted, the power requirement goes up to the 3rd power with velocity. I drew in a representative curve shape, ignore the position. Be careful about trusting AI.

A couple of quick points: first this is not an ai output, it is an excel model (which i am happy to upload if you would like). Where the AI is useful is in validating the methodology and gathering the key variables from around the internet (cd, curb weight, frontal area, fill rate, etc. etc.).

That said, the curve shape is actually correct. You're right that power scales with v³, but range is energy per mile, which divides power by velocity one more time. So consumption scales with v², and range follows a reciprocal quadratic — exactly the shape the blue dots show. Your quadratic fit with R²=1.0 is actually confirming the model, not contradicting it. The red curve you drew would be correct for a power-vs-speed plot, not a range-vs-speed plot.

Power = ½ρv³CdA (aero scales with v³)

But consumption per mile = Power / velocity = ½ρv²CdA (scales with v², not v³)

And range = Battery / Consumption ≈ Battery / (av² + b)

So range versus speed follows a reciprocal quadratic, not an exponential. It's a gentle curve, exactly like the blue dots show. The quadratic fit with R²=1.0 actually confirms the model is producing the correct mathematical shape.

 

[DCFC]

I'm a bit sleepy, but check my equations:
energy = power * time, ex. 100kW x 1hr = 100kWh
time = distance/speed, ex. 50 miles / 100 mph = 0.5h
energy = power * (distance/speed)
distance = (energy/power)*speed

Power scales with speed^3 and is in the denominator. Energy= battery capacity = constant. So as speed increases, the denominator should increase faster, so the distance shall decrease?

Again, a bit sleepy, not all the neurons firing, but it didn't look correct to me at first glance.

 

[racekarl]

So, physics is physics, regardless of EV or ICE, and you already know that maintaining a constant speed requires much less energy than getting up to the speed (hence highway MPG is much higher than city, where acceleration is more prone). In EVs, the current required to maintain a speed is lower too.

What you wrote is true of ICE vehicles, but EVs do not behave the same way. While it may take more power to accelerate, EVs are able to recapture much of that when decelerating. It's the steady state power needed to overcome air resistance that is unrecoverable, and hence the main driver of EV energy use.

EVs typically get HIGHER MPGe ratings on the city cycle than the highway cycle. E.g., the Gen 1 R1T has an EPA rated MPGe of 74 city, but 66 highway.

 

[Mikey]

This is a longwinded post but the short version is I figured it was pretty straightforward to build a decent model to predict highway range. So I got together with Claude and Gemini and we did. I did this for another reason but then ran R2 through it and thought you folks would find it useful. The result is this: an AWD Launch Edition with 21" road tires should deliver ~250 miles of highway range at 70mph sea level in good weather (note Out of Spec at higher altitude add ~20 miles). This is pretty competitive with Model Y (Motortrend just benchmarked it at 252 which is consistent with the model).

I've summarized the model details below and attached the full writeup in pdf. It is sensitive to input data so that as always is the key caution.

EDIT - Updated the EPA estimate to 335 from 330 (no impact on model)

Here is the modeled 2026 Model Y for comparison (AWD 20" wheels)

The Calculation
1 Mechanical Power Requirements
The mechanical power required at the wheels to sustain a constant velocity is the sum of aerodynamic drag power and rolling resistance power.
Aerodynamic drag power, which overcomes air resistance, scales with the cube of velocity:
P_aero = ½ × ρ × v³ × Cd × A
Rolling resistance power, which overcomes tire deformation and surface friction, scales linearly with velocity:
P_rr = m × g × Crr × v
Where ρ is air density (kg/m³), v is velocity (m/s), Cd is the drag coefficient, A is the dynamic frontal area (m²), m is total vehicle mass including payload (kg), g is gravitational acceleration (9.81 m/s²), and Crr is the rolling resistance coefficient.

2 Electrical Power Draw
The total draw from the battery pack adjusts the mechanical power requirement for drivetrain efficiency and adds the constant auxiliary power demand:
P_total = (P_aero + P_rr) / η + P_aux
Where η is the hardware-specific drivetrain efficiency and P_aux is the baseline auxiliary power draw for thermal management, computing, and HVAC systems.

3 Range Calculation
The consumption rate in Wh/mi is derived by dividing total power by velocity and converting units. The theoretical range is the usable battery capacity divided by this consumption rate, multiplied by the 0.97 real-ideal adjustment factor:
Consumption (Wh/mi) = (P_total / v) × 0.44704
Range (mi) = (E_bat × 1000 / Consumption) × 0.97
The 0.97 adjustment factor (3% haircut) was derived empirically from calibration against multiple benchmark vehicles and accounts for BMS management overhead, parasitic losses in power distribution, and minor regenerative braking losses during speed maintenance that are not captured in the steady-state power equation.

The Validation
The model was validated against two independent benchmark datasets: Arena EV (controlled-speed road tests at 56 and 81 mph in Europe) and Out of Spec Reviews (70 mph highway range tests in the United States). The steady-state physics model described in this paper provides a reliable, transparent, and validated method for predicting highway range of battery electric vehicles at constant speeds of 70–81 mph. The model achieves ±3–5% accuracy against independently measured benchmark data when environmental conditions (altitude, temperature) are accounted for.

The Critical Input Variables
The precision of the model depends entirely on the rigorous extraction of its physical constants. Generic baselines are avoided in favor of platform-specific hardware tagging and dynamic geometric extraction.
1 Aerodynamic Variables (Cd and A)
The drag area (CdA) is the primary determinant of high-speed range and requires the most critical refinement.
Drag Coefficient (Cd) is sourced from published manufacturer wind-tunnel data where available. Where manufacturers do not publish Cd (notably Rivian), values are estimated from independent CFD simulations and cross-referenced against range test results. The sensitivity of range predictions to Cd uncertainty is documented for each such vehicle.
Dynamic Frontal Area (A) is calculated from published vehicle dimensions rather than static bounding-box estimation. The formula subtracts the ground clearance void from the gross bounding box, then applies a vehicle-class fill factor:

A = [(Width × Height) − GC × (Width − 2 × TireWidth)] × FillFactor​

The ground clearance void calculation excludes the area beneath the chassis where air passes freely, while retaining the tire footprint in the blocking profile. The fill factor accounts for greenhouse taper, fascia radius, and body sculpting that reduce the actual air-blocking area below the rectangular envelope.

A key finding of this research is that the fill factor for electric vehicles collapses to two categories rather than the three or four typically used for ICE vehicles. Because every EV designer optimizes the frontal profile for efficiency (CdA equals range), even vehicles that appear boxy from the side — including the Rivian R1S, Tesla Cybertruck, and Chevrolet Silverado EV — have heavily sculpted frontal profiles. The “boxy SUV” fill factor category that exists in ICE vehicle analysis does not exist in EV analysis.

2 Drivetrain Efficiency (η)
Drivetrain efficiency is tagged according to the specific electrical architecture of the vehicle, reflecting the combined efficiency of inverter, motor, and gearbox at steady-state highway operation. Generic industry averages are rejected in favor of architecture-specific multipliers.

Examples

800V SiC + Permanent Magnet
0.92 – 0.94
BMW Neue Klasse, Lucid, Porsche PPE, Hyundai E-GMP

Optimized 400V SiC
0.90 – 0.91
Tesla Model 3/Y/S/X, CT, Rivian R2

Standard 400V Silicon IGBT
0.88 – 0.89
Rivian R1, legacy GM/Ford platforms

3.3 Rolling Resistance (Crr)
Rolling resistance is tagged according to the physical tread pattern and compound of the specific tire fitted to the vehicle, not a generic vehicle-class assumption.

Examples

Optimized EV Road Tire (19–20")
0.0080 – 0.0085
Michelin LTX, Pirelli Scorpion Zero, Tempest

Performance / Sport Summer (21"+)
0.0090 – 0.0100
Eagle F1 Asymmetric, Pilot Sport EV

Aggressive All-Terrain
0.0120+
Pirelli AT Plus, BFGoodrich Trail-Terrain

3.4 Auxiliary Power (P_aux)
Auxiliary power represents the baseline thermal, computational, and HVAC load at steady-state highway conditions in the normalized 15°C environment. Two tiers are used: 1.2 kW for highly integrated heat pump systems (Tesla Octovalve architecture), and 1.5 kW for standard EV thermal management platforms.

3.5 Battery Capacity (E_bat)
The model uses BMS-accessible usable capacity, not gross pack size. Where discrepancies exist between manufacturer specifications and measured data (e.g., from OBD scans or drive-to-zero tests), the measured value is preferred. The Out of Spec Reviews dataset provides independently measured usable capacities for many vehicles and is used as the primary reference where available.

Sponsored