Power-Mix Optimization for a HybridUltracapacitor/Battery Pack in an Electric Vehicle

using Real-time GPS Data

Mazhar MoshirvaziriECE, University of TorontoToronto, Ontario, Canadamazhar@ele.utoronto.ca

Christo MalherbeECE, University of TorontoToronto, Ontario, Canada

Andishe MoshirvaziriECE, University of TorontoToronto, Ontario, Canada

Olivier TrescasesECE, University of TorontoToronto, Ontario, Canadaot@ele.utoronto.ca

Abstract—The objective of this work is to investigate the effectof an ultracapacitor/battery based hybrid energy storage system(HESS) in an electric vehicle (EV) prototype having a 200 kmrange. Global positioning system (GPS) data is used to enhancethe HESS power-mix optimization in real-time, based on therelative position of stop signs and traffic signals. It is shownthat the GPS information, which is already available in the car,is beneficial in managing the u-cap voltage and reducing thedynamic currents on the battery. Simulations comparing variouspower sharing algorithms show the superior performance of theGPS enhanced HESS control scheme, based on the experimentaldrive-cycle. It is shown that utilizing the GPS data in the poweroptimizer can reduce the battery’s peak charge current by 38%compared to a standard HESS.

Keywords—Electric vehicle, hybrid energy storage system,lithium-ion battery, ultracapacitor, global positioning system, stopprediction, real-time power-mix optimization, dc-dc converter.

I. INTRODUCTION

Mass adoption of Electric Vehicles (EVs) has thus far beenlimited by high cost, despite generous government incentives,and concerns regarding the long-term performance of thebattery pack. Ultracapacitors (u-caps) have symmetric inputand output specific power of 0.5-25 kW/kg, which is at leastone order of magnitude higher than typical lithium-ion (Li-Ion) based batteries [1]. U-caps also offer very low EquivalentSeries Resistance (ESR), vastly improved cycling lifetime andthus they are complimentary to batteries in high-power auto-motive applications. Hybrid Energy Storage Systems (HESS)that combine batteries and u-caps intelligently have beenmainly studied through system-level simulations [2]–[6] withreported driving-range improvements of up to 46% [3]. Themain objectives of an automotive HESS are to (1) minimizethe battery stress during rapid acceleration in order to limitlong-term capacity fading and (2) maximize the capture of theregenerative power (Regen), while reducing the wear on themechanical brakes. Accurately predicting the battery lifetimeextension due to the reduction in dynamic currents under realdrive-cycle conditions is a major challenge, and is currentlyunder testing.

As the lithium battery dominates the EV system cost,extending the pack lifetime significantly with minimum in-cremental cost helps to increase EV adoption. The negativeeffect of high charge/discharge rates on the capacity fadingwas demonstrated in [7].

The prototype EV and chosen HESS topology is shownin Fig. 1(a) and (b), respectively. The HESS includes a non-isolated bi-directional dc-dc converter between Vuc and Vbt.

The chosen architecture allows (1) effective power-sharingcontrol within the HESS, (2) flexible voltage swing and thusgood utilization of the u-cap energy, and (3) minimal numberof conversion stages from the battery to the load for highefficiency. This architecture was also used in [2] with a modelpredictive control to demonstrate the reduction of the dischargeintensity of the battery. The main HESS control objective in[8] is to set the power-mix to operate at the optimal pointof each source, minimizing the system losses and optimizingthe u-cap State of Charge (SOC). Utilizing the same HESSconfiguration, [9] investigated the benefits of two differentcontrol approaches, namely u-cap SOC control and optimalneural network control.

(a)

VbtBMS

PMSM

Vehicle CAN Bus

GPS

Vuc

FeedbackGating

==

dc-dc Inverter

=

~

FPGA CRIOCPURAM

CANAIDOSPI

IbtIuc

IloadI’uc

(b)

Fig. 1. (a) Prototype EV used in this work. The A2B weighs 750 kg with a379 kg battery pack. (b) HESS architecture.

The goal of this project is to integrate an experimentalHESS into the targeted EV prototype shown in Fig. 1(a),

known as the A2B. This paper reports the first major phase ofthe project, where the objectives are to (1) create a detailedelectro-mechanical system model using experimental drive-cycle measurements, (2) develop a new real-time power-mix optimization algorithm that leverages Global PositioningSystem (GPS) information and (3) simulate the system perfor-mance using measured drive-cycle data to quantify the currentdistribution and energy consumption.

A. EV Prototype

The EV shown in Fig. 1(a), which weighs 750 kg, was builtby Toronto Electric. The chassis and body were specificallydesigned to carry a 33.8 kWh, 300 V, 380 kg Lithium IronMagnesium Phosphate battery pack. Selected vehicle specifi-cations are listed in Table I.

TABLE I. A2B VEHICLE SPECIFICATIONS

Vehicle Value UnitMax. Vehicle Speed, νmax 116 km/hCar Mass (without HESS) 750 kgEstimated Range 210 kmGearbox Ratio, KG 9.5Wheel Radius, Rw 0.28 mBattery Pack Value UnitNumber of Series modules 24Pack Mass 379.2 kgPack Volume 241.5 LBattery Sub-Module (U24-12XP) Value UnitNominal Voltage, Vbt,nom 12.8 VModule Capacity 110 AhrModule ESR 6 mΩSpecific-Energy 89.1 Wh/kgSpecific-Power 0.432 kW/kgCycle Life (20% degradation at 0.18C) 2,800 CyclesDrive-train Value UnitMax. Motor Speed, ωmax 10000 rpmMax. Torque (continuous), TMax 65 NmMax. Torque (for 30 sec), TPeak 170 NmMax. Power (continuous), PMax 37 kWMax. Power (for 30 sec), PPeak 80 kWEfficiency at Nominal Operation 93 %Operating Voltage 220- 400 VMotor Weight 36 kgInverter Weight 34 kg

The battery pack consists of 24 Valence U24-12XP LithiumIron Magnesium Phosphate (LiFeMgPO4) battery modules [1],connected in series to generate a 307 V bus. The distributedBattery Management System (BMS) performs cell and modulebalancing. The chosen battery chemistry provides a good trade-off between cycle-life, Equivalent Series Resistance (ESR) andspecific energy. The pack has a total mass and energy of 380kg (50% of the vehicle mass) and 33.8 kWh, respectively.According to the module manufacturer, the capacity dropsby 20% after 2800 cycles with 20 A charge and dischargerate, which corresponds to 0.18 C. It is well known that thecapacity-fade under real-world conditions is a complex func-tion of temperature, time-varying current profile and depth-of-discharge [10].

II. DRIVE-CYCLE DATA ACQUISITION AND ANALYSIS

The EV is equipped with two CAN buses that are fed intothe data acquisition system. At this stage in the testing, Regen

braking is electronically disabled as a precaution to protect thebattery pack from high charge currents, increasing the pack’slifetime in the Canadian climate. A 3 hour, 61 km (roundtrip) typical urban drive-cycle was performed in downtownToronto in April 2012. All internal sub-system parameters, aswell as the GPS trajectory were recorded to develop a com-plete electro-mechanical model of the vehicle. The availablemechanical parameters are, the vehicle speed (km/h), motorspeed (rpm), motor torque (Nm), gas pedal position (%) andbrake pedal status (0 or 1). Electrical parameters available onthe CAN bus includes the battery voltage, current and SOC.

The measured vehicle speed, motor speed and motor torqueare shown in Fig. 2. The motor torque is always positive, sinceRegen is disabled. The battery voltage and current measuredby the Battery Management System (BMS), and the calculatedload power are shown in Fig. 3. This data, which is criticalfor creating the system model, is generally not available inmass production EVs. Note that the peak battery current isnear 150 A (1.36 C), which is more than 10× higher than thedischarge rate used in the capacity fade specification from themanufacturer. The battery pack SOC is shown in Fig. 4. TheEV consumed 11.97 kWh net electrical energy over 61 km.The results show an average energy consumption of 19.62 kWhper 100 km (706 kJ/km), which compares favorably with theChevy Volt [11], with an official EPA measurement of 22.4kWh per 100 km (810 kJ/km) and a much smaller 16 kWhbattery pack. Of course, this is only a basic comparison, sincethe drive-cycles and EV payloads differ.

8800 9000 9200 9400 96000

20

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Veh

icle

spe

ed (k

m/h

)

(a)

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2000

4000

6000

t (s)

Mot

or s

peed

(rpm

)

(b)

8800 9000 9200 9400 96000

50

100

150

t (s)

Mot

or T

orqu

e (N

m)

(c)

Fig. 2. Measured (a) vehicle speed, (b) motor speed and (c) motor torquefor a segment of the urban drive-cycle.

III. HESS DESCRIPTION

The designed HESS specifications are listed in Table II.The HESS is loaded by an inverter and a 37 kW rated (90 kWpeak) three-phase permanent magnet electric motor. Three u-cap modules (BMOD0165) weighing a total of 40.5 kg (11 %

8800 9000 9200 9400 9600300

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t (s)

V bt (V

)

(a)

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t (s)

I bt (A

)

(b)

8800 9000 9200 9400 96000

20

40

60

t (s)

P Load

(kW

)

(c)

Fig. 3. Measured (a) battery voltage, (b) battery current and (c) load powerfor a segment of the urban drive-cycle.

0 2000 4000 6000 8000 100000.6

0.7

0.8

0.9

1

t (s)

SOC bt

Fig. 4. Measured battery SOC during the drive-cycle.

of the battery pack) are connected in series to build a 144 V,55 F pack having an ESR of 18.9 mΩ. The u-cap pack isinterfaced to Vbus using a 30 kW digitally controlled, non-isolated bi-directional dc-dc converter. The detailed design ofthis multi-phase converter is beyond the scope of this paper.

TABLE II. HESS PARAMETERS

U-cap Pack Value UnitNumber of Series modules 3Pack Mass 40.5 kgPack Volume 43.5 LU-cap Module (BMOD0165) Value UnitNominal Voltage, Vuc,nom 48 VModule Capacitance 165 FModule ESR 6.3 mΩSpecific-Energy 3.9 Wh/kgSpecific-Power 6.77 kW/kgCycle Life (20% Capacitance degradation) 1,000,000 Cycles4 Phase DC-DC Converter Value UnitInput Voltage 0-144 VOutput Voltage 240-350 VConverter Mass 12 kgConverter Volume 13.5 LMaximum Power 30 kW

The embedded system control is split into two control-targets, contained inside a 9025 CompactRIO (CRIO) modulewith a 9114 Chassis from National Instruments. The real-

time controller features an 800 MHz processor with 4 GBof nonvolatile storage and 512 MB of DDR2 memory. The 8-slot reconfigurable embedded chassis features a Xilinx Virtex-5reconfigurable I/O (RIO) FPGA. The high-level control, whichincludes the vehicle CAN bus and GPS monitoring, street mapanalysis and power-mix calculation are done in the CPU. Thehigh-speed IGBT gating signals, digital average current-modecontrol compensator and protection functions are implementedin the FPGA for minimum latency.

A. GPS Based Power Optimizer Algorithm

Keeping the u-cap SOC, SOCuc, near an optimal levelbased on vehicle speed is one of the key controller objectives.This conflicts with the short-term optimum power-mix, basedsolely on the immediate losses. As demonstrated in [12], thedc-dc converter efficiency varies with SOCuc, which makesit more lossy (or expensive from the optimization point ofview) to draw energy from the u-cap as Vuc drops. Predictingupcoming stops is extremely valuable to manage the trade-offbetween power losses and SOCuc management. The controllerfinds the optimal u-cap current, Iuc,opt, each second, bysolving

Iuc,opt = arg min fcostIuc

, (1)

subject to the constraints

I ′uc + Ibt = Iload, (2)0.4 ≤ SOCbt ≤ 1, (3)

−110 ≤ Ibt ≤ 300, (4)0.6 ≤ SOCuc ≤ 1, (5)

−216 ≤ Iuc ≤ 216, (6)

where I ′uc is the output current of the u-cap converter and fcost

is the cost function defined by

fcost = Pbt,loss · mbt + (Puc,loss + Pconv,loss) · muc

+ Pmech,loss,(7)

where Pbt,loss = I2btRbt is the battery ESR loss, Puc,loss =

I2ucRuc is the u-cap ESR loss and Pconv,loss is the load-dependent dc-dc converter loss. The mechanical braking loss,Pmech,loss, is calculated based on the available energy fromthe braking event and the maximum energy that the HESS cansafely absorb. The constantsmuc andmbt are adaptive weightsused to influence the power-mix.

Another controller objective is to limit the peak batterycharge and discharge current to Imin and Imax, respectively,as long as the u-cap has sufficient capacity. This is doneby penalizing the higher battery peak currents by marginallyincreasing the cost of the battery contribution. The controlleroperates in five modes, based on SOCuc, as defined in Fig. 5.In each mode (1) is solved, while muc and mbt are adjustedas follows

muc ={

A1 Iuc < 0A2 0 ≤ Iuc

mbt =

⎧⎪⎪⎨⎪⎪⎩

∞ Ibt < Imin

1 + A3 · Ibt

IminImin ≤ Ibt < 0

1 0 ≤ Ibt ≤ Imax

1 + A4 · I2bt

I2max

Imax < Ibt.

where the coefficients, A = {A1, A2, A3, A4}, are defined inTable III. In all modes, the GPS data is used to adaptivelyadjust muc and mbt. When a stop is predicted, the coefficientsin A are adjusted towards higher efficiency and less conser-vative SOC management, as listed in Table III. This helps tominimize the losses during the large bursts of power in the next200 m, since the Regen energy from braking will compensatethe SOCuc. Without stop prediction, the power-mix decisionbased on minimum losses causes an undesired drop in SOCuc.

• Mode 1: The dc-dc converter has the highest efficiencyand the power-mix is chosen to minimize the systemlosses.

• Mode 2-3: The maximum Vuc that allows the u-cap tofully absorb a Regen burst, SOCuc,des,h, is calculatedbased on the vehicle’s kinetic energy. A band of 10%is defined according to SOCuc,des,h, in order to set thedesired lower limit, SOCuc,des,l. Operating in Modes2 and 3, within ±5% of SOCuc,des, is optimal fromthe Regen, acceleration and dc-dc converter efficiencypoint of view.

• Mode 4: The u-cap is rarely activated to limit thebattery current, while it tries to absorb most of theRegen energy, despite the dc-dc converter losses forlow Vuc.

• Mode 5: In this mode the u-cap draws a minimumamount of power from the battery whenever the bat-tery current is below Imax to increase SOCuc. This isa critical mode, where the u-cap is charged with lowerefficiency, at the expense of higher battery current andshould ideally be avoided.

TABLE III. COST FUNCTION COEFFICIENTS

Mode Weight Coefficients A = {A1, A2, A3, A4}Regular Driving Stop Predicted

1 {1, 1, 0, 3} {1, 1, 0, 3}2 {0.4, 1.5, 2, 1.8} {0.5, 1, 1, 1.8}3 {0.2, 3, 2, 1.5} {0.3, 2.5, 1, 1.5}4 {0.1, 4, 3, 1} {0.2, 3, 2, 1}

SOCuc

SOCuc,max

tSOCuc,min

SOCuc,crit

SOCuc,des

SOCuc,des,h

SOCuc,des,l0.05

1

0.64

Mode:12345

0.05

0.6

Fig. 5. Controller modes based on u-cap SOC.

B. Open Street Map and GPS Data Processing

A post-processed version of the vectorized street map ofToronto [13] is stored in the vehicle controller. GPS dataprocessing is performed to detect the presence and relativeposition of stop signs and traffic lights that are within 200 m ofthe car trajectory. There are numerous challenges in predictingwhen the EV will likely come to a complete stop due to the

traffic conditions, the exact location of traffic stops and the factthat the state of the traffic lights is unknown to the controller.

Consider the map shown in Fig. 6. Points A and C representtraffic signals at which the vehicle might stop, dependingon the state of the traffic lights. Points B and F representstop signs which can potentially be falsely detected. PointD shows the specific pedestrian crossing point which onlyoccasionally results in a stop. Point E also falls within the 200m detection zone, however it can be neglected as it is out ofthe vehicle moving direction. This is achieved by calculatingthe vehicle moving direction and the displacement vector forthe position of the stop sign/traffic signal. Point G shows astop sign that might be falsely picked up, like points B andF. Although at this point the vehicle is turning, the vehicledoes not necessarily need to stop. A detailed analysis of theexperimental drive-cycle showed that vehicle stopped at 66%of the stop signs and 49% of the traffic lights that were detectedby the GPS system. Despite this uncertainty, the GPS systemsbring a significant value to the HESS that would otherwisehave no indication of impending stops.

Stop

Stop

Stop

Stop

Stop

Sto

p

Stop

Sto

p

X

X

Stop

Stop

Stop

Stop

200m

ABC

DE

F

G

Fig. 6. A hypothetical driving situation with the stop signs and traffic signalsshown over a map. The route is shown in blue and the possible points thatthe GPS data processor may pick up are marked in purple.

IV. SYSTEM SIMULATIONS BASED ON EXPERIMENTALDRIVE-CYCLE

MATLAB is used for system level simulations to investi-gate the benefits of the proposed GPS based HESS approach,based on the experimental drive-cycle data from the EV.

A. Vehicle Modeling

The battery, u-cap and dc-dc converter models used inthis work are identical to our prior work on Light ElectricVehicles (LEV) [12], hence this section is focused on thevehicle modeling.

The equivalent force available on the wheels is given by−−→Fnet =

−−→Facc +

−→FF +

−→FD, (8)

where−−→Facc is the needed force for acceleration,

−→FF is the

rolling friction resistance and−→FD is the drag resistance.

Rolling friction force is modeled by:

FF = Crr · N, (9)

where Crr is the dimensionless rolling resistance coefficientand N is the normal force to the surface that the wheels are

rolling on. Drag force is also modeled using:

FD =12ρ · ν2 · CD · A, (10)

where ρ is the mass density of air, ν is the vehicle speed, CD

is the drag coefficient and A is the orthogonal projection areaof the vehicle to the direction of motion.

It is assumed that the vehicle travels on a flat surface andthe torque delivered to the wheels is modeled by

−−→τnet =−→Rw ×−−→

Fnet, (11)

where Rw is the displacement vector. Based on typical valuesof Crr and CD for standard sedan vehicles, Crr = 0.015 andCD = 0.4 are used. The torque delivered to the wheels is afunction of mechanical system efficiency:

τnet = KG · ηm · τm, (12)

where KG is the gearbox ratio, ηm is the mechanical systemefficiency that depends on the operating point and τm is thetorque available on the motor shaft. The linear approximation:

ηm = η0 +|Tm|Tmax

(ηTmax − η0), (13)

is used in this work, where η0 and ηTmax are optimized usingthe least square method, such that the calculated motor torquebest matches the drive-cycle measurements. The load currentcan be calculated using the drive-train efficiency data:

Iload =Pe

Vbt, (14)

Pe =Pm

ηD, (15)

Pm = τ · ω, (16)ηD = f(τ, ω). (17)

B. Simulation Procedure and Test Scenarios

Several test scenarios are considered to simulate the impactof the HESS using the experimental drive-cycle:

1. Sim #1: SESS with No Regen:Baseline EV with Regen disabled.

2. Sim #2: SESS with -0.25 C < Ibt:Regen is enabled, however the battery is unable toabsorb all the Regen energy due to its limited chargecurrent, based on battery lifetime considerations. Re-gen energy for which -0.25 C < Ibt is returned tothe electrical system, while the rest is dissipated inthe mechanical brakes.

3. Sim #3: SESS with No Limits:Similar to Sim #2, where the charge and dischargecurrents are not explicitly limited.

4. Sim #4: HESS without GPS Data Processing:HESS with Regen enabled. The battery charge currentis limited to -0.25 C and therefore having a u-capwith enough capacity for the charge helps to recovermore energy. The battery discharge current is unlim-ited, but the power optimizer tries to limit batterydischarge current to 0.75 C. This limit can be violatedif SOCuc is too low and the u-cap cannot handle theload demand. This feature maintains the EV driving

experience with improved battery lifetime.5. Sim #5: HESS with GPS Data Processing:

Similar to Sim #4, with the GPS based stop predictionenabled.

C. Simulation Results and Discussion

The comparison for the five scenarios outlined in Sec-tion IV-B for the measured 3 hour urban drive-cycle is pre-sented in Table IV. The simulated energy consumption for thebaseline Sim #1 based on the detailed EV model was within0.4% of the measured experimental SESS data.

Enabling Regen in the EV, with and without charginglimits, results in an energy consumption reduction of 13.4%(Sim #2) and 14.5% (Sim #3), respectively, compared to thebaseline SESS (Sim #1). The HESS without GPS processing(Sim #4) achieves the same energy consumption as the SESSwith no limits (Sim #3), while drastically reducing the batterycurrent range. Finally the HESS with GPS processing de-creases the peak battery charge and discharge rates by 76% and47%, respectively, compared to Sim #3. Interestingly, the GPSprocessing reduces the peak charge current by 38% comparedto the baseline HESS in Sim #4, with virtually no addedsystem cost. The reduction in required peak battery currentcan translate into a different choice of battery chemistry withhigher specific-energy and lower peak power requirement.

The partial simulation results for Sim #3, Sim #4 and Sim#5 are shown in Fig. 7(c), (d). It is interesting to see that withthe predicted up-coming stop based on the real-time GPS dataprocessing shown in Fig. 7(b), the u-cap has been effectivelyused to further reduce the battery peak current between t =9350s and t = 9365s. Furthermore, future Regen bursts are alsoabsorbed by the u-cap (t = 9520s) to compensate for the extracharge that has been used and further limit the battery chargingcurrent. The HESS effectively limits the battery current, basedon the Ibt histogram, as shown in Fig. 8(a). Fig. 8(b) showsthe battery current based on the SOCbt. In this work, thecharge/discharge limits of the battery current for the HESSsystem are fixed, but they can be decreased at lower SOCbt,improving the battery operating condition.

V. CONCLUSIONS

The model of the prototype EV and the proposed HESShas been developed to provide a powerful and accurate toolfor system simulations. It is shown that utilizing the GPSdata in the power optimizer can reduce the battery chargingpeak current by 38% compared to a standard HESS, withoutsubstantially increasing the cost, since the GPS is alreadyavailable in the EV. In addition to reducing long-term capacityfading in the battery, the GPS enhanced HESS can relaxthe battery module power specifications and allow the use ofchemistries optimized for high specific-energy, while the peakpower demands are met by the u-cap.

ACKNOWLEDGMENT

The authors thank Steve Dallas from Toronto Electric andFeisal Hurzook from of Archronix for their valuable help. Thiswork was supported by the Natural Sciences and EngineeringResearch Council of Canada, the Canadian Foundation forInnovation and the Ontario Research Fund.

TABLE IV. SESS VERSUS HESS ANALYSIS

1. SESS 2. SESS 3. SESS 4. HESS without GPS 5. HESS with GPS(No Regen) (Ibt > −C

4) (No limits) (Ibt > −C

4) (Ibt > −C

4)

Net Electric Energy Usage(kWh) 12.01 10.40 10.26 10.27 10.28Energy Benefits (%) 0 13.4 14.6 14.5 14.4Battery Current Range, Ibt,min/max (A) 0 /154.7 -27.5 /154.7 -68.9 /154.7 -27.0 /82.5 -16.8 /82.5

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S F

lag

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100150

t (s)

I bt (A

)

(c)

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0.8

0.9

1

t (s)

SOC uc

(d)

Fig. 7. (a) Measured EV speed, (b) GPS lstop prediction signal. (c) SimulatedIbt for SESS with no limit (blue), HESS with GPS (green) and without GPS(red).(d) U-cap SOC for HESS with (green) and without GPS (red).

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-50 0 50 100 1500

200

400

600

800

1000

Ibt (A)

No.

of o

ccur

ance

s

SESS(No limits)HESS w/o GPSHESS with GPS

(a)

0.7 0.8 0.9 1-100

-50

0

50

100

150

200

SOCbt

I bt (A

)

SESS(No limits)HESS w/o GPSHESS with GPS

(b)

Fig. 8. (a) Simulated battery current histogram and (b) simulated batterycurrent versus SOCbt for the 3 hour urban drive-cycle.

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