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Optimising multi-objective control methodology ofa hydrogen powered car

Tien Ho a,*, Vishy Karri b

a School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001, AustraliabAustralian College of Kuwait, P.O. Box 1411, Safat 13015, Kuwait

a r t i c l e i n f o

Article history:

Received 7 September 2010

Received in revised form

8 February 2011

Accepted 8 February 2011

Available online 2 April 2011

Keywords:

Hydrogen powered car

Ignition timing

Ignition advance

Two stage model

Statistical engine modelling

Hydrogen engine fine tuning

* Corresponding author.E-mail addresses: ntho@utas.edu.au (T. H

0360-3199/$ e see front matter Copyright ªdoi:10.1016/j.ijhydene.2011.02.041

a b s t r a c t

This paper presents the optimising control technique for a Toyota Corolla four-cylinder,

1.8-L hydrogen powered car. Based on the extensive experimental tuning data, statistical

two stage models and calibration generation methodology are carried out, in which igni-

tion timing, injection timing, injection duration and corresponding lambda value (indicate

air to fuel ratio) are chosen as control variables while engine output torque and exhaust

NOx emissions are chosen as performance index functions. The trade-off study is

employed to optimise performance of hydrogen engine by considering different optimi-

sation objectives at different engine operating states. Those engine operating states are

defined by the throttle position and opening speed of throttle, except start and idle load

states that need the auxiliary control parameters to be added in. Each value of ignition

advance, lambda, injection duration and injection end angle are tested and the hydrogen

engine is found to have good drivability and reliable on road optimisation. This work is

a step towards establishing optimising control methodology of hydrogen powered car via

application of advanced power train techniques while saving time, money and limiting

damage for innovative hydrogen engine in early experimental fine tuning process.

Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights

reserved.

1. Introduction effect on the performance of hydrogen engine includes:

The paper covers the due diligence research work [2e5] of the

same authors. Previous papers included: the conversion of

hydrogen powered car; tuning procedures and online

measuring of hydrogen engine parameters; trade-off studies

and two stage modelling build up process; and discussion of

the basic tuning of hydrogen car. This paper is presented as

the cutting edge technologies to present a trade-off study of

multi-objective control algorithm for hydrogen internal

combustion engine. The study will cover entire operating

range of the hydrogen conversion vehiclewithmore details on

howdifferent objectives at different states of engine operation

can be achieved. The control parameters which have great

o), v.karri@ack.edu.kw (V2011, Hydrogen Energy P

injection duration, injection timing, ignition timing, and the

corresponding lambda value.

The objective of this work is to produce optimised look-up

tables of hydrogen engine’s control parameters resulting from

the trade-off study of multi-objectives optimisation tech-

niques. These results will be applied in hydrogen engine

control unit (ECU) which is an aftermarket MoTeC ECU.

The research assessed the methodology referenced by

Yang Z.Z. et al. [6] and Sheridan L.A.D. et al. [7] and conducted

studies to verify that the proposed optimising control model

on hydrogen-fuelled engine using Model-Based Calibration

(MBC) and Calibration Generation (CAGE) techniques are

appropriated. Accordingly, all of the coefficients for look-up

. Karri).ublications, LLC. Published by Elsevier Ltd. All rights reserved.

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control tables are to meet the requirements of the proposed

optimisation technique by validating with the extensive

testing hydrogen engine tuning database and on-road opti-

misation. The research results from this paper are presented

in three parts:

1. Brief description of tuning procedures and online

measuring of hydrogen engine parameters as well as the

constructed two-stage models of hydrogen powered car;

2. Propose a trade-off study of multi-objective optimisation

technique on hydrogen engine’s performance of torque and

NOx emissions;

3. Use calibration generation tool to optimise the control

model. Then, amend new coefficients of look-up control

tables for MoTeC ECU and its margins of different engine

operating conditions as required.

Table 2 e Engine parameters and appropriate source ofmeasurement sensors [8].

2. Brief description of hydrogen car tuningprocedures and the constructed two-stagemodels

2.1. Hydrogen car tuning procedures and data loggingmethod

The Toyota Corolla four cylinders, 1.8 L engine running on

petrol are systematically converted to run on either gasoline

or hydrogen (depending on the driver’s choice). Several

ancillary instruments for measuring various engine operating

parameters and emissions are fitted to appraise the perfor-

mance of the hydrogen car. Themajor aspects of investigation

of internal combustion (IC) engine running on hydrogen are to

build appropriate engine tuning maps for ignition timing and

injection timings for smoother knock free combustion while

minimising NOx emissions and maximising generated torque

as well as good drivability. Table 1 below details the specifi-

cation of the converted vehicle.

A number of engine operating parameters were measured

so that theMoTeC ECU of the converted hydrogen powered car

Table 1 e Specifications of the conversion vehicle [5].

Manufacturer Toyota

Model Corolla

Series Ascent

Body type Hatchback

Year of manufacture 2002

Type Inline, 4 Cylinders, DOHC, VVTi

Total displacement 1794 cm3

Compression ratio 10.0:1

Fuel type Unleaded Petrol RON 91 or Higher

Maximum power output 100 kw @ 6000 RPM

Maximum torque 171 Nm @ 4200 RPM

Length 4385 mm

Width 1695 mm

Height 1475 mm

Wheelbase 2600 mm

Driven wheels Front wheel drive

Transmission 5 speed manual

can operate and control correctly. All parameters were logged

to Advanced Dash Logger (ADL) supplied by MoTeC. Some of

themweremade available via MoTeC ECU or directly from the

sensors, while others obtained from the stock ECU as supplied

by its manufacturer. Table 2 below presents those engine

parameters and appropriate source of measurement sensors.

The engine speed was measured by using the standard

crank sensor as supplied on the engine in its original form. The

throttle position was measured by using the standard throttle

position sensor, as supplied on the engine by itsmanufacturer.

A MoTeC professional lambda metre (PLM) and its associated

wide band lambda sensor (Bosch LSU Sensor) was installed to

measure theexhaust lambdavalue, theoutput fromwhichwas

then wired into the M400 MoTeC ECU allowing for closed loop

lambda tuning and data logging via the ADL. A piezoresistive

manifold air pressure Bosch 0-261-230-030was installed at the

“T” junction of manifold vacuum tube. The ignition timing or

ignition advance was the value of angle (degree before top

death centre, �BTDC) to start to ignite the hydrogen fuel while

the chosen injection timing was end of injection angle and

injection duration (hydrogen fuel injector pulsewidth)was the

percentage opening of injector’s duty cycle. The hydrogen

pressure within the fuel rail was measured using a pressure

gauge. The flow rate of hydrogen was calculated using the

injector pulse width time and the pressure differential across

the hydrogen fuel injectors (between the hydrogen fuel rail

pressure and the inlet manifold pressure).

Engine parameters were logged at a rate of 2 Hz through

a MoTeC ADL Unit. Results were found to be adequate at that

rate and increasing the logging rate did not further improve

accuracy of the recorded data. The ADL has a total of 1 MB of

memory with logging time being dependant on the number of

parameters being logged as well as logging frequency. The

vehicle was then placed on a dynamometer while a 5-way gas

analyser was installed on the exhaust port. The gas analyser

was a commercial unit from OTX-SPC. The set up for the gas

Parameter Designation Sensor/Source

1. Engine RPM RPM Stock ECU

2. Throttle position TP Stock ECU

3. Mass air flow AMass MAF Sensor via

Stock ECU

4. Manifold air pressure MAP MAP Sensor via

MoTeC ECU

5. Fuel actual

pulse width

FAPW MoTeC ECU

lookup table

6. Ignition advance IAdv MoTeC ECU

lookup table

7. Lambda La1 Lambda Sensor

via PLM

S. Intake air

temperature

AT Stock ECU

9. Engine temperature ET Stock ECU

10. Output power PWR Dynamometer

11. Exhaust gas

temperature

EGT Dynamometer

EGT Sensor

12. Exhaust emissions

gases

NOx, CO,

CO2, HC, O2

OTX-SPC gas

analyser

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analyser is similar to that for a normal vehicle with the

exception of additional moisture traps required due to the

high water vapour content in the exhaust stream [1,8,9]. Data

logging was set up for the device with a frequency of 2 Hz to

match the input frequency. The catalytic converter was

removed from the exhaust system of the vehicle in order to

measure the true emissions data.

The following are the experimental procedures used to

gather the dataset for two-stage models development.

1. Theenginespeedwasheldat1500rpm, theresistanceon the

dynamometer was set such that the throttle position is as

close to 25% as possible. Injection timing and injection

durationwere adjusted so that thedesired lambdavalue can

be achieved without abnormal engine combustion.

2. The spark angle is now swept. Approximately 10 data

points (average values) were recorded for both engine

speed, throttle position, air to fuel ratio, ignition advance,

torque and emissions for data processing. These data will

be used to build and test two-stage model.

3. Engine speed is then increased to another step (increments

of 500 rpm or 1000 rpm) while the dynamometer was set

such that the throttle position is kept close to 25% as

possible. Repeat step 2.

4. Step 3 is repeated for 5e6 RPM steps between 1000 RPM and

4000 RPM.

5. The results fromsteps1e4 canbecompiled intoadataset for

different engine characteristics at 25% throttle position.

6. The engine is then brought back to 1500 RPM and the

resistance on the dynamometer increased so that the

throttle position is at 50%.

7. Steps 2e5 are repeated for the dynamometer’s resistance

fixed at 50% throttle position.

8. Steps 2e5 are subsequently repeated for increasing throttle

position of 75%.

Fig. 1 e General MBC Developme

9. Steps 2e5 are subsequently repeated for increasing throttle

position of 100%.Note that at this step, the higher engine

speed of approximately 5000 rpm and 6000 rpm can be

achieved.

All datasets generated in steps 1e9 are now used to build

up the design of two-stage models and then import these

models into calibration generation to create look-up tables of

control parameters.

Other than simple full throttle ramp-up power testing

runs, all testing and data recorded were done under steady-

state conditions, according to the requirements of ISO

15550:2002 [10], including the requirement where the engine

must be tested at its proper engine operating temperature and

at the desired operating speed and throttle position.

2.2. Brief description of developed model basedcalibration

The general procedure for development of comprehensive

model based calibration to optimise performance of hydrogen

car is presented in Fig. 1.

2.3.1. Two-stage model of torqueThe local model of spark/torque curves is well studied

throughout extensive experimental tuning process. There-

fore, the chosen localmodelwas polynomial splinewhichwas

fitted by different pieces of polynomial joined smoothly

together. The point of the join is called knot which is the point

of spark angle achieve maximum torque [12,13].

Global model was built up with different structure so that

the comparisons can bemade and the investigation of the best

model can be achieved. These different structures of global

models included cubic polynomial globalmodel, hybrid linear-

radial basic function global model and LevenbergeMarquardt

nt Process of Hydrogen Car.

Table 3 e Comparison of two stage root mean squareerror with different global models of torque.

Response model corresponding to: Two-stage RMSE

Cubic global model 6.1895

Levenberg-Marquardt global model 4.3568

Radio basis function global model 3.7282

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global model. The accuracy of these three global models to

build up two-stagemodel of torque is shown in Table 3 below.

Based on the predicted root mean squared error (PRESS RMSE)

and root mean square error (RMSE) criteria, the best torque

model has been achieved with local RMSE at 0.6659 and two-

stage RMSE at 3.7282.

2.3.2. Two-stage model of NOx emissionsSimilar to the modelling process of torque model, the process

tomodel theNOx emissionswas basedon the createdprevious

test plan of Torquemodel. It has been decided that the chosen

response of the datum of NOx emissionmodel from hydrogen

car is based on the maximum brake torque (MBT) datum so

that the correlation between MBT on this model can be

Fig. 2 e Dedicated screenshot of multi-

demonstrated. The chosen point for the “knot” model is at the

minimumNOxemissions in thecreated two-stagemodels.The

best NOx emissionsmodel has been achieved with local RMSE

at 0.023737 and two-stage RMSE at 0.15922.

3. Trade-off study of multi-objectiveoptimisation technique

From Yang et al. paper [6] and Hafner M., Isermann R.’s paper

[14], the optimising control model was based on different

runningstatesofhydrogenenginetocontrol ignitionstartangle,

injection start angle and injection duration. Dedicated control

mode could be identified by throttle position and opening speed

of the throttle, except start and idle load states that can be

identified by special sensors. The control mode included: high

load or rated load state; and low up to mean load state.

In high load or rated load state, the optimal objective is to

maximise the generated output torque without any abnormal

combustion while NOx emissions is to be kept within the

limit. In low up to medium load state, the optimal objective is

to minimise hydrogen fuel consumption so that high

objective optimisation case study.

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combustion efficientcy can be achieved. Principle of the

control system utilised the model of fuzzy neural network or

combining neural networks and genetic algorithms [16,17].

In the review of Ref. [6], assuming the trade-off study of

multi-objective control algorithm where different engine

operating states will be carried out by a dedicated engine load

(throttle position). Based on this assumption, it is ascertained

so see that the proposed algorithm is applicable to the con-

verted hydrogen car where the engine speed has less affect

comparing with throttle position. Also, it is expected to be

adequate to assess the impact of changes in the control look-

up table coefficients. However, there was no information on

how engine operating states can be defined by particular

operating range of throttle positions.

In this paper, the margin for different engine operating

state will be defined and included in themethodology to verify

compliance of the proposed research study by using two-stage

modelling incorporated with calibration generation tech-

niques [15,18,19]. The histogram of 20 min of driving logged at

Fig. 3 e Trade-off study of multi-objective o

2 Hz (2400 samples) of the normal engine operating conditions

showed that the most driving time was operated with throttle

position lower than 40%, in fact more than 50% of the time

between the ranges of 5%e40% of load (throttle opening

position). The engine speed was within the range of 800 rpm

and 3500 rpm. Therefore in this case study, regardless of the

engine speed, the high load is defined as at throttle opening

position more than 40% while low up to medium load state is

defined as at throttle position from 5% to 40%.

3.1. Low up to medium load state

The multi-objective of hydrogen engine performance is

applied for this operating state. This is to ensure that the high

combustion efficiency can be achieved with multi objectives

of maximising torque and minimising NOx emissions subject

to NOx emissions constraint less than 2000 parts per million

(ppm) or 2 parts per thousand (ppt). The created optimisation

model in this operating state (low up to mean load) have to

ptimisation result for ignition advance.

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perform the constrained optimisation multi-objectives by

determine the optimal settings for the control variables of

ignition start angle, injection duration and corresponding

lambda value. It is noted that as the injection timing (injection

end angle) is only affected by engine speed. Therefore the

optimal results for this look up table is the combination of the

injection end angles values from:

- The constrained single objective optimisation of torque at

engine speed above 3000 rpm.

- The constrained multi-objective optimisation of torque at

engine speed below 3000 rpm.

So, the objective at this engine operating state can be

shown as below:

Fig. 4 e Trade-off study of multi-object

Objectives:

MinS;ID ;La

½NOXðNi;Li;S; ID;LaÞ�

And

MaxS;ID ;La

½TQðNi;Li;S; ID;LaÞ�

Subject to constraint on:

NOXðNi;Li;S; ID; LaÞ � 2000 ppmðor 2 pptÞ

3.2. High load or rated load state

Within this engine operating state, the main objective is to

maximise the generated output torque while NOx emissions

ive optimisation results for FAPW.

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are limited below 2000 ppm. Therefore, the constrained single

objective is demonstrated as below.

Objective:

MaxS;ID ;La

½TQðNi;Li;S; ID;LaÞ�

Subject to constraints on:

NOXðNi; Li;S; ID;La� � 2000 ppm

�or 2 ppt

�

3.3. CAGE implementation

3.3.1. Constrained single objective optimisationThe optimisation is performed on a grid of 3D surface view

where speed and load of hydrogen engine are shown in the x

and y axis respectively, corresponding to desired operating

points of ECU look-up control tables of spark advance, lambda

Fig. 5 e Trade-off study of multi-objectiv

value, fuel absolute pulse width and injection end angle. The

single-objective optimisation subject to constraints “foptcon”

optimisation algorithm (available from MATLAB optimisation

toolbox) was utilised and specified themaximisation objective

function of torque in brake torque/spark two-stagemodel. We

chose NOx model to constraint value of NOx emissions to be

less than or equal to 2000 ppm or 2 ppt.

3.3.2. Constrained multi-objectives optimisationThe multi-objective optimisation algorithm “NBI” (available

fromMATLAB optimisation toolbox)was utilised and specified

the maximisation objective function of torque in brake tor-

que/spark two-stage model while minimising the NOx emis-

sions in NOx flow two-stagemodel. Similar to the constrained

single objective optimisation, four variables of spark, lambda,

injection end angle and FAPW were chosen to fill in the look

e optimisation results for Lambda.

Fig. 6 e Trade-off study of multi-objective optimisation results for injection end angle.

Fig. 7 e Percentage difference between the research optimisation results and on-road optimisation results for (a) ignition

advance; (b) FAPW; (c) Lambda; (d) injection end angle.

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up tables. Also, NOx model was again chosen to constraint

value of NOx emissions to be less than or equal to 2 ppt so that

at any time of engine operating point where specific solution

of multi-objective could not achieve, the constraint would be

applied. The dedicated screenshot of optimisation result is

shown in Fig. 2.

4. Optimisation results

4.1. Calibration results

The detailed results from trade-off study of multi-objective

optimisation for the control variables of ignition advance,

injection duration, injection end angle and corresponding

lambda are shown in Figs. 3e6 respectively.

4.2. On-road optimisation results and discussion

Each value of ignition advance, lambda, FAPW and injection

end angle were programmed into MoTeC ECU and the

hydrogen engine was found to have good drivability and

Fig. 8 e Trim function for engine tempe

reliable for on-road optimisation. It is further encouraging to

note that the estimation and online measured lambda values

are within �5% error while achieving the desired engine

power and NOx emissions. Fig. 7 shows percentage difference

between the research optimisation results and on-road opti-

misation results that were implemented into MoTeC ECU.

However, in implementation of the aftermarket MoTeC

ECU [11], there are a few limitations and their solutions are

presented as below:

� The cold start is the most troublesome state of hydrogen

engine. The correct amount of hydrogen that needs to inject

to start the engine is difficult to predict. Then, the decision

has been made to use overall trim table in this case as cold

engine require more fuel. This is done manually by simply

testing different value in the cell of engine temperature

compensation table. Note that the amount of additional fuel

reduces as the enginewarms up and has no effect above 60 �

C. The look-up table for hydrogen engine fuel requirements

at different operating temperatures was built up against

throttle position. Note that the value in the compensation

table was the percentage of themain fuel table (FAPW table)

rature compensation at cold start.

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value. Based on the quick lambda reading of PLM, the

amount of extra fuel required at different engine operating

temperatures was estimated. Moreover, it is necessary to

have the engine completely cool before the testing of a cold

start engine. The implementation of the engine temperature

compensation table, and 3D surface in MoTeC ECUManager

Software, is shown in Fig. 8 below.

� Fuel compensation associated with air temperature: the

density of air depends on the air temperature (environment

compensation). Cold air is denser than hot air and hence

more hydrogen fuel is required tomaintain the desired air to

fuel ratio. The Tasmania weather is fairly different between

seasons and therefore, the tuning in very hot or very cold

temperatures are also considered in the final ECU tuning

and setting file. The standard compensation table for air

temperature (supplied by MoTeC) has been modified and

applied. A number of tests have been done in a number of

days to confirm the fine tuning (Fig. 9).

� There will be some sites that engine cannot physically

achieve such as high speed (8000 rpm) at low load (10%

throttle position). The statistical model has considered the

boundary operating region for normal operating conditions

to be calibrated and did not concentrated on the unrealistic

Fig. 9 e Fuel compensation asso

engine operating conditions. Future calibration could need

to set or modify manually for neatness.

� Idle speed: Injector timings have significant influence on

idling speed and its stability as well as exhaust emissions.

The tuning and optimisation results found that a little bit

richer of fuel mixture at idle speed than cruising speed is

required. The testing cases of the end of injection timing at

about 450� CA (compensation) gave a good result at idle

speed and starting of the engine.

� Acceleration enrichment: There is driving situation where

acceleration enrichment is required. As such an extra

amount of hydrogen fuel for sudden throttle position

changes is needed. The acceleration enrichment has the

major impact on lower engine speed region. However, there

isnoaccelerationenrichmentat 0 rpmsite.Two tableswhich

weremodified to allow the better drivability for acceleration

enrichment including: fuel acceleration clamp table, fuel

acceleration sensitivity and fuel acceleration decay rate.

þ The fuel acceleration clamp table: the value in each cell of

this table is the percentage of injector scaling. The settings

for this table are shown in Fig. 10 below.

þ The fuel acceleration sensitivity: it was decided that the

approximation of the rate of change of throttle, and the rate

ciated with air temperature.

Fig. 10 e The acceleration enrichment clamp table.

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of change of the extra amount of fuel to be injected into the

combustion chamber is a linear relationship. A value of 75

was applied for this setting. For instance, if the throttle

change quickly at 100%/revolution, the extra amount of

75%/revolution of fuel in the main fuel table (FAPW table)

will be added. Note that the sensitivity level is a multiplying

factor of the rate of change of the throttle position.

þ The fuel acceleration decay rate: it is also important to

define how quickly to take the extra amount of fuel away

using the fuel acceleration decay function [11]. The accel-

eration decay (%IJPU/Rev) sets the percentage of the accel-

eration enrichment pulse width that will be removed with

each revolution of the crank. A value of 10 (% of IJPU/revo-

lution) was applied for this setting. Hence, it took the extra

pulse width away completely after 10 crank revolutions.

It is also to be noted that the setting of injector scaling

(IJPU) in MoTeC ECU is 22 ms (corresponding to 100% IJPU).

Thus, the injection duration are implemented into fuel main

table of MoTeC ECU as the percentage of IJPU.

5. Conclusion

It has been shown that using the developed input two-stage

models together with the presented auxiliary control parame-

ters, the calibration generations with different objectives and

study case have been demonstrated. The desired performance

optimisation control tables in all engine operating states were

successfully achieved. Each value of ignition advance, lambda,

FAPW and injection end angle were tested and the hydrogen

engine was found to have good drivability and reliable on road

optimisation. Inconclusion, thedevelopedtwo-stagemodeland

optimisationcalibrationsystemsachievedall theobjectivesand

meet all the criteria for a successful intelligent control meth-

odology tool to use in advanced power train of hydrogen car

using anaftermarketMoteCM400ECU. It is further encouraging

noting that the estimation and measured NOx emissions are

within�5%errorwhile achieving the desired enginepower. It is

a clear benefit to see that if at any time there are changes in

hydrogen engine performance objective, the tuner would just

only proceeds further study by calibrating directly on the

developed CAGE models and quickly achieves the calibration

results. Hence, the application of advanced power train tech-

niques can be utilised to save time,money and limiting damage

for the innovativehydrogen internal combustionengine inearly

experimental advanced tuning process.

Acknowledgements

The authors are deeply grateful to Hydro Tasmania Pty. Ltd.,

for financial support and all of the HART researchmembers as

well as Intelligent Hydrogen Car project for sharing ideas and

concept along the way.

Nomenclature

UTAS University of Tasmania, Australia

HART Hydrogen & Allied Renewable Technology research

program

AS Australian Standard

ECU engine control unit

L engine load

N engine speed

RPM round per minute

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ADL Advanced Dash Logger

ppm parts per million

ppt parts per thousand

RMSE root mean square erroroBTDC degree before top death centre

MBT maximum brake torque

AMass mass air flow

MAP manifold air pressure

FAPW fuel actual pulse width

EGT exhaust gas temperature

ET engine temperature

MLE maximum likelihood estimation

RBF radial basis function

PRESS predicted sum square error

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