140 120 85 - EVS28 · 2015-04-12 · gearbox power train The expected main benefits from a vehicle level are twofold. drive Figure 2: Consumption comparison between a two speed gearbox

EVS28 International Electric Vehicle Symposium and Exhibition 1

EVS28

KINTEX, Korea, May 3-6, 2015

Advanced Shifting Control of a Two Speed Gearbox for an

Electric Vehicle

Allende, M.1, Prieto, P.1, Hériz, B. 2, Cubert, J.M.3, Gassman, T.4 1Tecnalia Research & Innovation. Parque Tecn.Zamudio. Ed.700. Bizkaia, Spain. [email protected]

2University of the Basque Country (UPV/EHU), Intelligent Control Research Group, ETSI, Bizkaia, Spain 3GKN Driveline. Sagarbidea Bidea, 2, 20750 Zumaia, Spain 4GKN Driveline. Hauptstraße 130, 53797 Lohmar, Germany

Abstract

The apparition of electric vehicles on the market generates new challenges. Within them, one of the most

important is related to the vehicle autonomy. In this paper an advanced shifting control system for a two

speed gearbox is presented. It combines driving conditions and driving style inference techniques in

conjunction with an expert decision control system for selecting the most suitable gear for energy saving.

The expert control system is offline-tuned using a set of pre-specified simulation trips which characterize

different scenarios. These pre-specific trips are recognized in real time to apply the expert control rules. The

strategy is combined with an automatic shifting sequence with speed synchronization which reduces shifting

times and torque transitions. Compared to a single-geared vehicle, an energy saving up to 10% can be

achieved keeping the driver sensations while driving. An ad-hoc HIL mechatronic test bed has been

developed for the complete system testing purposes.

Keywords: HEV, EV, Powertrain control, Gearbox control, GKN, HIL, SVM, Driving cycle

1 Introduction Electric motors are very well suited to vehicle

drive needs, as torque is provided at low speed.

Advances in motor design and power electronics

have allowed significant torque at very high

speeds, which are interesting when medium and

high cruise speeds are needed. Nevertheless, the

significant cost of large electric motors and drives

make the implementation of two speed gearboxes

an appealing design option from an integrated

power train system point of view. These two speed

gearboxes allow the electric power train to be

designed for smaller maximum torque, which is

the main key when defining motor diameter (and

hence weight), reducing the required currents and

using higher rotational speeds, which are feasible

using advanced power electronics techniques.

Figure 1: Motor operating points for same power deliver

at different gears

70

70

80

80

85

85

90

90

9192

93

94

95

Speed (rpm)

Torq

ue (

Nm

)

EFFICIENCY MAP

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 120000

20

40

60

80

100

120

140

160

180

200

70

75

80

85

90

95

s

s

s

s

(

s

s

Motor operating point for

the same power transfer

at different gears.

(4923 rpm, 60 Nm)

(2764 rpm, 106 Nm)

mailto:[email protected]


Additionally to the already commented

advantages, and depending on the driver torque

requests and the vehicle speeds, the two speed

gear box allows the system to provide the same

power at both gear ratios, but with different

efficiency.

This paper is focused on controlling the

powertrain for an electric vehicle and selecting the

most efficient gear according to the driver

behaviour and on performing a seamless gear

shifting.

The basic principle for saving energy can be seen

in Figure 1. Same power can be delivered in both

gears with different motor operating points. Each

point has its own efficiency value for converting

electric energy into mechanical energy.

For instance, considering a vehicle speed of 50

km/h and a requested torque of 710 Nm (outside

gearbox) with reduction ratios of 8,835 and 5,462

in 1st and 2nd gear respectively, the delivered

power is (outside gearbox).

kWwMP 93.3060

·2·49,4923·60·1

(1)

kWwMP 93.3060

·2·2764·84,106·2

(2)

In case of 1st gear the motor efficiency (Fig. 1) is

near to 95% and in case of 2nd gear it is near to

93%.

2 Benefits of the GKN two speed

gearbox power train The expected main benefits from a vehicle level

are twofold.

Figure 2: Consumption comparison between a two

speed gearbox vehicle vs no gearbox vehicle for a

known driving cycle.

First, the efficiency of the power train is improved

as it is possible to choose between two gear ratios

for many operating points (estimated efficiency

increases of 10% depending on driving cycle, as can

be seen in Figure 2).

Secondly, the driveability of the vehicle is improved,

as there is higher torque at low speed and an

extended maximum speed, making this

configuration optimum for medium to high power

fully electric or plug in hybrids.

3 Potential problems and design

constraints It has to be taken into account that the gear selection

will have to be carried out automatically, without

any input from the driver. In order to ensure that the

benefits on efficiency and driveability are

maintained, it is necessary to develop a control

strategy that fulfils the next three requirements.

1) The first one is related to select the right gear for

a given condition taking into account the efficiency

map of the electric drive for optimal powertrain

efficiency. The minimum time between gear

shifting is 30 seconds, so a new gear shifting cannot

be performed less than this time.

2) The second one is that the system should work in

any vehicle so the algorithm must use only already

available data (for example: vehicle speed, throttle

position, yaw angle, etc.).

3) The third one is related the gear shifting. It will

execute the gear shift sequence in the most

appropriate manner, controlling all the relevant

actuators (clutches) in coordination with the electric

drive, in order to provide a seamless torque delivery

to the vehicle.

4 System architecture The general system architecture is presented in this

section. As can be seen in Figure 3 the ECU must

request the effective torque to drive the vehicle and

control the gearbox according to the external

requests which are read through the vehicle CAN

network. In order to fulfill these requirements the

developed powertrain ECU has four main functions:

It calculates the torque which must be

delivered by the inverter for traction or

regenerative purposes depending on the

driver requests (throttle, brake), vehicle

stability (ABS, ESP), system status

(inverter and battery) and motor operating

point. This part is described in section 4.1.

It selects the most efficient gear for the

actual trip using driving pattern recognition


techniques. This part is described in

section 4.2.

It manages all the inputs and outputs for

controlling the pump and valves involved

in the gear shifting. This part is described

in section 4.3.

It informs the driver through the cluster

about the powertrain status, energy usage

and vehicle estimated autonomy.

Moreover, the powertrain ECU is connected to the

vehicle and to the inverter CAN network (Fig.3).

Figure 3: ECU Powertrain system architecture.

4.1 Powertrain control

As stated previously, one of the main function of

this ECU is calculating the torque setpoint which

is requested to the inverter for driving/braking the

vehicle. It has to take into account 1) the driver

requests, 2) the stability system’s requests and 3)

the global system status. In order to fulfil this

functionality the powertrain ECU needs to

exchange data with the energy storage system,

brake, throttle, inverter and body ECUs (Fig.3).

Additionally, and with the purpose of controlling

the vehicle to perform the right torque setpoint

calculation a state logic (Fig.4) has been

developed.

Figure 4: Powertrain ECU state logic for calculating

the effective torque set point to the inverter

This information generates a CAN communication

matrix with all the relevant variables.

As well, the powertrain ECU controls the different

vehicle modes which can be selected by the driver,

as ECO (economy) mode and SPORT mode. The

system changes the motor torque vs speed curve and

the jerks for a most aggressive or economic driving.

The energy algorithm is only activated in ECO

mode.

4.2 Energy saving algorithm

This algorithm is in charge of selecting the most

suitable gear for energy savings and to increase the

system global efficiency. It is based on the principle

that the requested power can be delivered with both

gears but with different motor operation point and

consequently different efficiency point. In order to

ensure a correct motor operating point in both gears

(to avoid overspeed and overtorque) it has a

supervisory control that checks the possibility of

supplying this power and to enable/disable this

algorithm.

As can be seen in Figure 2 the maximum energy

savings which can be achieved is 10%, but using an

already known cycle so the gear shifting has been

previously scheduled. However it’s a no real

situation because during a normal trip the cycle is

unknown and depends on the system status and

driver requests while driving.

One possibility for saving energy is to select the

most suitable gear while the vehicle is moving,

using the actual vehicle speed, but it is not efficient

due to the number of shifting required.

The problem of detecting the driving cycle and

driving style has been studied several times because

it’s a key for optimization algorithms, by extracting

its statistical parameters[1, 2].

As commented before one constraint is to use

already available data in modern vehicles which

limits the algorithm capabilities (no GPS data is

available for trip estimator).

Another important issue in automotive domain is

that all the developed algorithms must be stable in

all operating points and depending on the adopted

solution this can be a complicated task.

Considering all these constraints the problem has

been split into two phases:

1) First stage (PC environment): Two main tasks are

developed. The first one is the implementation of an

optimization algorithm which searches for the best

gear shifting speed points over a set of predefined

micro trips (see section 4.2.1), using a vehicle

backward model. This model can be adapted

according to the vehicle characteristics and

according to the motor, inverter and gearbox

InverterECU

POWERTRAIN

AND

GEAR SHIFTING

PMSM

2 speed transmission

Pre

ssu

re m

easu

rem

en

ts

Actu

ato

rs

Actu

ato

r

CANbus / FlexRay

CANbus /

FlexRay

To Vehicle bus

Brake ECU Battery ECU Body ECU Body ECU


efficiencies tables. The result for this is a tuned

look up table that is included in the real time

control system and contains the optimum speed

change points for each micro trip. The second task

is the Multiclass Support Vector Machine

training. It uses several statistical calculations

from the 13 micro trips and calculates the relevant

parameters for the SVM.

2) Second stage (Real time environment): A

Multiclass Support Vector Machine (SVM)

detects the actual micro trip using several speed

and acceleration statistical parameters of the

recent past and obtains from the previous tuned

look up table the speeds pre-sets for gear shifting.

These phases can be identified in Figure 5. The

green block corresponds to the look up table tuned

on PC environment. The blue blocks correspond

to the on-line cycle identification. The red block

is an expert block in charge of selecting the best

gear which takes into account other constraints

apart from the energy savings, like vehicle status,

driver requests, vehicle speed, vehicle stability…

Figure 5: High level scheme of gear selection

algorithm.

4.2.1 Multiclass Support Vector Machine

The Support Vector Machine is a well-known

supervised classification algorithm from Data

Mining that tries to identify the hyper plane that

maximizes the margin, i.e. the distance, between

the scattered points in a multidimensional space.

The more distance, the less misclassification

could be obtained.

The frontier is performed by a kernel function that

typically can be linear, polynomial, Gaussian

radial basis function or sigmoidal. During the

learning phase, the algorithm identifies the points,

called Support Vectors, which belong to both

classification groups and maximize the distances

to the frontier.

Figure 6: Support vector machine classification

example.

Once trained, the algorithm infers from those points

a classification value so that if this value is positive

the inputted example is classified to group 1 and to

group -1 otherwise.

The Statistics toolbox from MATLAB includes a

two-class SVM algorithm for both training and

inference. After the learning, the inference is

performed by the following equation:

bXSKSn

i

iii 1

),(· (3)

where:

n is the number of support vectors.

α is a nx1 column vector.

b is a bias factor.

K(Si,Xi) is the kernel function, where:

S is the support vector matrix.

X is a vector containing the example to

classify.

Two extra parameters are needed for the

normalization of the inputted example: shift and

factor scales, which are 1x2 row vectors.

In case of a lineal kernel, 𝐾(𝑆𝑖, 𝑋𝑖) is performed by

the dot product, i.e., ∑ 𝑆𝑖 𝑋𝑖 𝑛𝑖=1 .In case of

polynomial function, the kernel is implemented by

∑ 𝑆𝑖 𝑋𝑖 𝑛𝑖=1 (1 + ∑ 𝑆𝑖 𝑋𝑖 𝑛

𝑖=1 )𝑑−1 where d is the

grade.

However, the shifting strategy presented in this

paper is based on a multi-class SVM. For that

purpose the one-versus-all approach has been

followed. In this variant so many SVMs as

classification types must be trained. The training

procedure of each SVM is faced considering that the

machine will not distinguish between two specific

classes. It classifies between the expected class and

the rest (one-versus-all) in such a way that in order

to find the class an iterative process along all classes

must be executed. During off line work it was

discovered that in some cases more than one “trues”

were found. In order to solve this drawback, the


class with the smallest classification value is just

considered as the correct one.

4.2.2 Cycles to be used and characterization

parameters

As already commented there are several standard

cycles for energy calculation and optimization

techniques, like NEDC, EUDC. The problem

appears when the results for these off-line

simulations are used as control rules on real time

while driving, because the real cycles differs a lot

from theses ones.

For implementation of vehicle energy

management strategies in real-time operations,

driving patterns need to be predicted. Because the

road type and traffic condition, trend and style,

and vehicle operating modes have various degrees

of impacts on vehicle efficiency, prediction of

these driving patterns are of great importance.

Recent studies tries to characterize these patterns

[1,2,3] using speed information and data derived

from it. For example, roadway types can be

predicted and classified in terms of maximum

speed, maximum acceleration, maximum

deceleration and so on in a short term [4]. Driving

trends, operation modes and drive styles can be

predicted using features such as average speed,

average acceleration, the standard deviation of

acceleration and so on.

Table 1: Summary of key statistics for the LOS and

Speed freeway cycles.

Taking into account the previous considerations

non-standard cycles have been used. In this case it

has been selected the sierra report [5,6] driving

cycles which considers level of services of traffic

density and urban freeway operation at different

average speeds. The level of service (LOS) is

coded from A to F where F is the highest

congestion ratio. These cycles represent a more

realistic situation than standard cycles and all the

statistical values are available to be used in cycle

identification.

A high quantity of studies are focused on the

characterization of driving cycles by using the

speed and data derived from it. Up to 62 parameters

have been calculated and studied, but only 16 have

more influence in fuel consumption [1]. In this

project 8 more parameters have been included and

a total of 24 statistical parameters [7] are used for

the energy algorithm to characterize the driving

cycle. They can be grouped into 4 domains:

Time related:

Percent time stopped.

Percent time driving.

Percent time cruising.

Percent time accelerating.

Percent time decelerating.

Percent time braking.

Speed related:

Average driving speed.

Average speed.

Speed standard deviation.

Maximum speed.

Acceleration related:

Average acceleration.

Average positive acceleration.

Average negative acceleration.

Acceleration standard deviation.

Positive acceleration standard deviation.

Dynamics related:

Relative positive acceleration.

Positive kinetic energy.

Relative positive speed

Relative real speed.

Relative square speed

Relative positive square speed.

Relative real square speed.

Relative real cubic speed.

Root mean square of acceleration.

4.2.3 PC Environment stage.

The main purpose of this stage is double. The first

task consists in computing an optimization

algorithm to search for the most efficient speed set

points for gear shifting in order to maximize the

system efficiency for a known cycle.

A backward model (Fig.7) has been prepared

including vehicle model (block 1), gearbox model

(block 2), battery model (block 4) and electric

motor model (block 3) including its efficiencies

using the QSS approach [8].

%Tcruise %Taccel %Tdeccel

AVG

Speed

(km_h)

STD

Speed

(km_h)

AVG

Accel

(m_s2)

STD

Accel

(m_s2)STANDARD:LOSA 0,2306 0,4085 0,3609 109,069 8,8712 0,084 1,5265STANDARD:LOSB 0,2459 0,3825 0,3716 107,6805 9,4502 0,0811 1,6306STANDARD:LOSC 0,2254 0,3906 0,3839 107,022 9,2482 0,0714 1,3276STANDARD:LOSD 0,1963 0,4365 0,3672 105,0064 9,4453 0,0754 1,4665STANDARD:LOSE 0,1783 0,4076 0,414 92,1098 18,6901 0,0564 1,455STANDARD:LOSF 0,1175 0,5019 0,3806 52,3945 22,854 0,0264 0,6725STANDARD:S1020 0,1099 0,4193 0,4596 24,6464 16,5774 0,0192 1,4507STANDARD:S2030 0,0872 0,5436 0,3692 40,8262 10,8588 0,0607 0,701STANDARD:S3040 0,0899 0,4882 0,4218 52,7257 15,735 0,0385 1,0271STANDARD:S4050 0,1106 0,4749 0,4146 73,4637 12,0189 0,0597 1,0117STANDARD:S5060 0,1834 0,4459 0,3707 90,9871 7,7009 0,0526 0,7787STANDARD:S6070 0,2247 0,3897 0,3857 105,0711 5,5858 0,0584 1,3166STANDARD:S7080 0,2519 0,3721 0,376 117,3005 4,5249 0,0593 1,3963


Figure 7: Backward modelling with vehicle, gearbox,

battery and electric motor models and efficiency

evaluator algorithm.

The basic equations for vehicle (4), electric motor

(5) and gearbox model (6) are presented below.

m

tvAc

m

cgm

m

tFtv

ffwLrfa

f

)(····2

1··)(

)(

2 (4)

),(

1··

EMEMEM

EMEMEMTw

TwP

(5)

·inPPOUT

(6)

Additionally to the previous equations, an

efficiency evaluator block (Fig.7, block 5) has

been included in the model in order to compute the

cycle global efficiency. This block generates three

important outputs for the system optimization:

1. Maximum global efficiency which can be

obtained in the actual cycle in case the

best gear is engaged.

2. Minimum global efficiency which can be

obtained in the actual cycle in case the

worst gear is engaged.

3. Efficiency obtained with the gear

engaged in the actual cycle.

The block is calculating each ten milliseconds

which is the best and worst gear (1st or 2nd) from

the efficiency point of view according to the

instantaneous torque and speed point required by

the driving cycle. It also calculates the efficiency

obtained with the actual gear shifting logic. These

three values are very important because the first

two show the maximum and minimum efficiency

that can be reached with the actual gearbox

configuration in the best and worst gear shifting

situation, and the last one shows the obtained with

the implemented shifting logic.

The optimization algorithm tries to minimize the

energy consumption by selecting the right speed

points for gear shifting, so the problem can be

resumed as:

Value to be minimized: Energy

consumption.

Variables to manipulate: Speed point to

shift from 1st to 2nd gear and speed point to

shift from 2nd to 1st gear.

The algorithm is based on a Nelder-Mead nonlinear

optimization technique, which is a heuristic search

method for minimizing an objective function. This

function needs starting points for solving the

problem, but it can lead into local minimum points.

In this case 15 different starting points have been

considered to find a global minimum value. The

final algorithm also takes into account the

mechanical system limits, as maximum speed for

first gear and maximum torque for second gear.

Each iteration the Nelder-Mead algorithm executes

the Simulink model (see Figure 7) using the selected

speed changes and compute the energy

consumption (calculated by the efficiency block),

which is the value to be minimized. In order to

compare results from different simulations it has

been calculated the “optimization algorithm

success” (see equation 7) which is a comparative

between the energy consumption obtained with the

actual shifting logic and the maximum/minimum

which can be obtained.

100·_

1MinMax

MinEnergyAVSuccess

(7)

Figure 8: Evolution of speed changes values and

consumption during optimization process.

The algorithm evaluates the energy consumption

(see Figure 8) with 15 different starting points and

generates a report showing the local minimum

points which have been found, as can be seen in

Table 2. The best one (44.37 km/h and 64.12 km/h

in this case) is selected to be included on the real

time look up table.

1

2

3

5

4


Table 2: Result of optimization algorithm with 15

different starting points in LOSF cycle.

Speed point to

engage 1st gear

[km/h]

Speed point to

engage 2nd gear

[km/h]

Algorithm

success [%]

11.49 19.00 77.68

10.49 30.00 82.53

10.49 42.00 88.62

10.37 48.75 90.60

11.43 48.75 90.60

21.00 31.49 83.00

20.99 41.99 89.27

19.87 48.59 90.60

19.99 54.00 89.85

31.50 41.99 89.61

31.12 48.75 90.29

35.62 50.99 91.30

44.31 50.78 91.61

44.16 64.39 91.87

44.37 64.12 91.87

This procedure is repeated for the 13 micro trips

in order to fill the real time LUT.

The second task of this stage consists in tuning the

support vector machine variables. First of all the

24 statistical (see 4.2.2 section) values of the 13

microtrips are calculated. Using this information a

Matlab script, programmed for this application,

trains the SVM calculating the n, α, b and the

scaling factors (see section 4.2.1). These values

characterize the SVM and are saved into the

microcontroller to be used on-line.

4.2.4 Real time environment

At the beginning of this stage a LUT with the

optimum speed points for gear shifting for each

one of the selected microtrips and a SVM

parameterized are available. So in real time an

online identification of the actual trip is needed.

Several studies have been focused on detecting the

driving cycle [9, 10, 11, 12, 13] using different

techniques. In this approach a support vector

machine (SVM) is used to match the actual

driving cycle with one of the thirteen known

microtrips.

The on-line classification algorithm has two

different parts.

The first one is in charge of calculating the 24

statistical parameters for the actual driving cycle.

A buffer is storing every 1 second the actual

vehicle speed and the statistical parameters are

calculated (the first time ignition is pressed the

software waits until the 150 seconds buffer is

filled).

Figure 9: SVM on-line execution tasks.

The second one is the SVM itself where the actual

cycle is classified according to the 13 microtrips

already known.

The complete scheme can be seen in Figure 9 where

the online task can be identified in T1 and T2

blocks. T1 is in charge of speed buffering and T2 is

in charge of statistical parameters calculation and

SVM classifier.

The result of the classifier is the identified

microtrip. Later in the ECU software and using the

off-line tuned table (see Table 2) the speed change

set points for gear shifting are selected.

4.2.5 Expert decision block

The expert block has been included on the ECU to

perform the final decision for gear shifting (upper

block on the Figure 5). It takes into account (in this

order):

System status for all the mechanical and

electric components involved on gear

shifting.

Time between shifts must be at least 30

seconds.

In case the system is performing an electric

braking (due to driver or braking ECU

request) gear shifting is not allowed to

avoid torque discontinuities.

The block takes into account the driving

mode selected by the user. In SPORT mode

the energy algorithm is not used because

the system tries to maximize the power

delivered to the powertrain.

The vehicle speed and the torque requested

by the driver. As can be seen in Figure 10

only the area marked as “Smart energy

algorithm” is suitable for energy algorithm

execution. 1st gear must be engaged for

high torque requests and 2nd gear must engaged for high speeds.


Figure 10: Areas for gear selection.

4.3 Gear shifting

The proposed powertrain (fully electric vehicle

with a two speed gearbox) and due to the

possibilities of motor control allows to perform a

smooth gear shifting. The motor can be

synchronized to its new speed depending on the

gearbox final ratio and the vehicle speed, avoiding

torque bumps while engaging the new gear. This

is known as the “perfect gear shifting” because a

continuous power flow can be delivered to the

powertrain without any discontinuity as usually

happens on a traditional manual gear box

(obviously it depends on the driver skills).

In Figure 11 can be seen the implemented state

flow for gear shifting. When a shift is requested

the system releases the motor torque, shifts to

neutral, synchronizes the speeds and engages the

new gear.

This sequence must be done as fast as possible in

order to have a “continuous power transfer”. Two

main controls were optimized to decrease the gear

shifting time.

The first one is related to pressure control. Times

less than 30 milliseconds were obtained for clutch

management and gear engage/disengage by

improving the control system using fine-tuned

models as feedforward.

The second one is related to motor speed control.

A speed/current cascade control was developed

and tuned using motor models. As it is known (see

equation 8) the torque consumption for a speed

controlled motor without any load (turning

decoupled) is equal to the friction torque plus the

torque due to inertia.

·JMM losses (8)

The mechanical losses, which depend on the

motor speed, and the inertia can be obtained by

calibration tests. The angular acceleration is

calculated to track a predefined path, so it is also

known. So using all these values a bias torque can

be added to the speed regulator in order to have a

faster response, needed for this application.

Figure 11: Gear shifting sequence.

5 Embedded system After developing and testing the main

functionalities in MIL and HIL (using a rapid

prototyping system) the code was downloaded to a

microcontroller. For this development a customized

hardware was developed (ECU) including all the

necessary inputs and outputs for managing the

system. A Freescale automotive microcontroller

was selected because of its processing capabilities,

memory and ASIL level. It also has available

drivers to be included in the Simulink model in

order to generate code automatically for the

microcontroller. This capability is very important

because the same model used in PC environment for

MIL tests is later compiled into a fast prototyping

hardware for HIL tests and then compiled into the

final microcontroller by just changing the

inputs/outputs drivers. It allows to have the same

platform (Simulink) and to work with the same

model from the beginning to the end of the project

which saves a lot of time. As it is known, this type

of algorithms, like SVMs, have a high memory

consumption and high processing needs [14]. In this

case two microcontrollers are running in parallel

(see Figure 12). One of them is in charge of the

powertrain control (including CAN network

communications) and the gearbox shifting

sequence, and the other one is in charge of

executing the SVM for the energy algorithm. The

first one sends the vehicle speed and the second one

informs about the most suitable gear. In order to

decrease the final ECU costs a new board will be

developed for integrating the complete solution.

2nd Gear1st Gear

0 INITIAL

2Release Torque

(SP_Cm=0 Nm)

1

Set Neutral pressures

(SP_P2=0;SP_P3=25)

Set Motor in

SPEED CTRL

SPw=SP_w_Powertrain*i2

SP_Cm=20 Nm

GEAR SHIFT SEQUENCE


SP_Cm=20 Nm

Set 2nd Gear pressures

(SP_P2=0;SP_P3=0)

Set 1st Gear pressures

(SP_P2=25;SP_P3=25)


SP_Cm=20 NmSPw=SP_w_Powertrain*i1

SP_Cm=20 Nm

Set Motor in

TORQUE CTRL

Set Motor in

TORQUE CTRL

Set Motor in

SPEED CTRL

3

8 18

9 19

10 20

11 21

12 22

13 23


Figure 12: Customized hardware designed for the

application.

6 HIL Tests In order to validate the control algorithms, a series

of physical test data had been conducted. The data

had been obtained with the complete powertrain,

featuring power inverter, electric motor, gearbox

including actuators and drive shafts in a “state of

the art” 2 wheel mechatronic test bench, running

against a forward vehicle model [15, 16],

following “Power train in the loop” approach. As

commented before the powertrain ECU exchanges

data with different ECUs (brake, throttle, battery,

inverter and vehicle body).

Figure 13: Powertrain ECU connections scheme with

vehicle virtual ECUs.

These ECUs were not present at the time of testing

so they were modelled and installed on a real time

system (see Figure 13). All the necessary data for

these ECUs to work where collected from the

virtual car (Dynacar) [16] which includes up to 90

virtual sensors and from the vehicle electric plant,

where has been modelled the battery, inverter and

electric motor.

At the end of the test bench commissioning a full

HIL system emulating a vehicle where available

with the components showed on table 3.

Table 3: List of real and simulated components in the

test bench

Real components

installed on test

bench

Simulated components

installed on test bench

Traction Motor Vehicle dynamics model

Traction inverter Vehicle electric plant

model

Powertrain ECU Brake ECU

Gearbox Throttle ECU

Pump and valves for

gearbox control

Battery ECU

Differential Body ECU

Drive shafts

Dynacar model allows manual driving by using a

steering wheel and throttle/brake pedals thorough a

virtual scenario, which helps a lot to test the

complete system. Using this possibility a lot of

testing can be carried out on virtual scenarios to

validate the powertrain ECU functionalities.

Figure 14: Test bench installed on GKN facilities.

6.1 Results

The complete system was tested on several recorded

real driving cycles focusing on energy

consumption.

In the figure 15.a can be seen the cycle speed and

the torque needed. In the figure 15.b are the speed set point and actual value. The figure 15.c and 15.d


are the most important. The first one shows the

efficiency evolution, where can be seen three

logged variables. The blue one is the maximum

efficiency which can be obtained with a perfect

gear shifting, and the black one is the worst (it’s

calculated using the same code of efficiency

evaluator block explained on 4.2.2 section). The

red one is efficiency evolution obtained using the

energetic algorithm explained in previous

sections. It can be checked that the cycle value is

nearest the maximum value. It also can be checked

the actual gear selection (green lines), which

fulfils with the requirement of 30 seconds between

shifts.

The second one (fig.15.c) shows the consumption

evolution and can be also checked how the cycle

value is near to the minimum one.

Figure 15: Energy algorithm data logging results

The shifting sequence and speed synchronization

can be seen in Figure 16. The blue line

corresponds to speed set point and the red line

corresponds to speed actual value. The

synchronization is performed in terms of 80

milliseconds. In the lower graph of the same

figure can be seen the torque interruption due to

gear shifting.

Figure 16: Shifting sequence loggings.

7 Conclusion In this paper a complete powertrain ECU

development has been explained including the

gearbox control, torque management and an

algorithm for selecting the most appropriate gear

in real time while driving using cycle pattern

detection through a SVM. The results obtained

during the tests show that the selected gear using

this method decreases the global cycle energy consumption. The most important result of this

work is that an already working software has been

developed and now it is possible to include/exclude

different microtrips in the off-line software and to

check the on-line influence of each one using a state

of the art mechatronic test bed. These microtrips can

take into account different driving styles, types of

road and so on.

The energy saving earned due to the algorithm,

added to the benefits of high torque at low speed, an

extended vehicle speed because of having a two

speed gearbox and the downsizing of the electric

motor confirm the viability of installing this system

in an electric vehicle.

Acknowledgments We thank GKN Driveline for its support rising this

project and for giving us the opportunity of working

together developing a new control concept for a two

speed gearbox. We also want to thanks to the

Basque Government Etorgai program, because this

publication has been carried out during the project

“PowerTrainv Eléctrico de Nueva Generación para

Vehículos Híbridos HEV y Eléctricos EV”, founded

under this program.

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Transportation Research Part D, 2000

[2] Independent Driving Pattern Factors and their

influence on fuel-use and exhaust emission factors,

Eva Ericcson, Transportation Research Part D,

2001

[3] Investigation of Automobile Driving Pattern on

Real-Road Condition in Tianjin, DU Qing, YANG

Yan-xiang, ZHU Di, CAI Xiao-lin, Transactions of

Tianjin University, Dec 2002

[4] Vehicle Power Management. Modeling, Control

and Optimization. Xi Zhang, Chris Mi. Springer.

2011

[5] SCF Improvement – Cycle Development. Sierra

Research, Sierra Report No. SR2003-06-02, 2003.

[6] Development of speed correction cycles. T.R.

Carlson and R.C. Austin,.Sierra Research, Inc.,

Sacramento, CA, Report SR97-04-01, 1997.

[7] A reference book of driving cycles for use in the

measurement of road vehicle emissions. T.J.Barlow,

S.Latham, I.S. McCrae and P.G.Boulter. Published

Project Report PP3354. TRL Limited. Version 3.

[8] Introduction to Modeling and Control of Internal

Combustion Engine Systems. Guzzella L., Onder

C.H..Springer Verlag, Berlin, 2004


[9] Neural Learning of Driving Environment

Prediction for Vehicle Power Management, Yi L.

Murphey, et al.IEEE, 2008

[10] Driving Condition Recognition for a Genetic-

Fuzzy HEV Control, M. Montazeri-Gh, et al., 3rd

International Workshop on Genetic and Evolving

Fuzzy Systems, Witten-Bommerholz, Germany,

March 2008

[11] Review of Driving Conditions Prediction and

Driving Style Recognition Based Control

Algorithms for Hybrid Electric Vehicles, Rui

Wang, Srdjan M. Lukic, IEEE, 2011

[12] On Adaptive-ECMS strategies for Hybrid Electric

Vehicles, Simona Onori, Lorenzo Serrao, RHEVE

2011, 6-7 December 2011

[13] Driving Style Recognition Using Fuzzy Logic,

Ahmad Aljaafreh, Nabeel Alshabatat, Munaf S,

Najim Al-Din, IEEE International Conference on

Vehicular Electronics and Safety, July 24-27

2012, Istanbul.

[14] System-on-Chip-based highly integrated

Powertrain Control Unit for next-generation

Electric Vehicles: harnessing the potential of

Hybrid Embedded Platforms for Advanced

Model-Based Control Algorithms. M.Dendaluce,

et al. EVS28

[15] Development and validation of Dynacar RT

software, a new integrated solution for design of

electric and hybrid vehicles. A. Pena, I. Iglesias,

J.J. Valera, A. Martin. EVS26

[16] Using LabVIEW, NI VeriStand, and INERTIA to

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http://sine.ni.com/cs/app/doc/p/id/cs-14647

Authors

Miguel Allende earned a BEng in

Electronic Engineer from University of

Basque Country in 2001, a MEng degree

in Industrial Engineer in 2013, awarded

for the best academic record of the year.

Since 2012 is responsible for the control

system department into automotive

domain in Tecnalia R&D. His field of

consideration are control system design,

modelling and simulation of hybrid and

electric powertrains. He is postgraduating

in Control Systems and Industrial

Automation Engineering in Madrid

Complutense University.

Pablo Prieto Arce –Senior Researcher

with degree in Telecomunication

Engineer by University of Valladolid

(2006). Specialized in Digital Signal

Processing Algorithms implemented in

FPGA and microcontroller. Currently, he

works on developing electrical and hybrid

powertrain controllers for automotive

sector.

Borja Heriz received his Electronic &

Control Engineering degree in 2008 and

his postgraduate in Control Systems and

Industrial Automation Engineering in

2011, both from the University of the

Basque Country. Since 2014 he works at

Optimitive Group, as Artificial

Intelligence Engineer. Before, he worked

at Tecnalia developing control algorithms

for both electric and hybrid powertrains.

He is a Ph.D student from the University

of the Basque Country.

Dr. Jose Manuel Cubert is graduated in

Mechanical and Electrical Engineering

from the University of Navarra and holds

a Doctoral Thesis in design and control of

synchronous motors by the University of

the Basque Country. Engineering Director

at Indar, S.A., Basque manufacturer of

electric rotating machines during 17 years.

Engineering Director at GKN Driveline

Zumaia since 1994 up to date. Besides this

position he has been Head of Global

Product Centre for Fixed Joints,

Interconnecting Shafts, dampers, coatings

and assembly from 2000 to 2013 and he

has been appointed as the Head of

TRACTION motors Global Product

Centre since 2013. GKN Fellow since

2013

Theo Gassmann holds a Dipl. Ing.

Mechanical Engineering and started with

GKN in August 1989 in the R&D Centre

Lohmar and developed AWD components

and Limited Slip Differential for

Viscodrive. From 1997 to 2000 he worked

in Auburn Hills as Chief Engineer AWD

Systems and Compondents. In 2000

becoming Engineering Director,

Americas for DriveTek. Since 2010, as

Director Advanced Engineering and

eDrive Systems, he is globally responsible

for Software Development and Vehicle

Engineering as well as the development of

new Driveline Products and Systems for

AWD, Hybrid and Electric Vehicles.

http://sine.ni.com/cs/app/doc/p/id/cs-14647

Documents

140 120 85 - EVS28 · 2015-04-12 · gearbox power train The expected main benefits from a vehicle level are twofold. drive Figure 2: Consumption comparison between a two speed gearbox