View
0
Download
0
Category
Preview:
Citation preview
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. miguel.allende@tecnalia.com
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)
EVS28 International Electric Vehicle Symposium and Exhibition 2
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
EVS28 International Electric Vehicle Symposium and Exhibition 3
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
EVS28 International Electric Vehicle Symposium and Exhibition 4
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
EVS28 International Electric Vehicle Symposium and Exhibition 5
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
EVS28 International Electric Vehicle Symposium and Exhibition 6
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
EVS28 International Electric Vehicle Symposium and Exhibition 7
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.
EVS28 International Electric Vehicle Symposium and Exhibition 8
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
SPw=SP_w_Powertrain*i1
SP_Cm=20 Nm
Set 2nd Gear pressures
(SP_P2=0;SP_P3=0)
Set 1st Gear pressures
(SP_P2=25;SP_P3=25)
SPw=SP_w_Powertrain*i2
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
EVS28 International Electric Vehicle Symposium and Exhibition 9
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
EVS28 International Electric Vehicle Symposium and Exhibition 10
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.
References [1] Variability in urban driving patterns, Eva Ericcson,
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
EVS28 International Electric Vehicle Symposium and Exhibition 11
[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
Create DYNACAR, a Model-Based Dynamometer
with Full Vehicle Simulation. M. Allende, et al.
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.
Recommended