How the Saber simulation environment helps develop increasingly demanding and complex vehicle power systems. A Volkswagen vehicle power net serves as an illustration.

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SAE TECHNICAL
PAPER SERIES 2006-01-0299
Analysis of Vehicle Power Supply
Systems Using System Simulation
Thorsten Gerke
Synopsys GmbH
Carsten Petsch
Volkswagen AG
Reprinted From: Simulation & Modeling Mechatronics
(SP-2030)
2006 SAE World Congress
Detroit, Michigan
April 3-6, 2006

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3. ABSTRACT
Due to the introduction of new safety and comfort
systems in modern automobiles, stability of the vehicle
electrical system is increasingly important. The
increasing number of electrical components demands
that additional electrical energy be provided from robust,
reliable supply sources in vehicles. When designing such
systems, simulation is the development tool that is used
to quickly obtain information regarding electrical system
stability, battery charge level, and the distribution of
power to the consumer systems. This paper describes
how the Saber simulation environment from Synopsys
Corporation helps develop increasingly demanding and
complex vehicle power systems. A Volkswagen vehicle
power net serves as an illustration.
INTRODUCTION AND OBJECTIVES
Automobiles do not have a constant energy supply
nevertheless energy is required for starting the engine as
well as for consumer systems that remain active when
the engine is off. To supply this need, the battery serves
as a storage device as well as a buffer. The battery has
to deliver energy when there are peaks in the demand of
consumption. These consumption peaks have increased
by kilowatts in recent years, and this trend appears likely
to continue into the foreseeable future. To increase
battery life and optimally use existing resources,
intelligent energy management (EM) algorithms are
being introduced to ensure reliable, efficient energy and
battery management. In addition, systems simulation is
being turned to accelerate the development process
since it provides a convenient way to quickly obtain
information regarding electrical system stability, battery
charge level, and the distribution of power to the
consumer systems. Most importantly, simulation allows
the performance of an automobile under different
environmental conditions to be quickly and easily
determined. Variations of ambient temperature, driving
cycles or power consumption, and other dynamic factors
can be easily simulated. However, the essential
advantage of simulation is that it allows automobiles to
be optimized at an early stage of development, before a
prototype is produced. This advantage reduces time to
market while delivering superior vehicles. Simulation also
allows quick and easy development of energy
management algorithms, as development is possible at a
higher level of abstraction for the electronic control unit
(ECU). Of course simulation models must be validated
with conventional assessment methods, to help ensure
that simulation results remain reliable. One application of
simulation is energy balance simulation for quickly
obtaining information about power net stability, battery
charge level, and the distribution of power to
conveniences for operator and passenger comfort
without requiring a large amount of time for technical
measurements. The following sections describe the
necessary elements for designing a simulation model for
automotive applications focussing on the analysis of
overall electrical power consumption in the vehicle.
Vehicle
Equipment
• Voltages
• Currents
• State of charge
• Alternator
utilization
• EM validation
Drive Cycle
Environment
Power
Supply
User input Output
Simulation
Figure 1 Development flow using system
simulation for vehicle power nets
2006-01-0299
Analysis of Vehicle Power Supply Systems
Using System Simulation
Thorsten Gerke
Synopsys GmbH
Carsten Petsch
Volkswagen AG
Copyright © 2006 SAE International

4. BASICS
VEHICLE POWER NET ARCHITECTURE
Modern vehicle networks are very complex but, for the
purpose of analyzing the electrical power consumption, it
is sufficient to consider the significant sources of power
consumption. The general architecture of a vehicle
charging system is illustrated in figure 2. The entire
system can be divided into three major parts
• Energy sources
• Energy consumers
• Energy management.
The first two items represent the physical parts of the
power net. Alternator and battery are the electrical
sources that supply the energy to all consumers in a
vehicle. The battery delivers electrical energy during the
cranking phase when the starter accelerates the engine.
Energy is also drawn from the battery when the
alternator has reached maximum supply current but the
current demand from consumers exceeds this threshold.
Energy consumers represent significant power sinks
such as heaters or lights that are used according to
environmental conditions and occupant preferences.
Modern vehicles include an Energy Management module
to ensure that energy demand is not allowed to
discharge the battery and that there is sufficient energy
on reserve to provide an adequate State of Charge
(SOC) for the next vehicle start. The Energy
Management module is designed to check the voltage
level of the power net and control the alternator and
loads based on the requested amount of electrical
energy as well as the utilization of alternator and battery
SOC. The vehicle power net architecture reduces the
entire vehicle network to the parts specifically relevant for
simulation and analysis so electronic control units and
any actuator and sensor components are either
represented by their electrical power behavior or
completely disregarded, if they do not significantly impact
the behavior of the vehicle power net.
ENERGY MANAGEMENT (EM)
Beside the physical parts in the power net, the energy
management module ensures the robust behavior of the
power network by controlling the power distribution and
battery energy management. EM includes the following
responsibilities:
• Coordination of the energy flow
• Stabilization of the power net
• Maintenance of energy reserves for the engine
start
• Control of charging cycles so optimum battery
life is ensured.
Figure 3 shows the concept of energy management for
vehicle power supply systems. To perform its function,
EM requires a steady flow of information about the
vehicle electrical system and energy demands. EM
monitors the overall performance of the alternator to
optimally utilize generator resources EM distributes the
energy to power consumers according to individual need.
In addition, the battery is supplied with sufficient energy
so that the vehicle can be started even under the most
unfavorable operating conditions. EM receives additional
information to help optimize power distribution, including
system voltage and the status of consumers that make
high current demands. Continuous monitoring allows EM
Energy
Management
Front
Lights
Rear
Lights
Seat
Heater
Seat
Heater
Power net
Load control
Alternator
Alternator
control
Engine
Speed
Figure 2 Simplified vehicle power net
Battery

5. to quickly increase the charge current to the battery if
necessary. EM also includes raising idle running speed
to increase the current supply and to distribute the power
via a prioritized activation of consumers provided for the
comfort of passengers.
SIMULATION
OVERALL SIMULATION MODEL
Physical power net
An appropriate simulation model with sufficient accuracy
is necessary to effectively simulate the overall vehicle
power net. Such a model was constructed by splitting the
design task into two tasks. First, the physical
components, such as alternator, consumer elements and
battery, are simulated using the Saber Simulator™.
Saber is very strong in modeling network-oriented
systems like electrical networks. Figure 4 shows the
Saber schematic of the entire simulation model for the
physical part of the power net. The Saber simulation
model contains
• Alternator
• Battery
• Electrical consumers
• Interface to the energy management algorithm
• Simulation stimuli
The simulation stimuli represents environmental
quantities during simulation such as temperature or
engine speed.
Energy management
Unlike the power components of the vehicle, the energy
management algorithm is usually developed as a
software algorithm to increase reusability for this part in
the model. The simulation model of this algorithm was
developed as executable specification in the signal flow
simulator Matlab/Simulink. Matlab/Simulink has its
Alternator
utilization
Power net
voltage
Figure 3 Function of energy management
Energy Management
Status of
high current
consumers
Controlling
alternator
current
Activation
of comfort
consumers
Consumers
Battery
Alternator
Energy
Management
Simulation stimuli
Figure 4 Physical power net in Saber

6. strength in the control system area by providing solutions
for automated ECU code generation. The Saber
schematic of the power net contains an interface block to
connect the physical power net in Saber with the
algorithmic implementation of energy management in
Simulink.
PARTIAL SIMULATION MODELS
Battery model
The role of the battery within a vehicle electrical charging
system is to act as chemical storage device for the
electrical energy generated by the alternator. The lead-
acid battery has always been the most important
rechargeable electrochemical storage device for
automotive applications, due to its relative simplicity and
ease of installation. The lead-acid battery remains the
choice of automotive manufacturers, although different
technologies are becoming more common with the
increasing popularity of hybrid vehicles. The battery
model, used in our vehicle model, provides the behavior
of a lead acid battery that is used as energy storage in
the vehicle. Since the battery’s state of charge is one of
the simulation results being used to evaluate the entire
power net concept, this model must accurately provide
the charging and discharging behavior of a lead acid
battery. The battery model from the German Institut für
Solare Energieversorgungstechnik (ISET) is used since it
is the most advanced model of a lead-acid battery on the
market. The model takes into account the physical and
electrochemical structure of lead-acid batteries and
can be initialized by providing some geometrical data,
the amount of substances in the electrodes and the
values of porosity for the separators and the electrodes
in charged state. Values such as battery capacity,
electrolyte concentration, operating voltage in addition to
the physical parameters of the battery, such as the
number of plates for positive and negative electrodes,
grid mass, length, height and width for example, can be
set to represent the vehicle battery as accurately as
possible. The impact of ambient temperature can be
taken into account as well to test the power supply under
different environmental conditions. The battery model
delivers information about
• Battery voltage
• Battery current
• State of charge
Additional information that is useful for energy balance
analysis is also provided.
Alternator model
The generator was modeled with concern for a specific
type of operating scenarios. For energy balance
simulation a semi-physical model from the alternator
manufacturer Valeo [5] is used. The alternator model
consists of two parts
• The electrical machine serving as electrical
power generator
• Regulator to control the electrical current of the
excitation field.
Both components are modeled as separate instances in
the alternator model whereas the electrical machine is
split up into different instances covering different physical
aspects of the electrical machine
• Electrical model describing the electrical
behavior for the stator and armature windings
• Thermal model covering the electrical energy
losses in the alternator
Figure 6 shows the general architecture of the alternator
model from the French company Valeo. It illustrates the
behavior of the alternator model by means of the energy
flow. The diagram does not reflect the actual model of
this component since the quantities are bidirectional and
not unidirectional as illustrated. Each winding is
Electrical
model
Thermal
model
Ambient
Temperatur
Vehicle Speed
Losses
Alternator components
temperature
Figure 6 Architecture of alternator model
Vehicle
Speed
Battery
model
Physical and
chemical
battery data
Ambient
temperature
SOC
Voltage
Current
Figure 5 Battery data and output

7. modeled by the equivalent circuit shown in figure 7 and
includes consideration of winding resistance as well as
inductance. The interaction with the magnetic part of the
alternator is modeled by an electromotive force caused
by the excitation field of the alternator’s armature. The
Potier diagram provides the mathematical description of
the relationship between the electrical and magnetic
domain. From the mechanical perspective, the model
deals with changes in speed as quasi-stationary effects
since the speed is specified by the developer and is not
computed dynamically. For the description of the thermal
transient behavior of the alternator, an equivalent simple
thermal network consisting of thermal capacitance and
resistance is applied. Figure 8 shows a comparison
between simulated and measured alternator behavior for
the maximum deliverable current using the model above.
For all tested simulation scenarios the difference
between simulated results and measurements was within
a range of -5 up to 5 %. To check the validation of the
model, a test bench [5] was specified by the VDA
(Verband der deutschen Automobilhersteller) working
group AK30, a consortium of automotive manufacturers
and suppliers concerned about model exchange
between different parties in the automotive market. The
test bench includes worst case and consists of a
complex network of dynamic high loads and different
driving cycles without battery. Figure 9 shows some
results of the simulated test bench showing the alternator
behavior of one driving cycle period. The upper signal
shows the set voltage that the alternator is supposed to
follow as well as the actual alternator. The middle signal
is the profile of the engine speed so it serves as the
driving cycle that is applied for the test bench. The lower
signal shows the utilization of the alternator over the
whole driving cycle period. The simulation clearly shows
that the transient response of the alternator depends on
the set voltage and the engine speed of the vehicle. In
some areas, where the engine speed is too low, the
alternator reaches the saturation area and can no longer
deliver enough current to maintain the voltage at the
intended voltage level. It is important to note, that this
test bench runs without the battery model. Based on the
Figure 9 Alternator transient response
Set voltage
Alternator voltage
Vehicle speed
Alternator utilization
1
2
3
Measurement
Simulation
1 -
2 -
3 -
Test at -15°C
Test at +25°C
Test at +80°C
Figure 8 Alternator maximum deliverable
current
V V
Electromotive
force
Resistance Inductance
Rectifier
voltage
Figure 7 Winding model and Potier diagram
Ej
jLωI
RI
UB+
JT
jαI
φ
I
θ

8. test bench results and the comparisons with
measurements from the lab, the simulation model shows
good accuracy. Simulation speed was very good.
Load model
The energy consumers are usually modeled as resistive
loads as well as power sinks. For some loads lab
measurements are incorporated into the model by using
data tables. The developer can specify different load
cycles so the load changes its energy consumption
during a driving cycle — such as when a driver turns the
heater on and later, turns it off. Different levels of energy
consumption are associated with different load levels so
the developer specifies time ranges for the energy level
or physical behavior of this device. Figure 10 illustrates
the concept of the load model. Energy management can
control each individual load via an additional control pin
on the device symbol allowing it to reduce power
consumption or disable a load if available electrical
alternator output voltage energy in the power net
approaches a critical lower level.
MERGING PHYSICAL POWER NET AND ENERGY
MANAGEMENT CONTROL ALGORITHM
Since the energy management control algorithm runs as
embedded software on an Electronic Control Unit (ECU)
this part must be merged with the simulation model of
the physical power net in Saber. There are different
methods possible for doing this, and each of them has
different benefits:
• Using a model of the controller hardware (VHDL
or Verilog, SystemC, Instruction Set Simulator)
and running the software on this controller
model. These approach provides accurate timing
information depending on the preferred
approach but is usually to expensive with respect
to simulation time needed to simulate the entire
model.
• If the control algorithm was developed using a
software development tool, then cosimulation is
another possibility to merge both parts. This
approach does not provide any information
regarding the timing of the controller since only
the correct functionality of the control algorithm
can be validated.
Since only the functionality of the energy management
control algorithm is supposed to be validated and the
timing can be disregarded, the second method is the
appropriate one for load balance analysis. The control
algorithm was developed in Simulink. Two possibilities
are available in order to merge both models together
• Cosimulation [2]
• Model import/export [3]
The cosimulation requires an interface to allow both
simulators to communicate by exchanging information
across a communication channel. Signal values are
mainly exchanged but it might become necessary to
exchange additional information, such as parameter
values. in order to initialize one model by calling the other
one. The benefit of the cosimulation is that both
graphical user environments are available during the
entire simulation process allowing the developer to
debug the software model by simultaneously evaluating
the hardware model of the power net in Saber. Figure 11
shows the general methodology of this approach. The
second method, model import/export, is also a
cosimulation but is based on a different procedure.
Software development tools usually compile the control
algorithm model into C-code. Saber provides an interface
that allows software modules written in C to be used.
The developer can run the generated control code
directly together with the physical design in Saber. This
approach requires that the developer build around the c-
code a wrapper that is written in MAST, one of the
modeling languages supported by the Saber Simulator.
An automated process can be used in Saber to integrate
Physical power net
Figure 11 Concept of cosimulation
Energy management
Electrical
network Control state diagram
Interface
channel
Load
Power net
Levels
1 25 W
2 50 W
3 75 W
Load cycle
Figure 10 Load model concept
Energy
management

9. models produced by the Simulink C-code generator.
Figure 12 illustrates the procedure for this. The tool
generates C-code for the model based on the Simulink
block diagram. Some additional scripts are automatically
executed during this procedure to generate all required
instances. The output contains the following components
• Saber Simulink model compiled and linked into a
library
• Mast wrapper to integrate the C-model
• Symbol of this model being required for the
schematic
The entire procedure is fully automated and does not
require any manual intervention by the developer. Since
the exported model runs as separate process during the
Saber simulation, it is actually also a cosimulation
between both simulation engines. The benefit of the
second method is faster simulation speed since the
compiled model runs as binary source that is
immediately executed. From the developer perspective,
the software is a black box with a certain number of input
and output signals. This approach is very attractive as
soon as the model has reached a certain level of
complexity and stage of maturity such that simulation
becomes time consuming or when it longer is intended to
modify the software model.
APPLICATION SCENARIOS AT VOLKSWAGEN
The models and methods described above can be used
to build up a virtual vehicle power net for the purpose of
energy balance. We will analyze two different scenarios.
FIRST SCENARIO
For the first scenario the vehicle is equipped with the
following components:
• Valeo Alternator
• Battery lead acid Varta 72Ah
• Consumers such as seat heating, radio, etc.
The driving cycle NEFZ (new European driving cycle) is
supposed to be analyzed under the condition of
environment temperature of 5°C. Figure 13 shows some
results of the simulated driving cycle. The upper signal is
the vehicle battery’s SOC shows that electrical energy is
almost continuously drawn from the battery since the
value of SOC is during 50% of the driving cycle smaller
than zero. This plot considers only the amount of energy
that was taken out of the battery relative to the initial
SOC. Therefore, the signal starts with an initial value of
zero at the beginning of the driving cycle. The set value
of the battery voltage is 14 Volts. According to the
simulation results, this voltage level is not permanently
achieved during the entire driving cycle. Variation occurs
when the alternator is fully utilized at the appropriate
vehicle speed and supplies maximum current. When the
consumers in the vehicle request more electrical energy
than is available, even if some of the comfort consumers
are disabled, the electrical voltage stays in some area for
Battery SOC
Battery voltage
Vehicle speed
Alternator utilization
Figure 13 Results of first scenario
Power net and
control software in
Saber
Control state diagram
Energy
management
1001101010
0010001111
0010101000
Measurement
Control
C-code
#include<math.h>
....
....
out[1]=x1+x2
....
Figure 12 Concept of model import/export
Code
generator

10. a significant amount of time under the desired threshold
of 14 volts. Results from the simulated driving cycle
indicate that either the chosen alternator is too small or
energy management must disable more comfort
consumers. In this case the maximum possible number
of comfort consumers was disabled implying that another
alternator size should be chosen to fulfill the
requirements.
SECOND SCENARIO
In the second scenario the same vehicle equipment is
taken except for the alternator. When a larger alternator
is supplied, the simulation delivers updated results as
shown in figure 14. With the larger alternator, the battery
is permanently charged during the entire driving cycle
and this will guarantee enough power supply for all
consumers in the vehicle. In addition, enough electrical
energy can be supplied to restart the combustion engine.
The intended set value of 14 volts is achieved almost
throughout the entire driving cycle. This configuration of
energy sources and energy management would fulfill the
Volkswagen requirements in the first stage. The next
step might be further optimization of the system to keep
the alternator type as small as possible while providing
enough power capability. This example illustrates how
system simulation can help the developer analyze the
system performance and the robustness of the design
with acceptable effort and within a short time frame.
SHORTEN DEVELOPMENT TIME
Building up a real prototype network requires
approximately 1.5 years before the first results can be
obtained. When simulation is used in the concept
phase, there is an enormous benefit to the designing
engineers because they have access to system behavior
information not available otherwise. This information
allows as significant reduction in prototype development
time. Figure 15 shows time spent during testing of the
physical prototype compared with the equivalent
simulation prototype.
Before drive cycle performance analysis can be done
using the prototype vehicle once it becomes available,
several preparation steps are needed with the vehicle.
Two phases of the development process have to be
taken into consideration. First, during the concept phase,
measured driving cycle data cannot be obtained to check
the correctness of the initial design. Simulation is a
necessary tool for producing data to validate conceptual
ideas. Development of a new simulation model for an
entire power net takes maximum a half year (modeling of
all components and collecting required data). Derivatives
of this architecture might be modeled in a few minutes by
simply modifying the existing architecture model.
In the second phase (following the concept phase), when
a prototype vehicle is available to produce measured
data, simulation still helps reduce development time.
Before any testing can be performed on a physical
prototype the equipment must be prepared, including
setting the vehicle battery to a predefined state. Usually,
a full battery is discharged to a certain level and allowed
to rest for a certain time to recover (typically, two days).
In addition, the vehicle must be equipped with
measurement instruments. After preparation has been
completed, the driving cycles are performed with the
Figure 15 Testing phases
Preparation
Driving
Cycle
Post
processing
Simulation
Real test
Man-Days
Cumulative
effort
6
12
Battery SOC
Battery voltage
Vehicle speed
Alternator utilization
Figure 14 Results of second scenario

11. prototype vehicle (one week is a typical timeframe).
Following the driving cycles, all measured data must be
processed and results evaluated (three days). Simulation
can complete this process in a matter of hours (6) and
require fewer resources — a developer working on a
single personal computer versus two or three engineers
required by the longer, manual process.
CONCLUSION
The purpose of this article is to demonstrate how system
simulation helps to develop and analyze vehicle power
supply systems. System simulation is already a
mandatory part of the development process at
Volkswagen and several other vehicle manufacturers.
The development phase can be further shortened since
simulation allows a power net design to be analyzed and
validated without data from a prototype of the real
network or the vehicle. Performing power net stability
analysis is already an important part of development and
will become more important in the future since the
number of electronic consumers can be expected to
increase.
REFERENCES
1. Saber simulation product documentation, 2004,
Synopsys
2. Saber-Simulink cosimulation documentation, 2004,
Synopsys
3. Saber-Simulink model import documentation, 2002,
Synopsys
4. Alternator test bench spec, 2004, VDA AK30 Group
5. Valeo alternator model, 2004, Valeo
CONTACT
Carsten Petsch
Volkswagen AG
Energymanagement & Power net simulation
Mailbox 1784
38436 Wolfsburg
carsten.petsch@volkswagen.de
Phone: +49 (5361) 944164
Thorsten Gerke
Synopsys GmbH
Application Engineer Automotive Applications
Karl-Hammer-Schmidtstraße 34
85609 Aschheim/Dornach
Thorsten.gerke@synopsys.com
Phone: +49 (89) 99320227