Structuring of Methods to Estimate Benefits of Partial Networking

Alexandr Vasenev

ESI, TNO Joint Innovation Centre, Eindhoven, The Netherlands

Keywords: Partial Networking, ECUs, Potential Energy Savings, System Segmentation.

Abstract: Partial Networking, as a mechanism for moving-to-sleep and waking-up embedded systems, is beneficial for

saving energy within a vehicle (or within other complex distributed systems). Even though a number of

models exist which identify benefits of partial networking, they often address rather specific cases. Moreover,

these fragmented efforts do not necessarily make explicit which methodological steps were taken. Explicating

and analysing methodologies of existing research is beneficial to construct an overarching structure how to

estimate potential energy savings for partial networking implementations. This structure can be used to select

which steps to take to investigate the savings, and how to construct an argument for presenting the findings.

This paper describes initial results of such a research. It reviews several models, illuminates their (sometimes

not explicitly documented) methods, and outlines a generalized sequence for estimating partial networking

benefits. Besides, it provides a list of questions to consider when introducing partial networking. The outlined

methods and the analysis can be of interest to other domains interested in energy savings, such as smart grids,

smart cities, and internet of things.

1 INTRODUCTION

Saving energy by reducing consumption of in-vehicle

components, such as ECUs (Electronic Control

Units), is essential for both electric as traditional

vehicles. Specifically, switching ECUs from a fully

active state to a less power demanding state can

provide several advantages (Butzkamm and Bollati,

2012):

- Reducing energy consumption which leads

to less CO2 emission (thus, tax advantage)

and increased range of electric vehicles;

- Optimizing of operational strategy when the

vehicle is at rest can support: new comfort

functionalities (e.g., the sun roof control);

reduced strain on the battery; longer time

periods when the engine can be re-started;

less fuel is needed to charge the battery;

- Shorter operating time of embedded systems

can lead to reduced lifetime requirements for

ECUs.

Partial Networking (PartialNW), as a mechanism

to put to sleep and wake up ECUs, can provide

significant energy savings. It requires models to track

and optimize system-level energy consumptions.

Such models can provide inputs for the following

considerations (Lingadahalli et al., 2016.):

- Feature deployment to ECUs for optimum

power consumption;

- Comparison of various electrical

architecture alternatives;

- Power budgeting for features;

- Defining the fuel economy drive cycle

impact.

This paper reports intermediate outcomes of

research directed at systematizing methodologies and

constructing a framework to estimate benefits of

partial networking. It aims to provide inputs for

designing various PartialNW models best suited at

different product development stages, based on the

available level of details. New models can be

constructed by linking inputs (functions/ECUs) to

resulting values (CO

, energy reduction) through the

calculation stage. Some values relevant for models

are mentioned next to references to best practices of

modelling. To note, these values are referred as they

are mentioned in the cited publications and are thus

linked to contexts and assumptions of those articles.

Nevertheless, they can serves as first order estimates

and provide useful insights to the models.

The next sections introduce PartialNW and

overview models to estimating its benefits. A

generalized sequence of estimating benefits and list

some relevant issues is presented afterwards.

Vasenev, A.

Structuring of Methods to Estimate Beneﬁts of Partial Networking.

DOI: 10.5220/0006803205750581

In Proceedings of the 4th Inter national Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 575-581

ISBN: 978-989-758-293-6

575

2 BACKGROUND

This section starts with an overview of the structure

of power savings related to ECUs, continues by

introducing partial networking, and then lists

potential ECU candidates for partial networking.

2.1 Power Saving Techniques

In general, techniques to obtain power savings for

embedded systems include the following (Heinrich

and Prehofer, 2013):

1. Improving consumption of an ECU (i.e. CPU)

by dynamic hardware resource management:

a. Dynamic voltage/frequency scaling;

b. Reducing quality of service to decrease

energy consumption;

c. Energy-efficient task scheduling;

2. Improving system-wide energy consumption:

a. Dynamic power management (DPM),

which deactivates unused components of a

system to save energy;

b. Dynamic voltage scaling and DPM -

considers the energy consumption of

peripherals (e.g., memory) within standby

and activation/deactivation time of the

processor.

Such opportunities benefit from three degenerated

states of ECUs (Schmutzler et al., 2010:

1. ECU degradation – decreasing the clock rate or

feature set;

2. Stop mode – the microprocessor is in a

powered stop mode, while all subsystems

except the communication subsystem are

deactivated;

3. Sleep mode.

This paper concentrates on the latter state, i.e., sleep

mode of ECUs. Specifically, the paper concerns

methods of estimating benefits of Partial Networking

(PartialNW) in connection to energy savings. Other

drivers, such as wiring rationalization, system

segmentation, and potential impact on security (e.g.,

in connection to e.g. the “extended vehicle” concept)

are not covered here in detail.

2.2 Introduction to Partial Networking

PartialNW, is a coordinated go-to-sleep and wake-up

protocol. As part of a network management

middleware it aims to maximize the time ECUs sleep

to save energy. Its use can realize an additional

number of viable sleep scenarios and thus be

beneficial for the current consumption of functions

when the vehicle is at rest or moving.

PartialNW is a further step in granularity of

selective sleep of ECUs, compared to the bus-wide

sleep. This bus-level sleep is directly linked to ECUs

located on a specific bus, and thus to reasons why and

how the bus was constructed. This can constrain the

network architecture, because of a bus (as a sub-

network) purpose to: (1) correspond to a functional

domain, (2) help to restrict the impact of bus or node

malfunctions, and (3) decrease the wire length of

individual bus arms. For instance, residential

functions can be linked to a specific CAN. PartialNW

relaxes assumptions behind such bus composition

logics. In PartialNW, nodes form clusters which wake

up and respond on demand. If a particular cluster is

not requested, its participants can move to sleep. This

mechanism allows ECU nodes to enter sleep mode

even if the bus is active.

Introduction and elaboration of selective sleep

affects network communications and the process of

its design due to challenging the following

paradigms:

- Cyclic communication: network management

policies allow nodes to move to sleep and wake

up again. Therefore, some nodes do not always

participate in communication;

- Deployment (Heinrich et al., 2016): In 'Energy-

focused allocation', software (SW) components

are places to reduce the energy demand of the

system. For instance, some SW components can

be grouped. Within 'Function based allocation' –

contrarywise – suppliers provide hardware

(HW) and SW components as an integrated

system, which can reduce number of active

ECUs needed. Modern SW architectures, such

as AUTOSAR, introduce independence of SW

components from HW components and help to

move from the function-based to energy-

focused allocations.

While participating in PartialNW, a

microcontroller may switch into an unpowered mode.

The sleep to active transition time can be up to 100

ms. Sleep mode switches off the power supply of

peripherals and microcontroller. Starting can be

compared to a cold boot of an ECU (in comparison,

Pretended Networking can bring the ECU into the

stop mode, where the current consumption would be

higher). After moving from sleep to active, the node

has an obsolete representation of the system, which

might need to be identified again. Potentially, that

status can be stored in a persistent memory, but it may

require bigger memories.

In case of Automotive Ethernet, additional wake

up specifics relevant to PartialNW apply due to the

need to establish communication links. Three wake-

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576

up mechanisms can be differentiated as follows

(Suermann and Müller, 2014):

- Selective wake-up. For instance, the time taken

to establish a link according to the BroadR-

Reach specification this can be up to 200 ms. If

four consequent links shall be established it

might sum up to 800 ms, which is unacceptably

long for many in-vehicle functions;

- Global wake-up via a separate wake-up line

(therefore, the need for a wake-up line arises);

- Global wake-up via the Ethernet network, when

transceivers re-send the wake-up signal. The

wake-up message spreads swiftly throughout

the network. Unneeded clusters are then

switched off again.

PartialNW could employ a local decision unit

which analyses bus traffic and decides if a s single

node is needed (thus, generate a local wake up event).

In addition, other nodes must be informed about

ECUs which are about to sleep, because a sleeping

ECU could generate errors in other ECUs. Equipped

with the state of other ECUs, nodes they can decide

whether a signal is missing because the responsible

cluster or ECU is sleeping, or if an error occurred.

2.3 Candidates for Partial Networking

To benefit from PartialNW, the distributed system

properties should include (Huber, 2010):

1. Functions need to have large execution periods,

so ECUs can go to sleep;

2. Functions should be distributed to ECUs

adequately (in the view of a distributed vs

centralized ECU architecture). In other words,

sleep modes cannot be applied, if a single unit

performs many essential vehicle functions.

As a result, PartialNW candidates are ECUs that

do not require cyclic CAN communications, e.g.:

- parking assistance, lane departure, or other

systems to be used at specific speeds;

- door, mirror, roof, and seat control modules;

- some elements from the infotainment segment or

special accessories (e. g. trailer control units).

3 OVERVIEW OF MODELS TO

ESTIMATE BENEFITS OF

PARTIAL NETWORKING

This section overviews state of the art solutions to

estimate PartialNW benefits. It explicitly outlines the

methods behind such calculations. The section starts

with first order estimates, introduces the logic of

grouping ECUs based on power consumption, and

finally overviews several models.

3.1 First Order Estimations of CO2

and Fuel Savings

To calculate fuel savings, one can employ the

heuristic that an increase of 100 Watt means that fuel

consumption rises by 0.1 litre per 100 km. This in turn

leads to an increase in CO2 emissions of 2.5 gram per

km (Monetti et al., 2012). Alternatively, energy saved

by ECUs can be linked to savings in fuel using caloric

value of petrol (Schmutzler et al., 2010):

- Assume a caloric value of 10 kWh per liter of

petrol and energy efficiency of a car with a

combustion engine as 0.225 (efficiency of

engine 0.25 * efficiency of a generator 0.9);

- With 3.84 W*t energy savings of sleeping

ECUs, Fuel consumption = 3.84/(0.225*10) =

1.71 ml/h * t;

- Fuel reduction is 0.56ml (With Motor Vehicle

Emissions Group A driving cycle t=1180s and

11km);

- Reduction of CO

is 0.12 g/km (Assuming

burning 1L of petrol to produce 2.33 kg CO2).

Also, PartialNW can be linked to the amount of

money saved by avoiding CO

penalties (Huber,

2010)):

- Energy saved per ECU is found by multiplying

differences in current and voltage

consumptions.

- Assuming that N network nodes can be put into

selective sleep, total energy saved per vehicle

can be calculated by multiplying the previous

value by this number N;

- Assuming 0.0265 grams of CO

emissions per

kmW, CO

saved per km can be determined;

- The resultant CO

savings are multiplied by 95

Euro penalties for exceeded emission (CO

per

km) to find savings per vehicle;

- Cost reduction per ECU can be found by

dividing the savings by N nodes.

NB: the values of 0.0265 grams of CO

emissions

per kmW and 95 Euro penalties (as intended for the

EU starting in 2015) are mentioned in (Huber, 2010)

without further details. These values depend on the

car model used for calculation and the regulatory

context of the car use. While the values can change,

the outlined method might still hold.

3.2 Inputs: Classes of ECUs

For modelling purposes, power requirements of

ECUs can be grouped into classes. For instance, such

Structuring of Methods to Estimate Beneﬁts of Partial Networking

577

classes can be linked to how, e.g., NXP

Semiconductors differentiates between several types

of applications: advance driver assistance systems, in-

vehicle networking, body, chassis, powertrain and

safety (NXP, 2018). In another example (Schmutzler

et al., 2010), four classes of ECUs are distinguished

in connection to average power consumptions, i.e.,

high-end power train, high-end, mid-end, and low-

end (average current consumptions in the mentioned

article are 400mA, 70 mA, 20 mA, and n/a).

3.3 Estimations using High Level

Scenarios

Scenarios may be uses to determine energy savings

(such as driving and hybrid charging). (Schmutzler et

al., 2010). The logic behind this method is as follows:

1. The power usage of automotive controllers is

assumed to correlate with their feature sets and

processing power;

2. Several power classes are distinguished: high-

end power train, high-end, mid-end, and low-

end based on their consumption (Section 3.2);

3. The supply voltage is used for calculations, as

most ECUs use linear regulators. Assumptions

are: average supply voltage level (12.6V) and

an average 1mA quiescent current per ECU

while sleeping;

4. Two scenarios are outlined (driving scenario A

and a stationary charging scenario B);

5. By estimating which ECUs can sleep, power

categories per scenario are outlined. In scenario

A 2 high-end and 13 mid-end ECUs are

required only 20% of time.

6. Energy savings are estimated as follows:

- Energy savings in Scenario A are calculated as

(320mA-15 (sleeping ECUs) *1mA) *12.6V *

t =3.84W * t;

- Energy savings in Scenario B are calculated

24.26W * t (with 35 ECU sleeping);

- Yearly savings=60.64 kWh (charging cycle 10

hours, 250 times a year) = 24.26*10*250.

3.4 Detailing Scenarios as a Set of

Functions

To detail a scenario, a sequence of function can be

linked to a timeline. For instance, w.r.t. scenario A

from the previous subsection (i.e., driving), functions

linked to ECUs can be as shown in Table 1 (example

from Yi and Jeon, 2015).

Table 1: Sequence of functions within a scenario.

Operating

sequence

Operating

Detail

Operating

ECU

ECU(s)

Ignition on

All ECUs

All

Adjust Seat

ECU 1

2,3,4,

…

Operating Rear

View Camera

ECU 9

10,11,12

Ignition off

3.5 Steps to Estimate Impact of

Networking Mechanisms on

Measured Data

Steps to estimate impact of sleep mechanisms using

measured data (e.g., on prototype vehicles) can be

structured as implied in (Hong et al., 2016):

1. Select ECUs that can be assessed:

- Obtain measurement data;

- Identify if an ECU can sleep (with not too

much centralized architecture) or move to

stop. Consider whether an ECU does not need

to operate permanently (e.g., other ECUs

don’t rely on the messages) and is not safety-

critical, e.g. powertrain.

2. Perform test drives to identify ECUs that don’t

sleep (in the mentioned paper – Vacuum Pump

ECU and Body Control Module ECU (as a

combination of two ECUs: 1.with accessories

ON and 2.Accessories OFF)):

- Document behavior at startup;

- Analyze when and how the ECU starts to work

(switch-on requirements);

- Document applicable scenarios (e.g., 1. Urban

Test Drive and 2. Freeway Test Drive). Note

relevant events and context (e.g., rain

conditions are relevant to wipers; speed can be

derived from the driving cycle);

3. Estimate energy savings if the ECU could sleep

or move to stand-by in such scenarios.

3.6 Estimations in the Design Process

Different product development stages can provide

inputs for estimating PartialNW savings as follows

(Heinrich and Prehofer, 2013b):

- At the System requirements stage designers

can envision Hardware (number of ECUs,

ECU Energy Classes (see Section 3.2) and

software (Number of features, feature

computation class) elements;

- System Architecture stage adds details on HW

(Topology, Network Bandwidth) and SW

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

578

(architecture as black box, tasks, task

computation efforts);

- System design adds to HW network-specific

energy consumption and elaborates, e.g.,

using function point analysis, SW

computation efforts of tasks;

- HW/SW Development elaborates ECU Active

and sleep times and Task details.

3.7 Function Deployment

Lingadahalli et al., 2016 outline the following way to

detail function deployment and alternatives (using

Simulink SimEvents):

- Inputs to the model: (1) Feature activation

times (as time_deactivated-time_triggered);

(2) Feature-to-ECU deployments (with

alternatives), e.g. a feature deployment with

participating ECUs; and active and inactive

power consumption values (Table 2).

- Output: Power partial NW=sum(consumption

of all active ECUs)+sum(consumption of all

inactive ECUs)+x (extra power due to NW

delays and internal times).

Table 2: Consumptions of ECUs w.r.t. deployments.

ECU

Active

State

Current

Inactive

State

Current

CAN

Bus

Deploy-

ment 1

(All

ECUs)

Deploy-

ment 2

(alter-

native)

ECU1

300 mA

100 uA

Bus1

Active

ECU2

200 mA

50 uA

Bus3

Active

ECU3

500 mA

200 uA

Bus2

Active

Inactive

ECU4

800 mA

100 uA

Bus1

Active

ECU5

50 mA

5 uA

Bus1

Active

As a next step, mapping functional chains (Walla

et al., 2012) (which are active in specific conditions)

to different ECUs can help to investigate potential

power savings in more detail. For instance, if a

functional chain is not needed for a vehicle speed

greater than 20 km/h, corresponding ECUs can

degrade their performance (a technique mentioned in

Section 2.1).

3.8 Detailed Communications Model

A detailed model of power savings can consider

activation/deactivation of components (including SW

components within an ECU) and communication

power demands (Heinrich et al., 2016). It can include

CAN/Ethernet communications (CAN/Ethernet

communication controllers, transceivers, power

needed to send a message between networks, time and

power for activate/de-activate an ECU and SW

components). The corresponding steps could be as

follows:

1. Assume power consumptions w.r.t.

communications, such as:

- CAN: data rate 500 Kbit/s. Eight bytes per

message are user data and 44 bits are

communication overhead;

- Ethernet: data rate 10 Mbit/s. Maximum 1500

bytes per message are user data and 144 -

communication overhead;

- ECU boot time: 250 ms (100-200 ms to boot,

then to receive messages to work with), 2.5 W.

To de-activate -2.5W;

- To activate/deactivate a SW component

5.61mWs (5ms for self-test, load data, 1122

MW power by microcontroller);

- To transfer a message between NWs: 0.12

mWs, transmission time 0.1s;

- Energy relevant parameters of the network

(including communication controllers and

transceivers);

2. Construct dependencies of functional elements

per function; account for transferred bytes;

3. Identify components needed for speed ranges;

4. Use a context parameter (speed from the New

European Driving Cycle);

5. Consider alternative allocations of [SW

component / sensor / actuator] structure.

Compare Function-based vs Energy-focused

allocations (as mentioned in Section 2.2);

6. Obtain energy demands for: CPU, ECU offset

(the energy demand of components such as the

power supply unit and the voltage regulator),

Sensors/actuators, Communication (Comm.

Connections, transfer, listener, energy saving

mode), and adaptivity (activation/de-activation

of SW components, activation/de-activation of

ECUs)).

4 ANALYSIS

The models outlined above work with different (types

of) inputs and address specific questions. They can be

linked to each other in a generalized sequence of

estimating partial networking benefits as follows:

1. Inputs: scenarios, functions, details of ECUs

(potential function deployments, energy

consumption or energy class), communication

details (if available); →

2. Processing as actions to: Consider (alternative)

function deployments to ECUs → Identify

ECUs that can be put to sleep (by estimating

the amount of sleeping ECUs or identifying

Structuring of Methods to Estimate Beneﬁts of Partial Networking

579

ECUs based on function-to-ECUs mappings]

→ Identify ECU consumption (measurements

or estimations – individually or per ECU class);

→

3. Output as values of: fuel saved → less CO

→

money saved by avoiding the CO

penalty.

The ‘processing’ step heavily depends on the

adopted function/feature deployment (i.e., mapping)

alternatives. As the result of analyzing the literature

listed in this publication, the following (exemplary)

questions can be considered:

1. Can an ECU avoid sending those cyclic

messages that other ECUs rely on?

2. Can an ECU stay free from functions that

demand very low-latency replies?

3. Can repetitive tasks (that do not require the

processing power and flexibility of a

microprocessor) be offloaded? E.g., can an

ambient light system driven by Pulse Width

Modulation (PWM) run on a dedicated

hardware (not on the microcontroller)?

4. Can a mapping improve the response time?

5. If a group of sub-functions within an ECU is

not needed, can that ECU reduce its energy

consumption (in connection to overall structure

of energy savings mentioned in Section 2.1)?

6. Can a specific set of (sub-) functions be linked

to an ECU for energy saving:

- sub-functions not needed at specific

conditions (such as parking assist, if the

vehicle speed is greater than 20 km/h);

- a specific mode (e.g., an ECU with comfort

functionality within a parked vehicle)?

Some issues mentioned above need to be addressed

before adopting PartialNW, e.g.:

- state of ECUs after wake-up might need to be

renewed;

- errors shall be envisioned if ECUs don't know

whether other ECUs are sleeping or awake;

- potential need for extra Sleep Support that

tracks ECUs and supports wake-up/shut-down

sequences.

- timing needed (i.e., budgeting) for specific

functions, if some ECUs still need to wake up.

Related to the latter, analysing Ethernet wake-up

specifics can suggest ways to account for

performance and system segmentation based on

separating wake-up, go-to-sleep mechanisms, and in-

vehicle network segments. Specifically, it includes:

- Step-by-step wakeup. (Time to establish all

links shall be compared to the performance

needed);

- Need for an extra wire. This measure to

partition the system corresponds to a wake-up

mechanism that acts in parallel and helps to

avoid the delay of the step-by-step wakeup.

Such a measure is linked to the wiring

rationalization, when a power line or a relay is

used to wake up a group of ECUs;

- Global wake-up can complement a selective

go-to-sleep mechanism.

Altogether, moving away from the logic of having

an ECU per major (group of) functions, which can

restrict possibilities of selective sleep, Partial NW can

assist in energy savings. This section highlighted

several aspects to facilitate (cross-related) use of

relevant models, including a generalized sequence of

estimating PartialNW benefits, relevant processing-

related questions, and emerging budgeting and other

related issues.

5 CONCLUSIONS

Considering the potential energy savings that

PartialNW can provide, there is a need to understand

ways to capitalize on it. Unfortunately, the literature

on estimated potential savings sometimes misses the

description of the methodology steps, does not

explicitly outline the context, nor contain discussions

on applicability of such methods. Explicating

methods behind calculations and aligning existing

them can help to construct a structure for future

research on estimating potential energy savings. New

methods may address practical questions how to

assess specific cases by integrating (parts of) different

models and thus the decisions if specific approaches

should be adopted. While following the general

methodical steps, those new methods could be more

detailed and tailored to a specific implementation.

This paper reviewed articles related to partial

networking and illuminated their (sometimes not

explicitly documented) methods. Equipped with this

information, a designer can estimate power savings

for their system of interest using these methods and

first order values listed in this paper, as well can

further study the mentioned literature. A researcher

may further investigate the methods and construct

new models and methods by tabulating and linked

existing approaches.

Within a future research agenda, several aspects

missing in the relevant literature can be addressed,

such as connections between the context of this

research and moving-to-sleep and waking-up

mechanisms. The relation to design science could be

further strengthened. To provide such a

comprehensive view, future research may study the

generalizability limits of methods to estimate benefits

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

580

of selective sleep. It can describe a case on applying

generalized sequence of estimating partial

networking benefits, including relating it to concerns

of stakeholders. Moreover, the links between the in-

vehicle partial networking and other energy-

conscious domains (and their approaches) can be

investigated. On example is the applicability of the

methods described in this paper to other systems, such

as smart grids or smart cities.

ACKNOWLEDGEMENTS

This work was partially supported by industrial

funding and TKI (Topconsortia voor Kennis en

Innovatie) program. I would also like to thank Teun

Hendriks for comments and suggestions.

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