Process Process Model input - output Residual generation Residual evaluation + Knowledge of faults Aug. 2017 - Feb. 2021 March 2021- Model-based development with “eFMI” From Physical Models to ECU Software Recorded July 17, 2021 Oliver Lenord (Bosch Research) voice
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Tips & Tricks Project Presentation <Delete this slide from
your final slide set>Model-based development with “eFMI” From
Physical Models to ECU Software
Recorded July 17, 2021 Oliver Lenord (Bosch Research) voice
Presenter
Presentation Notes
Welcome to “Model-based development with eFMI”. In the next 8 Minutes you’ll learn about the new eFMI standard its motivation, application and benefits. eFMI is one of the key achievements of the publicly funded European ITEA project EMPHYSIS over the past 3½ years. The eFMI specification has been handed over to the Modelica Association for further development in preparation of the first official release expected within 2021.
2
Developer
Su pp
lie rseFMI
Publicly Funded Project EMPHYSIS Partners by Country and Position in the Value Chain
OEM
Germany Bosch DLR ETAS ESI ITI AbsInt PikeTec dSPACE EFS
Sweden Dassault Systèmes AB Volvo Cars Modelon Linköping University SICS East
France Siemens SAS Dassault Systèmes SE Renault CEA University of Grenoble FH Electronics OSE Soben
Belgium Siemens NV Dana University of Antwerp
Canada Maplesoft
Presenter
Presentation Notes
The EMPHYSIS project with 25 partners from 5 countries was led by Prof. Martin Otter from DLR and Oliver Lenord from Bosch. The partners of the consortium and the members of the associated OEM Advisory Board cover the entire value chain from physical modeling to ECU software including tool vendors as well as automotive suppliers and OEMs. Leading companies ensured a good market access. The OEM Advisory Board provided the market pull and direction. All partners, even competing companies, joint forces to work out a conceptually compelling and technically outstanding open standard to enable new ways of model-based development of ECU software.
3
5
Benefits of Lower SW Complexity
Less SW maintenance effort
Less SW calibration effort
Less ECU resources demand
Area LoC (Lines of Code) Color BMI (Bosch Maintainability Index )
Engine Control SW Complexity Measurement
Presenter
Presentation Notes
The motivation for Bosch Corporate Research to initiate this project back in 2015 was driven by the business needs expressed by our industrial partners in the Bosch business units. A growing software stack whose complexity has become a threat in terms of maintenance costs and a limiting factor for new innovative functions for systems of increasing complexity. New ways of developing embedded software in a faster and more sustainable way was the ambitious project goal.
4
Presentation Notes
Starting point of the eFMI approach is the observation that understanding how things work is fundamental for developing successful products. <click> Modeling & simulation has been used over decades to build-up and utilize this knowledge. Thousands of engineers at Bosch leverage simulation techniques to <click> build the high quality products we’re selling world wide. With “code” being the fuel of the 21st century, <click> software has become a driver for new innovations for smarter products. Finding ways to dissolve simulation models into software to inject them directly into the products is the key to derive advanced functions faster. <click> Bridging the gap between the modeling and simulation domain and the embedded software world is the core idea behind the EMPHYSIS project leading to eFMI as new exchange format between these domains.
5
Presentation Notes
This idea has not only been specified on paper in form of the eFMI specification, but also realized by a large number of tools from modeling tools over ECU code generating tools to validation and verification tools. With 13 tool prototypes at a high level of maturity one could say that eFMI is established before its official release. The developed tool chain has been thoroughly tested and cross checked. An eFMU Compliance Checker is available open source for everybody to verify their implementation against the standard.
6
ECU Runtime Performance: In all cases the eFMI generated code is
below the +25% KPI margin. In 5 of 6 examples an eFMI exists that
outperforms the hand code. In average the best performing eFMUs are
26% faster than the hand code.
eFMI Readiness D7.2 eFMI Performance Assessment (Bosch)
# Name Difficulty* Average Min. Max. M03 PID low -7% -27% +29% M04 Drivetrain medium +9% -21% +44% M15 Air System medium +38% -7% +132% M10 Inverse Slider Crank high -65% -66% -64% M16 ROM high +4% +1% +6% M14 Rectifier high +3% -33% +44%
Average -3% -26% 32%
ECU Runtime Performance
eFMI
KPI
*Difficulty for an automated procedure to achieve same quality as manual implementation.
Relative ECU Runtime
Textual modeling compact formulation
Presentation Notes
Key for Bosch was to assess the quality of the generated code. It was clear to us that compromising on runtime performance and resource demand would undermine the user acceptance. A better productivity through an automated process was considered acceptable at the cost of 25% loss. Six test cases have been defined by our expert embedded software developers and manually implemented to be the baseline for the benchmark of the eFMU code evaluated on a Bosch MDG1 control unit. As a result of this assessment did the auto-generated code in all case reach the KPI of less than 25% and to our surprise even outperformed the hand coded solution in 5 out of the 6 examples. This is a technically outstanding result.
7
93 %
52 %
18 %
-20%
0%
20%
40%
60%
80%
100%
0
20
40
60
80
100
120
140
Manual eFMI Manual eFMI Manual eFMI Manual eFMI Manual eFMI Manual eFMI
PID Drivetrain Inverse Slider Crank Rectifier Air System ROM
M03 M04 M10 M14 M15 M16
De ve
lo pm
Better code, less effort!
Presentation Notes
Furthermore the working hours have been counted for both the eFMI workflow and the state-of-the-art manual implementation. As a result it could be shown that especially for the examples taking advantage of a high level of reuse based on Modelica libraries a tremendous gain in productivity can be achieved. Bottom-line: <click> eFMI enables better code with less effort.
8
Binary Code
Advanced Emergency Braking System controller
Hybrid engine torque prediction using scale model Neural Network
Kalman Filter air filling estimation using scale model Neural Network predictor
D7.06 Renault Demonstrator
Vehicle dynamics control by Parameterized Nonlinear Model Predictive Control for semi-active control with Neural Network prediction model
Transmission model as virtual sensor
EMPHYSIS Demonstrators
D7.4 Model-based Diagnosis of Thermo System
eFMI DAE Eq. Code
D7.14 Dassault Systèmes Demonstrator
Presenter
Presentation Notes
Over the course of the project nine demonstrators have been developed to show case and evaluate the industrial grade readiness of the eFMI tool chain. The impressive results include a wide variety of physics-based state estimators that convinced not only our industrial partners but also the ITEA review board to consider the EMPHYSIS project with excellent ratings. Finally we're looking forward to the ITEA Award Ceremony in September 2021, when the EMPHYSIS project will be honored with the Vice-chairman Award of Excellence for outstanding results in all three categories: innovation, business impact and standardization.
9
Automation Model Transformations Code Generation
Seamless Tool Chain
Separation of Concerns Physical Behavior Data Flow Embedded Code
Software Innovations Tool Vendors Added Value Expand Market in MBD Domain
Supplier/OEM New Advanced Functions Replace HW with SW New Modes of Collaboration
eFMI Outlook Business Impact
Component Libraries
Data Flow
Services Functions RTPC, e.g. ETAS RTPC
.bin
Presentation Notes
This brings us back to our initial goals. As briefly outlined in this talk: <click> By tearing down the walls between physical modeling and embedded software, the overall productivity can be largely improved. The eFMI workflow helps to manage complexity better by a separation of concerns and allowing to manage complex system on a higher level of abstraction. Finally all this is an enabler of new software innovations for suppliers and OEMs to sell more and more advanced functions and tool vendors to be among the first to provide tool support for this breaking technology.
10
Voice of the Customer Statements of Members of the OEM Advisory Board
“Reusing the same, universal and physical plant model as eFMU in MiL, SiL, HiL and on the ECU is a technological breakthrough with considerable potential to reduce the development time.” (Zdenk Husár, Daimler)
“What we demonstrated using eFMI for the model-based development of a virtual sensor is the way to do it.” (Per Jacobsson, Volvo Cars)
“eFMI will revolutionize the translation of models to embedded SW.” (Yutaka Hirano, JSAE)
JSAE: Japanese Society of Automotive Engineering
Presenter
Presentation Notes
As a closing note I want to refer to what the members of our OEM Advisory Board said. These positive statements encourage us to expect a strong market pull for eFMI so that in a couple years from know we can truly say: Yes, eFMI has revolutionized the model-based development of embedded software. Thanks for watching.
Process
Model-based development with “eFMI” From Physical Models to ECU Software
Recorded July 14, 2021 Oliver Lenord (Bosch Research)
Look-up EMPHYSIS results: https://emphysis.github.io/ Visit us on https://efmi-standard.org/ Join the Modelica Association Project: MAP-efmi
https://modelica.org/
Presenter
Presentation Notes
Does this sound exciting to you also? Don’t hesitate to contact us. We're looking forward to welcome you in the eFMI community. Wanna get involved? Become member of the new Modelica Association project MAP-efmi.
Model-based development with “eFMI”From Physical Models to ECU Software
Publicly Funded Project EMPHYSISPartners by Country and Position in the Value Chain
Why?Challenges in the Field of Automotive Embedded Systems
Why?Bridge the gap
ReadinesseFMI Tool Chain
Slide Number 8
eFMI OutlookBusiness Impact
Voice of the CustomerStatements of Members of the OEM Advisory Board
Recorded July 17, 2021 Oliver Lenord (Bosch Research) voice
Presenter
Presentation Notes
Welcome to “Model-based development with eFMI”. In the next 8 Minutes you’ll learn about the new eFMI standard its motivation, application and benefits. eFMI is one of the key achievements of the publicly funded European ITEA project EMPHYSIS over the past 3½ years. The eFMI specification has been handed over to the Modelica Association for further development in preparation of the first official release expected within 2021.
2
Developer
Su pp
lie rseFMI
Publicly Funded Project EMPHYSIS Partners by Country and Position in the Value Chain
OEM
Germany Bosch DLR ETAS ESI ITI AbsInt PikeTec dSPACE EFS
Sweden Dassault Systèmes AB Volvo Cars Modelon Linköping University SICS East
France Siemens SAS Dassault Systèmes SE Renault CEA University of Grenoble FH Electronics OSE Soben
Belgium Siemens NV Dana University of Antwerp
Canada Maplesoft
Presenter
Presentation Notes
The EMPHYSIS project with 25 partners from 5 countries was led by Prof. Martin Otter from DLR and Oliver Lenord from Bosch. The partners of the consortium and the members of the associated OEM Advisory Board cover the entire value chain from physical modeling to ECU software including tool vendors as well as automotive suppliers and OEMs. Leading companies ensured a good market access. The OEM Advisory Board provided the market pull and direction. All partners, even competing companies, joint forces to work out a conceptually compelling and technically outstanding open standard to enable new ways of model-based development of ECU software.
3
5
Benefits of Lower SW Complexity
Less SW maintenance effort
Less SW calibration effort
Less ECU resources demand
Area LoC (Lines of Code) Color BMI (Bosch Maintainability Index )
Engine Control SW Complexity Measurement
Presenter
Presentation Notes
The motivation for Bosch Corporate Research to initiate this project back in 2015 was driven by the business needs expressed by our industrial partners in the Bosch business units. A growing software stack whose complexity has become a threat in terms of maintenance costs and a limiting factor for new innovative functions for systems of increasing complexity. New ways of developing embedded software in a faster and more sustainable way was the ambitious project goal.
4
Presentation Notes
Starting point of the eFMI approach is the observation that understanding how things work is fundamental for developing successful products. <click> Modeling & simulation has been used over decades to build-up and utilize this knowledge. Thousands of engineers at Bosch leverage simulation techniques to <click> build the high quality products we’re selling world wide. With “code” being the fuel of the 21st century, <click> software has become a driver for new innovations for smarter products. Finding ways to dissolve simulation models into software to inject them directly into the products is the key to derive advanced functions faster. <click> Bridging the gap between the modeling and simulation domain and the embedded software world is the core idea behind the EMPHYSIS project leading to eFMI as new exchange format between these domains.
5
Presentation Notes
This idea has not only been specified on paper in form of the eFMI specification, but also realized by a large number of tools from modeling tools over ECU code generating tools to validation and verification tools. With 13 tool prototypes at a high level of maturity one could say that eFMI is established before its official release. The developed tool chain has been thoroughly tested and cross checked. An eFMU Compliance Checker is available open source for everybody to verify their implementation against the standard.
6
ECU Runtime Performance: In all cases the eFMI generated code is
below the +25% KPI margin. In 5 of 6 examples an eFMI exists that
outperforms the hand code. In average the best performing eFMUs are
26% faster than the hand code.
eFMI Readiness D7.2 eFMI Performance Assessment (Bosch)
# Name Difficulty* Average Min. Max. M03 PID low -7% -27% +29% M04 Drivetrain medium +9% -21% +44% M15 Air System medium +38% -7% +132% M10 Inverse Slider Crank high -65% -66% -64% M16 ROM high +4% +1% +6% M14 Rectifier high +3% -33% +44%
Average -3% -26% 32%
ECU Runtime Performance
eFMI
KPI
*Difficulty for an automated procedure to achieve same quality as manual implementation.
Relative ECU Runtime
Textual modeling compact formulation
Presentation Notes
Key for Bosch was to assess the quality of the generated code. It was clear to us that compromising on runtime performance and resource demand would undermine the user acceptance. A better productivity through an automated process was considered acceptable at the cost of 25% loss. Six test cases have been defined by our expert embedded software developers and manually implemented to be the baseline for the benchmark of the eFMU code evaluated on a Bosch MDG1 control unit. As a result of this assessment did the auto-generated code in all case reach the KPI of less than 25% and to our surprise even outperformed the hand coded solution in 5 out of the 6 examples. This is a technically outstanding result.
7
93 %
52 %
18 %
-20%
0%
20%
40%
60%
80%
100%
0
20
40
60
80
100
120
140
Manual eFMI Manual eFMI Manual eFMI Manual eFMI Manual eFMI Manual eFMI
PID Drivetrain Inverse Slider Crank Rectifier Air System ROM
M03 M04 M10 M14 M15 M16
De ve
lo pm
Better code, less effort!
Presentation Notes
Furthermore the working hours have been counted for both the eFMI workflow and the state-of-the-art manual implementation. As a result it could be shown that especially for the examples taking advantage of a high level of reuse based on Modelica libraries a tremendous gain in productivity can be achieved. Bottom-line: <click> eFMI enables better code with less effort.
8
Binary Code
Advanced Emergency Braking System controller
Hybrid engine torque prediction using scale model Neural Network
Kalman Filter air filling estimation using scale model Neural Network predictor
D7.06 Renault Demonstrator
Vehicle dynamics control by Parameterized Nonlinear Model Predictive Control for semi-active control with Neural Network prediction model
Transmission model as virtual sensor
EMPHYSIS Demonstrators
D7.4 Model-based Diagnosis of Thermo System
eFMI DAE Eq. Code
D7.14 Dassault Systèmes Demonstrator
Presenter
Presentation Notes
Over the course of the project nine demonstrators have been developed to show case and evaluate the industrial grade readiness of the eFMI tool chain. The impressive results include a wide variety of physics-based state estimators that convinced not only our industrial partners but also the ITEA review board to consider the EMPHYSIS project with excellent ratings. Finally we're looking forward to the ITEA Award Ceremony in September 2021, when the EMPHYSIS project will be honored with the Vice-chairman Award of Excellence for outstanding results in all three categories: innovation, business impact and standardization.
9
Automation Model Transformations Code Generation
Seamless Tool Chain
Separation of Concerns Physical Behavior Data Flow Embedded Code
Software Innovations Tool Vendors Added Value Expand Market in MBD Domain
Supplier/OEM New Advanced Functions Replace HW with SW New Modes of Collaboration
eFMI Outlook Business Impact
Component Libraries
Data Flow
Services Functions RTPC, e.g. ETAS RTPC
.bin
Presentation Notes
This brings us back to our initial goals. As briefly outlined in this talk: <click> By tearing down the walls between physical modeling and embedded software, the overall productivity can be largely improved. The eFMI workflow helps to manage complexity better by a separation of concerns and allowing to manage complex system on a higher level of abstraction. Finally all this is an enabler of new software innovations for suppliers and OEMs to sell more and more advanced functions and tool vendors to be among the first to provide tool support for this breaking technology.
10
Voice of the Customer Statements of Members of the OEM Advisory Board
“Reusing the same, universal and physical plant model as eFMU in MiL, SiL, HiL and on the ECU is a technological breakthrough with considerable potential to reduce the development time.” (Zdenk Husár, Daimler)
“What we demonstrated using eFMI for the model-based development of a virtual sensor is the way to do it.” (Per Jacobsson, Volvo Cars)
“eFMI will revolutionize the translation of models to embedded SW.” (Yutaka Hirano, JSAE)
JSAE: Japanese Society of Automotive Engineering
Presenter
Presentation Notes
As a closing note I want to refer to what the members of our OEM Advisory Board said. These positive statements encourage us to expect a strong market pull for eFMI so that in a couple years from know we can truly say: Yes, eFMI has revolutionized the model-based development of embedded software. Thanks for watching.
Process
Model-based development with “eFMI” From Physical Models to ECU Software
Recorded July 14, 2021 Oliver Lenord (Bosch Research)
Look-up EMPHYSIS results: https://emphysis.github.io/ Visit us on https://efmi-standard.org/ Join the Modelica Association Project: MAP-efmi
https://modelica.org/
Presenter
Presentation Notes
Does this sound exciting to you also? Don’t hesitate to contact us. We're looking forward to welcome you in the eFMI community. Wanna get involved? Become member of the new Modelica Association project MAP-efmi.
Model-based development with “eFMI”From Physical Models to ECU Software
Publicly Funded Project EMPHYSISPartners by Country and Position in the Value Chain
Why?Challenges in the Field of Automotive Embedded Systems
Why?Bridge the gap
ReadinesseFMI Tool Chain
Slide Number 8
eFMI OutlookBusiness Impact
Voice of the CustomerStatements of Members of the OEM Advisory Board
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