32nd Digital Avionics Systems Conference

32nd Digital Avionics Systems Conference Knowledge Based Engineering to Support Electric and Electronic System Design and Automatic Control Software Development Fengnian Tian Mark Voskuijl

Challenge the future

1

1. Introduction

The Percentage of Safety Recalls Vs. Global Motor Vehicle Production

The Percentage of Electronic Failures in the Safety Recalls


2

2. Methodology

2.1 Design and Engineering Engine (DEE) • • • •

Initiator Multi-Model Generator (MMG) Converger & Evaluator Communication module


3

2. Methodology

2.2 Integration E/E System Development into the DEE

2.2.1 Logical System Architecture Integration


4

2. Methodology


2.2.2 Technical System Architecture Integration


5

2. Methodology


2.2.3 Software Components Integration


6

2. Methodology

2.2 Integration E/E System Development into the DEE 2.2.4 Test and Parameter Optimization for Software Components


7

3. Test Case - The Development of the Antilock Braking System (ABS) for a Novel Electric Vehicle Configuration It is well known that the driving range of electric vehicles is always a critical technical specification for the designer, car manufacture and consumer. Nowadays, the driving range is limited by the battery pack equipped on the vehicle. We propose a novel electric vehicle configuration named A-line, where passengers are seated in line.  Small frontal area Long driving range  Reduced curb weight Alleviating traffic  Less parking area congestion Challenge the future

8

3. Test Case

3.1 Modeling the Physical Plant of ABS

Trimetric View of the Geometries for the Physical Plant (1 - Wheel Speed Sensor, 2 - ECU, 3 Electromagnetic Valve, 4 - Master Cylinder, 5 - Brake Pedal, 6 - Disk Brake, 7 - Wheel) Challenge the future

9

3. Test Case

3.2 Software Component Generation


10

3. Test Case

3.2 Software Component Generation 3.2.1 Data Model Generation for ABS a1 = a1'× (1 + (

G×R − 1) ×100%) G '× R '

Where a1 is the new value of the wheel speed derivative threshold; a1’ is the old value of the wheel speed derivative threshold; G is the new value of gross weight; G’ is the old value of gross weight; R is the new value of front axle load ratio; R’ is the old value of front axle load ratio.

Generation of Data Model for the ABS Software Components Challenge the future

11

3. Test Case

3.2 Software Component Generation 3.2.2 Behavior Model Generation for ABS The behavior model of the logic controller is responsible for switching stages and outputting control logic.

Generation of Behavior Model for the ABS Software Components Challenge the future

12

3. Test Case

3.2 Software Component Generation 3.2.3 Real-time Model Generation


13

3. Test Case

3.3 Simulation and Comparison Parameters for the Dynamic Simulation Parameters Gross Weight [kg] Number of Axles Front Axle Load Ratio

Concept 1 1000 2 49.2%

Concept 2 1000 2 51%

Wheelbase [mm] Track [mm] Tire

2400 1000 205/55 R16

2400 1000 205/55 R16

Initial Velocity [km/h] Hydraulic Pressure [Mpa]

100 1

100 1

Hydraulic Delay [s]

0.01

0.01

Wheel Speed Derivative Threshold a1 and a2 Front axle load ratio

Front Wheels

Rear Wheels

49.2% Concept1 51% Concept2

-92 -95

-80 -77

94 94


0 0

14

3. Test Case

3.3 Simulation and Comparison

Vehicle Speed Vs. Front Wheel Speed for the Concept 1 and 2

Relative Slip for the Concept 1 and 2


15

4. Conclusion •

• • • •

The Design and Engineering Engine (DEE) has been shown its ability able to support the development of E/E systems, finishing a complete design from the logical, technical system architecture to the software components. The physical plant as well as the software components of the ABS have been generated successfully by the DEE. The data model of the software components can be automatically calculated and updated when the physical plant of the E/E systems or even the top level overall design changes. In future, the MMG is expected to support the generation of generic real-time models of the software components. The proposed design methods and tools can in principle be applied to any dynamic system with a high level of software integration, such as e.g. unmanned aerial vehicles.


16