Time-Discrete Selective State Space Model for Vehicle Control
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Solution Overview
Problem
The existing controller development process for motor vehicles faces challenges in efficiently managing program and data versions, leading to increased tuning complexity and resource wastage due to the need for frequent changes and individual project requirements, which results in a conflict between maintaining a common building block software and accommodating unique project needs.
Innovation Solution
A universal modeling method is introduced, involving the provision of an input signal set from vehicle sensors, selection of a modeling signal set based on system architecture, and determination of an output signal set using a time-discrete selective state space model modeling function, allowing for automated control of actuators and enabling accurate mapping of motor vehicle modes and physical responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a common building block software framework is maintained across multiple projects, then resource wastage is reduced and development efficiency is improved, but tuning complexity increases and individual project requirements cannot be easily accommodated
Solution Approach 1:
The controller software is segmented into distinct modules: a common building block framework containing reusable program code, and separate data version files containing project-specific parameters and tuning data. This segmentation allows the framework to remain unchanged across projects while individual project requirements are met through data configuration.
Solution Approach 2:
Project-specific customization data is extracted into separate data version files, distinct from the common program framework. This extraction enables individual project requirements to be accommodated without modifying the shared software framework, reducing both tuning complexity and resource wastage.
2Adaptability or versatility
If the building block software framework is changed to accommodate individual project requirements, then project-specific needs are met, but the duration of development loops increases due to confirmation overhead
Solution Approach 1:
The software is divided into immutable framework code and mutable data versions. This segmentation allows project-specific customization through data configuration rather than code modification, eliminating the need for extensive confirmation processes and reducing development loop duration.
Solution Approach 2:
The common building block framework is prepared in advance with generic functionality that can accommodate multiple projects. This preliminary preparation allows project-specific requirements to be met through simple data configuration rather than time-consuming framework modifications and confirmations.
3Loss of energy
If the building block software contains only the strict overlap of functionality across all projects, then resource wastage is minimized, but individual project requirements must be incorporated as balcony solutions increasing complexity
Solution Approach 1:
The software architecture segments common functionality into the building block framework and project-specific requirements into separate data version files. This eliminates the need for balcony solutions by providing a formal mechanism for incorporating individual project requirements without increasing overall system complexity.
Solution Approach 2:
The building block framework is designed with universal functionality that can serve multiple projects through configuration rather than code changes. This multi-functionality reduces resource wastage by avoiding duplication while maintaining the ability to meet individual project requirements through data customization.
Data Source
AI summary
A universal modeling method is provided for a motor vehicle, the universal modeling method including: providing an input signal set, the input signal set comprising those signals of respective sensors of the motor vehicle which can be relevant for the control of devices of the motor vehicle; selecting a set of modeling signals from the input signal set as a function of a system architecture of the motor vehicle; and determining an output signal set by way of a time-discrete selective state space model modeling function taking into account the set of modeling signals. In this case, the output signal set functions as a signal set for controlling corresponding actuators of the devices of the motor vehicle.