Motor Vehicle Control Device Dynamic Optimization Method Selection
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Solution Overview
Problem
Existing methods for optimizing motor vehicle operation rely on a single predefined optimization algorithm, which is not robust and efficient across varying driving situations, such as uphill driving with a combustion engine, and fail to account for factors like road topology and presence of other road users.
Innovation Solution
A method that selects the most suitable optimization method from a plurality of alternatives based on the current driving situation, using techniques like dynamic programming, heuristics, or genetic optimization, to determine an optimal operating mode that minimizes operating costs, fuel consumption, or pollutant emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single predefined optimization algorithm is used, then the method is simple to implement, but the speed and robustness of optimization are strongly influenced by the driving situation
Solution Approach 1:
The system dynamically selects from multiple optimization algorithms based on the current driving situation. The control device determines the driving situation and selects an appropriate optimization algorithm from a plurality of available algorithms, allowing the optimization approach to adapt to varying conditions such as uphill/downhill driving, traffic jams, or open roads, thereby improving robustness while maintaining implementation feasibility
Solution Approach 2:
The system changes the optimization algorithm parameter (which specific algorithm is used) based on driving situation parameters. By monitoring driving situation characteristics and selecting different algorithms accordingly, the system optimizes performance for each specific scenario while keeping the overall implementation structure manageable
2Measurement precision
If complex heuristic optimization methods are used for uphill driving, then optimization accuracy may be improved, but the method becomes not particularly robust and difficult to implement
Solution Approach 1:
The system dynamically adjusts the complexity of the optimization method by selecting different algorithms based on the driving situation. For uphill driving scenarios, appropriate algorithms are selected from the plurality of available optimization methods, balancing accuracy requirements with implementation complexity without requiring a single overly complex method to handle all scenarios
Solution Approach 2:
The optimization process is segmented into different algorithmic approaches for different driving situations. Instead of using one complex heuristic method for all cases, the system divides the optimization task by selecting from multiple specialized algorithms suited to specific conditions, reducing the complexity burden on any single method while maintaining overall optimization accuracy
3Ease of manufacture
If closed mathematical methods are used, then implementation becomes easier, but many areas of the solution space are excluded causing problems with computing time and memory consumption
Solution Approach 1:
The system dynamically selects between closed mathematical methods and other optimization approaches based on the driving situation. By monitoring the current scenario and selecting the most appropriate algorithm from multiple options, the system achieves ease of implementation when using closed methods while avoiding their limitations in exploring the full solution space when other methods are selected
Solution Approach 2:
The control device is designed with multi-functionality to execute multiple different optimization algorithms. This universal capability allows the system to switch between closed mathematical methods for ease of implementation and other algorithms when broader solution space exploration is needed, maintaining both implementation simplicity and computing efficiency across different scenarios
4Reliability
If route topology and presence of other road users are considered, then optimization robustness is improved, but the complexity of determining the driving situation increases
Solution Approach 1:
The determination of driving situation is segmented into consideration of route topology characteristics and presence of other road users as separate but integrated factors. The control device evaluates these distinct aspects and uses their combination to select appropriate optimization algorithms, improving robustness through comprehensive situation awareness while managing complexity through structured analysis of individual factors
Solution Approach 2:
The driving situation determination acts as an intermediary layer between raw sensor data and optimization algorithm selection. This intermediary process synthesizes information about route topology and other road users into a categorized driving situation that then guides algorithm selection, improving robustness while managing the complexity of situation determination through a structured intermediate representation
Data Source
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AI summary
The invention relates, inter alia, to a method for optimizing the operation of a motor vehicle (10). The method comprises providing (S1) a plurality of optimization methods (M1, ..., MN); determining (S2) a driving situation of the motor vehicle (10); selecting (S3) an optimization method from the provided plurality of optimization methods (M1, ..., MN) depending on the determined driving situation of the motor vehicle (10); and determining (S4) an optimal operating mode of the motor vehicle (10) with respect to a given target criterion using the selected optimization method. Furthermore, the invention relates to a control device (1) configured for carrying out the method, and to a motor vehicle (10) with such a control device (1).