Powertrain Extrema Identification via Constraint Segmentation
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Solution Overview
Problem
Existing powertrain control systems face challenges in rapidly and accurately identifying extrema for real-time control due to the computational intensity of evaluating all combinations of constraints, limiting their ability to optimize torque and rotational speed efficiently.
Innovation Solution
A method is introduced that determines an objective function for powertrain components, evaluates permutations of independent and dependent variables, and identifies overall minimum and maximum values to control powertrain operations, reducing computational load by optimizing the evaluation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Simplex method is used to identify extrema by evaluating all combinations of constraints, then accurate results are achieved, but substantial processor resources are consumed and real-time control capability is limited
Solution Approach 1:
The patent segments the constraint evaluation process by identifying and evaluating only the critical permutations that define the extrema, rather than evaluating all possible constraint combinations. This segmentation reduces the computational scope while maintaining accuracy in identifying the optimal operating points for the powertrain system.
Solution Approach 2:
The patent applies partial action by evaluating only the necessary permutations required to identify extrema, rather than performing exhaustive evaluation of all constraint combinations. This selective approach achieves sufficient accuracy for real-time control without the excessive computational burden of complete enumeration.
2Reliability
If all combinations of constraints are evaluated to identify extrema, then comprehensive optimization is achieved, but the computational load increases substantially
Solution Approach 1:
The patent extracts and identifies only the critical permutations that directly influence the extrema of the objective function, separating these essential evaluations from the redundant evaluation of all constraint combinations. This extraction maintains comprehensive optimization for the critical cases while eliminating unnecessary computational overhead.
Solution Approach 2:
The patent performs preliminary identification of critical permutations and their relationships to the objective function extrema before executing the full optimization process. This preliminary action allows the system to focus computational resources on the most impactful evaluations, reducing overall processor resource consumption while maintaining optimization reliability.
Data Source
AI summary
A method for operating a powertrain system includes determining an objective function for an object component of interest of the powertrain system. Constraints are determined for a plurality of independent variables and dependent variables. Permutations of the objective function are evaluated with reference to the independent variables and the dependent variables. The objective function is evaluated to determine maximum and minimum values for the objective function for each of the permutations. Overall minimum and maximum values for the objective function are determined based upon the maximum and minimum values for the objective function for each of the permutations. Operation of the powertrain system associated with the object component of interest is controlled based upon the overall minimum and maximum values for the objective function.