Vehicle Powertrain Co-Optimization Through Cost Function Decomposition
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Solution Overview
Problem
Existing methods struggle to efficiently model and predict the performance of vehicles due to the complexity of interrelated components, necessitating a need for improved methods and systems for co-optimization of vehicle systems.
Innovation Solution
A computer-implemented method that decomposes a cost function into multiple control problems to optimize vehicle powertrain performance, considering various optimization variables and control variables, and generates a solution to control the powertrain based on these problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive vehicle model including all interrelated components is created to predict vehicle performance, then the accuracy of performance prediction is improved, but the model complexity increases significantly
Solution Approach 1:
The patent segments the complex vehicle model into multiple subsystems (powertrain subsystem, chassis subsystem, body subsystem, etc.), each modeled separately with its own state variables and parameters. This allows accurate representation of interrelated components while managing complexity through modular subsystem modeling rather than a monolithic model.
2Productivity
If co-optimization of all vehicle systems is performed to maximize efficiency and reduce emissions, then the overall vehicle performance is improved, but the computational complexity and difficulty of control increases
Solution Approach 1:
The control system is segmented into subsystem controllers (powertrain controller, chassis controller, body controller) that each optimize their respective subsystems. The powertrain controller handles engine, transmission, and energy management separately from other subsystems, reducing computational complexity while achieving co-optimization through coordinated control of multiple independent controllers.
Solution Approach 2:
The patent implements dynamic control strategies where controller parameters and optimization objectives are adjusted in real-time based on operating conditions. The powertrain controller adapts its control parameters dynamically to maximize efficiency across varying vehicle states, allowing flexible optimization without requiring a static complex control architecture.
3Manufacturing precision
If detailed state variables and parameters are tracked for all components to enable precise control, then the control accuracy is improved, but the data processing and computational requirements increase
Solution Approach 1:
The patent extracts and focuses on only the critical state variables and parameters needed for powertrain control (engine speed, torque, transmission gear, battery state of charge, etc.) rather than processing all possible vehicle system data. This selective extraction of essential variables maintains control accuracy while significantly reducing computational burden and data processing requirements.
Data Source
AI summary
An example computer implemented method includes receiving a plurality of optimization variables; receiving a cost function representing a vehicle system, where the cost function includes a plurality of weights assigned to the plurality of optimization variables; decomposing the cost function into a plurality of problems; and generating a solution to the cost function by solving the plurality of problems. An example system includes a vehicle powertrain and a computing device configured to receive a plurality of optimization variables; receive a cost function representing a vehicle system, where the cost function comprises a plurality of weights assigned to the plurality of optimization variables; decompose the cost function into a plurality of control problems; generate a solution to the cost function by solving the plurality of control problems; and control the vehicle powertrain based on the solution to the cost function.