Dynamic Vehicle Parameter Estimation for Powertrain Control
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Solution Overview
Problem
Existing vehicle control systems face challenges in dynamically determining vehicle parameters such as power loss due to aerodynamic drag, rolling resistance, and powertrain losses, due to complexity, computational burden, and reliability issues.
Innovation Solution
A method involving an electronic control system that estimates coefficients of a vehicle loss model, evaluates convergence criteria, sets converged values, determines vehicle powertrain commands, and transmits these commands to control powertrain components, using a combination of sensors, actuators, and predictive models to optimize powertrain operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic determination of vehicle parameters is implemented, then powertrain performance is improved, but computational burden increases
Solution Approach 1:
The vehicle loss model is segmented into multiple coefficients (aero drag coefficient, rolling resistance coefficient, powertrain loss coefficient) that are estimated independently through iterative optimization. This segmentation allows the complex dynamic determination problem to be broken down into manageable sub-problems, reducing computational burden while maintaining accuracy in powertrain performance optimization.
Solution Approach 2:
The system performs preliminary estimation of vehicle parameters using sensor data before final powertrain control decisions are made. By pre-estimating coefficients such as aerodynamic drag and rolling resistance using available sensor measurements and iterative optimization, the system reduces the computational load during real-time control while ensuring accurate powertrain performance optimization.
2Measurement precision
If dynamic determination of vehicle parameters is implemented, then control accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs feedback mechanisms where sensor measurements (vehicle speed, acceleration, engine parameters) are continuously fed into the vehicle loss model, and the estimated coefficients are used to adjust powertrain control in real-time. This feedback loop ensures high control accuracy by adapting to actual vehicle conditions while managing system complexity through structured estimation algorithms.
Solution Approach 2:
The vehicle control system performs self-characterization by autonomously estimating its own loss coefficients (aerodynamic drag, rolling resistance, powertrain losses) using onboard sensor data and iterative optimization algorithms. This self-service capability eliminates the need for external calibration equipment or complex manual testing, improving control accuracy while keeping the system relatively simple.
3Measurement precision
If iterative coefficient estimation is used, then parameter accuracy is improved, but computation time increases
Solution Approach 1:
The iterative coefficient estimation is performed periodically at predetermined intervals rather than continuously, balancing parameter accuracy with computation time. The system estimates vehicle loss coefficients at specific time points or under certain conditions, allowing sufficient time for accurate iterative optimization while avoiding excessive computational burden from continuous real-time estimation.
Solution Approach 2:
The system performs preliminary estimation of vehicle parameters using available sensor data before final powertrain control decisions are made. By pre-estimating coefficients such as aerodynamic drag and rolling resistance using available sensor measurements and iterative optimization, the system reduces the computational load during real-time control while ensuring accurate powertrain performance optimization.
Data Source
AI summary
Apparatuses, methods, systems and controls including dynamic vehicle parameter determination are disclosed. One embodiment is a method of operating a vehicle system including a powertrain comprising a prime mover structured to propel the vehicle, and an electronic control system in operative communication with the prime mover and the transmission. The method includes estimating a plurality of coefficients of a vehicle loss model, evaluating a convergence criterion for the plurality of estimated coefficients, setting converged values of the plurality of coefficients if the convergence criterion is satisfied, determining a vehicle powertrain command utilizing the converged values of the plurality of coefficients, and transmitting a vehicle powertrain command to control operation of one or more powertrain components.