Electric Machine Torque Estimation With Speed-Segmented Polynomials
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Solution Overview
Problem
Existing torque estimation methods for electric machines in vehicles are prone to interference from system calculations and do not adequately compensate for inverter losses, leading to inaccuracies and reduced reliability.
Innovation Solution
Generating polynomials to estimate torque output, with separate polynomials for lower and higher electric machine speeds, to simplify and improve the reliability of torque estimation, compensating for inverter losses and reducing system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional torque estimation methods are used, then torque calculation is performed, but the estimate is prone to interference from system calculations and does not compensate for inverter losses, leading to reduced accuracy and reliability
Solution Approach 1:
The torque estimation is segmented into two distinct polynomial models: one for lower speed ranges and another for higher speed ranges. Each polynomial is optimized for its specific speed range, allowing accurate torque estimation across the entire operating spectrum while compensating for inverter losses in each regime.
2Measurement precision
If complex torque estimation calculations are performed, then more comprehensive torque data is obtained, but processing time increases and system complexity increases
Solution Approach 1:
The patent uses simple polynomial equations that can be rapidly evaluated computationally. These lightweight mathematical models provide sufficient accuracy for control applications without requiring complex, time-consuming calculations, enabling real-time torque estimation.
3Measurement precision
If multiple parameters are used in torque estimation, then more comprehensive torque calculation is achieved, but the system complexity and processing requirements increase
Solution Approach 1:
The patent extracts and compensates specifically for inverter losses from the overall torque estimation process. By identifying and separately accounting for the dominant source of error (inverter losses), the system achieves improved accuracy without requiring complex multi-parameter models that would increase system complexity.
Data Source
AI summary
Methods and systems for estimating torque of an electric machine that propels a vehicle are described. The methods and systems may generate torque estimates that may be applied to higher and lower priority systems. In one example, a contactor may be selectively opened according to a torque estimate that is based on interpolating between two curves.