Series Hybrid Engine Economic Curve Self-Learning for Fuel Use
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Solution Overview
Problem
Fixed optimal economic curves in series hybrid electric vehicles fail to account for manufacturing dispersion and environmental variations, leading to suboptimal energy consumption.
Innovation Solution
A method and system for self-learning an optimal economic curve by adding a time-varying test torque to the engine's target torque, determining the minimum fuel consumption rate, and correcting the preset curve based on the test operating point to achieve a corrected optimal economic curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed optimal economic curve is used based on universal characteristics, then the control system is simple and easy to implement, but the energy consumption is suboptimal due to manufacturing dispersion and environmental variations
Solution Approach 1:
The system performs self-learning by automatically conducting tests at different operating points, collecting fuel consumption data, and generating a customized optimal economic curve without requiring manual intervention or external calibration, enabling the vehicle to adapt to its specific component characteristics
Solution Approach 2:
The optimal economic curve is transformed from a fixed universal parameter set to a variable parameter set that is customized for each vehicle based on its specific engine and motor characteristics, allowing the system to optimize energy consumption by adapting parameters to actual component variations
2Ease of manufacture
If a fixed optimal economic curve is used, then the system has low complexity and is easy to manufacture, but it cannot adapt to manufacturing dispersion and environmental changes
Solution Approach 1:
The system performs preliminary self-learning tests during the vehicle's operation to establish a customized optimal economic curve before normal operation begins, proactively adapting to the specific vehicle's characteristics rather than relying on pre-manufactured fixed curves
Solution Approach 2:
The optimal economic curve transitions from a static fixed value to a dynamic parameter that can be customized for each vehicle through self-learning, enabling the system to adapt to manufacturing dispersion and environmental variations while maintaining ease of manufacture through automated processes
Data Source
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AI summary
The present disclosure relates to the technical field of hybrid vehicle model power control, and provides a self-learning method and system for the optimal economic curve of series power generation for a hybrid vehicle model. The method comprises: adding a time-varying test torque to a target torque of an engine operating at an operating point on a preset optimal economic curve, so that the engine adjusts, on the basis of a final torque, a time-varying test operating point on a target equal-power line; acquiring a time-varying fuel consumption rate corresponding to the engine at the time-varying test operating point; determining a minimum value of the fuel consumption rate from the time-varying fuel consumption rate, and determining a test operating point of the engine corresponding to the minimum value; and on the basis of the test operating point corresponding to the minimum value, performing a self-learning correction on the preset optimal economic curve, so as to obtain a corrected optimal economic curve for a hybrid vehicle. The present disclosure mitigates the technical problem in the related art of the inability of a fixed optimal economic curve to guarantee optimal energy consumption.