Propulsion Battery RUL Estimation from Charging Voltage Aging Modes
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Solution Overview
Problem
Current methods for determining the remaining useful lifetime of propulsion batteries in vehicles lack accuracy due to insufficient consideration of different aging mechanisms, which affects the prediction of battery performance and service planning.
Innovation Solution
A method that constructs a voltage feature vector from charging event data, maps it to a battery state of health model incorporating aging mode distribution, and uses a physical aging model with state of health parameters and aging mode distribution to predict the remaining useful lifetime, accounting for various aging mechanisms such as active material loss and solid electrolyte interface growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional state of health monitoring methods are used, then the monitoring process is simple, but the prediction accuracy of remaining useful lifetime is insufficient
Solution Approach 1:
The patent segments the battery aging process into multiple distinct aging modes (e.g., lithium plating, electrolyte decomposition, electrode material degradation). Each aging mode is modeled separately with its own kinetic parameters and degradation pathways. This segmentation allows the complex aging process to be broken down into manageable components that can be individually monitored and predicted, thereby improving overall prediction accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces multiple state variables beyond traditional state of health, including state of lithium plating, state of electrolyte health, and state of electrode integrity. These additional parameters capture different aspects of battery degradation. By tracking changes in multiple parameters simultaneously and their interrelationships, the model achieves higher prediction accuracy while maintaining tractable complexity through systematic parameter management.
2Reliability
If multiple state of health parameters are monitored, then the prediction reliability improves, but the data processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the estimated remaining useful lifetime predictions are continuously compared against actual battery performance data. The discrepancies feed back into the model to refine the aging rate parameters and update the state of each aging mode. This feedback loop improves prediction reliability over time while the systematic feedback structure prevents processing complexity from becoming unmanageable.
Solution Approach 2:
The patent performs preliminary classification of degradation patterns by analyzing voltage and current data to identify which aging modes are currently dominant. This preliminary action allows the system to focus computational resources on the most relevant aging modes at each time step, improving prediction reliability while reducing overall data processing complexity by avoiding unnecessary calculations for inactive aging modes.
Data Source
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AI summary
The invention relates to a method for determining a remaining useful lifetime of a propulsion battery (2) of a vehicle (1), the method comprising: constructing (S102) a voltage feature vector (204) from voltage data of a charging event for the battery (2), mapping (S104) the voltage feature vector to a battery state of health model (206) including an aging mode distribution of different battery aging mechanisms to determine state of health parameters (210) and the present aging mode distribution (211) of the battery; predicting (S106) the remaining useful lifetime (RUL) using a physical aging model (212) with the state of health parameters and the present aging mode distribution as inputs to the physical aging model.