Online Battery SOH and RUL Estimation via Dynamic State Detection
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Solution Overview
Problem
Existing battery management systems rely on static historical data for Remaining Useful Life (RUL) estimation, which is inefficient and does not account for real-time dynamics affecting battery performance.
Innovation Solution
A processor-based method and system for online battery management that determines real-time voltage and current values, identifies the battery state, and processes cumulative charge, time elapsed, and operation time using a battery performance model to estimate State of Health (SOH) and RUL dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static historical data is used for RUL estimation, then the system complexity is reduced, but the accuracy and reliability of battery life prediction deteriorates
Solution Approach 1:
The patent transitions from static historical data to dynamic real-time data collection and processing. The system continuously monitors battery parameters (voltage, current, temperature) and updates RUL predictions dynamically, allowing the estimation model to adapt to changing battery conditions and usage patterns, thereby improving prediction accuracy without excessive complexity increase
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing real-time battery performance data with the performance model predictions. This feedback loop enables the system to refine its RUL estimates based on actual battery behavior, improving prediction accuracy while maintaining a manageable system architecture through iterative optimization
2Measurement precision
If real-time data collection and processing is implemented, then the accuracy of SOH and RUL estimation is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the battery management system into distinct functional modules: real-time data acquisition module, state determination module (charging/discharging/rest), parameter calculation module (cumulative charge, operation time), and prediction module. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high estimation accuracy
Solution Approach 2:
The system performs preliminary actions by pre-establishing the battery performance model with expected degradation patterns and by continuously calculating intermediate parameters (cumulative charge Q_char, operation time T_opn, rest time T_rest) in advance. This preparation reduces the computational burden during real-time RUL prediction, balancing accuracy with processing complexity
Data Source
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AI summary
Performance and lifespan of batteries deteriorate with time due to various factors. Existing systems for battery management use different approaches for the battery management, and also rely on static value of parameters for State of Health (SOH) and Remaining Useful Life (RUL) estimation, thereby failing to consider current condition of the battery. The disclosure herein generally relates to battery management, and, more particularly, to a method and system for online battery management involving real-time estimation of State of Health (SOH) and Remaining Useful Life (RUL) of a battery, based on real-time data collected from the battery. The system determines state of the battery as one of charging, discharging, and rest. Further, corresponding to the determined state, the system determines values of one or more parameters, and processes the determined values with a battery performance model for online determination of the SOH and RUL.