Battery SOH and RUL Estimation Using Real-Time Operating Data
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Solution Overview
Problem
Conventional battery management systems rely on static values for Remaining Useful Life (RUL) estimation, which is inadequate for accurately monitoring battery health and predicting battery performance over time.
Innovation Solution
A processor-based method and system for online battery management that determines real-time voltage and current, calculates cumulative charge, operation time, and charging time, and processes these values with a battery performance model to estimate State of Health (SOH) and RUL, using real-time data and machine learning techniques for dynamic correlation and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static values are used for RUL estimation, then the system complexity is reduced, but the estimation accuracy and reliability deteriorate
Solution Approach 1:
The patent transitions from static RUL estimation to dynamic online estimation that continuously updates predictions based on real-time battery parameters. The system dynamically adjusts estimation models according to current operating conditions, battery state, and historical data, making the estimation process adaptive and responsive to changing battery characteristics throughout its lifecycle.
Solution Approach 2:
The patent changes the estimation approach from using fixed static values to utilizing multiple dynamic parameters including real-time voltage, current, temperature, charge-discharge cycles, and state of charge. These parameters are continuously monitored and fed into the estimation model, allowing the system to capture the evolving behavior of the battery and improve prediction accuracy.
2Measurement precision
If real-time dynamic parameters are monitored and processed, then the estimation accuracy improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the battery management system into distinct functional modules: real-time parameter acquisition module, state determination module, cumulative parameter calculation module, and RUL estimation module. Each module performs a specific function, processing data in a structured pipeline that reduces overall system complexity while maintaining high estimation accuracy.
Solution Approach 2:
The patent introduces intermediate cumulative parameters (such as cumulative charge-discharge cycles, cumulative operating time, and cumulative stress exposure) that serve as mediators between raw real-time measurements and final RUL predictions. These intermediate variables simplify the estimation process by aggregating complex temporal patterns into meaningful metrics that feed into the prediction model.
3Reliability
If multiple cumulative parameters (Qchar, Telap, Topn, Topn_ch) are tracked and processed, then the RUL estimation reliability improves, but the loss of time and computational resources increases
Solution Approach 1:
The patent performs preliminary calculations of cumulative parameters (Qchar, Telap, Topn, Topn_ch) as data is being collected during normal battery operation. Rather than computing these complex aggregations in real-time during RUL estimation, the system continuously updates these cumulative values in the background, preparing processed data ahead of time to reduce computational burden during critical prediction moments.
Solution Approach 2:
The patent maintains continuous monitoring and updating of all battery parameters and cumulative metrics without interruption. This continuous data flow ensures that the RUL estimation always has access to the most current and relevant information, improving reliability while the ongoing nature of data collection amortizes the computational cost over time rather than requiring intensive batch processing.
Data Source
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.