BESS SOH Forecasting Using Iterative Temperature-Degradation Modeling
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Solution Overview
Problem
Accurately predicting the state of health (SOH) of battery energy storage systems (BESS) is crucial due to battery degradation over time, which affects their efficiency and remaining useful life, but existing methods are inadequate for precise forecasting.
Innovation Solution
A system and method that estimates BESS SOH by executing an iterative process using an average temperature look-up table and cell degradation equations, considering historical and future usage profiles, to determine SOH over time, displayed through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing SOH prediction methods are used, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments the SOH prediction process into multiple iterative cycles, each comprising temperature determination, degradation calculation, and SOH update steps. This segmentation allows the system to process complex degradation mechanisms through manageable iterative steps, improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent employs preliminary actions by pre-establishing temperature-SOH relationships and degradation models before actual prediction. The system pre-processes temperature data and establishes lookup tables or mathematical models that guide subsequent SOH calculations, enabling accurate predictions while maintaining computational efficiency
2Measurement precision
If iterative prediction process with multiple parameters is implemented, then SOH prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent implements periodic action through iterative cycles that systematically update SOH predictions at regular intervals. Each iteration processes temperature and degradation data in a structured sequence, balancing computational thoroughness with efficiency by repeating the calculation process multiple times with refined inputs
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting temperature values, degradation rates, and SOH estimates across iterations. The system modifies these parameters based on previous iteration results, allowing the model to converge toward accurate predictions while adapting to changing battery conditions
Data Source
AI summary
Systems and methods for estimating battery degradation of a battery energy storage system (BESS) are disclosed. An iterative process is executed over a pre-defined time period divided into iterations. For each iteration, an average temperature of the BESS is determined by inputting a state of health (SOH) and charge rate into an average temperature look-up-table (LUT). The SOH for the next iteration is determined by inputting the determined average temperature into a set of cell degradation equations. The charge rate for the next iteration is derived from a usage profile which defines the charging and discharging cycles over the pre-defined time period and includes power and SOC over the pre-defined time period. The SOH of the BESS over the pre-defined time period may then be displayed on a user interface.