BESS SOH Prediction 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 for optimizing energy usage and extending the life of batteries, but existing methods are inadequate in addressing battery degradation over time.
Innovation Solution
A system and method for estimating BESS SOH through 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.
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 to accurately capture battery degradation over time
Solution Approach 1:
The patent segments the SOH prediction process into multiple iterative cycles, where each cycle performs localized calculations using degradation equations and temperature look-up tables. This segmentation allows the complex prediction to be broken down into manageable steps, improving accuracy through repeated refinement while keeping each individual calculation step relatively simple.
Solution Approach 2:
The patent pre-calculates and stores temperature values in look-up tables based on charge rates and states of charge before the actual SOH prediction is needed. This preliminary action eliminates the need for complex real-time thermal modeling during prediction, maintaining computational simplicity while enabling accurate temperature-dependent degradation calculations.
2Measurement precision
If iterative prediction process with multiple calculations is implemented, then SOH prediction accuracy improves, but computational time and resources increase
Solution Approach 1:
Temperature look-up tables are pre-computed and stored before the iterative prediction process begins. This preliminary action eliminates the need for complex thermal calculations during each iteration, significantly reducing computational time while maintaining the accuracy benefits of the iterative approach.
Solution Approach 2:
The patent uses look-up tables that store pre-calculated temperature values as copies of the results of complex thermal models. Instead of re-running thermal simulations during each iteration, the system copies and retrieves pre-computed values, dramatically reducing computational overhead while preserving accuracy.
3Measurement precision
If temperature-dependent degradation equations are used, then prediction accuracy improves, but the complexity of the prediction model increases
Solution Approach 1:
Temperature values are pre-calculated and stored in look-up tables based on charge rates and states of charge. This preliminary action transforms complex temperature-dependent degradation equations into simpler table-lookup operations, maintaining accuracy while reducing model complexity during actual prediction execution.
Solution Approach 2:
The patent introduces temperature look-up tables as an intermediary between the charge rate/SOC inputs and the degradation equations. This intermediary pre-processes the temperature calculations, allowing the degradation model to use simple table lookups instead of complex thermal modeling, thus reducing overall model complexity while preserving temperature-dependent accuracy.
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.