ESS Residual Capacity Estimation From Actual SOC Usage Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for estimating the residual capacity of Energy Storage Systems (ESS) at End Of Life (EOL) are time-consuming and costly, requiring extensive data analysis of usage patterns, which burdens manufacturers with significant human and physical resource costs.
Innovation Solution
A system and method using an ESS controller to analyze actual usage patterns, determine residual capacity at EOL, and record deviations from design patterns, allowing for real-time estimation and management of ESS residual capacity without the need for periodic testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic residual capacity testing is performed to ensure EOL lifespan guarantee, then reliability of EOL lifespan guarantee is improved, but loss of time and manufacturing costs increase due to extensive data analysis requirements
Solution Approach 1:
The patent creates a virtual copy of the testing process by developing a residual capacity estimation model that replicates the functionality of physical testing. The model uses usage log data to simulate and predict residual capacity at EOL, eliminating the need for actual periodic testing while maintaining reliability assessment capabilities.
Solution Approach 2:
The patent replaces the mechanical testing system with an information-based estimation system. Instead of performing physical capacity measurements and analyzing test results, the system uses computational models to estimate residual capacity based on usage patterns, substituting mechanical testing operations with information processing operations.
2Reliability
If periodic residual capacity testing is performed to ensure EOL lifespan guarantee, then reliability of EOL lifespan guarantee is improved, but manufacturing costs increase due to extensive human and physical resources required
Solution Approach 1:
The patent creates a virtual copy of the testing process by developing a residual capacity estimation model that replicates the functionality of physical testing. The model uses usage log data to simulate and predict residual capacity at EOL, eliminating the need for actual periodic testing while maintaining reliability assessment capabilities.
Solution Approach 2:
The system enables self-service by automatically collecting usage logs and performing residual capacity estimation without requiring external testing resources. The ESS controller itself generates the estimation results, eliminating the need for manufacturer-sponsored testing facilities and expert personnel.
3Measurement precision
If extensive data analysis of usage patterns is performed to determine residual capacity, then measurement precision of residual capacity is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential data elements needed for residual capacity estimation from the complete usage log dataset. The model focuses on extracting relevant usage patterns such as charge/discharge cycles, depth of discharge, and operational conditions, rather than analyzing all available data, thereby reducing complexity while maintaining precision.
Solution Approach 2:
The patent transforms the complex usage log data into simplified parameters that the estimation model can process efficiently. By converting raw usage data into meaningful metrics such as equivalent full cycles, average depth of discharge, and operational temperature ranges, the system reduces data complexity while preserving the information needed for accurate residual capacity estimation.
Data Source
AI summary
Disclosed is a system and method for estimating a residual capacity of an Energy Storage System (ESS). The system includes an ESS controller operably coupled to the ESS including a plurality of battery racks and rack controllers. The ESS controller acquires a state of charge (SOC) of the battery racks from the rack controllers, determines an actual usage pattern of the ESS indicating a change in SOC of the ESS for each reference time period, determines a first ESS residual capacity at End of Life (EOL) by applying an average of the actual usage patterns over a whole period of an EOL lifespan, and records a first deviation between the first ESS residual capacity and a reference residual capacity.