Battery Capacity Estimation Using Recursive Filtering and SOC Drift Correction
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Solution Overview
Problem
Existing methods for determining battery capacity are invasive, inaccurate, and not widely applicable across different battery types, particularly for lithium-ion batteries used in stationary energy storage and electric vehicles.
Innovation Solution
A computer-implemented method using a recursive filter to estimate battery capacity based on state-of-charge (SOC) difference values and charge throughput values, incorporating a cost function to update capacity estimates with each half-cycle, utilizing machine learning models for SOC prediction and Coulomb-counting to integrate current flow, and employing filters like RLS or Kalman filters to minimize measurement errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Coulomb-counting is used to determine battery capacity, then capacity can be measured during normal operation, but measurement accuracy deteriorates due to accumulation of errors over time
Solution Approach 1:
The patent implements a feedback mechanism where the estimated capacity from the recursive filter is used to correct the Coulomb-counting measurements. The cost function continuously compares the measured SOC difference with the reference value derived from capacity estimate and charge throughput, and adjusts the capacity estimate to minimize this difference, thereby preventing error accumulation.
Solution Approach 2:
The patent performs preliminary estimation of battery capacity using the recursive filter before relying on Coulomb-counting measurements. By establishing an accurate reference capacity estimate in advance, the system can correct subsequent measurements and prevent error propagation over time.
2Measurement precision
If invasive methods are used to measure battery capacity, then measurement accuracy improves, but battery life deteriorates due to additional stress on the battery
Solution Approach 1:
The patent enables the battery system to perform self-diagnosis and capacity estimation using only its normal operational data (current, voltage, temperature measurements during regular charging and discharging). No external invasive testing is required, and the battery continues to serve its primary function while providing capacity information.
Solution Approach 2:
The patent introduces a computational intermediary (the recursive filter algorithm) that processes normal operational measurements to derive capacity information. This intermediary transforms routine operational data into accurate capacity estimates without requiring direct invasive measurement that would stress the battery.
3Measurement precision
If traditional capacity measurement methods are used, then results are accurate for specific battery types, but applicability deteriorates across different battery types
Solution Approach 1:
The patent develops a universal recursive filter algorithm that can estimate capacity for different battery types (lithium-ion, lead-acid, nickel-based) using the same fundamental approach. The method adapts to different battery chemistries by using battery-specific parameters in the cost function while maintaining the same overall estimation framework.
Solution Approach 2:
The patent adjusts parameters within the cost function to accommodate different battery types. By modifying parameters such as the relationship between charge throughput and SOC change, the same recursive filter structure can accurately estimate capacity across various battery chemistries and applications.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, non-invasive, and scalable capacity estimation for various battery types, reducing measurement noise and improving accuracy by focusing on recent data, thus extending battery life and enhancing operational control.
Implementation Method 1
determining a current estimate of the capacity for each charging or discharging half-cycle of the sequence of charging or discharging half-cycles. The current estimate of the capacity is determined by updating a previous estimate of the capacity of the rechargeable battery using a recursive filter
Implementation Method 2
By measuring a SOC difference between two operating points of the battery and implementing Coulomb-counting, i.e., integrating the current flow over time, it is possible to determine the capacity
Data Source
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AI summary
Various examples of the disclosure pertain to determining an estimate of a capacity of a battery. A recursive filter is used. The recursive filter operates based on state-of-charge difference values and charge throughput values obtained for a sequence of charging or discharging half-cycles.