Battery SOC Estimation Using Heat Flow Peak Correction
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Solution Overview
Problem
Existing battery units face low accuracy in estimating the state of charge (SOC) of battery cells, particularly those with graphite as a negative electrode, due to small voltage changes and gradual degradation, which affects the accuracy of SOC estimation based on open circuit voltage (OCV) and closed circuit voltage (CCV) characteristics.
Innovation Solution
The method involves using heat flow (HF) and enthalpy potential (UH) characteristics to improve SOC estimation accuracy by detecting peaks and patterns in HF vs. SOC and UH vs. SOC differential characteristics, which maintain sharper spectra and higher signal-to-noise ratios even during degradation, allowing for precise correction of SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voltage-based SOC estimation is used for battery cells with graphite negative electrode, then the estimation method is simple, but the estimation accuracy is low due to small voltage changes
Solution Approach 1:
The invention changes the parameter used for SOC estimation from voltage to heat flow characteristics. By detecting heat flow during charging/discharging processes and comparing it with reference heat flow characteristics, the system achieves accurate SOC estimation for graphite-based battery cells where voltage changes are minimal, thus resolving the contradiction between simplicity and accuracy.
2Ease of operation
If periodical CCV measurement is used to correct SOC estimation error, then the correction can be performed during actual use, but the peaks have small magnitudes and low S/N ratios resulting in low estimation accuracy
Solution Approach 1:
The invention changes the measurement parameter from CCV (closed circuit voltage) to heat flow. By detecting heat flow characteristics during charging/discharging and comparing them with reference patterns, the system achieves both real-time correction capability and high estimation accuracy, overcoming the low S/N ratio problem associated with CCV measurement.
Solution Approach 2:
The invention substitutes electrical voltage measurement with thermal heat flow measurement for the purpose of SOC correction. This substitution enhances the signal magnitude and S/N ratio during actual use conditions, enabling accurate real-time correction of SOC estimation errors that were not achievable with CCV measurement alone.
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
This approach enhances the accuracy of SOC estimation and state of health (SOH) calculation by leveraging the distinct patterns in heat flow and enthalpy potential characteristics, improving estimation accuracy and maintaining precision even as battery cells degrade.
Implementation Method 1
a battery heat flow detector configured to detect a heat flow of the battery cell
Implementation Method 2
a voltage detector configured to detect an open circuit voltage or a closed circuit voltage of the battery cell
Implementation Method 3
a current detector configured to detect a current of the battery cell
Implementation Method 4
detects a peak of differential characteristics of the measured HF vs. SOC present characteristics, and determines a SOC at the detected peak as a SOC(HF)
Data Source
AI summary
The battery unit includes a storage configured to store (A1) and (A2) below, and a battery state estimator.(A1): a table map OCV vs. SOC characteristics.(A2): HF vs. SOC initial characteristics.The battery state estimator estimates, based on (A1), a start SOC corresponding to a detected OCV at a start of charge; measures HF vs. SOC present characteristics and detects a peak of differential characteristics of the measured HF vs. SOC present characteristics during the charge; determines a SOC at the detected peak as a SOC(HF) based on (A2); calculates a charge capacity from the start of the charge to detection of the peak; calculates a SOC(OCV) based on the calculated charge capacity and the start SOC; and in a case where the SOC(OCV) deviates with respect to the SOC(HF) by an amount of deviation equal to or greater than a predetermined value, corrects (A1).