Battery Entropic Heat Coefficient Measurement Algorithm
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Solution Overview
Problem
Existing methods for determining the entropic heat coefficient (EHC) of a battery are either too time-consuming, require complex and expensive equipment, or provide discontinuous and inaccurate results.
Innovation Solution
A method and system that automatically determine the EHC of a battery by receiving and processing current, voltage, and temperature data during a constant charge and discharge cycle, along with open-circuit voltage data as a function of state-of-charge, using an identification algorithm based on a battery thermal model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the potentiometric method is used to determine EHC, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces the traditional potentiometric measurement system with an identification algorithm based on a battery thermal model. Instead of using complex voltage measurement equipment and prolonged potentiometric testing, the system uses standard sensors (temperature, voltage, current) combined with computational algorithms to determine EHC, significantly reducing test time while maintaining accuracy
Solution Approach 2:
The patent changes the measurement approach from direct potentiometric voltage measurement to an indirect method using thermal model parameters. By measuring temperature, voltage, and current during charge/discharge cycles and processing them through identification algorithms, the system extracts EHC information without requiring the time-consuming potentiometric procedure
2Measurement precision
If the calorimetric method is used to determine EHC, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential measurement requirements from the complex calorimetric system, keeping only the necessary sensors (temperature, voltage, current) and removing unnecessary complex equipment like calorimeters and thermal isolation housings. The core EHC determination function is preserved through algorithmic processing of basic measurements
Solution Approach 2:
Instead of using expensive specialized calorimetric equipment, the patent creates a virtual thermal model that replicates the thermal behavior analysis functions. The identification algorithm processes standard sensor data to produce EHC results equivalent to those from complex calorimetric systems, effectively copying the analytical capability without the physical complexity
3Productivity
If the frequency analysis method is used to determine EHC, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the identification algorithm continuously adjusts EHC values based on the difference between measured and simulated temperatures during charge/discharge cycles. This iterative optimization process improves measurement precision by minimizing the temperature prediction error, overcoming the accuracy limitations of the frequency analysis method while maintaining its speed advantage
Solution Approach 2:
The patent uses dynamic charge and discharge cycles with varying currents and temperatures, processed through a time-varying thermal model. This dynamic approach captures the true thermal behavior more accurately than static frequency analysis, improving precision while maintaining the productivity benefit of faster testing
4Measurement precision
If the potentiometric method is used to determine EHC, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a multi-functional system that uses standard battery test equipment (temperature sensors, voltage and current measurements during charge/discharge) to perform both routine battery characterization and EHC determination. This universal approach eliminates the need for specialized potentiometric measurement equipment, reducing device complexity while maintaining the ability to obtain accurate EHC values
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
The method provides faster, simpler, and more accurate determination of the EHC, reducing experimental effort and enabling in-situ measurements without the need for specialized equipment or thermal isolation.
Implementation Method 1
the total heat generation H gen for a battery in operation is the sum of an irreversible heat Q irrev (ohmic loss, diffusion loss, etc.)
Implementation Method 2
a reversible heat Q rev resulting from the entropic effect: wherein with I being the battery current, U batt its voltage in operation, T cell the battery temperature, and U oc its open-circuit voltage
Data Source
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Figure 3A~3C
Figure 4
AI summary
The present invention concerns a method (200) and system (110) for automatically estimating an entropic heat coefficient (hereafter "EHC") of a battery (120), the method comprising: a) receiving or acquiring (210) a current, voltage, and temperature of the battery in function of the time, wherein said current, voltage and temperature have been measured simultaneously during a constant charge and discharge cycle; b) receiving or acquiring (220) an open-circuit voltage, hereafter "OCV", of the battery as a function of its state-of-charge, hereafter "SoC"; c) feeding (230) said current, voltage and temperature in function of the time and said OCV in function of the SoC in an identification algorithm based on a battery thermal model, said identification algorithm being configured for determining said EHC as a function of the SoC, wherein, for said determination, the EHC is defined as a function of N+1 real parameters a0,...,aN, wherein the identification algorithm is configured for determining the value of each real parameter a0,...,aN, by minimizing a difference between an estimated battery temperature and said measured temperature, wherein the estimated battery temperature is obtained by modeling, with said battery thermal model, a heat generated by the battery during said charge and discharge cycle; d) outputting (250), by said identification algorithm, the determined EHC as function of the SoC.