Battery Management System EIS Segmentation
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Solution Overview
Problem
Existing battery management systems face challenges in accurately determining the state of charge and state of health of lithium-ion batteries during use, as models derived from Electrochemical Impedance Spectroscopy (EIS) measurements become inaccurate over time, especially in long-lasting applications like electric vehicles.
Innovation Solution
A method that uses on-board EIS measurements during charging and discharging, employing two excitation signals across different frequency ranges to reduce computational complexity, allowing for real-time calculation of battery parameters like state of charge and state of health, and enabling cell balancing by determining the real and imaginary parts of the impedance spectrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EIS measurements are performed using a single excitation signal covering a wide frequency range, then measurement precision is improved, but device complexity and computational burden increase
Solution Approach 1:
The frequency range is divided into two separate excitation signals: a first excitation signal for low frequencies (0.1-10 Hz) and a second excitation signal for high frequencies (10-1000 Hz). This segmentation allows the system to maintain measurement precision across the full frequency spectrum while reducing the computational complexity of processing each frequency range separately, making the system feasible for implementation in standard battery management system microcontrollers.
2Ease of operation
If factory models are used for battery parameter prediction, then ease of operation is improved, but measurement precision deteriorates over time
Solution Approach 1:
The system performs on-board EIS measurements during battery operation to obtain actual impedance data, which is then used to update and refine the battery model parameters. This feedback mechanism ensures that the battery management system maintains high measurement precision throughout the battery's life by continuously adapting to actual battery conditions rather than relying solely on static factory models.
3Measurement precision
If on-board EIS measurements are implemented, then measurement precision is improved over time, but device complexity increases
Solution Approach 1:
The on-board EIS measurement system is divided into two separate excitation signal generators, each operating in a specific frequency range. This segmentation reduces the computational burden on the microcontroller compared to implementing a single wide-band EIS measurement system, making the complex functionality feasible for standard battery management system hardware while maintaining high measurement precision.
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 battery state parameters over the battery's life, allows for smarter balancing, and provides early warnings for potential safety issues, improving battery longevity and enabling second-life applications.
Implementation Method 1
determining electrochemical impedance spectroscopy (EIS) measurement data of the rechargeable battery during use
Data Source
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AI summary
The invention pertains to a method of determining the State of Health (SoH) and/or State of Charge (SoC) of a rechargeable battery during use of said battery, the method comprising the steps of: generating a first excitation signal within a first selected frequency range, generating a second excitation signal within a second selected frequency range, applying said first and second excitation signals on said rechargeable battery, measuring the response signal for each of said two excitation signals, and then calculate the Electrochemical Impedance (El) as the ratio between the excitation signals and respective response signals, and then determine the SoH and/or SoC of the rechargeable battery by comparing the calculated El to a circuit model for the battery and/or determining the SoH and/or SoC of the rechargeable battery by directly evaluating characteristics of the El. The invention also pertains to a battery management system configured for executing the steps of the method according to the invention.