Li-Ion Battery SOH Estimation via Liquid-Phase Diffusivity
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Solution Overview
Problem
Existing methods for estimating the state of health (SOH) of hybrid vehicle batteries are inefficient due to errors, high costs, and complexity, particularly in accurately calculating capacity fade, which can lead to overcharging or overdischarging and potentially dangerous situations.
Innovation Solution
A system and method using liquid-phase diffusivity of Li-ion parameters, which are highly correlated with battery capacity fade, to estimate SOH by extracting these parameters from voltage data during charging and referencing a mapping table to calculate the SOH, independent of charging speed and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware impedance measurement method is used to estimate SOH, then measurement capability is provided, but measurement precision deteriorates due to errors in circuit configuration and sensors
Solution Approach 1:
The patent replaces the hardware-based impedance measurement system with a software-based parameter extraction system. Instead of using physical circuit configurations and sensors to measure impedance, the system extracts battery model parameters (R0, R1, C1, R2, C2) from voltage data during charging/discharging processes. This substitution eliminates errors from hardware configuration and sensor inaccuracies, improving both measurement precision and reliability.
2Measurement precision
If current and voltage pair data acquisition method is used to infer impedance, then indirect measurement capability is provided, but device complexity increases due to complex logic for data acquisition and inference
Solution Approach 1:
The patent extracts only the essential voltage data during charging and discharging processes, eliminating the need for complex current and voltage pair data acquisition. By focusing on voltage data alone and using pre-determined battery model parameters, the system simplifies the measurement logic while maintaining the capability to infer impedance and estimate SOH, thereby reducing device complexity.
Solution Approach 2:
The patent performs preliminary determination of battery model parameters (R0, R1, C1, R2, C2) through offline testing and stores them for online use. This preliminary action eliminates the need for complex real-time inference logic during operation, as the system only needs to extract voltage data and apply the pre-determined parameters to calculate SOH, significantly reducing system complexity.
3Ease of operation
If charging capacity calculation method is used to estimate SOH, then ease of operation is improved, but measurement precision deteriorates due to accumulated errors of current sensor
Solution Approach 1:
The patent replaces the current sensor-based charging capacity calculation method with a voltage-based parameter extraction method. Instead of accumulating current sensor data to calculate charging capacity, the system extracts battery model parameters from voltage data during charging/discharging. This substitution eliminates accumulated errors from current sensors while maintaining ease of operation, as the voltage-based method is equally simple to implement.
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 allows for more accurate and reliable estimation of SOH, improving the safety and efficiency of hybrid vehicle operations by preventing battery overcharging or overdischarging and enhancing the reliability of state of charge calculations.
Implementation Method 1
extracting a liquid-phase diffusivity of Li-ion parameter included in the battery model parameters
Data Source
AI summary
A method and system for estimating a state of health using battery model parameters are provided. The system includes a battery model parameter extractor that is configured to extract liquid-phase diffusivity of Li-ion parameters and a storage unit that is configured to store a mapping table in which states of health (SOH) for each liquid-phase diffusivity of Li-ion parameter are mapped. In addition, a SOH estimator is configured to use the mapping table to estimate the SOH that corresponds to a liquid-phase diffusivity of Li-ion extracted from the battery model parameter extractor.