Internal Battery Temperature Estimation via Multi-Frequency Impedance
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Solution Overview
Problem
Current methods for monitoring internal battery temperature, such as surface-mounted thermocouples for larger batteries and single-frequency impedance measurements for smaller batteries, are non-ideal due to delays in heat conductivity, high costs, and failure to account for state-of-charge (SOC) and state-of-health (SOH) variations.
Innovation Solution
Estimating internal battery temperature using terminal impedance measurements at multiple frequencies combined through multivariable polynomial regression, which reduces the effects of SOC and SOH variations and requires fewer computational resources, enabling lower-cost and less-intrusive monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface-mounted thermocouples are used for monitoring internal battery temperature, then temperature monitoring is achieved, but heat conductivity delays cause measurement lag and response time is slow
Solution Approach 1:
The patent replaces the mechanical/thermal sensing system (thermocouples requiring physical heat conduction) with an electrical measurement system (impedance spectroscopy). By measuring electrical impedance at multiple frequencies, the system directly detects internal battery temperature without relying on thermal conduction, thereby eliminating measurement lag while maintaining temperature monitoring capability.
2Ease of manufacture
If single-frequency impedance measurements are used for smaller batteries, then cost is reduced, but SOC and SOH variations are not accounted for leading to reduced measurement precision
Solution Approach 1:
The patent transitions from single-frequency impedance measurement (one-dimensional) to multi-frequency impedance measurement (multi-dimensional). By measuring impedance across multiple frequencies and using multivariable polynomial regression, the system captures additional information about battery state, enabling accurate temperature estimation that accounts for SOC and SOH variations while remaining cost-effective.
Solution Approach 2:
The patent changes the measurement parameter from single-frequency impedance to multi-frequency impedance spectrum. By analyzing impedance at multiple frequencies and applying polynomial regression models that incorporate frequency as a variable, the system achieves accurate temperature estimation that compensates for SOC and SOH effects, improving measurement precision without significantly increasing cost.
3Measurement precision
If multiple-frequency impedance measurements with multivariable polynomial regression are used, then temperature estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary work by pre-calibrating polynomial regression models during battery manufacturing or initial use. The multi-frequency impedance characteristics and their relationship with temperature are characterized in advance, creating lookup tables or pre-computed coefficients. During actual temperature monitoring, the system only needs to perform simple regression calculations using pre-determined parameters, significantly reducing real-time computational complexity while maintaining high accuracy.
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 provides accurate and efficient internal battery temperature monitoring, reducing the impact of SOC and SOH variations and lowering computational costs, while being applicable to both larger and smaller batteries.
Implementation Method 1
Estimating internal battery temperature using terminal impedance measurements at multiple frequencies
Data Source
AI summary
One embodiment is a method for estimating an internal temperature of a battery, the method comprising obtaining multiple terminal impedance measurements for the battery, wherein each of the terminal impedance measurements is obtained at a different one of a plurality of frequencies; automatically selecting one of a plurality of battery models using on a value of a parameter of the battery, wherein each of the battery models has been trained and corresponds to a different range of values for the battery parameter and wherein the value of the parameter of the battery falls within the range of values for the battery parameter corresponding to the selected one of the plurality of battery models; and applying the selected one of the plurality of battery models to the multiple terminal impedance measurements to estimate the internal temperature of the battery.