Electro-Thermal Battery Model Using Segmented Parameter Fitting
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Solution Overview
Problem
Conventional battery modeling techniques struggle to accurately estimate electrical parameters across varying temperature, state of charge, and age conditions, often relying on complex equations that require significant processing power and are not efficient for real-time monitoring and control.
Innovation Solution
The development of an electro-thermal model that separates parameters into convex and non-convex sets, using a fitting procedure to determine these parameters efficiently, allowing for the creation of a predictive model that can monitor battery performance with reduced processing power by focusing on fitting non-convex parameters first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery modeling techniques use complex equations to accurately estimate electrical parameters across varying conditions, then measurement precision is improved, but device complexity increases and processing power requirements increase
Solution Approach 1:
The patent segments the parameter fitting process into two distinct phases: an outer loop that fits non-convex parameters and an inner loop that fits convex parameters. This segmentation allows each loop to use optimized algorithms appropriate for its parameter type, improving both accuracy and computational efficiency without requiring overly complex equations.
Solution Approach 2:
The patent transforms the parameter estimation problem by separating parameters into convex and non-convex categories, allowing different fitting strategies to be applied to each group. This parameter transformation enables the use of efficient convex optimization techniques for the majority of parameters while only requiring iterative methods for the smaller set of non-convex parameters.
2Measurement precision
If conventional battery modeling techniques use complex equations to accurately estimate electrical parameters, then measurement precision is improved, but processing power requirements increase
Solution Approach 1:
By segmenting the computational workload into outer and inner loops with different algorithmic complexities, the patent reduces the overall processing power requirements. The inner loop uses efficient convex optimization that converges quickly, while the outer loop handles the smaller set of non-convex parameters, resulting in lower total computational demand compared to using complex iterative methods for all parameters.
Solution Approach 2:
The transformation of parameters into convex and non-convex groups enables the application of computationally efficient convex optimization techniques to the majority of parameters, significantly reducing processing power requirements while maintaining estimation accuracy.
3Measurement precision
If conventional battery modeling techniques are used for real-time monitoring, then measurement precision is improved, but productivity decreases due to inefficient processing
Solution Approach 1:
The segmented fitting approach with separate outer and inner loops enables real-time monitoring by reducing computational burden. The efficient convex optimization in the inner loop can be executed quickly for real-time applications, while the outer loop updates less frequently, achieving both high precision and real-time productivity.
Solution Approach 2:
By changing the mathematical formulation to separate convex and non-convex parameters, the patent enables the use of fast convex optimization algorithms that are suitable for real-time processing, thereby improving monitoring efficiency without sacrificing precision.
4Measurement precision
If conventional battery modeling techniques are used, then measurement precision is improved, but loss of time increases due to computationally intensive processing
Solution Approach 1:
The time-consuming parameter fitting process is segmented into two loops, with the inner loop using fast convex optimization that converges in fewer iterations. This segmentation significantly reduces the total time required for model fitting while maintaining estimation precision.
Solution Approach 2:
The mathematical transformation that separates convex and non-convex parameters enables the application of efficient optimization algorithms, dramatically reducing the computational time required for parameter estimation while preserving accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for the generation and use of an electro-thermal battery model. One of the methods includes obtaining battery data comprising voltage values, with each voltage value corresponding to an operating state of the battery. The method includes selecting a battery model, the battery model having convex parameters and non-convex parameters. The method includes processing the battery data by performing a fitting procedure to determine values of the convex parameters and non-convex parameters. The fitting procedure includes fitting the convex parameters with respect to the battery data during which the non-convex parameters are held fixed. The fitting procedure includes fitting the non-convex parameters with respect to the battery data. The fitting procedure also includes creating an electro-thermal model for a battery from the selected battery model using the fitted values of the convex and non-convex parameters.