Electrochemical-Thermal Battery Model Vector Parameter Optimization
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Solution Overview
Problem
Current electrochemical-thermal (ECT) battery models struggle to accurately estimate battery voltage due to one-dimensional scalar optimization of diffusion parameters, which fails to account for varying stoichiometry values of anodes and cathodes, leading to inaccuracies in voltage estimation as current dynamics increase.
Innovation Solution
The method involves acquiring an initial vector-type parameter for the ECT model, extracting a predetermined point based on stoichiometry differentiation, generating a target parameter to minimize error between actual and modeled battery states, and interpolating parameters to estimate battery state, including voltage and temperature, using a processor and memory to optimize the ECT model as a multidimensional vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If diffusion parameters are optimized using one-dimensional scalar modeling, then the ECT model is simpler and easier to compute, but the voltage estimation accuracy deteriorates when current dynamics increase due to inability to capture stoichiometry variations
Solution Approach 1:
The patent transitions from one-dimensional scalar diffusion parameters to two-dimensional vector-type parameters that simultaneously capture stoichiometry values and diffusion characteristics. This dimensional expansion enables the model to represent the coupled variations of stoichiometry and diffusion more accurately, resolving the contradiction between model simplicity and voltage estimation accuracy under dynamic current conditions.
2Measurement precision
If vector-type parameters are used to model ECT model, then voltage estimation accuracy improves by capturing stoichiometry variations, but the parameter optimization becomes more complex requiring multidimensional search
Solution Approach 1:
The patent segments the parameter optimization process into two distinct stages: (1) extracting initial values for vector-type parameters based on stoichiometry relationships, and (2) optimizing only the diffusion parameters while keeping stoichiometry values fixed. This segmentation transforms the complex multidimensional optimization into a more manageable process, maintaining accuracy while reducing computational complexity.
Solution Approach 2:
The patent performs preliminary extraction of stoichiometry-based initial values for vector-type parameters before the actual optimization process. By pre-computing these initial values using stoichiometry relationships, the optimization algorithm starts from a more informed position, reducing the search space and computational effort required during the optimization phase.
3Use of energy by moving object
If diffusion parameters are modeled as scalar values, then the computational cost is lower, but the model cannot accurately represent the coupling between stoichiometry and diffusion under dynamic operating conditions
Solution Approach 1:
The patent introduces a second dimension to the parameter representation by using vector-type parameters that simultaneously encode stoichiometry values and diffusion characteristics. This dimensional enhancement allows the model to capture the coupling between stoichiometry and diffusion without requiring a complete redesign of the computational framework, balancing accuracy improvements with acceptable computational costs.
Data Source
AI summary
A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.