Convex Space Filtering for Battery Formation State Estimation
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Solution Overview
Problem
Existing state estimation methods for time-delay systems in power battery formation processes are computationally complex and inefficient, leading to inaccurate results and increased production costs.
Innovation Solution
A state estimation method based on convex space filtering is introduced, which involves obtaining prediction and update steps, combining them into linear inequalities, and solving these inequalities to obtain upper and lower bounds of the system state, thereby reducing computation complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state dimension expansion is performed to transform the time-delay system into an augmented system, then state estimation can be performed, but computation complexity increases and estimated result conservation deteriorates
Solution Approach 1:
The patent extracts and processes only the necessary state information through measurement update representations rather than expanding the entire state dimension. By taking out only the essential components needed for estimation and processing them through convex space filtering, the method avoids the computational burden of augmented system transformation while maintaining estimation accuracy.
Solution Approach 2:
The patent changes the parameter representation from expanded state dimensions to convex space filtering parameters. By transforming the estimation problem into a convex optimization framework with linear inequalities, the method achieves accurate state estimation without the computational complexity associated with traditional state dimension expansion techniques.
2Measurement precision
If state dimension expansion is performed to transform the time-delay system into an augmented system, then state estimation can be performed, but estimated result conservation deteriorates
Solution Approach 1:
The patent extracts and processes only the necessary state information through measurement update representations rather than expanding the entire state dimension. By taking out only the essential components needed for estimation and processing them through convex space filtering, the method avoids the computational burden of augmented system transformation while maintaining estimation accuracy.
Solution Approach 2:
The patent implements a two-round measurement update mechanism that provides feedback refinement. The first-round update establishes initial constraints, and the second-round update refines these constraints by incorporating prediction information, thereby improving the conservation and reliability of estimated results through iterative feedback optimization.
Data Source
AI summary
Disclosed is a state estimation method for a power battery formation process based on convex space filtering, belonging to the technical field of power battery manufacturing. The method performs state estimation on a time delay system by a filtering method, and an iterative replacement method is provided for converting the state quantity at a time k to the state quantity at a time k−h and subsequent items, so as to combine time delay items, thereby avoiding the problem that the dimension is increased when a state matrix A and a state matrix Ah of a time-delay state quantity are subsequently combined into a new state matrix, and reducing the computation complexity and computation time in subsequent computations. Moreover, the estimation accuracy is also improved to a certain extent because of the cancellation of the same items in the iterative replacement. In addition, the method of this application uses two times of update when obtaining an update step, so that the obtained convex space is wrapped more compactly, so as to improve the state estimation accuracy for the battery formation process.