Battery State Estimation Using Adaptive G and H Parameters
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Solution Overview
Problem
Existing battery state estimation methods are inaccurate and costly, requiring extensive experimental data and being inflexible to changes in battery characteristics over time, making them unsuitable for real-time and scalable applications.
Innovation Solution
A battery state estimation method using adaptive filters with recursive least squares (RLS) to generate G and H parameters from real-time voltage and current measurements, allowing for direct estimation of battery state without the need for extensive data storage or complex experimental models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery state estimation methods are used, then measurement precision may be maintained, but device complexity and cost increase due to extensive experimental data requirements
Solution Approach 1:
The patent extracts only the essential parameters (G and H parameters) needed for battery state estimation from the complex electrochemical model, eliminating the need for extensive experimental data while maintaining estimation accuracy. This reduces device complexity by focusing on critical components only.
Solution Approach 2:
Instead of building complex models from extensive experimental data, the patent inverts the approach by directly estimating battery state from real-time voltage and current measurements using adaptive filters, bypassing the need for large data sets and complex model construction.
2Measurement precision
If conventional battery state estimation methods are used, then measurement precision may be maintained, but productivity decreases due to extensive data processing requirements
Solution Approach 1:
The patent performs preliminary actions by pre-defining the relationship between voltage, current, and battery state through the G and H parameters. This allows real-time estimation without extensive data processing, as the estimation framework is already established and ready for immediate application.
Solution Approach 2:
The patent replaces the mechanical data processing system (extensive experimental data collection and analysis) with a mathematical substitution approach using adaptive filters and parameter estimation, enabling rapid real-time calculations with minimal computational overhead.
3Productivity
If adaptive filters with RLS are used, then productivity improves for real-time estimation, but device complexity increases due to filter implementation
Solution Approach 1:
The patent changes the parameters being estimated from complex electrochemical states to simplified G and H parameters that can be directly derived from voltage and current measurements. This parameter transformation reduces algorithm complexity while maintaining real-time estimation capability.
Solution Approach 2:
The adaptive filter with RLS is designed to automatically adapt to changing battery conditions without requiring external calibration or extensive data input. The algorithm self-adjusts its parameters based on real-time measurements, reducing the need for complex external control systems.
4Measurement precision
If extensive experimental data is collected, then measurement precision improves, but loss of time increases due to data collection requirements
Solution Approach 1:
The patent enables continuous real-time estimation using ongoing voltage and current measurements, eliminating the need for separate data collection phases. The useful action of state estimation continues uninterrupted throughout battery operation, providing accurate results without time loss.
Solution Approach 2:
The estimation framework is preliminarily established with predefined G and H parameter relationships, allowing immediate real-time estimation without requiring preliminary data collection periods. The system is ready for accurate estimation from the start of operation.
Data Source
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AI summary
Provided is a battery state estimation method. The battery state estimation method comprises: a step for periodically measuring the voltage and current of a battery in use to generate a voltage value and a current value; a step for using an adaptive filter to generate a G parameter value and an H parameter value in real time from the voltage value and the current value, said parameters indicating the present state of the battery; and a step for using the G parameter value and the H parameter value to estimate the state of the battery in real time. The G parameter is a parameter that represents the sensitivity of the voltage to changes in the current of the battery, and the H parameter is a parameter that represents an effective potential determined by the local equilibrium potential distribution and resistance distribution inside the battery. According to the battery state estimation method, the state of the battery can be accurately estimated in real time on the basis of the voltage value and the current value of the battery.