Battery State Estimation Using Coupled Digital Filters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mathematical algorithms are not sufficiently optimized for batteries with non-linear operational characteristics, leading to inaccurate estimation of battery state and parameters, which is crucial for maintaining battery health and efficiency in applications like HEVs and BEVs.
Innovation Solution
A system and method using digital filtering techniques, specifically employing coupled filters to estimate battery state and parameter vectors by determining augmented state vectors, input and sensor noise uncertainties, and predicting output vectors, allowing for precise calculation of battery state and parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mathematical algorithms are used to estimate battery state and parameters, then the system is simple to implement, but the estimation accuracy is insufficient for batteries with non-linear operational characteristics
Solution Approach 1:
The algorithm segments the battery state estimation into two distinct components: state vector estimation (SOC, terminal voltage) and parameter vector estimation (capacity, resistance). This segmentation allows each component to be optimized independently, improving overall accuracy while managing computational complexity through modular processing steps.
Solution Approach 2:
The algorithm dynamically adapts to non-linear battery characteristics by using time-varying parameter estimation. The parameter vectors (capacity, resistance) are continuously updated based on current operating conditions, allowing the system to respond to changing battery states rather than assuming fixed parameters, thereby improving accuracy for non-linear operations.
2Duration of action of stationary object
If detailed parameter estimation is performed to maintain precision over battery lifetime, then the service time is extended, but the computational load increases
Solution Approach 1:
The algorithm performs preliminary parameter estimation using readily available measurement data (voltage, current, temperature) before more intensive computations are needed. By continuously updating parameter vectors in the background using incremental calculations, the system prepares accurate baseline values that reduce the computational burden during critical state estimation moments, thereby extending service time with manageable energy consumption.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system and a method for determining an estimated battery state vector and an estimated battery parameter vector are provided. The method includes determining an estimated battery parameter vector indicative of a parameter of the battery at the first predetermined time based on a plurality of predicted battery parameter vectors, a first plurality of predicted battery output vectors, and a battery output vector. The method further includes determining the estimated battery state vector indicative of a state of the battery at the first predetermined time based on a plurality of predicted battery state vectors, a second plurality of predicted battery output vectors, and the battery output vector.