Battery Module SOC Estimation Using Composite Probability Variables
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Solution Overview
Problem
Existing battery management systems require extensive sensor networks and computational resources to accurately monitor and estimate the state of charge (SOC) of multiple battery cells, which increases cost and affects performance.
Innovation Solution
A method and system using a composite probability variable model and Kalman filter operation, leveraging a microcontroller unit (MCU) and neural processing unit (NPU) co-processing to estimate SOC based on voltage measurements of selected battery cells, reducing the need for comprehensive monitoring of all cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive monitoring of all battery cells is implemented, then measurement precision of SOC is improved, but device complexity and cost increase
Solution Approach 1:
The patent divides the battery module monitoring into two segments: directly measured cells (with voltage sensors) and indirectly estimated cells (without sensors). By segmenting the monitoring approach, the system achieves comprehensive SOC estimation without requiring sensors on all cells, thus reducing device complexity while maintaining measurement precision through the probabilistic model that combines direct measurements with statistical inference about unmeasured cells.
2Measurement precision
If comprehensive monitoring of all battery cells is implemented, then measurement precision of SOC is improved, but manufacturing cost increases
Solution Approach 1:
The patent extracts the voltage measurement function from every battery cell and implements it only in selected representative cells. By taking out the sensor requirement from the universal level and applying it selectively, the system reduces manufacturing costs significantly while maintaining SOC estimation accuracy through the composite probability variable model that infers the state of unmeasured cells based on measured ones.
3Measurement precision
If extensive computational resources are allocated, then SOC estimation accuracy is improved, but processing time increases
Solution Approach 1:
The patent transforms the computational problem from solving complex differential equations to evaluating probability density functions and performing Kalman filter operations. By changing the mathematical parameters and approach, the system achieves accurate SOC estimation with reduced computational burden and faster processing time, as the probabilistic model allows for efficient calculation using standard statistical methods rather than intensive numerical simulation.
Data Source
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AI summary
The present disclosure relates to a method of estimating a state of charge of a battery module, comprising: obtaining, by a microcontroller unit, a composite probability variable model associated with a plurality of battery cells included in a particular battery module, receiving, by the microcontroller unit, voltage measurement data of a first battery cell and voltage measurement data of a second battery cell of the plurality of battery cells included in the particular battery module, and estimating, by the microcontroller unit and/or a neural processing unit, an SOC of the particular battery module via a Kalman filter operation based on the composite probability variable model, the voltage measurement data of the first battery cell, and the voltage measurement data of the second battery cell.