Battery SOC Estimation via Stochastic Reduced Order Model
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Solution Overview
Problem
Existing battery management systems face challenges in accurately estimating the state of charge (SOC) and other unobservable variables of lithium-ion batteries, particularly due to limitations in electric equivalent circuit models, which struggle to estimate degradation states and health metrics effectively.
Innovation Solution
The implementation of a stochastic Pseudo 2-dimensional electrochemical thermal (P2D-ECT) model, which involves acquiring battery conservation equations, calculating a stochastic reduced order model (SROM) and mean, combining sensor-measured state information, and determining SOC through basis conversion using Hermite polynomials, allowing for more accurate estimation of SOC and internal states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric equivalent circuit model (ECM) is used to estimate battery state, then the estimation process is simple and fast, but it is difficult to estimate immeasurable variables such as degradation state and state of health
Solution Approach 1:
The patent merges the electric equivalent circuit model (ECM) with the stochastic Pseudo 2-dimensional electrochemical thermal (P2D-ECT) model to create a hybrid approach. The ECM provides fast computational speed while the stochastic P2D-ECT model enables accurate estimation of immeasurable variables such as degradation state and state of health, thus resolving the contradiction between estimation speed and accuracy.
Solution Approach 2:
The patent introduces a stochastic reduced order model (SROM) as an intermediary between the complex stochastic P2D-ECT model and the simple ECM. The SROM acts as a bridge that captures the essential features of the stochastic P2D-ECT model while maintaining computational efficiency, enabling accurate estimation of immeasurable variables without sacrificing estimation speed.
2Measurement precision
If stochastic Pseudo 2-dimensional electrochemical thermal (P2D-ECT) model is used to estimate battery variables, then immeasurable variables including degradation state can be estimated accurately, but the calculation complexity and computational burden increase significantly
Solution Approach 1:
The patent extracts the essential features from the complex stochastic P2D-ECT model to create a stochastic reduced order model (SROM). By taking out only the critical components needed for estimating immeasurable variables while removing unnecessary complexity, the SROM achieves accurate estimation of degradation state and state of health with significantly reduced computational burden.
Solution Approach 2:
The patent changes the parameters of the stochastic P2D-ECT model by reducing the spatial and temporal resolution to create the SROM. This parameter change maintains the ability to estimate immeasurable variables accurately while reducing the computational complexity and making the model suitable for real-time battery management applications.
3Device complexity
If traditional battery management systems are used, then the system structure is simple, but the accuracy of SOC tracking and prediction is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the stochastic reduced order model continuously updates the state of charge estimation by incorporating real-time battery measurements and correcting deviations. This feedback loop significantly improves SOC tracking accuracy while maintaining a relatively simple system structure suitable for practical battery management applications.
Data Source
AI summary
Disclosed is a battery state of charge (SOC) determining method and a battery managing apparatus that acquires a stochastic reduced order model (SROM) and a mean using battery conservation equations acquired based on a stochastic Pseudo 2-dimensional electrochemical thermal (P2D-ECT) model, measures state information, and assimilates the state information with the SROM and the mean to determine an accurate SOC at a relatively low calculation cost.