Battery SOC Estimation Using Dynamic OCV Reference Range
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Solution Overview
Problem
Existing battery state of charge (SOC) estimation methods, such as those using ampere counting and equivalent circuit models, face inaccuracies due to measurement errors and the difficulty in simulating nonlinear battery characteristics, especially when the SOC is close to full discharge.
Innovation Solution
A battery management system employing an extended Kalman filter that determines open circuit voltage (OCV) information based on comparisons with a reference range and adjusts the maximum value of this range according to battery degradation, thereby improving SOC estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ampere counting is used to estimate SOC, then the estimation is simple to implement, but measurement errors and external noise cause discrepancy between estimated and actual SOC
Solution Approach 1:
The patent combines ampere counting with equivalent circuit model and extended Kalman filter to create a hybrid estimation system. The ampere counting provides cumulative charge information while the equivalent circuit model and EKF correct for measurement errors and nonlinear effects, achieving both simplicity and accuracy.
Solution Approach 2:
The extended Kalman filter implements feedback by continuously comparing the estimated SOC with measurements from the equivalent circuit model and correcting the estimation accordingly. This feedback mechanism reduces the impact of measurement errors and external noise on SOC accuracy.
2Adaptability or versatility
If equivalent circuit model is used to simulate battery characteristics, then electrochemical properties can be modeled, but it is very difficult to sufficiently simulate nonlinear characteristic associated with rapid OCV change near full discharge
Solution Approach 1:
The patent dynamically adjusts the reference range for OCV based on the current SOC level. When SOC is near full discharge where nonlinear effects are strong, the reference range is reduced to a first value, while when SOC is away from this region, the reference range is increased to a second value. This dynamic adjustment allows the model to adapt to changing battery characteristics.
Solution Approach 2:
The patent changes the parameter of reference range size based on operating conditions. By reducing the reference range when nonlinear characteristics are strong and increasing it when nonlinear effects are weak, the system optimizes its ability to handle different battery states and improves overall SOC estimation accuracy.
3Reliability
If extended Kalman filter is used to combine ampere counting and equivalent circuit model, then drawbacks of each method are compensated, but when nonlinear characteristic is very strong, error still occurs in SOC estimation
Solution Approach 1:
The patent introduces dynamic adjustment of the reference range based on detected nonlinear characteristic strength. When strong nonlinear characteristics are detected, the reference range is reduced to minimize estimation error. This dynamic adaptation enhances the EKF's ability to handle extreme operating conditions.
Solution Approach 2:
The patent takes preliminary action by detecting the strength of nonlinear characteristics before performing SOC estimation. Based on this detection, the reference range is pre-adjusted to counteract the expected estimation errors that would occur under strong nonlinear conditions, preventing accuracy degradation before it happens.
Data Source
AI summary
Provided are a battery management system, a battery management method, a battery pack and an electric vehicle. The battery management system includes a sensing unit to generate battery information indicating a current, a voltage and a temperature of a battery, and a control unit. The control unit determines a temporary estimate for a SOC in a current cycle using a time update process of an extended Kalman filter based on a previous estimate indicating a SOC in a previous cycle and the battery information. The control unit determines open circuit voltage (OCV) information based on the temporary estimate. The control unit determines a definitive estimate indicating the SOC in the current cycle using a measurement update process of the extended Kalman filter based on the temporary estimate, the OCV information and the battery information.