Battery Pack OCV-SOC Curve Updating for Aging-Aware State Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As electric vehicle batteries age, changes in open circuit voltage (OCV) lead to increasing errors in estimating their online states, such as state of charge (SOC) and state of health (SOH), due to the gradual aging of the battery pack.
Innovation Solution
A method to update the OCV-SOC curve of a battery pack based on its aging state by quantifying aging characteristic parameters like scale-down and translation ratios of electrode curves, allowing for more accurate online state estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If the battery pack is used for a long time, then the service time increases, but the OCV-SOC curve deviates from the actual aging state leading to increased estimation errors
Solution Approach 1:
The OCV-SOC curve is transformed from a static parameter to a dynamic one that automatically adapts to battery aging. The system continuously updates the curve based on real-time detection of OCV values at different SOC levels, allowing the curve to evolve with the battery's aging state rather than remaining fixed throughout the battery's service life.
Solution Approach 2:
The battery management system performs self-updating of the OCV-SOC curve using its own operational data. By detecting OCV values during normal operation and automatically comparing them with the stored curve, the system updates the curve without requiring external intervention or manual recalibration, enabling the system to maintain accuracy through self-service.
2Measurement precision
If the OCV-SOC curve is updated frequently to maintain accuracy, then the state estimation accuracy improves, but the system complexity and computational load increase
Solution Approach 1:
The system performs rapid OCV detection and curve update operations only when necessary conditions are met, such as when the battery reaches specific SOC thresholds or when deviations exceed predetermined limits. This selective updating approach allows the system to maintain accuracy while avoiding unnecessary computational overhead and simplifying the control logic.
3Measurement precision
If the OCV-SOC curve is updated based on aging state, then the state estimation accuracy improves, but the data processing and calculation requirements increase
Solution Approach 1:
The system updates only the necessary portions of the OCV-SOC curve based on detected deviations, rather than recalculating the entire curve. By focusing computational resources on updating specific SOC ranges or curve segments where aging effects are most pronounced, the system achieves improved accuracy while minimizing overall computational energy consumption.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
This application relates to the field of battery technologies, and discloses a method for updating an OCV-SOC curve of a battery pack, a battery management system, and a vehicle. The method for updating an OCV-SOC curve of a battery pack includes: obtaining information that represents an aging state of the battery pack; obtaining a current aging characteristic parameter of the battery pack based on a current OCV-SOC curve of the battery pack and the information; and updating the OCV-SOC curve of the battery pack based on the current aging characteristic parameter and the current OCV-SOC curve of the battery pack. In this application, the OCV-SOC curve of the battery pack is updated based on the aging state of the battery pack, thereby obtaining an OCV-SOC curve that meets the aging state of the battery pack and making it convenient to estimate the online state of the battery pack more accurately.