Traction Battery SOC Uncertainty Bounding for Capacity Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating the state-of-charge (SOC) of traction batteries in electrified vehicles fail to accurately consider multiple uncertainty factors, leading to inaccurate capacity estimation and control of the battery and vehicle operations.
Innovation Solution
A battery controller (BECM) that considers multiple SOC uncertainty factors, including voltage measurement, ampere-hour integration, and distributed voltage measurements, to refine the SOC uncertainty bound, thereby improving the accuracy of capacity estimation and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple SOC uncertainty factors are considered to refine the SOC uncertainty bound, then measurement precision of capacity estimation is improved, but device complexity increases
Solution Approach 1:
The SOC uncertainty is segmented into multiple independent uncertainty factors (voltage measurement uncertainty, ampere-hour integration uncertainty, distributed voltage measurement uncertainty, etc.). Each factor is evaluated separately and then combined to determine the overall SOC uncertainty bound, allowing systematic refinement without overwhelming complexity
Solution Approach 2:
The controller continuously monitors and evaluates multiple SOC uncertainty factors in real-time, using feedback from voltage measurements, current integrations, and temperature data to dynamically refine the SOC uncertainty bound and adjust capacity estimation accordingly
2Reliability
If multiple SOC uncertainty factors are considered to refine the SOC uncertainty bound, then reliability of battery control is improved, but loss of time in calculation increases
Solution Approach 1:
The system pre-establishes the relationships between different uncertainty factors and their impact on SOC estimation. By preparing the framework for uncertainty analysis in advance and continuously monitoring individual factors, the system reduces real-time computational burden while maintaining high reliability
Solution Approach 2:
The controller dynamically adjusts the weight and significance of different uncertainty factors based on operating conditions (charge state, discharge rate, temperature). This adaptive parameter adjustment allows reliable uncertainty bounding with optimized calculation time by focusing on dominant uncertainty sources under specific conditions
Data Source
AI summary
A system includes a battery and a controller. The battery has a state-of-charge (SOC) with a SOC uncertainty. The controller is configured to charge and discharge the battery based on a capacity of the battery according to the SOC with a bound of the SOC uncertainty. The bound of the SOC uncertainty is based on consideration of multiple SOC uncertainty factors to thereby be reduced relative to being based on consideration of less than all of the SOC uncertainty factors.