Nonlinear Adaptive Observer for Battery SOC Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating battery state of charge (SOC) in electric vehicles, such as amp-hour integration, often drift from the real SOC due to their nature, leading to inaccurate calculations.
Innovation Solution
A nonlinear adaptive observer approach is implemented, combining a recursive parameter estimator and a nonlinear adaptive observer to estimate battery open circuit voltage (OCV) and SOC, with a system that operates in both open loop and closed loop modes, using gain scheduling and modular architecture to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If amp-hour integration method is used to calculate SOC, then the calculation is simple, but the SOC drifts from real SOC leading to inaccuracy
Solution Approach 1:
The patent introduces an open-circuit voltage (OCV) as an intermediary measurement to correct the SOC drift. The OCV is measured when the battery is in a relaxed state (no current flow), and this measurement serves as a reference point to recalibrate the SOC estimate, thereby improving accuracy without completely abandoning the simple amp-hour integration method
Solution Approach 2:
The system implements feedback by continuously monitoring the relationship between OCV and SOC. When the battery reaches a relaxed state, the measured OCV is compared with the expected OCV-SOC relationship, and the SOC estimate is adjusted accordingly. This feedback mechanism prevents drift accumulation and maintains long-term accuracy
2Measurement precision
If nonlinear adaptive observer approach is implemented, then SOC accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the SOC estimation process into distinct operational modes: open-loop mode for initial estimation and closed-loop mode for refined estimation. The system transitions between these modes based on operating conditions, allowing the complex nonlinear adaptive observer to be activated only when necessary (e.g., when OCV measurements are available), rather than continuously
Solution Approach 2:
The system dynamically adjusts its estimation approach based on real-time conditions. The adaptive observer gains are adjusted online based on the battery's operational state, and the system switches between open-loop and closed-loop modes. This dynamic adaptation allows the system to maintain high accuracy while reducing computational burden during transient or uncertain conditions
Data Source
AI summary
A method of controlling an electric vehicle including an internal combustion engine, a battery having a state of charge (SOC) and an open circuit voltage (OCV), includes establishing a system for estimating battery SOC. The system includes (i) a parameter estimation subsystem including a recursive parameter estimator for identifying battery parameters and (ii) an OCV estimation subsystem including a nonlinear adaptive observer for estimating battery OCV. Estimated battery OCV is related to estimated battery SOC by a mapping. An output is generated based on the estimated battery SOC.