Battery SOC Estimation with Dual Models and Voltage Feedback
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Solution Overview
Problem
Current battery state estimation methods face challenges in accurately predicting the state of charge (SOC) and relative SOC (RSOC) due to errors in electrochemical models, particularly when discharging batteries, as they struggle to account for variations in current, temperature, and degradation states.
Innovation Solution
A processor-implemented method using dual electrochemical models, where a first model corrects the SOC by comparing measured and estimated voltages, and a second model estimates end SOC by updating internal states based on voltage differences and open-circuit voltage tables, allowing for precise RSOC calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single electrochemical model is used to estimate battery SOC, then the model complexity is low, but the estimation accuracy deteriorates due to errors in predicting voltage under varying current and temperature conditions
Solution Approach 1:
The patent divides the battery modeling into two separate electrochemical models: a first model for estimating current SOC and a second model for estimating end SOC. Each model is optimized for specific conditions, with the first model handling general operation and the second model specializing in discharge scenarios. This segmentation allows each model to be simpler while collectively achieving higher accuracy.
Solution Approach 2:
The patent dynamically selects which electrochemical model to use based on the battery's operating conditions, specifically switching between the first and second models depending on whether the battery is charging or discharging. This dynamic adaptation allows the system to maintain high accuracy across varying conditions without requiring a single overly complex model.
2Measurement precision
If electrochemical models are used to account for variations in current and temperature, then the estimation accuracy improves, but the computational resources required increase
Solution Approach 1:
By segmenting the modeling into two specialized models rather than one comprehensive model, the patent reduces the computational burden of each individual model. Each model focuses on specific operating conditions, requiring fewer computational resources while maintaining or improving accuracy for those conditions.
Solution Approach 2:
The patent applies electrochemical modeling partially - using simplified models for specific conditions (charging or discharging) rather than attempting to model all possible operating scenarios with a single complex model. This partial application of electrochemical principles reduces computational requirements while achieving sufficient accuracy for practical purposes.
3Measurement precision
If the electrochemical model is corrected using voltage differences, then the SOC estimation accuracy improves, but the device complexity increases due to additional correction mechanisms
Solution Approach 1:
The patent implements feedback by continuously comparing the voltage estimated by the electrochemical model with the actual measured voltage, and using this voltage difference to correct the SOC estimation. This feedback mechanism systematically reduces estimation errors without requiring complex additional hardware or mechanisms.
Solution Approach 2:
The patent replaces complex mechanical or hardware-based correction mechanisms with an electrical/software-based voltage comparison and correction approach. By using voltage differences as a feedback signal to adjust SOC estimates, the system achieves high accuracy without adding mechanical complexity to the battery system.
Data Source
AI summary
A processor-implemented method with battery state estimation includes estimating a current state of charge (SOC) of a target battery by correcting a first electrochemical model corresponding to the target battery using a first voltage difference between a measured voltage of the target battery and an estimated voltage of the target battery that is estimated by the first electrochemical model, estimating an end SOC of the target battery by correcting a second electrochemical model using a second voltage difference between an estimated voltage of a virtual battery that is estimated by the second electrochemical model and a preset voltage, and estimating a relative SOC (RSOC) of the target battery based on the current SOC and the end SOC of the target battery, wherein the second electrochemical model is based on the virtual battery corresponding to the target battery being discharged to reach the preset voltage.