Lithium Battery State of Charge Estimation via Surface Density
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Solution Overview
Problem
Current methods for estimating the state of charge of lithium batteries in vehicles are not reliable and require significant calibration efforts.
Innovation Solution
A method and system that utilize a non-linear function and diffusion models to estimate the state of charge by determining the actual lithium surface density and varying parameter, with real-time correction and prediction based on actual and predicted surface densities, and display the estimated state of charge in the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional state of charge estimation methods are used, then the estimation can be obtained, but the reliability and accuracy of the estimation is insufficient
Solution Approach 1:
The patent segments the state of charge estimation process into multiple independent components: open circuit voltage estimation, ohmic resistance calculation, and diffusion resistance determination. Each component is calculated separately using specific equations (Eq. 1-3), allowing for more precise and reliable overall estimation by addressing each physical phenomenon independently rather than using a single conventional method.
Solution Approach 2:
The patent introduces lithium surface density as an intermediary parameter to bridge the relationship between voltage and state of charge. By using the non-linear function relating open circuit voltage to normalized lithium surface density (Eq. 1), the system achieves more accurate state of charge estimation through this intermediate physical quantity rather than direct voltage-to-capacity conversion.
2Measurement precision
If conventional calibration methods are applied, then state of charge estimation can be performed, but significant calibration effort and time are required
Solution Approach 1:
The patent enables the battery management system to automatically determine its own parameters (ohmic resistance, diffusion resistance, and capacity) through real-time voltage and current measurements during normal operation. The system uses self-service calibration where the battery itself provides the data needed for parameter identification, eliminating the need for external calibration equipment and procedures.
Solution Approach 2:
The patent performs preliminary determination of battery parameters (capacity, ohmic resistance, diffusion resistance) during initial system setup or manufacturing, storing these values for subsequent use. This preliminary action (Eq. 4-6) prepares the system in advance, reducing the need for extensive calibration efforts during vehicle operation and enabling faster state of charge estimation from the start.
3Device complexity
If simple voltage-based estimation is used, then the system is simple, but the estimation accuracy is insufficient for lithium batteries
Solution Approach 1:
The patent changes from using a single voltage parameter to utilizing multiple parameters including open circuit voltage, current, ohmic resistance, diffusion resistance, and lithium surface density. This multi-parameter approach (Eq. 1-6) significantly improves estimation accuracy by capturing the complex electrochemical behavior of lithium batteries, while the systematic organization keeps the increased complexity manageable.
Solution Approach 2:
The patent replaces simple voltage measurement with a more sophisticated electrochemical model that incorporates diffusion processes and resistance characteristics. By substituting the mechanical/electrical voltage-only approach with an electrochemical model based on lithium ion transport physics, the system achieves higher accuracy in state of charge estimation for lithium battery chemistry.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy and reliability of state of charge estimation in lithium batteries, reducing calibration efforts and providing real-time monitoring.
Implementation Method 1
determining a predicted lithium surface density and an estimated state of charge based on the varying parameter relative to a second diffusion model
Data Source
AI summary
A method and system for estimating state of charge of a lithium battery cell of a vehicle is provided. The method comprises providing a non-linear function of the lithium battery cell, a normalized lithium surface density and an actual voltage at a current of the lithium battery cell having an internal resistance. The method further comprises determining an actual lithium surface density based on the actual voltage relative to an inverse of the non-linear function. The method further comprises determining a varying parameter based on the actual lithium surface density relative to a first diffusion model. The method further comprises determining a predicted lithium surface density based on the varying parameter relative to a second diffusion model. The method further comprises determining an estimated state of charge of the lithium battery cell when a difference between the predicted and actual lithium surface densities is less than a first threshold.