Battery State of Health Estimation Using Standardized Temperature
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Solution Overview
Problem
Accurate State of Health estimation for batteries is challenging due to complex working conditions and temperature effects, leading to low accuracy and poor versatility in existing methods, especially across a wide temperature range.
Innovation Solution
An online State of Health estimation method using standardized temperature, which calculates the Incremental Capacity curve, establishes a quantitative relationship between voltage shifts and temperature through Arrhenius fitting, and applies Box-COX transformation to linearize the relationship between capacity-sensitive feature points and State of Health, ensuring accurate estimation across a wide temperature range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Incremental Capacity curve method is used for State of Health estimation at standard temperature, then the estimation accuracy is improved, but the accuracy varies greatly at different temperatures due to temperature-induced shifts in the curve
Solution Approach 1:
The patent applies parameter changes by introducing a temperature compensation parameter (voltage shift of temperature-sensitive feature point) that adjusts the Incremental Capacity curve based on temperature. The Arrhenius fitting function models how this parameter changes with temperature, allowing the curve to be dynamically adjusted to maintain accuracy across different temperature conditions.
Solution Approach 2:
The patent uses the voltage shift of the temperature-sensitive feature point as an intermediary parameter that mediates between temperature variations and the Incremental Capacity curve. This intermediary allows the system to account for temperature effects without directly modifying the core estimation methodology, enabling accurate State of Health estimation across wide temperature ranges.
2Measurement precision
If electrochemical model is used for State of Health estimation, then the calculation results are extremely accurate, but the model parameters are too numerous to obtain and the calculation process is very complicated
Solution Approach 1:
The patent extracts only the essential features needed for accurate State of Health estimation from the complex electrochemical model. Specifically, it extracts the Incremental Capacity curve and identifies key feature points (temperature-sensitive and capacity-sensitive points) that contain the necessary information, eliminating the need for numerous model parameters while maintaining accuracy.
Solution Approach 2:
The patent segments the complex electrochemical model into manageable components: the Incremental Capacity curve, temperature-sensitive feature points, and capacity-sensitive feature points. This segmentation allows each component to be analyzed and processed independently, significantly reducing calculation complexity while preserving the essential accuracy of the full model.
3Measurement precision
If empirical model is used for State of Health estimation, then the model can be established, but it requires a large amount of test data and takes a long time, resulting in poor versatility
Solution Approach 1:
The patent performs preliminary action by pre-identifying the temperature-sensitive feature point and establishing the Arrhenius fitting function relationship between voltage shift and temperature. This preliminary work allows the system to quickly compensate for temperature effects during actual State of Health estimation without requiring extensive real-time test data collection, significantly reducing the time required while maintaining accuracy.
4Speed
If equivalent circuit model is used for real-time State of Health monitoring, then the monitoring is performed in real time, but the accuracy is not high and multiple optimization algorithms are usually required
Solution Approach 1:
The patent replaces the mechanical/electrical equivalent circuit model with a thermally-based approach using the Arrhenius fitting function. Instead of using complex circuit optimizations, it substitutes a physics-based temperature compensation mechanism that directly addresses the root cause of accuracy degradation, achieving both real-time performance and high accuracy without requiring multiple optimization algorithms.
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
This method effectively broadens the temperature range for State of Health estimation, improves accuracy, and stabilizes the estimation by reducing random errors, enhancing the linearity and stability of the estimation process.
Implementation Method 1
an Arrhenius fitting function is utilized to fit the quantitative relationship
Implementation Method 2
Based on a BOX-COX transformation, the quantitative relationship between the transformed height of the capacity-sensitive feature point and the State of Health of the battery is established
Data Source
AI summary
An on-line State of Health estimation method of a battery in a wide temperature range based on “standardized temperature” includes: calculating battery Incremental Capacity curve of a battery, establishing a quantitative relationship between the voltage shift of the temperature-sensitive feature point and the temperature of a standard battery, standardized transformation of Incremental Capacity curves at different temperatures, establishing a quantitative relationship between the transformed height of the capacity-sensitive feature point and the State of Health based on a BOX-COX transformation. The BOX-COX transformation is expressed asyk(λ)={ykλ-1λλ≠0lnykλ=0.An maximum likelihood function is used to calculate the optimal λ, and the transformed height of the capacity-sensitive feature point y can be acquired. The quantitative relationship between transformed height of the capacity-sensitive feature point and the State of Health is established to obtain the State of Health.