Battery State of Charge Estimation Using Section-Wise Voltage Correlation
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Solution Overview
Problem
Existing battery state of charge estimation methods face indeterminacy due to non-linear voltage-state of charge correlations, which result in multiple possible state of charge values for a given voltage, making unique determination impossible.
Innovation Solution
A battery model is developed with section-wise defined correlations of terminal voltage values, ensuring monotonic dependence, allowing for biunique inverse functions and precise state of charge estimation by segmenting the voltage-state of charge relationship and applying a correction factor through coulomb counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a non-linear battery model is used to estimate state of charge from terminal voltage, then the estimation can account for non-linear battery behavior, but the function may return multiple state of charge values for a given voltage, causing indeterminacy
Solution Approach 1:
The battery model's voltage-state of charge function is divided into multiple monotonic sections. Each section represents a range where the function is strictly monotonic (either increasing or decreasing), ensuring that for any given voltage within a section's range, there is exactly one corresponding state of charge value. The system identifies which section applies based on operational conditions and retrieves the unique state of charge from that section, thereby eliminating the indeterminacy problem while preserving the ability to model non-linear battery behavior.
2Measurement precision
If the battery model is divided into multiple monotonic sections, then a unique state of charge value can be determined for a given voltage, but the model complexity increases
Solution Approach 1:
The battery model is segmented into monotonic sections, each representing a specific operational range. The system includes logic to identify which section is currently applicable based on operational conditions (such as charge/discharge state), then retrieves the unique state of charge from that section. This segmentation approach manages complexity by organizing the non-linear relationship into manageable monotonic segments rather than attempting to handle the entire non-linear curve as a single function.
Solution Approach 2:
The system dynamically identifies which monotonic section is currently applicable based on real-time operational conditions of the battery. Rather than using a static single-function model, the system adapts by selecting the appropriate monotonic section according to the battery's current state, allowing the model complexity to be activated only when needed and keeping the retrieval process simple once the section is identified.
3Measurement precision
If section identification based on operational conditions is implemented, then the correct monotonic section can be selected for accurate estimation, but additional computational steps are required
Solution Approach 1:
The system performs preliminary identification of the applicable monotonic section based on operational conditions before retrieving the state of charge value. By determining which section is currently relevant in advance, the system ensures that the subsequent state of charge retrieval is accurate and unique, avoiding the need for complex post-processing or multiple retrieval attempts.
Data Source
AI summary
A state of charge of a battery is estimated by a battery model specific to the battery. The battery model provides a section-wise defined correlation of terminal voltage values depending on state of charge values. Each of sections of the battery model delimits a monotonic dependence of the correlation from others of the sections. By segmenting the correlation of terminal voltage values depending on a state of charge value into sections, each segment within such the section-wise defined correlation of terminal voltage values depending on state of charge values is mathematically spoken a bi-unique function suitable for transformation into an inverse function defined within the section.The estimated state of charge may be continuously refined by an iterative feedback loop including coulomb counting for estimating a battery charge value, where refined estimated battery charge values state of charge values at a previous cycle are projected forward to the current cycle.