Battery State Estimation Using Single-Particle Diffusion Modeling
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Solution Overview
Problem
Current methods for estimating the internal state of batteries, such as state of charge (SOC) and state of health (SOH), lack accuracy and efficiency in real-time monitoring due to limitations in numerically modeling lithium-ion secondary batteries and understanding movement phenomena.
Innovation Solution
A method and apparatus using an electrochemical model based on a single particle model (SPM) to calculate inner lithium-ion concentrations in the cathode and anode, considering diffusion differences and temperature, to estimate the battery's internal state by discretizing the model into layers and using a diffusion coefficient and overpotential values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an electrochemical model based on single particle model (SPM) is used to calculate inner lithium-ion concentrations, then measurement precision of battery internal state is improved, but device complexity increases due to discretization into multiple layers and diffusion coefficient calculations
Solution Approach 1:
The patent applies segmentation by discretizing the continuous SPM into multiple concentric layers (e.g., 5-10 layers) within the particle. Each layer is assigned a specific lithium-ion concentration, transforming the continuous diffusion problem into a discrete multi-layer system. This segmentation enables numerical calculation of concentration distributions while maintaining the physical realism of the electrochemical model.
Solution Approach 2:
The patent employs parameter changes by introducing concentration-dependent diffusion coefficients that vary across different layers and conditions. The diffusion coefficient is adjusted based on local lithium-ion concentration, temperature, and layer position, allowing the model to capture non-linear diffusion behavior. This parameter adaptation enhances measurement precision by reflecting actual physical conditions at different stages of charging/discharging.
2Reliability
If discretization of SPM into multiple layers is performed to calculate inner lithium-ion concentrations, then reliability of battery state estimation is improved, but loss of time increases due to complex calculations
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing diffusion coefficient values for various concentration and temperature conditions in lookup tables before real-time operation. During battery monitoring, the model retrieves pre-computed parameters rather than performing full numerical integration, significantly reducing calculation time while maintaining reliability through the use of pre-validated diffusion characteristics.
Solution Approach 2:
The patent implements partial action by selectively calculating concentration profiles only in the regions and time steps where changes are significant. Rather than continuously updating all layers at full resolution, the model adjusts calculation frequency and spatial resolution based on the rate of change of battery state, reducing computational overhead while preserving reliability during critical transitions.
3Manufacturing precision
If diffusion coefficient calculations based on concentration and temperature are implemented, then manufacturing precision of the model is improved, but ease of operation deteriorates due to increased parameter requirements
Solution Approach 1:
The patent applies feedback by continuously monitoring actual battery temperature and concentration states, then using these measurements to dynamically adjust the diffusion coefficient values in the model. The measured temperature and voltage data feed back into the calculation loop, allowing the model to adapt to real-time conditions and maintain manufacturing precision without requiring manual parameter reconfiguration by operators.
Solution Approach 2:
The patent implements self-service through automated parameter estimation algorithms that derive diffusion coefficients directly from standard battery measurements (voltage, current, temperature) without requiring external calibration or manual input. The model self-adjusts its parameters based on observed battery behavior, eliminating the need for operators to manually input material properties or perform complex setup procedures.
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 approach provides accurate and efficient estimation of battery internal states, including SOC and SOH, by simulating lithium-ion concentration and voltage dynamics, enhancing real-time monitoring and management.
Implementation Method 1
calculating inner lithium-ion concentrations in the cathode and the anode based on a difference between diffusion of lithium ions in the cathode and the anode
Implementation Method 2
estimates a voltage of the battery based on an overpotential value defined as a difference between a measured voltage of the battery and an open-circuit voltage (OCV) of the battery
Data Source
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AI summary
A method of estimating an internal state of a battery (S110) includes acquiring at least one parameter of the battery, and estimating the internal state (S120) of the battery by using an electrochemical model based on the at least one parameter, the electrochemical model being calculated based on a single particle model (SPM) with respect to a cathode and an anode of the battery, wherein the electrochemical model includes a model configured to calculate inner lithium-ion concentrations in the cathode and the anode based on a difference between diffusion of lithium ions in the cathode and the anode of the battery and to estimate the internal state of the battery based on the difference between the inner lithium-ion concentrations in the cathode and the anode.