To optimize the performance, safety, and lifespan of li-ion batteries, different battery modeling techniques are used. This summary offers a basic understanding for the sake of simplicity.
1) Electrochemical Models:
Describe battery behavior by taking into account the chemical reactions occurring within the battery.
1.1) Single Particle Model (SPM): Simplifies a battery's complexity by presuming that all active particles in electrode have identical SoC. Offers valuable insights into the intercalation process.
1.2) Pseudo Two-Dimensional Model (P2DM): Provides detailed understanding by accounting for spatial distribution of variables like concentration & potential. Its detailed nature makes it computationally intensive.
2) Equivalent Circuit Models:
Represent the battery using familiar electrical components, making them easy to comprehend & implement.
2.1) Rint Model: A straightforward equivalent circuit involving an open-circuit voltage source & internal resistance, can accurately predict battery performance under steady-state conditions.
2.2) Thevenin Model: Uses resistors & capacitors to represent battery polarization phenomena. It is widely used to estimate SoC & predict dynamic behaviors.
2.3) Generic Non-Linear (GNL) Model: Captures complex battery behaviors such as capacity fading by including non-linear elements. It requires more computational resources and detailed data.
3) Empirical Models:
Derived from experimental observations & describe battery's behavior mathematically.
3.1) Shepherd Model: A simplistic model, represents discharge behavior of a battery. However, it often fails to account for factors like temperature effects and aging.
3.2) Unnewehr Universal Model: Comprehensive model accounting for a broader range of battery behaviors. Its complexity may limit its use in systems with computational constraints.
3.3) Nernst Model: Based on the Nernst equation & accounts for concentration of lithium ions, offering precise SoC estimation. Its inherent non-linearity, however, can make it computationally intensive.
4) Data-Driven Models:
Utilize ML algorithms to predict the system's behavior.
4.1) Radial Basis Function Neural Network (RBFNN): Excellent at handling non-linear multi-dimensional function approximations & predicting SoC, SoH & RUL.
4.2) Support Vector Machine (SVM): SVMs have proven robust in estimating SoH and SoH of li-ion batteries, even under varying working conditions.
4.3) Extreme Learning Machine (ELM): ELM's speed & scalability make it advantageous in real-time battery management systems.
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