With the automotive industry rapidly expanding their electric vehicle (EV) programs, fast development cycles are essential for time to market. Lithium-ion batteries are the foundation of this shift to electric vehicles, and accurate modeling of battery behavior is essential to maximize battery pack efficiency.Physics-based electrochemical models and empirical equivalent circuit models (ECMs) are two well-established and widely used techniques to predict the current-voltage behavior in lithium-ion cells. While physics-based models are typically very accurate and require relatively little experimental data to calibrate, they suffer from high mathematical and computational complexity. Conversely, empirical models are more computationally efficient and mathematically simpler, making them well-suited for applications in controls, diagnosis, and state estimation of lithium-ion battery packs. However, ECMs are not predicting the physical and chemical processes occurring in the cell, hence they are less accurate and require extensive and costly experimental campaigns to properly calibrate. With each class of model serving important roles in the EV development cycle, automakers typically calibrate both types of models.This research bridges the gap between these two classes of models by developing a method to directly define an ECM from an already-calibrated electrochemical model, eliminating the cost and time required for traditional ECM calibration methods. To achieve this, the Extended Single-Particle Model (ESPM), an electrochemical model, was chosen as the foundation for this work. The Padé Approximation was used to model the solid and liquid phase diffusion equations of the ESPM, with the approximation order chosen for mathematical compatibility with an ECM. Several governing equations of the ESPM were simplified via linearization and modeling approximations such that the mathematical structure of the approximated ESPM matched that of a second order ECM, making analytical definition of the ECM parameters straightforward.The newly defined electrochemistry-based ECM had minimal loss in accuracy compared to the high-fidelity ESPM, with only a 7% increase in RMS error across various validation test profiles at 25 C and reasonably low RMS error with temperatures ranging from 0 C to 50 C. With the developed ECM having analytical parameter definitions, the extensive experimental campaign typically required to calibrate a data-driven ECM was eliminated. Finally, since the electrochemical ECM voltage expression is strictly composed of physics-based equations, it is essentially a physics-based model that assumes the form of an ECM. This makes the electrochemical ECM well-suited for incorporating other physics-based models, such as capacity loss and power fade aging models.(Author did not follow requirements and enter the required 400 words to somplete the submission so staff as added this text)

Electrochemistry-Based Equivalent Circuit Model via Model Approximation

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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