Lithium-Ion Battery Model Development and State Estimation for an Extended Single Particle Model Coupled with Thermal Modeling

• Author / Creator

  Chevalier, Brianna L.

• Lithium-ion batteries are the predominant battery type in portable consumer electronics and electric vehicles. Compared to experimental studies, simulation studies tend to be more cost-effective. Trustworthy simulation results depend on high-fidelity electrochemical-thermal dynamic models. To balance computational efficiency with exactness, we propose an enhanced model through an extended single-particle model with thermal dynamics to forecast the internal battery states. Experimental results from literature verify the effectiveness of the developed model. Along this line, we provide a comprehensive analysis of dynamic behaviour of a lithium-ion battery cell using the proposed model. Simulation shows promising results while maintaining computation speed. The developed model does not depend on any commercial packages and provides a high-fidelity simulation platform for battery research and development. Moreover, to enable the effective operation of portable electronics and electric vehicles, accurate and quick estimations of state of charge (SOC) and internal cell temperature are vital to battery management systems. Therefore, a long-short-term-memory (LSTM) recurrent-neural network is proposed which completes the state estimation of SOC and internal average cell temperature (Tavg) of lithium-ion batteries under varying current loads. The network is trained and evaluated using data compiled from the developed extended single particle model coupled with a thermal dynamic model. Results are promising, with root mean square error values typically under 2 \% for SOC and 1.2K for Tavg, while maintaining quick training and testing times. Also examined was a comparison of a single-feature versus multi-feature network, as well as two different approaches to data partitioning.

• Subjects / Keywords

  • Lithium-ion battery
  • PDE
  • Electrochemical model
  • first principles model

• Graduation date

  Fall 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-6zk1-b936

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.