Supervisor:
Daniel- Ioan Stroe

Co-Supervisor:
Xin Sui

Assessment Committee:
Vincenzo Liso(Chair)
Professor Erik Dahlquist, Malardalen University, Sweden
Professor Daniel Gladwin, University of Sheffield, United Kingdom

Moderator:
Erik Schaltz

Abstract:

ASSESSMENT OF LITHIUM-ION BATTERY DEGRADATION UNDER PULSED CURRENT OPERATION

Lithium-ion batteries, characterized by their low-carbon footprint, green energy potential, and innovative technology, serve as a pivotal solution for achieving a green and low-carbon energy transition.  However, their lifespan is limited. Pulse current charging (PCC) emerges as an effective method to extend battery lifetime, yet the internal electrochemical mechanisms underlying its effectiveness remain unclear. This Ph.D. project provides a comprehensive analysis of lithium-ion battery degradation under PCC conditions, encompassing both cylindrical batteries and electrochemical perspectives. A novel feature extraction method tailored for PCC conditions was proposed, and the prediction results validated the effectiveness and general applicability of the extracted features.

Firstly, considering the external characteristics and internal electrochemical changes of lithium-ion batteries, a long-term PCC experiment was designed for NMC batteries (HTCNR18650) to evaluate the degradation in terms of capacity fade and internal resistance (IR) growth in cylindrical cells. The test results showed that PCC significantly mitigates battery degradation and extends battery lifetime compared to constant current (CC) charging. Subsequently, non-invasive and post-mortem analysis techniques were used to identify the degradation mechanisms occurring during PCC. The results revealed that PCC alleviates degradation by suppressing lithium inventory loss, active material loss, and kinetic barriers. Notably, PCC with a small duty cycle effectively suppressed the growth of interfacial impedance, reducing capacity fade and prolonging battery life. Finally, to accurately predict the state of health (SOH) of lithium-ion batteries, aging test data obtained under PCC conditions were used to extract ten features from the pulse charging voltage curves as candidate features for SOH prediction. These features were arranged, combined, and analyzed before SOH prediction was performed using ELM, SVM, DNN, and GPR algorithms. The results showed that the accuracy of SOH prediction does not exhibit a linear increase with the number of input features. The proposed feature extraction method exhibited strong applicability and effectiveness.

This Ph.D. project provides valuable insights into uncovering the degradation mechanisms of lithium-ion batteries under PCC conditions and offers new possibilities for improving SOH prediction in PCC scenarios.