Physics

Nonlinear system

Fade

Ageing

Biology

Operating system

Power (physics)

Materials science

Capacity loss

Lithium-ion battery

Computer science

Battery (electricity)

Quantum mechanics

Genetics

Thermodynamics

Journal of Power Sources

Forecasting the lifetime of Li-ion batteries is a critical challenge that limits the integration of battery electric vehicles (BEVs) into the automotive market. Cycle-life performance of Li-ion batteries is intrinsically linked to the fundamental understanding of ageing mechanisms. In contrast to most previous studies which utilise empirical trends (low real-time information) or rough simplifications on mathematical models to predict the lifetime of a Li-ion battery, we deployed a novel ageing formulation that includes heterogeneous dual-layer solid electrolyte interphase (SEI) and lithium-plating ageing mechanisms with porosity evaluation. The proposed model is parameterized and optimized for mass transport and ageing parameters based on fresh and an aged cell and validated against our experimental results. We show that our advanced ageing mechanisms can accurately calculate experimentally observed cell voltage and capacity fade with respect to cycling number and can predict future fade for new operating scenarios based on constant-current and a dynamic power profile cycling experimental data consisting of high discharge C-rates and fast-charging periods. Our model is able to capture the linear and nonlinear (knee-point) capacity fade characteristics with a high accuracy of 98% goodness-of-fit-error and we compared our model performance with well-accepted existing model in literature.

Theory of battery ageing in a lithium-ion battery: Capacity fade, nonlinear ageing and lifetime prediction