Real-time Lithium-ion battery state of health evaluation based on discrete wavelet transform: The effect of operating temperature

Abstract

Li-ion batteries (LIBs), thanks to high efficiencies and energy density, represent the mainstream technology to replace traditional internal combustion vehicles with electric ones. However, LIBs state of health (SoH) should be investigated to avoid fast degradation due to fast-charging, electrical, mechanical and thermal factors. Therefore, SoH prediction and monitoring for battery electric vehicles is necessary for extending LIB lifespan and avoiding failures. In this paper, an accurate real-time SoH prediction and monitoring method, based on discrete wavelet (DWT) analysis, is investigated through an extensive experimental campaign considering the effect of temperature variation. Specifically, moving from cycle aging performed on Li-ion NCR 18650 cells and applying two typical US test drive cycles at different SoHs, three different operating temperatures (i.e., 0 °C, 20 °C and 30 °C) were investigated. Applying DWT on the gathered LIB voltage profiles, it is demonstrated that temperature effect on the implemented method is easily recognizable from the one of cycle aging. Moreover, suitable linearized functions are identified to refer DWT outcomes assessed at the operative temperature to a reference temperature, at which a suitable equation is previously identified to assess capacity fading. Due to its general validity the method can be extended to stationary applications.