UC Riverside

Rapid development of renewable energy technologies and EV (Electric Vehicle)/PHEV (Plug-in Hybrid Electric Vehicle) has pushed the demand of energy storage systems with high energy density and power density to the next level. However, the lack of understanding of the degradation mechanisms of the batteries makes the industry facing two significant challenges: Fast charging and SOH (State of Health) determination. In order to be competitive with conventional ICE (Internal Combustion Engine) vehicles, the charging time of EV/PHEV needs to be shortened to around 5-10 minutes, while the existing charging technologies usually take more than 8 hours. Many researches in this area still follow the “optimal charging” curve back to 1972 for Lead-Acid batteries. Therefore, in this work, a novel adaptive fast charging technique is proposed based on the internal resistance analysis. NCR 18650B batteries that were cycled under industry based fast charging showed 78% increase in internal resistance after 120 cycles along with rapid capacity fading. Fracturing of the battery case occurred around the 60th cycle for industry based fast charging. In contrast, IR (Internal Resistance) based fast charged batteries showed 29.4% increase in internal resistance over 120 cycles. In addition, fractures were not observed, and the relative capacity fading was on a moderate level. Furthermore, this work could pave the way for the optimization of fast charging techniques to secure the lifespan and safety of various types of lithium-ion batteries. For SOH determination, the existing researches usually use capacity, resistance, temperature, or combination of them. However, these parameters cannot provide enough information for SOH determination and therefore lead to significant safety hazards. In this work, EIS (Electrochemical Impedance Spectroscopy) was used as a tool to characterize different electrodes, including CNT (Carbon Nano Tubes), silicon and lithium anodes. The results shown that EIS is a powerful technique to determine the degradation mechanisms and interfacial variations in molecular level, which is far beyond any existing techniques. Key parameters were also retrieved from the data to determine the SOH of the batteries with high precision.