UC Riverside

To bridge the gap between existing lithium ion batteries and future high energy density lithium ion batteries, device manufacturers have moved towards fast charging. Fast charging methods have been widely adopted for many applications, especially in electric vehicles and is therefore important to understand the long-term effects of this type of charging. Fast charging is commonly used for charging electric vehicles; people misunderstand and misuse fast charging and this has introduced safety and capacity issues for lithium ion batteries. It is thus of paramount importance that electric vehicle and portable device manufactures quantify the effect of fast charging on thermal stability, usable capacity and cycle life of the battery.

Fast charging tests were performed based on existing industry charging protocol and battery parameters like internal resistance and temperature were analyzed. Based on the analysis of the industry charging protocol A novel fast charging technique was proposed by analyzing the internal resistance of the battery and adjusting the charging current based on the analysis.

The batteries cycled under the industry based fast charging showed faster capacity fade compared to the battery cycled with the internal resistance (IR) based fast charging lasting an average of 11 cycles longer. The internal resistance of the battery, which is an important parameter for understanding the battery state of health was investigated. The battery cycled under the industry based fast charging showed 74.2% increase in internal resistance over 120 cycles whereas the battery cycled under the IR based fast charging showed a 29.4% increase in internal resistance over 120 cycles. The temperature of the battery cycled under the IR based fast charging was not significantly higher (± 2^o C), hence similar thermal solutions could be used for battery packs cycled under the IR based charging.

Using the internal resistance of the battery, the battery state of health can be mapped as the battery is cycled. This data can be used to create an adaptive charging algorithm where the battery fast charging algorithm was updated as the internal resistance of the battery evolved.