Adaptive temperature monitoring for battery thermal management

Abstract

Battery thermal management is crucial for avoiding disastrous consequences due to short circuits and thermal runaway. The temperature inside a battery (core temperature) is higher than the temperature outside (skin temperature) under high discharge/charge rates. Although the skin temperature is measurable, the core temperature is not. In this paper, a lumped thermal model is considered to estimate the core temperature from skin temperature readings. To take into account uncertainties in thermal model parameters, which are bound to occur as the battery ages, an adaptive closed-loop estimation algorithm called the adaptive Potter filter is derived. Finally, computer simulations are performed to validate the adaptive Potter filter's ability to track the skin and core temperatures under high charge/discharge current pulses and model mismatches.