Adaptive method for sensorless temperature estimation over the lifetime of lithium-ion batteries

Over the last decade, several impedance-based temperature estimation methods for lithium-ion cells have been proposed in the literature. However, the influence of cell degradation on these methods is rarely considered. In this paper, we therefore investigate the influence of aging on the temperature estimation method, presented in our previous work, by tracking the capacity fade and resistance change of a 6s1p module over 200 cycles. Both capacity fade and resistance change were found to affect the accuracy of the temperature estimation method, leading to a root mean square error (RMSE) of up to 15 K without adaptation to cell aging. Fitting the reference used for temperature estimation with linear operations and a nonlinear least-squares solver (NLS) to the aging data proved to be a valid method of compensating for the effects of aging. Derived from the fitting results, an online applicable aging adjustment scheme based on the checkup values is proposed to maintain a stable temperature estimation over battery lifetime. Using a simple resistance offset correction and an accurate state of charge and health estimation, the temperature estimation error stabilizes at an average RMSE of below 2 K for each cell in the module over its entire lifetime.