Model prediction-based battery-powered heating method for series-connected lithium-ion battery pack working at extremely cold temperatures

The degraded performance of lithium-ion batteries at low temperatures is a key obstacle to the development of battery energy storage system applied in extremely cold environment. Therefore, this paper proposes a heating method based on model prediction to support the low-temperature operation of battery pack without additional power sources. Battery pack model is developed based on Thevenin equivalent circuit model. A co-estimator is established to update model parameters and state-of-charge online using adaptive recursive least squares and extended Kalman filter. The permissible discharging current of pack is predicted based on multiple constraints to prevent over-discharge. Then, the battery powered heating structure, control circuit, and heating strategy are designed. The strategy contains a preheating process for cold-start and a holding process for stabilizing cell temperature. The method is verified experimentally through systematic battery-in-the-loop tests at the environmental temperature of e -40 degrees C. Results show that the method can uniformly preheat all in-pack cells from -40 degrees C to -20 degrees C in 330 s consuming 4.7% of nominal capacity. In holding process, it is energy-efficient to raise cell temperature continuously and then maintain at 5 degrees C, which makes 68.3% of nominal capacity available when loading a modified federal urban driving schedule. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The paper proposes a heating method based on model prediction to support the operation of battery pack in low temperature, and experimental results demonstrate its effectiveness and energy efficiency.