Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks. (July 2021) - Kleiner, Jan; Stuckenberger, Magdalena; Komsiyska, Lidiya; Endisch, Christian
Abstract: For modeling of the non-linear heat generation and thermal effects in Li-ion batteries, artificial neural networks are a great solution to represent the thermal behavior for the battery management system in an electric vehicle. Several studies have proven the high accuracy with large benefits in computing-time and complexity compared to detailed electrochemical–thermal models. Commonly used feedforward networks need to prove suitability for dynamic applications but are always limited due to missing information about the previous time steps or need external sensor information. In this work, a novel Nonlinear AutoRegressive with eXogenous (NARX)-network is developed and parameterized for a large 25Ah prismatic cell. The NARX is compared to a feedforward using the same general structure and input data in terms of training, validation behavior, long-term prediction and dynamic driving application. Both ANN approaches prove to be adequate for the temperature prediction with an accuracy within 1K during long-term prediction of 10h. Additionally, in a BEV application with real-time requirements the thermal models predicting the dynamic temperature behavior with high precision and robustness without even a temperature input in case of the NARX-approach. Graphical abstract: Highlights: Novel NARX approach is compared to common feedforward architecture. Both model approaches predict core temperature precisely and long-term stable. NARX approach reduces number of sensors in a BEV system and is more robust. Real-time temperature estimation as stand-alone model and in BEV-framework. For dynamic BEV application a mean model accuracy of ±0.5K is revealed.