A quick solution algorithm for online prediction of three-dimensional temperature distribution in lithium-ion battery based on thermal resistance network and influence of network parameters on calculation performance

Temperature has a crucial impact on the performance and safety of lithium-ion batteries. To achieve accurate prediction of battery temperature, this paper establishes a thermal resistance network model that can achieve online calculation of battery three-dimensional temperature field, and conducts a systematic study based on different thermal resistance network structures to solve two key issues: the balance between the model's prediction accuracy and computational efficiency, as well as the sensitivity of the prediction accuracy in different spatial dimensions. Firstly, the three-dimensional heat conduction inside the battery is reconstructed based on the thermal resistance network, and an online calculation algorithm for battery three-dimensional temperature field is established. The 6710 thermal model, with the most complex thermal network structure, can still control the computational time within 39 s. Secondly, four thermal resistance network structures ranging from simple to complex are constructed, the operational efficiency and prediction accuracy of these four thermal resistance network models are analyzed, and the parameter γ is introduced to determine the optimal thermal resistance network structure. When the γ of the thermal resistance network structure is 0.0071, it can not only ensure the prediction accuracy of the model but also ensure calculation efficiency. Finally, the sensitivity of the model's prediction accuracy in different spatial dimensions is explored. When the number of layers in the thermal resistance network structure changes, the ΔME of internal temperature along the thickness direction can reach 3.18 °C, while the ΔME along the length and height directions can be controlled within 1.36 °C. This indicates that changes in the number of layers along the length and height directions have a relatively small impact on the model accuracy, while the changes in the number of layers in the thickness direction have a significant impact on the model accuracy.