一种用于电池组多物理场的高保真在线监测算法

Abstract Accurate estimation of battery state of charge (SOC), state of temperature distribution (SOTD), and state of power (SOP) is crucial for ensuring the safety, efficiency, and longevity of modern energy storage systems, particularly in electric vehicles. Cross-coupled dynamics between these states require advanced modeling and estimation methods to enhance performance and reliability. In this study, a battery pack was selected as the research object to develop an electro-thermal coupled model. The electrical model is based on the first-order equivalent circuit model, which is extended to incorporate series-parallel relationships, providing electrical parameters for the thermal model. The thermal model establishes a detailed framework for heat generation and heat transfer in the battery pack, providing temperature feedback to the electrical model to correct its parameters. An online SOC-SOTD-SOP joint reconstruction scheme is then designed for the model. First, the SOC of each unit in the pack is estimated based on the electrical model and corrected using SOT. The SOC is then utilized in the heat generation model to calculate model parameters. Subsequently, the thermal model determines the core temperature of each cell. Using the designed fractal theory method, the 3D temperature field of the battery pack is reconstructed to obtain precise temperature distribution for each series-parallel unit. Finally, leveraging accurate SOC, SOT, and parameters provided by the electro-thermal coupled model, including the battery terminal voltage and current, the SOP is estimated. The real-time reconstruction results of key status interpret that the proposed method shows good accuracy and reliability under different operating conditions and temperatures, with AAEs of SOC no more than 1.52 %, MAE of 3D temperature field less than 1.32 °C, and SOP estimation method also shows excellent effect on peak current estimation, considering different constraints.