Li Marui, Dong Chaoyu, Yu Xiaodan, Xiao Qian, Jia Hongjie
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, 300072, China.
Key Laboratory of Smart Energy & Information Technology of Tianjin Municipality, Tianjin, 300072, China.
Sci Rep. 2021 Jul 28;11(1):15332. doi: 10.1038/s41598-021-93801-9.
The energy storage system is an important part of the energy system. Lithium-ion batteries have been widely used in energy storage systems because of their high energy density and long life. However, the temperature is still the key factor hindering the further development of lithium-ion battery energy storage systems. Both low temperature and high temperature will reduce the life and safety of lithium-ion batteries. In actual operation, the core temperature and the surface temperature of the lithium-ion battery energy storage system may have a large temperature difference. However, only the surface temperature of the lithium-ion battery energy storage system can be easily measured. The estimation method of the core temperature, which can better reflect the operation condition of the lithium-ion battery energy storage system, has not been commercialized. To secure the thermal safety of the energy storage system, a multi-step ahead thermal warning network for the energy storage system based on the core temperature detection is developed in this paper. The thermal warning network utilizes the measurement difference and an integrated long and short-term memory network to process the input time series. This thermal early warning network takes the core temperature of the energy storage system as the judgment criterion of early warning and can provide a warning signal in multi-step in advance. This detection network can use real-time measurement to predict whether the core temperature of the lithium-ion battery energy storage system will reach a critical value in the following time window. And the output of the established warning network model directly determines whether or not an early emergency signal should be sent out. In the end, the accuracy and effectiveness of the model are verified by numerous testing.
储能系统是能源系统的重要组成部分。锂离子电池因其高能量密度和长寿命而被广泛应用于储能系统。然而,温度仍然是阻碍锂离子电池储能系统进一步发展的关键因素。低温和高温都会降低锂离子电池的寿命和安全性。在实际运行中,锂离子电池储能系统的核心温度和表面温度可能会有较大温差。然而,锂离子电池储能系统的表面温度很容易测量,而能更好反映锂离子电池储能系统运行状况的核心温度估算方法尚未商业化。为确保储能系统的热安全,本文基于核心温度检测开发了一种储能系统多步超前热预警网络。该热预警网络利用测量差异和长短时记忆网络集成来处理输入时间序列。此热预警网络以储能系统的核心温度作为预警判断标准,能够提前多步提供预警信号。该检测网络可以利用实时测量来预测锂离子电池储能系统的核心温度在接下来的时间窗口内是否会达到临界值。并且所建立的预警网络模型输出直接决定是否应发出早期应急信号。最后,通过大量测试验证了模型的准确性和有效性。