Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

Marui Li,Qian Xiao,Hongjie Jia,Xiaodan Yu,Chaoyu Dong

doi:10.1038/s41598-021-93801-9

Abstract

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.

Highlights

The output gate determines what the LSTM cell outputs
0–1 value is generated according to whether the core temperature of the lithium battery in the 10 s corresponding to each window reaches the critical value, which is regarded as the training output of the neural network model
A novel multi-step ahead thermal warning network is proposed for the energy storage system as the core temperature overrun warning

Summary

Introduction

The output gate determines what the LSTM cell outputs. First, xt and ht−1 are input into Sigmod layer, and a value ot between 0 and 1 is output. The output of softmax and the real output calculate the network loss through cross entropy, and calculate the gradient of loss to update the weight and deviation of each hidden layer. The output ht−1 of the previous time and the input xt of the current time are accepted, and the signal ft is output through the Sigmod layer.

Results

Conclusion