In order to improve the early warning ability of lithium battery energy storage,an early warning system of lithium battery energy storage is designed by combining edge computing technology.A multi-mode voiceprint recognition device for lithium battery is used to collect the sound features emitted during the running of lithium battery,extract the voiceprint feature parameters,and calculate the power spectrum of the voiceprint signal.The FPGA chip and hardware accelerator are used in the voice print recognition device of lithium battery.By constructing the fault warning model of BP-SNN fusion neural network,combining the back-propagation neural network and pulse neural network,the time information is calculated and transmitted by the specific neuron model,so that the time series of lithium battery monitoring data can be processed effectively,and improve the lithium battery fault warning capability.