To guarantee rescue time and reduce medical accidents, a health degradation prediction model of medical lithium-ion batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current state of health prediction methods. The collected energy data of medical lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition and correlation analysis. Then, deep Boltzmann machines and long short-term memory were used to model the main trend and fluctuation data, respectively. The predicting outcomes of deep Boltzmann machines and long short-term memory were effectively integrated to obtain the health predicted results of medical lithium-ion battery. The experimental results show that the method can effectively fit the health trend of medical lithium-ion batteries and obtain accurate state of health prediction results. The performance of the method is better than other typical prediction methods.

为了保证救援时间，减少医疗事故，针对目前健康预测方法的模型适应性差，预测不准确的问题，提出了一种基于多尺度深度神经网络的医用锂离子电池健康退化预测模型。通过集成经验模态分解和相关分析，将收集的医用锂离子电池能量数据分解为主要趋势数据和波动数据。然后，使用深部的Boltzmann机器和较长的短期记忆分别对主要趋势和波动数据进行建模。深度Boltzmann机器的预测结果和长期的短期记忆被有效地整合在一起，以获得医疗锂离子电池的健康预测结果。实验结果表明，该方法可以有效地适应医用锂离子电池的健康趋势，并获得准确的健康状态预测结果。该方法的性能优于其他典型的预测方法。