Toward a function realization of multi-scale modeling for lithium-ion battery based on CHAIN framework

As the most mature portable power source,lithium-ion battery has become the mainstream of power source for electric vehicles(EVs)by virtue of its high energy density,long cycle life and relatively low cost.However,an excellent battery management system remained to be a problem for the operational states monitoring and safety guarantee for EVs.In this paper,a function realization of multi-scale modeling is proposed based on cyber hierarchy and interactional network framework,realizing basic functions such as multi-scale mapping and cloud-based modeling.Furthermore,to solve the problem of limited computing capability of the conventional vehicle-end battery management system,the novel system consists of hierarchies of side,edge and cloud,which are designed to take on different computing tasks methodically and swap data iteratively,providing specific services for drivers,enterprise users,etc.Due to a series of promising features,this system has a large range of application scenarios as well as some critical bottlenecks,which will be discussed at the end of the article.Please check and confirm D.-B.Shan"is correctly identified in author group.It’s not correct.There is no"D.-B.Shan"in author group,please just remove it.

State of health and remaining useful life prediction for lithiumion batteries based on differential thermal voltammetry and a long and short memory neural network

As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)prediction are crucial for battery management systems.In this paper,the core contribution is the construction of a datadriven model with the long short-term memory(LSTM)network applicable to the time-series regression prediction problem with the integration of two methods,data-driven methods and feature signal analysis.The input features of model are extracted from differential thermal voltammetry(DTV)curves,which could characterize the battery degradation characteristics,so that the accurate prediction of battery capacity fade could be accomplished.Firstly,the DTV curve is smoothed by the Savitzky-Golay filter,and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics.Then,a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model.The LSTM neural network is trained by using the root mean square propagation(RMSprop)technique and the dropout technique.Finally,the data of four batteries with different health levels are deployed for model construction,verification and comparison.The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated.This method can greatly reduce the cost and complexity,and increase the practicability,which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.