Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale featu...Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.展开更多
The increasing reliance on batteries in transportation and energy storage sectors plays a pivotal role in addressing the challenges of energy security and grid power instability.However,the recurrent occurrence of bat...The increasing reliance on batteries in transportation and energy storage sectors plays a pivotal role in addressing the challenges of energy security and grid power instability.However,the recurrent occurrence of battery safety incidents has emerged as the primary obstacle to their more extensive deployment.This necessitates the implementation of precise and efficient battery safety management technology to enhance the safety of batteries throughout their lifecycle,safeguarding the users’assets and optimizing energy utilization.This article explores battery safety management technologies for power and energy batteries,starting with an overview of battery technology and then reviewing battery applications,failure mechanisms,and the analysis of existing intelligent safety management technologies.Finally,the paper consolidates current advancements,pinpoints gaps,and projects future trends in intelligent safety management technologies for power and energy-storage batteries.The insights presented will serve as a valuable reference and guideline for future research and development of battery safety management technology.展开更多
The development of new energy industry is an essential guarantee for the sustainable development of society,and big data technology can enable new energy industrialization.Firstly,this paper presents an in-depth analy...The development of new energy industry is an essential guarantee for the sustainable development of society,and big data technology can enable new energy industrialization.Firstly,this paper presents an in-depth analysis and discussion of big data technology in new energy power and energy storage systems.Furthermore,the current status of big data technology application is discussed based on power generation,grid and user side,while future development trends are proposed based on the characteristics of big data technology.Finally,a comprehensive cloud-platform-based new energy power and energy storage system is proposed,which efficiently combines new energy power generation,consumption,and transmission sides to optimize energy allocation and improve energy utilization efficiency.This paper aims to provide certain guidance significance for new energy research and application.展开更多
基金supported in part by the National Natural Science Foundation of China(No.62172036).
文摘Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.
基金supported partly by the National Natural Science Foundation of China(No.52107220)Beijing Nova Program(No.20230484272)+3 种基金Postdoctoral Research Fund Project of China(No.2021M690353)the Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts,Ministry of Education(No.2022 KLMT03)Postdoctor Research Foundation of Shunde Graduate Innovation School of University of Science and Technology Beijing(No.2021BH007)Fundamental Research Funds for the Central Universities(No.FRF-BD-20-08A,NO.FRF-IDRY-21-013).
文摘The increasing reliance on batteries in transportation and energy storage sectors plays a pivotal role in addressing the challenges of energy security and grid power instability.However,the recurrent occurrence of battery safety incidents has emerged as the primary obstacle to their more extensive deployment.This necessitates the implementation of precise and efficient battery safety management technology to enhance the safety of batteries throughout their lifecycle,safeguarding the users’assets and optimizing energy utilization.This article explores battery safety management technologies for power and energy batteries,starting with an overview of battery technology and then reviewing battery applications,failure mechanisms,and the analysis of existing intelligent safety management technologies.Finally,the paper consolidates current advancements,pinpoints gaps,and projects future trends in intelligent safety management technologies for power and energy-storage batteries.The insights presented will serve as a valuable reference and guideline for future research and development of battery safety management technology.
文摘The development of new energy industry is an essential guarantee for the sustainable development of society,and big data technology can enable new energy industrialization.Firstly,this paper presents an in-depth analysis and discussion of big data technology in new energy power and energy storage systems.Furthermore,the current status of big data technology application is discussed based on power generation,grid and user side,while future development trends are proposed based on the characteristics of big data technology.Finally,a comprehensive cloud-platform-based new energy power and energy storage system is proposed,which efficiently combines new energy power generation,consumption,and transmission sides to optimize energy allocation and improve energy utilization efficiency.This paper aims to provide certain guidance significance for new energy research and application.
