Federated learning empowered microgrids: Lithium battery state-of-health prediction and multi-node co-optimisation

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Xiaofen Fang, Weidong Chen, Tao Hu, Zijie Chen, Qingqiang Zeng, Jianqing Li
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引用次数: 0

Abstract

With the wide application of lithium-ion batteries in microgrids, accurately predicting the State of Health (SOH) of the batteries and realising multi-node co-optimisation has become a key challenge to ensure system stability and prolong battery lifetime. In this paper, we propose a novel federated learning-driven SOH prediction and multi-node optimisation model for Li-ion batteries, named FedOptSOH. The framework is based on National Aeronautics and Space Administration (NASA)’s publicly available Li-ion battery dataset, and utilises heterogeneous data distributed across nodes to achieve highly accurate SOH prediction through local training and central server aggregation, while effectively protecting data privacy. FedOptSOH integrates advanced federated optimisation algorithms, Federated Average of Momentum (FedAvgM) and Adaptive Federated Optimisation (FedOpt), which significantly improves the training stability and convergence speed of the model. Based on the predicted SOH results, FedOptSOH further employs a multi-objective co-optimisation approach to achieve dynamic adjustment of charging and discharging strategies at each node in the microgrid, maximising energy efficiency and delaying battery degradation. Experimental results show that FedOptSOH achieves an Mean Absolute Error (MAE) of 4.17%, a Root Mean Square Error (RMSE) of 5.03%, and a coefficient of determination (R2) of 0.978 in SOH prediction, which are significantly better than the traditional methods; multi-node co-optimisation effectively reduces the energy loss of the system by 12%, and extends the battery life by 15%. FedOptSOH framework combines high accuracy, strong privacy protection and real-time co-optimisation, providing a solid theoretical foundation and technical support for lithium battery management in smart microgrids.
联合学习增强微电网:锂电池健康状态预测和多节点协同优化
随着锂离子电池在微电网中的广泛应用,准确预测电池的健康状态(SOH)并实现多节点协同优化已成为保证系统稳定性和延长电池寿命的关键挑战。在本文中,我们提出了一个新的联邦学习驱动的锂离子电池SOH预测和多节点优化模型,命名为FedOptSOH。该框架基于美国国家航空航天局(NASA)公开可用的锂离子电池数据集,并利用分布在节点上的异构数据,通过本地训练和中央服务器聚合实现高度精确的SOH预测,同时有效地保护数据隐私。FedOptSOH集成了先进的联邦优化算法,联邦平均动量(FedAvgM)和自适应联邦优化(FedOpt),显著提高了模型的训练稳定性和收敛速度。基于预测的SOH结果,FedOptSOH进一步采用多目标协同优化方法,实现微电网各节点充放电策略的动态调整,最大限度地提高能源效率,延缓电池退化。实验结果表明,FedOptSOH预测SOH的平均绝对误差(MAE)为4.17%,均方根误差(RMSE)为5.03%,决定系数(R2)为0.978,显著优于传统方法;多节点协同优化有效降低了系统12%的能量损耗,延长了15%的电池寿命。FedOptSOH框架结合了高精度、强隐私保护和实时协同优化,为智能微电网中锂电池管理提供了坚实的理论基础和技术支持。
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来源期刊
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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