The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯...为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。展开更多
【目的】锂电池健康状态(state of health, SOH)的精确预测评估可以提高电池设备的安全性,降低故障的发生率。针对数据驱动方法在模型训练过程中需要大量标签样本数据的问题,提出了一种新的基于扩散模型和双向长短期记忆网络的锂电池SO...【目的】锂电池健康状态(state of health, SOH)的精确预测评估可以提高电池设备的安全性,降低故障的发生率。针对数据驱动方法在模型训练过程中需要大量标签样本数据的问题,提出了一种新的基于扩散模型和双向长短期记忆网络的锂电池SOH估计方法。【方法】首先,建立电池充电时间、电压和温度三者间的长期依赖关系云图;其次,设计一个时空信息捕捉模块,将该模块捕获的长期依赖信息作为扩散模型的生成条件,赋予扩散模型电池SOH数据生成能力;最后,利用双向长短期记忆网络(Bi-LSTM)对部分由原始数据和生成数据混合而成的电池数据集进行训练,并利用剩余的原始数据作为测试集对所提方法进行验证。【结果】验证结果表明,该方法不仅可以减少收集电池数据类型的周期和成本,而且能够有效预测电池SOH。【结论】该方法在电池SOH估计上具备良好的精度,可进一步探索其他电池数据集组合,优化模型结构,提高电池管理系统。展开更多
当前铁路信号设备智能运维正在起步阶段,针对转辙机机械故障率高且存在模糊性与随机性的特点,建立一种结合云模型与主、客观组合赋权相融合的设备健康状态(SOH,state of health)评估模型。首先,从“设备-环境-人员-管理”4个方面建立影...当前铁路信号设备智能运维正在起步阶段,针对转辙机机械故障率高且存在模糊性与随机性的特点,建立一种结合云模型与主、客观组合赋权相融合的设备健康状态(SOH,state of health)评估模型。首先,从“设备-环境-人员-管理”4个方面建立影响转辙机SOH的综合指标体系;其次,选择改进AHP法(主观法)与CRITIC法(客观法)理论求取对应20组指标层的权重;再分别采用2种组合赋权法(乘法集成法、动态赋权法)对比求取对应7组部件层的组合权重。然后,通过云模型理论与组合赋权相交,结合云相似度计算设备当前SOH等级。最后,通过一个实例分析验证了该方法的可行性与有效性,为铁路信号设备智能运维提供借鉴。展开更多
文章提出了一种基于TCN-Transformer集成模型的锂离子电池健康状态(State of Health,SOH)预测方法。该方法结合时间卷积网络(Temporal Convolutional Network,TCN)捕捉时间序列局部特征和长期依赖的优势与Transformer自注意力机制建模...文章提出了一种基于TCN-Transformer集成模型的锂离子电池健康状态(State of Health,SOH)预测方法。该方法结合时间卷积网络(Temporal Convolutional Network,TCN)捕捉时间序列局部特征和长期依赖的优势与Transformer自注意力机制建模全局关系的能力,提高预测精度。文章通过电池循环老化实验,提取充放电过程中的电压和容量增量等特征,优化输入数据并构建TCN-Transformer模型进行预测。实验结果显示,该模型较传统单一模型性能更优,能准确反映电池健康状态变化趋势。展开更多
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
文摘为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。
文摘【目的】锂电池健康状态(state of health, SOH)的精确预测评估可以提高电池设备的安全性,降低故障的发生率。针对数据驱动方法在模型训练过程中需要大量标签样本数据的问题,提出了一种新的基于扩散模型和双向长短期记忆网络的锂电池SOH估计方法。【方法】首先,建立电池充电时间、电压和温度三者间的长期依赖关系云图;其次,设计一个时空信息捕捉模块,将该模块捕获的长期依赖信息作为扩散模型的生成条件,赋予扩散模型电池SOH数据生成能力;最后,利用双向长短期记忆网络(Bi-LSTM)对部分由原始数据和生成数据混合而成的电池数据集进行训练,并利用剩余的原始数据作为测试集对所提方法进行验证。【结果】验证结果表明,该方法不仅可以减少收集电池数据类型的周期和成本,而且能够有效预测电池SOH。【结论】该方法在电池SOH估计上具备良好的精度,可进一步探索其他电池数据集组合,优化模型结构,提高电池管理系统。
文摘当前铁路信号设备智能运维正在起步阶段,针对转辙机机械故障率高且存在模糊性与随机性的特点,建立一种结合云模型与主、客观组合赋权相融合的设备健康状态(SOH,state of health)评估模型。首先,从“设备-环境-人员-管理”4个方面建立影响转辙机SOH的综合指标体系;其次,选择改进AHP法(主观法)与CRITIC法(客观法)理论求取对应20组指标层的权重;再分别采用2种组合赋权法(乘法集成法、动态赋权法)对比求取对应7组部件层的组合权重。然后,通过云模型理论与组合赋权相交,结合云相似度计算设备当前SOH等级。最后,通过一个实例分析验证了该方法的可行性与有效性,为铁路信号设备智能运维提供借鉴。
文摘文章提出了一种基于TCN-Transformer集成模型的锂离子电池健康状态(State of Health,SOH)预测方法。该方法结合时间卷积网络(Temporal Convolutional Network,TCN)捕捉时间序列局部特征和长期依赖的优势与Transformer自注意力机制建模全局关系的能力,提高预测精度。文章通过电池循环老化实验,提取充放电过程中的电压和容量增量等特征,优化输入数据并构建TCN-Transformer模型进行预测。实验结果显示,该模型较传统单一模型性能更优,能准确反映电池健康状态变化趋势。
