电池的健康状态估计(state of health,SOH)是锂离子电池管理系统中的状态参数之一,影响电池荷电状态估计(state of charge,SOC)和峰值功率估计(state of power,SOF)的精度。本文中通过追踪SOC-OCV(open circuit of voltage,OCV)曲线特...电池的健康状态估计(state of health,SOH)是锂离子电池管理系统中的状态参数之一,影响电池荷电状态估计(state of charge,SOC)和峰值功率估计(state of power,SOF)的精度。本文中通过追踪SOC-OCV(open circuit of voltage,OCV)曲线特征的衍变规律,从热力学的角度提出了全新的SOH估计方法。利用三元锰酸锂复合材料为正极的锂离子电池循环寿命实验数据构建SOH与SOC-OCV曲线特征参数之间的关系,并验证所提SOH估计方法的精度。实验结果表明:SOH从100%衰退到50%,SOH估计精度在±1.5%以内。展开更多
The application of MicroLogixTM 1200 PLC made by American AB Company in battery automatic production line located in Inner Mongolia Rare Earth Ovonic High-Power MH/Ni Battery Co., Ltd was introduced.OCV namely open ci...The application of MicroLogixTM 1200 PLC made by American AB Company in battery automatic production line located in Inner Mongolia Rare Earth Ovonic High-Power MH/Ni Battery Co., Ltd was introduced.OCV namely open circuit voltage could be somewhat different because of the build materials of negative electrode, positive electrode and electrolyte.So OCV test can check the voltage value of electrolyte filled cells and figure out if the value is within the process specification.MicroLogixTM 1200 relay output type PLC, DeviceNet communication card, variable speed driver, Panelview 1000 color touch screen and some other hardwares were chosen.Then programme by using RSLOGIX500 software, a flexible and skillful use of shift and XOR command in PLC system can make the equipments realize the high automation.Through DeviceNet network based on CAN and with the advantages of quick response and high reliability transmit the information to supervise and control master panel, the system with these components can successfully realize proceeding the battery OCV test and control.The equipments are running stably, locating and test accuracy satisfy the process request.展开更多
随着大规模储能系统的广泛发展,快速准确地估计锂离子电池的荷电状态(state of charge,SOC)对系统的安全可靠运行至关重要。然而,在传统的固定串并联电池单元/模块拓扑结构中,无法直接测量电池单元/模块的开路电压(open circuit voltage...随着大规模储能系统的广泛发展,快速准确地估计锂离子电池的荷电状态(state of charge,SOC)对系统的安全可靠运行至关重要。然而,在传统的固定串并联电池单元/模块拓扑结构中,无法直接测量电池单元/模块的开路电压(open circuit voltage,OCV),也就无法建立OCV-SOC映射关系来准确估计SOC。对此,提出一种基于新型动态可重构电池网络的精准SOC估计方法。该方法可以在1s内测量得到OCV,然后使用梯度增强决策树估计电池单元/模块的准确SOC。实验结果表明该方法的高效率和有效性,为电池状态估计提供了一个范式结构。展开更多
准确的开路电压特性OCV-SOC(open-circuit voltage-state of charge)曲线是保证锂离子电池建模精度的基础。静置法与涓流法在获取磷酸铁锂电池OCV-SOC曲线时分别存在无法描述非静置点OCV特性与极化效应影响的问题,为此,基于对磷酸铁锂...准确的开路电压特性OCV-SOC(open-circuit voltage-state of charge)曲线是保证锂离子电池建模精度的基础。静置法与涓流法在获取磷酸铁锂电池OCV-SOC曲线时分别存在无法描述非静置点OCV特性与极化效应影响的问题,为此,基于对磷酸铁锂电池特性的分析,结合静置法与涓流法提出1种高精度磷酸铁锂电池OCV-SOC曲线获取方法。该方法以分段形式拟合的涓流放电曲线为优化对象,基于静置法测量数据与一阶RC等效电路模型设计约束条件,采用差分进化方法寻优获取OCV-SOC优化曲线。实验结果表明,OCV-SOC优化曲线能够精确模拟磷酸铁锂电池的OCV特性。相比静置法获取的OCV-SOC曲线,基于OCV-SOC优化曲线进行的电池建模与SOC估算精度更高,其模型精度提升41.8%,SOC估算精度提升58.3%。展开更多
文摘电池的健康状态估计(state of health,SOH)是锂离子电池管理系统中的状态参数之一,影响电池荷电状态估计(state of charge,SOC)和峰值功率估计(state of power,SOF)的精度。本文中通过追踪SOC-OCV(open circuit of voltage,OCV)曲线特征的衍变规律,从热力学的角度提出了全新的SOH估计方法。利用三元锰酸锂复合材料为正极的锂离子电池循环寿命实验数据构建SOH与SOC-OCV曲线特征参数之间的关系,并验证所提SOH估计方法的精度。实验结果表明:SOH从100%衰退到50%,SOH估计精度在±1.5%以内。
文摘The application of MicroLogixTM 1200 PLC made by American AB Company in battery automatic production line located in Inner Mongolia Rare Earth Ovonic High-Power MH/Ni Battery Co., Ltd was introduced.OCV namely open circuit voltage could be somewhat different because of the build materials of negative electrode, positive electrode and electrolyte.So OCV test can check the voltage value of electrolyte filled cells and figure out if the value is within the process specification.MicroLogixTM 1200 relay output type PLC, DeviceNet communication card, variable speed driver, Panelview 1000 color touch screen and some other hardwares were chosen.Then programme by using RSLOGIX500 software, a flexible and skillful use of shift and XOR command in PLC system can make the equipments realize the high automation.Through DeviceNet network based on CAN and with the advantages of quick response and high reliability transmit the information to supervise and control master panel, the system with these components can successfully realize proceeding the battery OCV test and control.The equipments are running stably, locating and test accuracy satisfy the process request.
文摘随着大规模储能系统的广泛发展,快速准确地估计锂离子电池的荷电状态(state of charge,SOC)对系统的安全可靠运行至关重要。然而,在传统的固定串并联电池单元/模块拓扑结构中,无法直接测量电池单元/模块的开路电压(open circuit voltage,OCV),也就无法建立OCV-SOC映射关系来准确估计SOC。对此,提出一种基于新型动态可重构电池网络的精准SOC估计方法。该方法可以在1s内测量得到OCV,然后使用梯度增强决策树估计电池单元/模块的准确SOC。实验结果表明该方法的高效率和有效性,为电池状态估计提供了一个范式结构。
文摘准确的开路电压特性OCV-SOC(open-circuit voltage-state of charge)曲线是保证锂离子电池建模精度的基础。静置法与涓流法在获取磷酸铁锂电池OCV-SOC曲线时分别存在无法描述非静置点OCV特性与极化效应影响的问题,为此,基于对磷酸铁锂电池特性的分析,结合静置法与涓流法提出1种高精度磷酸铁锂电池OCV-SOC曲线获取方法。该方法以分段形式拟合的涓流放电曲线为优化对象,基于静置法测量数据与一阶RC等效电路模型设计约束条件,采用差分进化方法寻优获取OCV-SOC优化曲线。实验结果表明,OCV-SOC优化曲线能够精确模拟磷酸铁锂电池的OCV特性。相比静置法获取的OCV-SOC曲线,基于OCV-SOC优化曲线进行的电池建模与SOC估算精度更高,其模型精度提升41.8%,SOC估算精度提升58.3%。
