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    • 1. 发明授权
    • Method of performance analysis for VRLA battery
    • VRLA电池性能分析方法
    • US08340934B2
    • 2012-12-25
    • US12596698
    • 2008-04-17
    • Jianhong XuLing YuanYi Zheng
    • Jianhong XuLing YuanYi Zheng
    • G01R31/36
    • G01R31/362G01R31/3648G01R31/3668H01M10/121H01M10/48
    • This invention discloses a method of performance analysis for VRLA battery which applies the method of using the float voltage dispersion ratio of the battery to evaluate the battery performance from a new perspective, and it is an online real-time test which applies several ways to evaluate the performance of the VRLA battery. According to the relevance between the dispersion of the float charge voltage of the battery and its performance, the method gets the result of the battery performance through calculating the battery float charge voltage dispersion ratio and making it relevant with the battery performance. It has small workload, convenient operation and no danger to the system when doing online testing. It also won't affect the cycle life of the VRLA battery.
    • 本发明公开了一种VRLA电池的性能分析方法,该方法采用电池的浮动电压分散比率的方法,从新的角度对电池的性能进行评估,是一种在线实时测试,采用多种方式进行评估 VRLA电池的性能。 根据电池浮充电压的偏差与其性能的关系,该方法通过计算电池浮充电压分散比并使其与电池性能相关,得到电池性能的结果。 工作量小,操作方便,在线测试时不会对系统造成危害。 它也不会影响VRLA电池的循环寿命。
    • 2. 发明授权
    • Professional diagnosis method of battery performance analysis
    • 电池性能分析的专业诊断方法
    • US08306781B2
    • 2012-11-06
    • US12596163
    • 2008-04-17
    • Jianhong XuLing YuanYi Zheng
    • Jianhong XuLing YuanYi Zheng
    • H02J7/00
    • G01R31/3651G01R31/3606
    • The present invention discloses a professional diagnosis method of battery performance analysis, Through the overall evaluation of experiential data library, several parameters about the battery are input into the artificial neural network, outputting capacity prediction and service life prediction of each battery, etc. and giving useful advices for each battery. Therefore the result is much more in conformity with the real condition of the battery. Besides, it designs an adaptive learning function of the abovementioned artificial neural network. This invention effectively avoids the defect of evaluating the VRLA battery performance at single moment, from single perspective and by single method, and it does the real-time monitoring and evaluating for the performance of the battery during VRLA battery working period, which is easy to operate, and avoids checking discharge test to the battery so that it doesn't affect the cycle life of the VRLA battery.
    • 本发明公开了一种电池性能分析的专业诊断方法,通过对经验数据库的整体评估,将电池的几个参数输入人工神经网络,输出每个电池的容量预测和使用寿命预测等,并给出 每个电池有用的建议。 因此,结果更符合电池的实际情况。 此外,它设计了上述人工神经网络的自适应学习功能。 本发明有效地避免了从单个角度和单一方法在单一时刻评估VRLA电池性能的缺陷,并且对VRLA电池工作期间的电池性能进行了实时监控和评估,这很容易 操作,并避免检查电池的放电测试,使其不影响VRLA电池的循环寿命。
    • 3. 发明申请
    • Method of Performance Analysis for VRLA Battery
    • VRLA电池性能分析方法
    • US20100131218A1
    • 2010-05-27
    • US12596698
    • 2008-04-17
    • Jianhong XuLing YuanYi Zheng
    • Jianhong XuLing YuanYi Zheng
    • G01R31/36
    • G01R31/362G01R31/3648G01R31/3668H01M10/121H01M10/48
    • This invention discloses a method of performance analysis for VRLA battery which applies the method of using the float voltage dispersion ratio of the battery to evaluate the battery performance from a new perspective, and it is an online real-time test which applies several ways to evaluate the performance of the VRLA battery. According to the relevance between the dispersion of the float charge voltage of the battery and its performance, the method gets the result of the battery performance through calculating the battery float charge voltage dispersion ratio and making it relevant with the battery performance. It has small workload, convenient operation and no danger to the system when doing online testing. It also won't affect the cycle life of the VRLA battery.
    • 本发明公开了一种VRLA电池的性能分析方法,该方法采用电池的浮动电压分散比率的方法,从新的角度对电池的性能进行评估,是一种在线实时测试,采用多种方式进行评估 VRLA电池的性能。 根据电池浮充电压的偏差与其性能的关系,该方法通过计算电池浮充电压分散比并使其与电池性能相关,得到电池性能的结果。 工作量小,操作方便,在线测试时不会对系统造成危害。 它也不会影响VRLA电池的循环寿命。
    • 4. 发明申请
    • PROFESSIONAL DIAGNOSIS METHOD OF BATTERY PERFORMANCE ANALYSIS
    • 电池性能分析专业诊断方法
    • US20110054815A1
    • 2011-03-03
    • US12596163
    • 2008-04-17
    • Jianhong XuYi Zheng
    • Jianhong XuYi Zheng
    • G01R31/36H02J7/00
    • G01R31/3651G01R31/3606
    • The present invention discloses a professional diagnosis method of battery performance analysis, Through the overall evaluation of experiential data library, several parameters about the battery are input into the artificial neural network, outputting capacity prediction and service life prediction of each battery, etc. and giving useful advices for each battery. Therefore the result is much more in conformity with the real condition of the battery. Besides, it designs an adaptive learning function of the abovementioned artificial neural network. This invention effectively avoids the defect of evaluating the VRLA battery performance at single moment, from single perspective and by single method, and it does the real-time monitoring and evaluating for the performance of the battery during VRLA battery working period, which is easy to operate, and avoids checking discharge test to the battery so that it doesn't affect the cycle life of the VRLA battery.
    • 本发明公开了一种电池性能分析的专业诊断方法,通过对经验数据库的整体评估,将电池的几个参数输入人工神经网络,输出每个电池的容量预测和使用寿命预测等,并给出 每个电池有用的建议。 因此,结果更符合电池的实际情况。 此外,它设计了上述人工神经网络的自适应学习功能。 本发明有效地避免了从单个角度和单一方法在单一时刻评估VRLA电池性能的缺陷,并且对VRLA电池工作期间的电池性能进行了实时监控和评估,这很容易 操作,并避免检查电池的放电测试,使其不影响VRLA电池的循环寿命。