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1. 发明申请

WO2005050810A1 METHOD FOR CALCULATING POWER CAPABILITY OF BATTERY PACKS USING ADVANCED CELL MODEL PREDICTIVE TECHNIQUES 审中-公开
标题翻译：使用高级细胞模型预测技术计算电池组功率的方法
公开(公告)号：WO2005050810A1
公开(公告)日：2005-06-02
申请号：PCT/KR2004/003001
申请日：2004-11-19
申请人： LG CHEM, LTD. , PLETT, GREGORY L.
发明人： PLETT, GREGORY L.
IPC分类号： H02J7/00
CPC分类号： G01R31/3648 , G01R31/361 , G01R31/3651 , G01R31/3675
摘要： The present invention relates to a method and an apparatus for estimating discharge and charge power of battery applications, including battery packs used in Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV). One charge/discharge power estimating method incorporates voltage, state-of-charge (SOC), power, and current design constraints and works for a user-specified prediction time horizon of ¥Ät. At least two cell models are used in calculating maximum charge/discharge power based on voltage limits. The first is a simple cell model that uses a Taylor-series expansion to linearize the equation involved. The second is a more complex and accurate model that models cell dynamics in discrete-time state-space form. The cell model can incorporate a inputs such as temperature, resistance, capacity, etc. One advantage of using model-based approach is that the same model may be used in both Kalman-filtering to produce the SOC and the estimation of maximum charge/discharge current based on voltage limits.
摘要翻译：本发明涉及一种用于估计电池应用的放电和充电功率的方法和装置，包括用于混合电动车辆（HEV）和电动车辆（EV）中的电池组。一种充电/放电功率估计方法包括电压，充电状态（SOC），功率和当前设计约束，并且可以用于用户指定的预测时间范围。至少有两个电池模型用于根据电压限制计算最大充电/放电功率。第一个是使用泰勒级数扩展来对所涉及的方程进行线性化的简单单元格模型。第二个是在离散时间状态空间形式中建模细胞动力学的更为复杂和准确的模型。电池模型可以包含诸如温度，电阻，容量等的输入。使用基于模型的方法的一个优点是在卡尔曼滤波中可以使用相同的模型来产生SOC和最大充电/放电的估计电流基于电压限制。

2. 发明申请

WO2006052044A1 METHOD AND SYSTEM FOR CELL EQUALIZATION USING STATE OF CHARGE 审中-公开
标题翻译：使用充电状态进行电池均衡的方法和系统
公开(公告)号：WO2006052044A1
公开(公告)日：2006-05-18
申请号：PCT/KR2004/003104
申请日：2004-11-29
申请人： LG CHEM, LTD.
发明人： PLETT, GREGORY L.
IPC分类号： H01M10/44
CPC分类号： H02J7/0014 , H02J7/0021
摘要： The present invention relates to an implementation of a method for cell equalization. The present invention uses other information such as the individual cell state-of-charge (SOC) estimates, and individual capacities and/or cell Coulombic efficiencies, possibly available from a dual extended Kalman filter. The present invention defines or refers to an operational SOC range between a minimum and a maximum. The minimum and maximum could be constants or could be dynamic based on functions of other variables. SOC can be measured relative to maximum charge or relative to maximum discharge for each cell. When the SOC is not consistent across the cells, a prioritizing scheme is used to determine which cells require equalization.
摘要翻译：本发明涉及一种用于小区均衡的方法。本发明使用可能从双扩展卡尔曼滤波器获得的其他信息，例如单个电池充电状态（SOC）估计，以及各个容量和/或电池库仑效率。本发明定义或指代最小和最大值之间的操作SOC范围。最小和最大值可以是常量，也可以是基于其他变量的函数的动态变量。可以相对于每个电池的最大电荷或相对于最大放电量测量SOC。当SOC在单元格之间不一致时，使用优先化方案来确定哪些单元需要均衡。

3. 发明申请

WO2006057469A1 METHOD AND SYSTEM FOR JOINT BATTERY STATEAND PARAMETER ESTIMATION 审中-公开
标题翻译：用于联合电池状态和参数估计的方法和系统
公开(公告)号：WO2006057469A1
公开(公告)日：2006-06-01
申请号：PCT/KR2004/003102
申请日：2004-11-29
申请人： LG CHEM, LTD.
发明人： PLETT, GREGORY L.
IPC分类号： H01M10/44
CPC分类号： H01M6/5083 , H01G9/26 , H01M10/052 , H01M10/0565 , H01M10/42 , H01M10/4285 , H01M10/44 , H01M10/48 , H01M10/486
摘要： A method and apparatus for estimation of the augmented state of an electrochemical cell, the method comprising: making an internal augmented states prediction of the cell where the augmented state comprises at least one internal state value and at least one internal parameter value; making an uncertainty prediction of the internal augmented states prediction; correcting the internal augmented states prediction and the uncertainty prediction; and applying an algorithm that iterates the making an internal augmented states prediction, the making an uncertainty prediction and the correcting to yield an ongoing estimation to the augmented state and an ongoing uncertainty to the augmented state estimation.
摘要翻译：一种用于估计电化学电池的增强状态的方法和装置，所述方法包括：对所述电池进行内部增强状态预测，其中所述增强状态包括至少一个内部状态值和至少一个内部参数值; 对内部增强状态预测进行不确定性预测; 纠正内部增强状态预测和不确定性预测; 以及应用迭代进行内部增强状态预测的算法，进行不确定性预测和校正以产生对增强状态的持续估计和对增强状态估计的持续不确定性。

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