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Calculation of the power capacity of the battery pack using the advanced cell model predictive techniques

机译:使用先进的电池模型预测技术计算电池组的功率容量

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 Deltat. 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 an 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.
机译:用于估计电池应用的放电和充电功率的方法和装置技术领域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,该电池应用包括在混合动力电动车辆(HEV)和电动车辆(EV)中使用的电池组。一种充电/放电功率估计方法结合了电压,充电状态(SOC),功率和电流设计约束条件,并且可用于用户指定的预测时间范围Deltat。至少两个电池模型用于基于电压限制来计算最大充电/放电功率。首先是一个简单的单元模型,它使用泰勒级数展开来线性化所涉及的方程。第二个是更复杂和准确的模型,该模型以离散时间状态空间形式对单元动力学进行建模。电池单元模型可以包含诸如温度,电阻,容量等输入。使用基于模型的方法的一个优点是,同一模型可以用于卡尔曼滤波以产生SOC以及估计最大充电/放电量电流基于电压限制。

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