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Battery Parameter Estimation from Recorded Fleet Data

机译:录制的舰队数据的电池参数估计

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Existing battery parameter model structures are evaluated by estimating model parameters on real driving data applying standard system identification methods. Models are then evaluated on the test data in terms of goodness of fit and RMSE in voltage predictions. This is different from previous battery model evaluations where a common approach is to train parameters using standardized tests, e.g. hybrid pulse-power capability (HPPC), with predetermined charge and discharge sequences. Equivalent linear circuit models of different complexity were tested and evaluated in order to identify parameter dependencies at different state of charge levels and temperatures. Models are then used to create voltage output given a current, state of charge and temperature. The average accuracy of modelling the DC bus voltage provides a model goodness of fit average higher than 90% for a single RC circuit model. Both single RC equivalent circuit model and R-equivalent circuit model produce goodness of fit at average 75 % or higher. The dual RC equivalent circuit model experienced larger errors in voltage estimations compared to single R and RC equivalent circuit models.
机译:通过在应用标准系统识别方法的实际驱动数据上估计模型参数来评估现有电池参数模型结构。然后根据适合的良好和RMSE在电压预测方面对测试数据进行评估模型。这与之前的电池模型评估不同,其中常用方法是使用标准化测试训练参数,例如,混合脉冲功率能力(HPPC),具有预定的充电和放电序列。测试和评估不同复杂性的等效线性电路模型,以便在不同的充电水平和温度下识别参数依赖性。然后使用模型来产生电流,充电状态和温度的电压输出。模拟直流母线电压的平均精度为单个RC电路模型提供了高于90%的适合平均值的型号。单个RC等效电路模型和r相对于等效电路模型的良好平均为75%或更高。与单个R和RC等效电路模型相比,双RC等效电路模型经历了电压估计的较大误差。

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