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首页> 外文会议>Power Electronics, Drive Systems, and Technologies Conference >State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter
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State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

机译：基于连续传递函数模型和扩展卡尔曼滤波器的NMC基锂离子电池的充电状态估计

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摘要

Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.
机译：基于镍 - 锰 - 钴（NMC）阴极的锂离子（LI）电池被出现为电动车辆动力总成（EVS）的最成功电池类型之一。 基于NMC的电池的有效管理依赖于精确估计其电池管理系统（BMS）中其充电状态（SOC）。 在本文中，应用了有效的系统识别方法来使用连续传递函数（CTF）模型来建立电池模型。 Akaike信息标准（AIC）应用于考虑到模型的准确性和实时效率的合适的模型结构。 然后，基于开发的模型和扩展卡尔曼滤波器（EKF）算法来满足SOC估计。 使用基于3.4AH 3.7V NMC的电池单元的实验数据进行评估和确认所提出的方法的正确性能。 同样，通过一些硬件循环（HIL）测试证明了嵌入式实现的可行性。

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