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Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter

机译：扩展卡尔曼滤波器预测混合动力汽车铅酸蓄电池的充电状态

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摘要

This paper describes and introduces a new nonlinear predictor and a novel battery model for estimating the state of charge (SoC) of lead-acid batteries for hybrid electric vehicles (HEV). Many problems occur for a traditional SoC indicator, such as offset, drift and long term state divergence, therefore this paper proposes a technique based on the extended Kalman filter (EKF) in order to overcome these problems. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface and end). The SoC is determined from the voltage present on the bulk capacitor. In this new model, the value of the surface capacitor is constant, whereas the value of the bulk capacitor is not. Although the structure of the model, with two constant capacitors, has been previously reported for lithium-ion cells, this model can also be valid and reliable for lead-acid cells when used in conjunction with an EKF to estimate SoC (with a little variation). Measurements using real-time road data are used to compare the performance of conventional internal resistance (R_(int)) based methods for estimating SoC with those predicted from the proposed state estimation schemes. The results show that the proposed method is superior to the more traditional techniques, with accuracy in estimating the SoC within 3%.
机译：本文介绍并介绍了一种新的非线性预测器和一种新颖的电池模型，用于估计混合动力汽车（HEV）的铅酸电池的充电状态（SoC）。传统的SoC指示器会出现很多问题，例如偏移，漂移和长期状态发散，因此，本文提出了一种基于扩展卡尔曼滤波器（EKF）的技术来克服这些问题。每个电池的基本动态行为使用两个电容器（体和表面）和三个电阻（端子，表面和末端）进行建模。 SoC由大容量电容器上的电压确定。在这个新模型中，表面电容器的值是恒定的，而大容量电容器的值则不是恒定的。尽管先前已经报道了用于锂离子电池的带有两个恒定电容器的模型结构，但是当与EKF结合使用以估算SoC时，该模型对于铅酸电池也可以是有效且可靠的（变化很小） ）。使用实时道路数据进行的测量可用于比较基于常规内部电阻（R_（int））的SoC估计方法的性能与根据提议的状态估计方案预测的方法的性能。结果表明，该方法优于传统技术，估计SoC的准确度在3％以内。

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  • 来源
    《Energy Conversion & Management》 |2008年第1期|75-82|共8页
  • 作者

    A. Vasebi; S.M.T. Bathaee; M. Partovibakhsh;

  • 作者单位

    Hybrid Electric Vehicle Research Center, Department of Electrical and Electronic Engineering, K.N. Toosi University of Technology, Tehran, Iran;

    Hybrid Electric Vehicle Research Center, Department of Electrical and Electronic Engineering, K.N. Toosi University of Technology, Tehran, Iran;

    Hybrid Electric Vehicle Research Center, Department of Electrical and Electronic Engineering, K.N. Toosi University of Technology, Tehran, Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    batteries; extended kalman filter; hybrid electric vehicle; state of charge;

    机译：电池;扩展卡尔曼滤波器混合动力汽车充电状态;

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