首页> 外文会议>2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)论文集 >State and Parameter Estimation of a HEV Li-ion Battery Pack Using Adaptive Kalman Filter with a New SOC-OCV Concept
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State and Parameter Estimation of a HEV Li-ion Battery Pack Using Adaptive Kalman Filter with a New SOC-OCV Concept

机译:带有新SOC-OCV概念的自适应卡尔曼滤波器的HEV锂离子电池组状态和参数估计

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摘要

A new methodology of defining the relationship between SOC (State of Charge) and OCV (Open Circuit Voltage) relationship of the Li-ion battery pack used on HEVs (Hybrid Electric Vehicles) which is independent of the battery condition was proposed. This methodology could avoid the problems resulting from the defects that the conventional SOC-OCV relationship differs between batteries and different working conditions. Based on the new definition, a state and parameter estimator of the Li-ion battery pack based on the Sage-Husa adaptive Kalman filter was proposed. This estimator recruited an equivalent circuit model to describe the dynamic characteristics of the battery pack. The estimator could estimate the SOC, the battery actual capacity and the inner resistance on-board. The implementation of the estimator on a FPGA platform was also introduced. Testing results show that the new definition and the estimator work very well in any specific working condition.
机译:提出了一种新的方法来定义混合动力汽车(混合动力汽车)上使用的锂离子电池组的SOC(充电状态)和OCV(开路电压)之间的关系,该关系与电池状况无关。这种方法可以避免由于传统的SOC-OCV关系在电池和不同的工作条件下不同而导致的问题。基于新定义,提出了基于Sage-Husa自适应卡尔曼滤波器的锂离子电池组状态和参数估计器。该估算器采用了等效电路模型来描述电池组的动态特性。估计器可以估计SOC,电池实际容量和车载内部电阻。还介绍了估算器在FPGA平台上的实现。测试结果表明,新定义和估计器在任何特定工作条件下均能很好地工作。

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