| 蓄电池荷电状态预测的改进新算法 |
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| 引用本文： | 吴红斌,孙辉.蓄电池荷电状态预测的改进新算法[J].电子测量与仪器学报,2010,24(11):993-998. |
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| 作者姓名： | 吴红斌 孙辉 |
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| 作者单位： | 合肥工业大学教育部光伏系统工程研究中心; |
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| 基金项目： | 国家自然科学基金资助项目，国家863 高技术基金项目，教育部留学回国人员科研启动基金资助项目 |
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| 摘 要： | 对蓄电池的荷电状态（state of charge,SOC）进行预测是蓄电池能量管理的前提。考虑蓄电池充放电电流、内部工作温度和充放电循环次数等因素的影响,结合卡尔曼滤波,提出了蓄电池SOC预测的改进能量-卡尔曼滤波算法。在蓄电池三阶动态模型的基础上,详细阐述了算法的计算步骤,并与传统的SOC预测方法进行了对比研究。仿真结果表明,改进的能量-卡尔曼预测算法可以有效跟踪蓄电池SOC的变化,其精度优于传统的预测方法。
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| 关 键 词： | 蓄电池 荷电状态 能量预测法 卡尔曼滤波 |
New improved prediction algorithm for state of charge of battery |
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| Wu Hongbin,Sun Hui.New improved prediction algorithm for state of charge of battery[J].Journal of Electronic Measurement and Instrument,2010,24(11):993-998. |
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| Authors: | Wu Hongbin Sun Hui |
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| Affiliation: | Wu Hongbin Sun Hui(Hefei University of Technology,Hefei 230009,China) |
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| Abstract: | It is elementary to predict State of Charge(SOC) of battery for battery energy management.Considering the battery charge-discharge current,the internal temperature and the number of charge-discharge cycles and combining the Kalman Filtering,an improved energy-Kalman method was proposed,which integrated with Kalman filtering.Based on the three-order battery model,the paper described in detail the calculation steps of this new prediction method and compared with the traditional prediction methods.The results ... |
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| Keywords: | battery state of charge energy estimation method Kalman filtering |
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