基于自适应无迹卡尔曼滤波算法的锂离子电池SOC估计技术研究,自适应无迹卡尔曼滤波算法在锂离子电池荷电状态SOC估计中的应用与优化,基于自适应无迹卡尔曼滤波算法(AUKF)锂离子电池荷电状态SOC估计
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基于自适应无迹卡尔曼滤波算法的锂离子电池SOC估计技术研究,自适应无迹卡尔曼滤波算法在锂离子电池荷电状态SOC估计中的应用与优化,基于自适应无迹卡尔曼滤波算法(AUKF)锂离子电池荷电状态SOC估计。,基于AUKF算法;锂离子电池;SOC估计;荷电状态;无迹卡尔曼滤波算法;电池状态估计,基于AUKF算法的锂离子电池SOC估计技术研究 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401728/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90401728/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">标题<span class="ff2">：</span>基于自适应无迹卡尔曼滤波算法的锂离子电池荷电状态<span class="_ _0"> </span><span class="ff3">SOC<span class="_ _1"> </span></span>估计</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff2">：</span>随着电动汽车等领域的快速发展<span class="ff2">，</span>锂离子电池作为重要的能源储存装置之一<span class="ff2">，</span>其荷电状态<span class="ff2">（</span></div><div class="t m0 x1 h2 y3 ff3 fs0 fc0 sc0 ls0 ws0">State of Charge<span class="ff2">，</span>SOC<span class="ff2">）<span class="ff1">估计的准确性变得越来越重要<span class="ff4">。</span>本文基于自适应无迹卡尔曼滤波算法</span>（</span></div><div class="t m0 x1 h2 y4 ff3 fs0 fc0 sc0 ls0 ws0">Adaptive Unscented Kalman Filter<span class="ff2">，</span>AUKF<span class="ff2">），<span class="ff1">对锂离子电池的<span class="_ _0"> </span></span></span>SOC<span class="_ _1"> </span><span class="ff1">进行估计<span class="ff4">。</span>通过对实际</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">测试数据的分析<span class="ff2">，</span>证明了<span class="_ _0"> </span><span class="ff3">AUKF<span class="_ _1"> </span></span>算法在<span class="_ _0"> </span><span class="ff3">SOC<span class="_ _1"> </span></span>估计方面的优越性和有效性<span class="ff4">。</span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">关键词<span class="ff2">：</span>锂离子电池<span class="ff2">；</span>荷电状态<span class="ff2">（<span class="ff3">SOC</span>）</span>估计<span class="ff2">；</span>自适应无迹卡尔曼滤波算法<span class="ff2">（<span class="ff3">AUKF</span>）</span></div><div class="t m0 x1 h2 y7 ff3 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">引言</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">电动汽车等能源领域的发展对锂离子电池荷电状态<span class="ff2">（<span class="ff3">State of Charge</span>，<span class="ff3">SOC</span>）</span>估计提出了更高的</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">要求<span class="ff4">。</span>准确的<span class="_ _0"> </span><span class="ff3">SOC<span class="_ _1"> </span></span>估计能够提高电池系统的可靠性<span class="ff4">、</span>增强对电池工作状态的了解<span class="ff2">，</span>并有效延长电池的</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">寿命<span class="ff4">。</span>然而<span class="ff2">，</span>由于电池的非线性<span class="ff4">、</span>不确定性以及不可观测性等因素<span class="ff2">，<span class="ff3">SOC<span class="_ _1"> </span></span></span>估计面临着一定的挑战<span class="ff4">。</span>因</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">此<span class="ff2">，</span>本文提出了一种基于自适应无迹卡尔曼滤波算法<span class="ff2">（<span class="ff3">AUKF</span>）</span>的<span class="_ _0"> </span><span class="ff3">SOC<span class="_ _1"> </span></span>估计方法<span class="ff4">。</span></div><div class="t m0 x1 h2 yc ff3 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">锂离子电池<span class="_ _0"> </span></span>SOC<span class="_ _1"> </span><span class="ff1">估计的问题与挑战</span></div><div class="t m0 x1 h2 yd ff3 fs0 fc0 sc0 ls0 ws0">2.1.<span class="_"> </span><span class="ff1">锂离子电池的特性与模型</span></div><div class="t m0 x1 h2 ye ff3 fs0 fc0 sc0 ls0 ws0">2.2.<span class="_"> </span>SOC<span class="_ _1"> </span><span class="ff1">估计的问题</span></div><div class="t m0 x1 h2 yf ff3 fs0 fc0 sc0 ls0 ws0">2.3.<span class="_"> </span><span class="ff1">传统<span class="_ _0"> </span></span>SOC<span class="_ _1"> </span><span class="ff1">估计方法存在的不足</span></div><div class="t m0 x1 h2 y10 ff3 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">自适应无迹卡尔曼滤波算法</span></div><div class="t m0 x1 h2 y11 ff3 fs0 fc0 sc0 ls0 ws0">3.1.<span class="_"> </span><span class="ff1">无迹卡尔曼滤波算法原理</span></div><div class="t m0 x1 h2 y12 ff3 fs0 fc0 sc0 ls0 ws0">3.2.<span class="_"> </span><span class="ff1">自适应无迹卡尔曼滤波算法的改进</span></div><div class="t m0 x1 h2 y13 ff3 fs0 fc0 sc0 ls0 ws0">3.3.<span class="_"> </span>AUKF<span class="_ _1"> </span><span class="ff1">算法的优势与适用性</span></div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff1">基于<span class="_ _0"> </span></span>AUKF<span class="_ _1"> </span><span class="ff1">的<span class="_ _0"> </span></span>SOC<span class="_ _1"> </span><span class="ff1">估计方法</span></div><div class="t m0 x1 h2 y15 ff3 fs0 fc0 sc0 ls0 ws0">4.1.<span class="_"> </span>SOC<span class="_ _1"> </span><span class="ff1">估计模型建立</span></div><div class="t m0 x1 h2 y16 ff3 fs0 fc0 sc0 ls0 ws0">4.2.<span class="_"> </span>AUKF<span class="_ _1"> </span><span class="ff1">算法在<span class="_ _0"> </span></span>SOC<span class="_ _1"> </span><span class="ff1">估计中的应用</span></div><div class="t m0 x1 h2 y17 ff3 fs0 fc0 sc0 ls0 ws0">4.3.<span class="_"> </span><span class="ff1">算法参数的选择与自适应调节</span></div><div class="t m0 x1 h2 y18 ff3 fs0 fc0 sc0 ls0 ws0">5.<span class="_ _2"> </span><span class="ff1">实验结果与分析</span></div><div class="t m0 x1 h2 y19 ff3 fs0 fc0 sc0 ls0 ws0">5.1.<span class="_"> </span><span class="ff1">实验环境与数据采集</span></div><div class="t m0 x1 h2 y1a ff3 fs0 fc0 sc0 ls0 ws0">5.2.<span class="_"> </span><span class="ff1">基于<span class="_ _0"> </span></span>AUKF<span class="_ _1"> </span><span class="ff1">的<span class="_ _0"> </span></span>SOC<span class="_ _1"> </span><span class="ff1">估计结果分析</span></div><div class="t m0 x1 h2 y1b ff3 fs0 fc0 sc0 ls0 ws0">5.3.<span class="_"> </span><span class="ff1">与传统方法的对比分析</span></div><div class="t m0 x1 h2 y1c ff3 fs0 fc0 sc0 ls0 ws0">6.<span class="_ _2"> </span><span class="ff1">结论与展望</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>