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letter.drawio
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<mxCell id="rjWY6HScssMi8YFPxf11-1" value="<p style="margin-bottom: 0.0001pt; line-height: 20pt; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">意见<span lang="EN-US">5</span>:<span lang="EN-US">2.2</span>的物理建模部分作者更注重的是介绍模型的基本原理。但是本文的主题是电池快充策略,因此重点应该是介绍这些模型如何用于快充策略开发的。</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin-bottom: 0cm; text-align: center; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" align="center" class="MsoNormal"><br><br></p><p style="margin: 7.8pt 0cm 3.9pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:<br/>宋体;mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:0pt;<br/>mso-ligatures:none">回复:电池模型在快速充电策略设计中起到了重要作用,物理模型有着坚实的理论基础,因此被广泛应用于快速充电设计研究。为了使物理建模部分紧扣快速充电策略设计的主题,我们在每一类基本模型的原理介绍之中补充了其在快充策略设计中的应用,具体如下。</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p><p style="margin: 7.8pt 0cm 3.9pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:<br/>宋体;mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:0pt;<br/>mso-ligatures:none"><br></span></b></p><p style="margin-bottom: 0.0001pt; line-height: 20pt; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">意见<span lang="EN-US">6</span>:本文的主题是快充策略,但是从图<span lang="EN-US">7</span>中完全没有看到快充相关的内容。</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin: 7.8pt 0cm 3.9pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">回复:由于依赖于实验数据,泛化能力较差等原因,目前的快速充电策略设计研究暂未广泛应用基于机器学习方法的电池模型(如图七中的各类模型)。然而机器学习方法的灵活性与高效性能够有效优化快速策略的设计过程。因此我们提出,尝试利用现有的机器学习电池模型<span lang="EN-US">/</span>开发适用于快速充电设计的机器学习电池模型,可能将是一个有意义的研究方向。</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 10pt; font-family: 宋体;">(</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">1</span><span style="font-size: 10pt; font-family: 宋体;">)</span><b><span style="font-size:10.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">图</span></b><b><span style="font-size:10.0pt;<br/>font-family:&quot;Times New Roman&quot;,serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US">7</span></b><span style="font-size: 10pt; font-family: 宋体;">归纳总结了电池建模中常用的机器学习方法,可将建模过程抽象为三层。第一层为物理信息</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">/</span><span style="font-size: 10pt; font-family: 宋体;">数据信息层,包含各类电池机理等先验物理知识,以及通过电池测试实验得到的充放电数据;第二层为机器学习方法层,融合第一层的物理信息,设计高效的机器学习模型,并利用数据进行模型更新与知识学习;最后一层为模型预测</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">/</span><span style="font-size: 10pt; font-family: 宋体;">验证层,通过对比模型预测输出与真实值的差距,对模型进行验证与评价。</span><b><span style="font-size:10.0pt;font-family:<br/>宋体;mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:0pt;<br/>mso-ligatures:none">通过机器学习方法建模得到的模型往往具有较高的计算效率,并且建模方式灵活,在一定程度上减弱了对复杂电化学原理的依赖。然而由于依赖于实验数据,泛化能力较差等原因,目前的快速充电策略设计研究暂未广泛应用此类电池模型。</span></b><span style="font-size: 10pt; font-family: 宋体;">(</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2.2</span><span style="font-size: 10pt; font-family: 宋体;">机器学习建模方法</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">/</span><span style="font-size: 10pt; font-family: 宋体;">第</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">1</span><span style="font-size: 10pt; font-family: 宋体;">段)</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin: 7.8pt 0cm 3.9pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><br></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 10pt; font-family: 宋体;">(</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2</span><span style="font-size: 10pt; font-family: 宋体;">)针对这些缺点,可以考虑利用小样本学习<sup><span lang="EN-US">[128]</span></sup>、迁移学习<sup><span lang="EN-US">[129]</span></sup>等方法对建模过程进行改善。除此之外,如何利用电池中的物理信息增强机器学习模型也是十分有意义的研究方向<sup><span lang="EN-US">[130]</span></sup>。</span><b><span style="font-size:10.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">因此尝试利用现有的机器学习电池模型<span lang="EN-US">/</span>开发适用于快速充电设计的电池模型是具有潜力的研究方向。</span></b><span style="font-size: 10pt; font-family: 宋体;">(</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2.2</span><span style="font-size: 10pt; font-family: 宋体;">机器学习建模方法</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">/</span><span style="font-size: 10pt; font-family: 宋体;">第</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2</span><span style="font-size: 10pt; font-family: 宋体;">段)</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 10pt; font-family: 宋体;"><br></span></p><p style="margin-bottom: 0.0001pt; line-height: 20pt; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">意见<span lang="EN-US">7</span>:<span lang="EN-US">2.2</span>的机器学习建模方法介绍的都是估计<span lang="EN-US">SOC</span>,老化的,并没有快充相关的机器学习算法。建议作者进行修改。</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin: 7.8pt 0cm 3.9pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">回复:感谢您提出的建议,除了具有应用潜力的研究,我们在<span lang="EN-US">2.2</span>机器学习建模方法部分补充了现有的应用了机器学习电池模型的研究介绍,具体补充内容如下。<span lang="EN-US"></span></span></b></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 10pt; font-family: 宋体;">在电压预测方面,</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Li</span><span style="font-size: 10pt; font-family: 宋体;">等</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[124]</span></sup><span style="font-size: 10pt; font-family: 宋体;">通过</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">P2D</span><span style="font-size: 10pt; font-family: 宋体;">模型仿真生成不同工况下的电池状态响应数据,并将这些数据用于训练一个可以模拟电池各种状态动态响应的深度神经网络。经验证,该网络的输出与验证数据之间的相对误差在</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2.93%</span><span style="font-size: 10pt; font-family: 宋体;">以内。在</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">SOC</span><span style="font-size: 10pt; font-family: 宋体;">估计方面,</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Chemali</span><span style="font-size: 10pt; font-family: 宋体;">等</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[119]</span></sup><span style="font-size: 10pt; font-family: 宋体;">利用一种长短时记忆</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">(Long<br>short term memory, LSTM)</span><span style="font-size: 10pt; font-family: 宋体;">网络实现了准确的</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">SOC</span><span style="font-size: 10pt; font-family: 宋体;">估计。在温度预测方面,</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Pang</span><span style="font-size: 10pt; font-family: 宋体;">等</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[123]</span></sup><span style="font-size: 10pt; font-family: 宋体;">提出了一种基于双向</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">LSTM</span><span style="font-size: 10pt; font-family: 宋体;">网络的产热率预测模型,并利用贝叶斯优化方法确定了最优的模型参数。此外,针对分布参数系统的时空分离建模技术也被用于设计简化模型</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[125-126]</span></sup><span style="font-size: 10pt; font-family: 宋体;">。</span><span style="font-size: 10pt; font-family: 宋体;">在老化模型方面,</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Severson</span><span style="font-size: 10pt; font-family: 宋体;">等</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[116]</span></sup><span style="font-size: 10pt; font-family: 宋体;">利用弹性网络</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Elastic<br>Net</span><span style="font-size: 10pt; font-family: 宋体;">建立了一种早期预测模型,用于预测电池的</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">RUL</span><span style="font-size: 10pt; font-family: 宋体;">。</span><b><span style="font-size:10.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">该模型能够根据电池前</span></b><b><span style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;<br/>mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none" lang="EN-US">100</span></b><b><span style="font-size:10.0pt;font-family:宋体;mso-bidi-font-family:<br/>Helvetica;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">轮的容量衰减数据预测电池最终的循环使用寿命。为了减少在快速充电策略设计过程中的协议测试次数,</span></b><b><span style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;<br/>mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none" lang="EN-US">Attia</span></b><b><span style="font-size:10.0pt;font-family:宋体;<br/>mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none">等</span></b><b><sup><span style="font-size:10.0pt;font-family:<br/>&quot;Times New Roman&quot;,serif;mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:<br/>0pt;mso-ligatures:none" lang="EN-US">[44]</span></sup></b><b><span style="font-size:10.0pt;<br/>font-family:宋体;mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:<br/>0pt;mso-ligatures:none">引入了这种早期预测模型,大大减少了单次协议测试的充放电次数,提高了优化实验的效率。</span></b><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">Wang</span><span style="font-size: 10pt; font-family: 宋体;">等</span><sup><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">[121]</span></sup><span style="font-size: 10pt; font-family: 宋体;">提出了一种基于</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">LSTM</span><span style="font-size: 10pt; font-family: 宋体;">网络的电池寿命预测方法,并利用迁移学习方法减少了模型训练所需的数据量。(</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2.2</span><span style="font-size: 10pt; font-family: 宋体;">机器学习建模方法</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">/</span><span style="font-size: 10pt; font-family: 宋体;">第</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">2</span><span style="font-size: 10pt; font-family: 宋体;">段)</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US"></span></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;, serif;" lang="EN-US">&nbsp;</span></p><p style="margin-bottom: 0.0001pt; line-height: 20pt; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">意见<span lang="EN-US">8</span>:文章的整体结构有待改进。论文中电池模型建立部分的内容没有和充电问题描述,充电方法设计两个部分关联起来。导致阅读的时候,感觉到电池模型建立脱离了文章主题。</span><span style="font-size: 9pt; font-family: Helvetica, sans-serif;" lang="EN-US"></span></p><p style="margin-bottom: 0.0001pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">回复:感谢您的建议,在撰写电池模型建立部分的初稿时,我们过于聚焦于电池模型本身,导致电池模型建立部分未能很好地融入快速充电策略设计的主题。为了改善这个重要的问题,我们对该章节进行了以下调整:</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p><p style="margin-bottom: 0.0001pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:&quot;Times New Roman&quot;,serif;<br/>mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none" lang="EN-US">1.</span></b><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">调整了该章节与第三章节(快速充电设计方法)之间的部分内容;</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p><p style="margin-bottom: 0.0001pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:&quot;Times New Roman&quot;,serif;<br/>mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none" lang="EN-US">2.</span></b><b><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:&quot;Times New Roman&quot;;<br/>mso-hansi-font-family:&quot;Times New Roman&quot;;mso-bidi-font-family:&quot;Times New Roman&quot;;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">在机器学习模型的介绍中</span></b><b><span style="font-size:12.0pt;font-family:<br/>宋体;mso-bidi-font-family:Helvetica;color:#6600FF;mso-font-kerning:0pt;<br/>mso-ligatures:none">补充了其于快速充电设计中的应用方法,在机器学习模型的小节中补充了该模型于快速充电设计的应用前景;</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p><p style="margin-bottom: 0.0001pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:&quot;Times New Roman&quot;,serif;<br/>mso-fareast-font-family:宋体;color:#6600FF;mso-font-kerning:0pt;mso-ligatures:<br/>none" lang="EN-US">3.</span></b><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">删减了不必要的内容,调整了文章的整体结构。<span lang="EN-US"></span></span></b></p><p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><br></p><p style="margin-bottom: 0.0001pt; line-height: normal; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;" class="MsoNormal"><b><span style="font-size:12.0pt;font-family:宋体;mso-bidi-font-family:Helvetica;<br/>color:#6600FF;mso-font-kerning:0pt;mso-ligatures:none">综上,通过上述修改,强调了稿件的第二章节与主题的联系,使得全文的中心主题连贯一致,具体的修改内容可以参见新提交稿件的第二章节。</span></b><b><span style="font-size:9.0pt;<br/>font-family:&quot;Helvetica&quot;,sans-serif;mso-fareast-font-family:宋体;color:#6600FF;<br/>mso-font-kerning:0pt;mso-ligatures:none" lang="EN-US"></span></b></p>" style="rounded=0;whiteSpace=wrap;html=1;align=left;verticalAlign=top;spacingLeft=4;" parent="1" vertex="1">
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