Estimation of Distributed Temperature of Large-format Fast-charging Lithium-ion Batteries Based on a Model-data Fusion Method Article Swipe
PANG Xiaoqing
,
Jiacheng Li
,
Liu Wen-xue
,
Zhongwei Deng
,
Xiao Hu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3901/jme.2023.22.020
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3901/jme.2023.22.020
摘要： 充电时间长、续航里程短、热安全性差等问题正成为阻碍电动汽车大规模应用的主要因素。高安全无损快充和高比能电池技术正成为发展趋势，具体表现为充电功率快速和电池单体大型化提高。然而，大尺寸锂离子电池的温度不一致性问题明显，高功率充电容易导致电池温度快速升高甚至引发热失控。因此，针对快充场景，开发准确高效的温度估计方法尤为重要。针对软包型锂离子电池的分布式温度估计问题，提出一种基于长短期记忆神经网络(Long short-term memory， LSTM)与产热模型融合的方法，并在 5～40 ℃的宽温度区间以及多种快充场景下验证了方法的适用性。该方法仅基于一个温度测点的温度信息即可准确估计电池平面其余多个关键测点的温度，可近似获取大尺寸电池二维的温度分布情况，包括最高温度和最大温差，还能有效降低电池成组时的传感器布置成本。融合模型将产热模型结果作为 LSTM 模型输入，讨论不同温度测点以及不同产热模型作为输入对模型精度的影响。结果表明，温度测点的选择对模型精度影响明显，相比于正极极耳温度，将正极极耳与电池本体连接处温度作为输入，其他测点的估计方均根误差可降低 50%，最大误差仅为 0.239 ℃。相较于无产热输入，选择接近电池分布式产热情况的产热模型，估计值方均根误差可减小约 11%。
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.3901/jme.2023.22.020
- OA Status
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- Cited By
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4392464517Canonical identifier for this work in OpenAlex
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https://doi.org/10.3901/jme.2023.22.020Digital Object Identifier
- Title
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Estimation of Distributed Temperature of Large-format Fast-charging Lithium-ion Batteries Based on a Model-data Fusion MethodWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
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2023-01-01Full publication date if available
- Authors
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PANG Xiaoqing, Jiacheng Li, Liu Wen-xue, Zhongwei Deng, Xiao HuList of authors in order
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https://doi.org/10.3901/jme.2023.22.020Publisher landing page
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diamondOpen access status per OpenAlex
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https://doi.org/10.3901/jme.2023.22.020Direct OA link when available
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Lithium (medication), Fusion, Ion, Sensor fusion, Computer science, Materials science, Estimation, Nuclear engineering, Chemistry, Engineering, Artificial intelligence, Systems engineering, Biology, Endocrinology, Philosophy, Linguistics, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.LSTM | 7 |
| abstract_inverted_index.0.239 | 10 |
| abstract_inverted_index.11%。 | 12 |
| abstract_inverted_index.5～40 | 5 |
| abstract_inverted_index.memory， | 3 |
| abstract_inverted_index.摘要： | 0 |
| abstract_inverted_index.short-term | 2 |
| abstract_inverted_index.50%，最大误差仅为 | 9 |
| abstract_inverted_index.LSTM)与产热模型融合的方法，并在 | 4 |
| abstract_inverted_index.℃。相较于无产热输入，选择接近电池分布式产热情况的产热模型，估计值方均根误差可减小约 | 11 |
| abstract_inverted_index.模型输入，讨论不同温度测点以及不同产热模型作为输入对模型精度的影响。结果表明，温度测点的选择对模型精度影响明显，相比于正极极耳温度，将正极极耳与电池本体连接处温度作为输入，其他测点的估计方均根误差可降低 | 8 |
| abstract_inverted_index.℃的宽温度区间以及多种快充场景下验证了方法的适用性。该方法仅基于一个温度测点的温度信息即可准确估计电池平面其余多个关键测点的温度，可近似获取大尺寸电池二维的温度分布情况，包括最高温度和最大温差，还能有效降低电池成组时的传感器布置成本。融合模型将产热模型结果作为 | 6 |
| abstract_inverted_index.充电时间长、续航里程短、热安全性差等问题正成为阻碍电动汽车大规模应用的主要因素。高安全无损快充和高比能电池技术正成为发展趋势，具体表现为充电功率快速和电池单体大型化提高。然而，大尺寸锂离子电池的温度不一致性问题明显，高功率充电容易导致电池温度快速升高甚至引发热失控。因此，针对快充场景，开发准确高效的温度估计方法尤为重要。针对软包型锂离子电池的分布式温度估计问题，提出一种基于长短期记忆神经网络(Long | 1 |
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