| 引用本文: | 李杰,袁博兴,张云龙,何永全,朱玮.机械滥用工况下锂电池组的安全性能预测[J].哈尔滨工业大学学报,2025,57(6):136.DOI:10.11918/202404036 |
| LI Jie,YUAN Boxing,ZHANG Yunlong,HE Yongquan,ZHU Wei.Prediction on safety performance of lithium battery packs under mechanical abuse conditions[J].Journal of Harbin Institute of Technology,2025,57(6):136.DOI:10.11918/202404036 |
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| 摘要: |
| 为研究电动汽车电池组在机械滥用工况下的安全性和可靠性,避免电池组在发生碰撞时造成短路、热失控、甚至火灾和爆炸等一系列安全事故,构建了一种适用于机械滥用工况下锂电池热安全测试的实验系统,设计并实施了平板、圆柱、球形和针刺4种机械滥用工况下的锂电池安全实验。通过大量机械滥用实验数据构建了锂电池单体匀质化有限元模型,并利用实验数据与锂电池单体匀质化有限元模型建立了完备的锂电池组数据集,在此基础上设计了一种由支持向量机（support vector machine,SVM）、麻雀搜索算法（sparrow search algorithm,SSA）和反向传播（back propagation,BP）神经网络组成的SVM-SSA-BP融合数学模型,对不同参数条件下锂电池组的失效状态及失效后的位移和最大载荷进行预测。实验结果表明,SVM-SSA-BP可以准确地预测电池组系统的失效状态、失效位移和最大载荷。将SVM-SSA-BP与其他4种竞争模型进行了对比验证,并在数据集中添加20%高斯噪声评估了SVM-SSA-BP的泛化能力。结果表明,SVM-SSA-BP融合数学模型的预测性能优于其他4种竞争模型,具有较好的准确性和稳定性。本文所提模型可实现机械滥用工况下锂电池组安全性能的准确预测。 |
| 关键词: 锂电池组 安全性能 机械滥用 神经网络 失效状态 |
| DOI：10.11918/202404036 |
| 分类号:TM912 |
| 文献标识码:A |
| 基金项目:陕西省重点研发计划(2022GY-178)；国家市场监督管理总局科技计划项目(2021MK104) |
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| Prediction on safety performance of lithium battery packs under mechanical abuse conditions |
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LI Jie1,YUAN Boxing2,ZHANG Yunlong2,HE Yongquan3,ZHU Wei2
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(1.School of Energy and Electrical Engineering, Chang′an University, Xi′an 710064, China; 2.School of Electronics and Control Engineering, Chang′an University, Xi′an 710064, China; 3.Shaanxi Institute of Metrology Science, Xi′an 710199, China)
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| Abstract: |
| To study the safety and reliability of electric vehicle battery packs under mechanical abuse conditions, and prevent a series of safety accidents such as short circuits, thermal runaway, even fires and explosions that may occur when battery packs are subjected to collisions, this paper builds a mechanical abuse conditions of lithium battery thermal safety testing experimental system. Then safety experiments on lithium-ion batteries were designed and implemented under 4 mechanical abuse conditions: flat plate compression, cylindrical compression, spherical compression, and nail penetration test. A lithium battery monomer homogenization model was built through extensive data from mechanical abuse experiments. A complete lithium battery pack data set was established combined with the experimental data and lithium battery monomer homogenization model. Building on this foundation, a fusion mathematical model comprising support vector machine (SVM), sparrow search algorithm (SSA), and back propagation (BP) neural network was designed to predict the failure states of lithium battery packs under different parameter conditions, as well as the displacement and maximum load following failure under various parameter conditions. The experimental results show that SVM-SSA-BP can accurately predict the failure state, failure displacement and maximum load of the battery pack system. A comparative validation was conducted against four other competing models, and the generalization ability of SVM-SSA-BP model was evaluated by adding 20% Gaussian noise to the dataset. The results show that the predictive performance of the SVM-SSA-BP fusion mathematical model is superior to four other competing models, and it demonstrates good accuracy and stability. The model proposed in this article can accurately predict the safety performance of lithium battery packs under mechanical abuse conditions. |
| Key words: lithium battery pack safety performance mechanical abuse neural network failure state |
