Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network
文献类型：期刊论文
| 作者 | Wang, Jinghan1; Lv, Jie1,2; Lin, Wenye1; Song, Wenji1; Feng, Ziping1 |
| 刊名 | APPLIED THERMAL ENGINEERING
 |
| 出版日期 | 2024-09-15 |
| 卷号 | 253页码:16 |
| 关键词 | Heat generation rate estimation BP-based optimized neural network Lithium -ion batteries Electric vehicles Public dataset |
| ISSN号 | 1359-4311 |
| DOI | 10.1016/j.applthermaleng.2024.123752 |
| 通讯作者 | Lv, Jie(lvjie1225@163.com) |
| 英文摘要 | Accurate estimation of heat generation rate (HGR) of lithium-ion batteries (LIBs) is a critical and essential task for their decent thermal management, thereby facilitating the safe driving of electric vehicles (EVs). In order to improve the accuracy of HGR estimation and reduce the structural complexity of network, a data-driven strategy is developed through integrating Bayesian optimization (BO), Adam optimization, and Principal Component Analysis (PCA) with Back Propagation (BP) neural network. The BO algorithm is utilized to optimize the hyperparameters of the BP neural network for prediction accuracy enhancement. The PCA is employed to extract the feature matrix thereby reducing the complexity of inputs. The Adam optimization algorithm is used to improve computational efficiency. The performance of the proposed strategy was validated based on a dataset derived from lab-scale experiments, as well as a publicly available dataset regarding practical driving conditions. The test results show that the proposed strategy can achieve accurate HGR estimation and result in a mean absolute error (MAE) of 0.0504 W, and a root mean square error (RMSE) of 0.0628 W, and a R2 of 0.9998. Compared to some other HGR estimation methods, the proposed strategy achieved a significant enhancement in accuracy indexes, indicating its superior accuracy and robustness. |
| WOS关键词 | THERMAL MANAGEMENT ; MODEL |
| 资助项目 | National Key Research and Develop- ment Program of China[2021YFE0112500] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels ; Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001260412500001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Key Research and Develop- ment Program of China |
| 源URL | [http://ir.giec.ac.cn/handle/344007/42261]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Lv, Jie |
| 作者单位 | 1.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China 2.Guangdong Polytech Normal Univ, Sch Mechatron Engn, Guangzhou 510450, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jinghan,Lv, Jie,Lin, Wenye,et al. Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network[J]. APPLIED THERMAL ENGINEERING,2024,253:16. |
| APA | Wang, Jinghan,Lv, Jie,Lin, Wenye,Song, Wenji,&Feng, Ziping.(2024).Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network.APPLIED THERMAL ENGINEERING,253,16. |
| MLA | Wang, Jinghan,et al."Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network".APPLIED THERMAL ENGINEERING 253(2024):16. |
入库方式： OAI收割
来源：广州能源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。