基于大语言模型RAG架构的电池加速研究：现状与展望

doi:10.19799/j.cnki.2095-4239.2024.0604

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (9): 3214-3225.doi: 10.19799/j.cnki.2095-4239.2024.0604

• AI辅助先进电池设计与应用专刊 • 上一篇下一篇

基于大语言模型RAG架构的电池加速研究：现状与展望

钟逸¹(), 冷彦¹(), 陈思慧¹, 李培义¹, 邹智¹, 刘洋²(), 万佳雨¹()

^1.上海交通大学溥渊未来技术学院，未来电池研究中心，上海 200240
^2.昆山杜克大学数据科学研究中心，江苏昆山 215316

收稿日期:2024-07-03 修回日期:2024-08-11 出版日期:2024-09-28 发布日期:2024-09-20
通讯作者: 刘洋,万佳雨 E-mail:zhongyeah@sjtu.edu.cn;ly234244@sjtu.edu.cn;yang.liu2@dukekunshan.edu.cn;wanjy@sjtu.edu.cn
作者简介:钟逸（2005—），男，本科，从事人工智能的可再生能源应用，E-mail：zhongyeah@sjtu.edu.cn
冷彦（2001—），男，硕士研究生，从事锂离子电池先进材料的开发、人工智能的可再生能源应用，E-mail：ly234244@sjtu.edu.cn；

Accelerating battery research with retrieval-augmented large language models: Present and future

Yi ZHONG¹(), Yan LENG¹(), Sihui CHEN¹, Peiyi LI¹, Zhi ZOU¹, Yang LIU²(), Jiayu WAN¹()

^1.Future Battery Research Center, Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China
^2.Data Science Research Center, Duke Kunshan University, Kunshan 215316, Jiangsu, China

Received:2024-07-03 Revised:2024-08-11 Online:2024-09-28 Published:2024-09-20
Contact: Yang LIU, Jiayu WAN E-mail:zhongyeah@sjtu.edu.cn;ly234244@sjtu.edu.cn;yang.liu2@dukekunshan.edu.cn;wanjy@sjtu.edu.cn

摘要/Abstract

摘要：

随着近年电池领域研究投入的激增，研究人员面临着前所未有的信息过载和知识盲区的挑战。针对这一问题，本文探讨了大语言模型(large language model，LLM)的检索增强生成(retrieval augmented generation，RAG)架构在电池领域的应用潜力，在此基础上对近期的研究文献进行综述，并提出展望。本文介绍了大语言模型RAG架构的工作原理，强调了该架构在垂直领域的可靠性，并基于此综述探讨了该架构在电池材料设计、电池单元设计和制造、电动交通与电网的电池管理系统三个领域的潜在应用。在电池材料设计部分，本文着重分析了大语言模型RAG架构的无幻觉生成能力在数据提取、研究方案设计和多模态数据问答中的优势。在电池单元设计和制造部分，本文从科研端指出该架构对电池单元设计方案分析的辅助作用，从制造端指出该架构桥接产业和科研的鸿沟、辅助产业管控的作用。在电动交通和电网的电池管理系统部分，本文指出该架构在跨领域知识联结、辅助系统级运维的作用。最后，本文讨论了多模态RAG技术在电池研究领域的应用潜力及其对电池研究效率的提升，并展望了RAG在电池领域的更多应用前景。

关键词: 大语言模型, 检索增强生成, 电池材料, 电芯, 电池管理系统

Abstract:

In recent years, the surge in research investment within the battery field has presented researchers with challenges of information overload and knowledge gaps. This study examines the Retrieval-Augmented Generation (RAG) architecture of large language models in the battery domain, offering a review of contemporary research and future prospects. We describe the working principles of the RAG architecture, affirm its reliability in specialized domains, and discuss its applications across three key areas as follows: battery material design, battery cell design and manufacturing, and battery management systems for e-mobility and electric grids. In the section on battery material design, the study highlights the hallucination-free generation capabilities of RAG in data extraction, research protocol design, and multimodal data querying. The section on battery cell design and manufacturing elucidates RAG's role in enhancing research-driven battery cell design and bridging the gap between industry and academia, thereby aiding industrial control processes. The discussion on battery management systems for e-mobility and electric grids underscores RAG's contribution to cross-domain knowledge integration and system-level operation and maintenance support. The study concludes by considering the application of multimodal RAG technology in battery research and anticipates further expansion of RAG applications in this field.

Key words: large language model, retrieval augmented generation, battery material, battery cell, battery management system

中图分类号:

O 6-39

钟逸, 冷彦, 陈思慧, 李培义, 邹智, 刘洋, 万佳雨. 基于大语言模型RAG架构的电池加速研究：现状与展望[J]. 储能科学与技术, 2024, 13(9): 3214-3225.

Yi ZHONG, Yan LENG, Sihui CHEN, Peiyi LI, Zhi ZOU, Yang LIU, Jiayu WAN. Accelerating battery research with retrieval-augmented large language models: Present and future[J]. Energy Storage Science and Technology, 2024, 13(9): 3214-3225.

图/表 4

参考文献 68

3	RADFORD A, KIM J W, HALLACY C, et al. Learning transferable visual models from natural language supervision[EB/OL]. 2021: 2103.00020.[2024-06-30]. https://arxiv.org/abs/2103.00020v1
4	OpenAI. Hello GPT-4o[EB/OL]. [2024-06-30]. https://openai.com/index/hello-gpt-4o/
5	OpenAI. SORA[EB/OL]. [2024-06-30]. https://openai.com/sora
6	BUEHLER M J. Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design[J]. ACS Engineering Au, 2024, 4(2): 241-277. DOI: 10.1021/acsengineeringau.3c00058.
7	BRAN A M, COX S, SCHILTER O, et al. Augmenting large language models with chemistry tools[J]. Nature Machine Intelligence, 2024, 6(5): 525-535. DOI: 10.1038/s42256-024-00832-8.
8	BOIKO D A, MACKNIGHT R, KLINE B, et al. Autonomous chemical research with large language models[J]. Nature, 2023, 624(7992): 570-578. DOI: 10.1038/s41586-023-06792-0.
9	KANG Y, KIM J. ChatMOF: An artificial intelligence system for predicting and generating metal-organic frameworks using large language models[J]. Nature Communications, 2024, 15(1): 4705. DOI: 10.1038/s41467-024-48998-4.
10	ZHENG Z L, ZHANG O F, BORGS C, et al. ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis[J]. Journal of the American Chemical Society, 2023, 145(32): 18048-18062. DOI: 10.1021/jacs.3c05819.
11	JI Z W, LEE N, FRIESKE R, et al. Survey of hallucination in natural language generation[EB/OL]. 2022: 2202.03629.[2024-06-30]. https://arxiv.org/abs/2202.03629v7
12	吴思远, 王雪龙, 肖睿娟, 等. 基于大型语言模型的工具对电池研究的机遇与挑战[J]. 储能科学与技术, 2023, 12(3): 992-997. DOI: 10.19799/j.cnki.2095-4239.2023.0071.
	WU S Y, WANG X L, XIAO R J, et al. Problem and perspective for battery researcher based on large language model[J]. Energy Storage Science and Technology, 2023, 12(3): 992-997. DOI: 10.19799/j.cnki.2095-4239.2023.0071.
13	KIRKPATRICK J, PASCANU R, RABINOWITZ N, et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(13): 3521-3526. DOI: 10.1073/pnas.1611835114.
14	TINN R, CHENG H, GU Y, et al. Fine-tuning large neural language models for biomedical natural language processing[J]. Patterns, 2023, 4(4): 100729. DOI: 10.1016/j.patter.2023.100729.
15	MOSBACH M, ANDRIUSHCHENKO M, KLAKOW D. On the stability of fine-tuning BERT: Misconceptions, explanations, and strong baselines[EB/OL]. 2020: 2006.04884.[2024-06-30]. https://arxiv.org/abs/2006.04884v3
16	KIM Y, OH J, KIM S, et al. How to fine-tune models with few samples: Update, data augmentation, and test-time augmentation[EB/OL]. 2022: 2205.07874.[2024-06-30].https://arxiv.org/abs/2205.07874v3
17	CHEN J W, LIN H Y, HAN X P, et al. Benchmarking large language models in retrieval-augmented generation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(16): 17754-17762. DOI: 10.1609/aaai.v38i16.29728.
18	GAO Y F, XIONG Y, GAO X Y, et al. Retrieval-augmented generation for large language models: A survey[EB/OL]. 2023: 2312.10997.[2024-06-30]. https://arxiv.org/abs/2312.10997v5
19	EIBICH M, NAGPAL S, FRED-OJALA A. ARAGOG: Advanced RAG output grading[EB/OL]. 2024: 2404.01037.[2024-06-30]. https://arxiv.org/abs/2404.01037v1
20	PAN S R, LUO L H, WANG Y F, et al. Unifying large language models and knowledge graphs: A roadmap[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 3580-3599. DOI: 10.1109/TKDE.2024.3352100.
21	ZHANG J, SUTING W, ZHAOXIANG W, et al. Solid electrolyte interphase (SEI) on graphite anode correlated with thermal runaway of lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(7): 2105-2118.
22	WANG A P, KADAM S, LI H, et al. Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries[J]. NPJ Computational Materials, 2018, 4: 15. DOI: 10.1038/s41524-018-0064-0.
23	WANG X X, NI L, XIE Q X, et al. Highly electrocatalytic active amorphous Al₂O₃ in porous carbon assembled on carbon cloth as an independent multifunctional interlayer for advanced lithium-sulfur batteries[J]. Applied Surface Science, 2023, 618: 156689. DOI: 10.1016/j.apsusc.2023.156689.
24	LAI G M, JIAO J Y, FANG C, et al. The mechanism of Li deposition on the Cu substrates in the anode-free Li metal batteries[J]. Small, 2023, 19(3): e2205416. DOI: 10.1002/smll. 202205416.
25	WANG F, SUN Y, CHENG J. Switching of redox levels leads to high reductive stability in water-in-salt electrolytes[J]. Journal of the American Chemical Society, 2023, 145(7): 4056-4064. DOI: 10.1021/jacs.2c11793.
26	HAN X, GU L H, SUN Z F, et al. Manipulating charge-transfer kinetics and a flow-domain LiF-rich interphase to enable high-performance microsized silicon-silver-carbon composite anodes for solid-state batteries[J]. Energy & Environmental Science, 2023, 16(11): 5395-5408. DOI: 10.1039/D3EE01696J.
27	CHEN G J, YU L W, GAN Y H, et al. Reinforcing the stability of cobalt-free lithium-rich layered oxides via Li-poor Ni-rich surface transformation[J]. Journal of Materials Chemistry A, 2024. DOI: 10.1039/d4ta01403k.
28	LI J, ZHOU M S, WU H H, et al. Machine learning-assisted property prediction of solid-state electrolyte[J]. Advanced Energy Materials, 2024, 14(20): 2304480. DOI: 10.1002/aenm.202304480.
29	MERCHANT A, BATZNER S, SCHOENHOLZ S S, et al. Scaling deep learning for materials discovery[J]. Nature, 2023, 624(7990): 80-85. DOI: 10.1038/s41586-023-06735-9.
30	XU J, XIAO R J, LI H. ESM cloud toolkit: A copilot for energy storage material research[J]. Chinese Physics Letters, 2024, 41(5): 054701. DOI: 10.1088/0256-307x/41/5/054701.
31	THIK J, WANG S W, WANG C H, et al. Realizing the cooking recipe of materials synthesis through large language models[J]. Journal of Materials Chemistry A, 2023, 11(47): 25849-25853. DOI: 10.1039/D3TA05457H.
32	DEB J, SAIKIA L, DIHINGIA K D, et al. ChatGPT in the material design: Selected case studies to assess the potential of ChatGPT[J]. Journal of Chemical Information and Modeling, 2024, 64(3): 799-811. DOI: 10.1021/acs.jcim.3c01702.
33	LIU Y, LI C, LI C X, et al. Porous, robust, thermally stable, and flame retardant nanocellulose/polyimide separators for safe lithium-ion batteries[J]. Journal of Materials Chemistry A, 2023, 11(43): 23360-23369. DOI: 10.1039/d3ta05148j.
34	凌仕刚, 许洁茹, 李泓. 锂电池研究中的EIS实验测量和分析方法[J]. 储能科学与技术, 2018, 7(4): 732-749. DOI: 10.12028/j.issn.2095-4239.2018.0092.
	LING S G, XU J R, LI H. Experimental measurement and analysis methods of electrochemical impedance spectroscopy for lithium batteries[J]. Energy Storage Science and Technology, 2018, 7(4): 732-749. DOI: 10.12028/j.issn.2095-4239.2018.0092.
35	LIU X H, HUANG J Y. In situ TEM electrochemistry of anode materials in lithium ion batteries[J]. Energy & Environmental Science, 2011, 4(10): 3844-3860. DOI: 10.1039/C1EE01918J.
36	KITAHARA A R, HOLM E A. Microstructure cluster analysis with transfer learning and unsupervised learning[J]. Integrating Materials and Manufacturing Innovation, 2018, 7(3): 148-156. DOI: 10.1007/s40192-018-0116-9.
37	ZHANG Y, LIN X Y, ZHAI W B, et al. Machine learning on microstructure-property relationship of lithium-ion conducting oxide solid electrolytes[J]. Nano Letters, 2024, 24(17): 5292-5300. DOI: 10.1021/acs.nanolett.4c00902.
38	KUSCHE C, RECLIK T, FREUND M, et al. Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning[J]. PLoS One, 2019, 14(5): e0216493. DOI: 10.1371/journal.pone.0216493.
39	WANG Z H, ZHOU X Z, ZHANG W G, et al. Parameter sensitivity analysis and parameter identifiability analysis of electrochemical model under wide discharge rate[J]. Journal of Energy Storage, 2023, 68: 107788. DOI: 10.1016/j.est.2023.107788.
40	黄晟贤,徐会升,王起鹏,等.冲击荷载下圆柱型动力锂离子电池的响应特性研究[J/OL].储能科学与技术.[2024-05-05]. https://doi.org/10.19799/j.cnki.2095-4239.2024.0274.
	HUANG S, XU H, WANG Q, et al. Study on the Response characteristics of cylindrical power lithium-ion batteries under impact load[J/OL]. Energy Storage Science and Technology. [2024-05-05]. https://doi.org/10.19799/j.cnki.2095-4239.2024.0274.
41	WU X K, SONG K F, ZHANG X Y, et al. Safety issues in lithium ion batteries: Materials and cell design[J]. Frontiers in Energy Research, 2019, 7: 65. DOI: 10.3389/fenrg.2019.00065.
42	OAKLEY J. Intelligent cognitive assistants [EB/OL].[2024-06-30]. https://www.src.org/program/ica/
43	KERNAN FREIRE S, FOOSHERIAN M, WANG C F, et al. Harnessing large language models for cognitive assistants in factories[C]// Proceedings of the 5th International Conference on Conversational User Interfaces. ACM, 2023: 1-6. DOI: 10.1145/3571884.3604313.
44	CHEN H S, HOU L, WU S Z, et al. Augmented reality, deep learning and vision-language query system for construction worker safety[J]. Automation in Construction, 2024, 157: 105158. DOI: 10.1016/j.autcon.2023.105158.
1	VASWANI A, SHAZEER N, PARMER N, et al. Attention is all you need[J]. arXiv preprint arXiv:, 2017.
2	ZHAO S, CHEN S H, ZHOU J Y, et al. Potential to transform words to Watts with large language models in battery research[J]. Cell Reports Physical Science, 2024, 5(3): 101844. DOI: 10.1016/j.xcrp.2024.101844.
45	PERES R S, JIA X D, LEE J, et al. Industrial artificial intelligence in industry 4.0 - Systematic review, challenges and outlook[J]. IEEE Access, 2874, 8: 220121-220139. DOI: 10.1109/ACCESS. 2020.3042874.
46	GUO N L, CHEN S H, TAO J, et al. Semi-supervised learning for explainable few-shot battery lifetime prediction[J]. Joule, 2024, 8(6): 1820-1836. DOI: 10.1016/j.joule.2024.02.020.
47	TRIVEDI C, BHATTACHARYA P, PRASAD V K, et al. Explainable AI for industry 5.0: Vision, architecture, and potential directions[J]. IEEE Open Journal of Industry Applications, 2024, 5: 177-208. DOI: 10.1109/OJIA.2024.3399057.
48	ZHOU B, LI X Y, LIU T Y, et al. CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing[J]. Advanced Engineering Informatics, 2024, 59: 102333. DOI: 10.1016/j.aei.2023.102333.
49	LIN T Z, CHEN S H, HARRIS S J, et al. Investigating explainable transfer learning for battery lifetime prediction under state transitions[J]. eScience, 2024: 100280. DOI: 10.1016/j.esci. 2024.100280.
50	朱伟杰, 史尤杰, 雷博. 锂离子电池储能系统BMS的功能安全分析与设计[J]. 储能科学与技术, 2020, 9(1): 271-278. DOI: 10.19799/j.cnki.2095-4239.2019.0177.
	ZHU W J, SHI Y J, LEI B. Functional safety analysis and design of BMS for lithium-ion battery energy storage system[J]. Energy Storage Science and Technology, 2020, 9(1): 271-278. DOI: 10.19799/j.cnki.2095-4239.2019.0177.
51	SHEN M, GAO Q. A review on battery management system from the modeling efforts to its multiapplication and integration[J]. International Journal of Energy Research, 2019, 43(10): 5042-5075.
52	RAHIMI-EICHI H, OJHA U, BARONTI F, et al. Battery management system: An overview of its application in the smart grid and electric vehicles[J]. IEEE Industrial Electronics Magazine, 2013, 7(2): 4-16. DOI: 10.1109/MIE.2013.2250351.
53	LIU Y, CHEN S H, LI P Y, et al. Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context[J]. IET Cyber-Physical Systems: Theory & Applications, 2024. DOI: 10.1049/cps2.12086.
54	LU T, ZHAI X A, CHEN S H, et al. Robust battery lifetime prediction with noisy measurements via total-least-squares regression[J]. Integration, 2024, 96: 102136. DOI: 10.1016/j.vlsi. 2023.102136.
55	LAWDER M T, SUTHAR B, NORTHROP P W C, et al. Battery energy storage system (BESS) and battery management system (BMS) for grid-scale applications[J]. Proceedings of the IEEE, 2014, 102(6): 1014-1030. DOI: 10.1109/JPROC.2014.2317451.
56	MISHRA S, SWAIN S C, SAMANTARAY R K. A Review on battery management system and its application in electric vehicle[C]// 2021 International Conference on Advances in Computing and Communications (ICACC). IEEE, 2021: 1-6. DOI: 10.1109/ICACC-202152719.2021.9708114.
57	WANG J, FENG X N, YU Y Z, et al. Rapid temperature-responsive thermal regulator for safety management of battery modules[J]. Nature Energy, 2024, 9: 939-946. DOI: 10.1038/s41560-024-01535-5.
58	CHEN X J, JIA S B, XIANG Y. A review: Knowledge reasoning over knowledge graph[J]. Expert Systems with Applications, 2020, 141: 112948. DOI: 10.1016/j.eswa.2019.112948.
59	XIONG R, LI L L, TIAN J P. Towards a smarter battery management system: A critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018, 405: 18-29. DOI: 10.1016/j.jpowsour.2018.10.019.
60	ZHAO X Z, SUN B X, ZHANG W G, et al. Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries[J]. Applied Energy, 2024, 353: 121992. DOI: 10.1016/j.apenergy.2023.121992.
61	XIONG R, SUN Y, WANG C X, et al. A data-driven method for extracting aging features to accurately predict the battery health[J]. Energy Storage Materials, 2023, 57: 460-470. DOI: 10.1016/j.ensm.2023.02.034.
62	WANG W T, YANG K Y, ZHANG L S, et al. An end-cloud collaboration approach for online state-of-health estimation of lithium-ion batteries based on multi-feature and transformer[J]. Journal of Power Sources, 2024, 608: 234669. DOI: 10.1016/j.jpowsour.2024.234669.
63	吴思远, 李泓. 发展基于"语义检测" 的低参数量、多模态预训练电池通用人工智能模型[J]. 储能科学与技术, 2024, 13(4): 1216-1224. DOI: 10.19799/j.cnki.2095-4239.2024.0092.
	WU S Y, LI H. Developing a general pretrained multimodal battery model with small parameters based on semantic detection[J]. Energy Storage Science and Technology, 2024, 13(4): 1216-1224. DOI: 10.19799/j.cnki.2095-4239.2024.0092.
64	WU S Q, FEI H, QU L G, et al. NExT-GPT: Any-to-any multimodal LLM[EB/OL]. 2023: 2309.05519. https://arxiv.org/abs/2309. 05519v3
65	JABLONKA K M, AI Q X, AL-FEGHALI A, et al. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon[J]. Digital Discovery, 2023, 2(5): 1233-1250. DOI: 10.1039/d3dd00113j.
66	BRUCKHAUS T. RAG does not work for enterprises[EB/OL]. 2024: 2406.04369.[2024-06-30]. https://arxiv.org/abs/2406.04369v1
67	LIU Y, HUANG L Z, LI S C, et al. RECALL: A benchmark for LLMs robustness against external counterfactual knowledge[EB/OL]. 2023: 2311.08147.[2024-06-30]. https://arxiv.org/abs/2311.08147v1
68	ANDERSON M, AMIT G, GOLDSTEEN A. Is my data in your retrieval database? membership inference attacks against retrieval augmented generation[EB/OL]. 2024: 2405.20446.[2024-06-30]. https://arxiv.org/abs/2405.20446v2

[1]	刘子玉, 姜泽坤, 邱伟, 徐泉, 牛迎春, 徐春明, 周天航. 人工智能在长时液流电池储能中的应用：性能优化和大模型[J]. 储能科学与技术, 2024, 13(9): 2871-2883.
[2]	袁誉杭, 高宇辰, 张俊东, 高岩斌, 王超珑, 陈翔, 张强. 大语言模型在储能研究中的应用[J]. 储能科学与技术, 2024, 13(9): 2907-2919.
[3]	朱振威, 苗嘉伟, 祝夏雨, 王晓旭, 邱景义, 张浩. 基于机器学习方法的锂电池剩余寿命预测研究进展[J]. 储能科学与技术, 2024, 13(9): 3134-3149.
[4]	童文欣, 黄中垣, 王睿, 邓司浩, 何伦华, 肖荫果. 基于空间分辨中子衍射方法的锂离子电池电化学反应均匀性研究[J]. 储能科学与技术, 2024, 13(1): 72-81.
[5]	张奇, 李晓东, 王文雯, 刘晓. 生物质纤维素基多功能材料构建及其在新型能量存储方面的应用[J]. 储能科学与技术, 2023, 12(5): 1427-1443.
[6]	邓林旺, 冯天宇, 舒时伟, 张子峰, 郭彬. 锂离子电池快充策略技术研究进展[J]. 储能科学与技术, 2022, 11(9): 2879-2890.
[7]	刘春辉, 任宏斌. 基于SOC的动力电池组主动均衡研究[J]. 储能科学与技术, 2022, 11(2): 667-672.
[8]	梁新成, 张　勉, 黄国钧. 基于BMS的锂离子电池建模方法综述[J]. 储能科学与技术, 2020, 9(6): 1933-1939.
[9]	朱伟杰, 史尤杰, 雷博. 锂离子电池储能系统BMS的功能安全分析与设计[J]. 储能科学与技术, 2020, 9(1): 271-278.
[10]	罗勇, 赵小帅, 祁朋伟, 刘增玥, 邓涛, 李沛然. 车用动力电池二阶RC建模及参数辨识[J]. 储能科学与技术, 2019, 8(4): 738-744.
[11]	李荣辉, 孔令丽, 王纪威, 李文轩, 樊沁娜. 钛酸锂电芯正负极容量匹配设计及其对电芯性能影响[J]. 储能科学与技术, 2019, 8(1): 191-194.
[12]	陈诗璇，金鹏. LTC6803在镍氢电池储能管理系统中的应用[J]. 储能科学与技术, 2017, 6(S1): 31-.
[13]	罗红斌，邓林旺，冯天宇，吕纯. LiFePO4锂离子动力电池内阻与放电倍率关系研究[J]. 储能科学与技术, 2017, 6(4): 799-805.
[14]	张建1，郭慰问1,2，邹海曙3，邵光杰2，娄豫皖1，夏保佳1. 100 kW/200 kW·h氢镍电池储能系统[J]. 储能科学与技术, 2016, 5(4): 596-601.
[15]	吴娇杨，刘品，胡勇胜，李泓. 锂离子电池和金属锂离子电池的能量密度计算[J]. 储能科学与技术, 2016, 5(4): 443-453.

基于大语言模型RAG架构的电池加速研究：现状与展望

Accelerating battery research with retrieval-augmented large language models: Present and future

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 68

相关文章 15

编辑推荐

Metrics

本文评价