# License of Wenzhou Randomized Battery Data
***Accessibility and Attribution***
The related data and code are accessible under the Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0). Proper attribution is mandatory when using or sharing the data or code; please name the data source as "Wenzhou Randomized Battery Data" and cite the source article: Dongzhen Lyu et al. Battery cumulative lifetime prognostics to bridge laboratory and real-life scenarios. Cell Reports Physical Science (2024), https://doi.org/10.1016/j.xcrp.2024.102164
***Data Storage Locations for International Use***
GitHub: https://github.com/lvdongzhen/Wenzhou-Randomized-Battery-Data
ResearchGate: https://www.researchgate.net/profile/Dongzhen-Lyu
Onedrive: https://1drv.ms/f/s!AnQLciP1URipksZQPfoVLhdf67Y8mg
Google Drive: https://drive.google.com/drive/folders/1YqQv7KYJyRGDVNZrgR00kfmZAeeSdixX
***Data Storage Location for China***
X_MOL: https://www.x-mol.com/groups/DongzhenLyu
Quark Drive: https://pan.quark.cn/s/0fe7ffce2dec
Baidu Drive: https://pan.baidu.com/s/1RSku3T35LkzRx7DUUUtHrA?pwd=WZRD
Backup Baidu Drive: https://pan.baidu.com/s/1XlkNSaAH4SMMRPTK15tQeQ?pwd=WZRD
China Mobile Drive (PWD:nj5b): https://caiyun.139.com/m/i?2h8KfEC7bMFk6
***Attribution-NoDerivatives Requirement***
When using or sharing the data or code, it is mandatory to name the data source as "Wenzhou Randomized Battery Data" and cite the source article mentioned above. When sharing the data or code, it is also obligatory to maintain its original integrity and provide original links for accessing the data; any modification, processing, restructuring, or repackaging is prohibited. The scenarios involved in this experiment are complex, and the degradation processes and influencing factors of each battery require further research. To ensure the scientific accuracy of the relevant analyses, please consult Dr. Dongzhen Lyu for detailed information about the experiment. Apart from the content already disclosed by Dr. Dongzhen Lyu, please refrain from making unauthorized guesses or interpretations regarding the experimental details.
***Commercial Use***
Commercial use of the relevant data is permitted in compliance with this agreement. However, when using the related code for commercial purposes, please ensure that you contact Dr. Dongzhen Lyu in advance to obtain patent permission and technical support.
***Cooperation Invitation***
Dr. Dongzhen Lyu has been deeply involved in the field of lithium battery lifetime prediction for almost ten years, conducting extensive research on technical bottlenecks and application challenges in real-world life prediction scenarios. He has developed a series of engineering methods, applied for multiple Chinese, PCT, and U.S. patents, and received authorizations. Interested parties in related research are welcome to contact at any time to collaborate on academic research or industrial cooperation.
***Application Case***
Here is a research case that adopts accumulated mileage as a lifetime indicator lifetime prediction method, consistent with the core technological concepts of Chinese Patent 202110798763.5 submitted by Dr. Lu Dongzhen on July 15, 2021. In December 2023, several international research institutions collaborated on technical research and develop a lifetime prediction method, with accumulated mileage as the lifetime indicator. Their successful application was demonstrated with 60 electric buses. This project was jointly led by Beijing Institute of Technology, National Engineering Research Center of Electric Vehicles of China, RWTH Aachen University, Jülich Aachen Research Alliance, and Helmholtz Institute Münster. It received joint funding from National Key Research and Development Program of China, the National Natural Science Foundation of China, the Federal Government of Germany, and the State of North Rhine-Westphalia. The application's viability is evidenced in the following publication:
Wang, Q.; Wang, Z.; Liu, P.; Zhang, L.; Sauer, D.U.; Li, W. Large-scale field data-based battery aging prediction driven by statistical features and machine learning. Cell Rep. Phys. Sci. 2023, 4, 101720.
***Competing interests declaration***
Dr. Dongzhen Lyu has filed a US application with No. 18/163,357 on February 2, 2023, which is a Bypass Continuation of the International Patent Application No. PCT/CN2022/097748 with an international filing date of June 9, 2022. The application further claims foreign priority benefits to Chinese Patent Application No. 202111513327.5 filed on December 12, 2021, Chinese Patent Application No. 202111178722.2 filed on October 11, 2021, and Chinese Patent Application No. 202110798763.5 filed on July 15, 2021. Dr. Dongzhen Lyu reserves all rights to these patents. Dr. Dongzhen Lyu and Ms. Yueqin Cui retain all rights to these patents, including ownership, licensing, transfer, claims, profits, and other related rights.
***License Agreement***
Any institution, organization, entity, or individual, upon adopting, using, or in any way utilizing the data provided here for research, development, education, commercial activities, or any other activities, is deemed to automatically agree to and fully comply with all terms of this license agreement. In the event of any breach of this license agreement, the user must immediately cease using the data and take all necessary measures to mitigate any adverse effects resulting from unauthorized use, and be willing to accept any legal responsibilities arising from such actions.
***Disclaimer***
Users bear the risk of using the data and code provided here. The author makes no express or implied representations or warranties regarding the completeness, accuracy, reliability, applicability, or availability of the data, code, or related graphics contained in this document. In no event shall the author be liable for any direct, indirect, or consequential losses or damages, including but not limited to loss of data or profits resulting from the use of this data and code.
***Revision Clause***
The terms of this license agreement may be revised periodically. All revisions will become effective automatically and will be reflected in the latest version of this agreement. Revisions will be incorporated directly into the agreement text, and users are expected to regularly check the latest version to ensure awareness of all applicable terms. Revisions do not require separate notification, and continued use of the data or code implies acceptance of the revised terms and conditions.
***Contact Information***
For inquiries or clarifications, please contact Dr. Dongzhen Lyu.
Email: lvdongzhen@hrbeu.edu.cn
WeChat ID: LyuDongzhen
August 1, 2024
# 温州大学随机电池退化数据的许可协议
***数据访问和署名***
相关数据和代码的访问和使用权限遵循知识共享署名-禁止演绎 4.0 国际许可协议（CC BY-ND 4.0）。在使用或向他人分享这些数据或代码时，请务必采用统一的命名格式“WZU随机电池退化数据”（英文版本为 "Wenzhou Randomized Battery Data"），并引用以下来源文章：Dongzhen Lyu et al., Battery Cumulative Lifetime Prognostics to Bridge Laboratory and Real-Life Scenarios, Cell Reports Physical Science (2024), https://doi.org/10.1016/j.xcrp.2024.102164
***数据国际存储位置***
ResearchGate: https://www.researchgate.net/profile/Dongzhen-Lyu
GitHub: https://github.com/lvdongzhen/Wenzhou-Randomized-Battery-Data
Onedrive: https://1drv.ms/f/s!AnQLciP1URipksZQPfoVLhdf67Y8mg
谷歌网盘: https://drive.google.com/drive/folders/1YqQv7KYJyRGDVNZrgR00kfmZAeeSdixX
***数据中国存储位置***
课题组X_MOL主页: https://www.x-mol.com/groups/DongzhenLyu
夸克网盘: https://pan.quark.cn/s/0fe7ffce2dec
百度网盘: https://pan.baidu.com/s/1RSku3T35LkzRx7DUUUtHrA?pwd=WZRD
百度网盘备用: https://pan.baidu.com/s/1XlkNSaAH4SMMRPTK15tQeQ?pwd=WZRD
中国移动云盘(提取码:nj5b): https://caiyun.139.com/m/i?2h8KfEC7bMFk6
***署名-禁止演绎要求***
在使用或分享相关的数据或代码时，请务必将数据源命名为“温州大学随机电池退化数据”或者“Wenzhou Randomized Battery Data”，并引用上述来源文章。在分享数据或代码时，必须保持数据集的原始完整性，并提供直接访问原始数据的链接；严禁进行任何修改、处理、重组或重新打包。本实验中涉及的场景复杂，各个电池的退化过程及影响因素尚需进一步研究，为了确保相关分析的科学性，可具体咨询吕东祯博士交流实验细节。除了吕东祯博士已经公开披露的内容之外，请避免对实验细节进行未经授权的猜测或解读。
***商用条款***
在遵守本协议相关内容的情况下可以对相关数据进行商业使用，但对相关代码进行商用时请务必提前联系吕东祯博士本人以获得专利许可和技术支持。
***合作邀请***
吕东祯博士在锂电池寿命预测领域深耕近十年，针对实车工况寿命预测中的技术瓶颈和应用难点进行了深入研究，开发了一系列工程方法，申请多项中国、国际、美国发明专利并授权。欢迎对该相关研究感兴趣的人士随时联系，共同开展学术研究或者产业化合作。
***应用案例***
下面介绍的研究案例采用累计里程量作为寿命指标来开发寿命预测方法，与吕东祯博士早在2021年7月15日提交的中国专利202110798763.5中的核心技术理念（其中权利要求1）保持一致。2023年12月，多家国内外研究机构合作进行技术研发，开发出一种采用累计里程量作为寿命指标的寿命预测方法，并成功采用60台电动公交车进行示范性应用。该示范性应用由北京理工大学、国家电动汽车工程研究中心，德国亚琛工业大学，尤利希-亚琛联合研究中心，明斯特亥姆霍兹研究所共同主导。同时受到了中国国家重点研发、中国自然科学基金联合项目、德国联邦政府和北莱茵州的共同资助，应用证明可见下述文献：
Wang, Q.;Wang, Z.; Liu, P.; Zhang, L.; Sauer, D.U.; Li,W. Large-scale field data-based battery aging prediction driven by statistical features and machine learning. Cell Rep. Phys. Sci. 2023, 4, 101720.
***利益相关声明***
吕东祯博士已经申请了一系列与本数据研究相关的专利，欢迎感兴趣的人士随时联系。
吕东祯博士已于 2023 年 2 月 2 日提交了美国申请 (申请号 18/163,357)，它是国际专利申请 (申请号 PCT/CN2022/097748) 的延续申请，该国际申请的提交日期为 2022 年 6 月 9 日。该申请进一步要求以下中国专利申请的优先权：2021 年 12 月 12 日提交的申请号 202111513327.5、2021 年 10 月 11 日提交的申请号 202111178722.2 和 2021 年 7 月 15 日提交的申请号 202110798763.5。吕东祯博士和崔跃芹女士保留这些专利的所有权利，包括所有权、许可、转让、索赔、利润及其他相关权利。
***许可协议***
任何机构、组织、单位或个人，一旦采用、使用或以任何方式利用本数据进行任何研究、开发、教育、商业活动或其他任何活动，即视为自动同意并完全遵守本许可协议的所有条款；如有违反本许可协议的情况发生，应立即停止使用本数据，并采取一切必要措施消除因违规使用而产生的不良影响，并愿意承担由此产生的法律责任。
***免责声明***
用户自行承担使用此处提供的数据和代码的风险。作者对本文包含的数据和代码或相关图形的完整性、准确性、可靠性、适用性或可用性不作任何明示或暗示的陈述或保证。在任何情况下，作者均不对任何直接、间接或后果性损失或损害负责，包括但不限于因使用此数据和代码而导致的任何数据丢失或利润损失。
***联系方式***
如有任何疑问需要澄清，请联系吕东祯博士。
电子邮箱: lvdongzhen@hrbeu.edu.cn
微信号: LyuDongzhen
2024年8月1日