SIMT Technology Observation is a cutting-edge technology and industry transformation in-depth dialogue column launched by Sodium Yuan New Materials (SIMT). As an important component of the Na Yuan New Materials Technology ecosystem, this column will regularly invite frontline PhDs and researchers from fields such as new energy, artificial intelligence, materials science, and intelligent manufacturing to engage in dialogue and analyze their technical challenges, the latest developments in the laboratory, and the real path from theory to industrialization.
👤 Interview guests for this issue:
Yan Bo, a Ph.D. in German chemistry, studied under Professor Martin Winter, an academician of the German National Academy of Sciences and Engineering. Formerly served as the head of the Yuanjing Ruitai Power Intelligent Research and Development Platform, leading the establishment of a battery intelligent research and development system from scratch,Shorten development cycle by over 40%, increase data processing efficiency by 8 times, and save 30% of cyclic testing resources+Currently serving as an associate researcher in the Department of Artificial Intelligence and Energy Systems at Ningbo Digital Twin (Oriental Institute of Technology) Research Institute, focusing on research in the intersection of AI and energy systems, and deeply collaborating with multiple partner companies to jointly promote the implementation of digital transformation projects.
Published 23 SCI papers (including Nature Commun., Adv. Funct. Mater., etc.), obtained 1 international patent, 1 German patent, and 3 Chinese patents.
01. Traditional R&D model ceiling: When the "trial and error method" encounters complex systems
Research and development has always been the lifeline of battery development. Related studies indicate that the R&D investment intensity in the global battery industry remains at around 6.2%. Compared to the average R&D investment of 2.3% -2.6% in the A-share market, if calculated based on billions of revenue, it means an additional 4 billion yuan in R&D investment per year.
Among them, innovation in battery cell structure and optimization of manufacturing processes have become the focus of investment. But with the development of the battery industry, the market's requirements for product performance and iteration speed are becoming increasingly high. Dr. Yan pointed out that the drawbacks of the past research and development model, which relied solely on expert experience, intuition, and repeated experimental verification, have become increasingly apparent
1. The R&D cycle is too long and the iteration efficiency is low. Traditional battery development requires researchers to manually adjust parameters and repeat tests. Taking battery cell research and development as an example, testing only for cycle life and calendar life takes years, making it extremely difficult to keep up with the rapid pace of market iteration.
2. The overall cost is high and resources are severely wasted. The consumption of manpower, high-end equipment, and raw materials is enormous. A large amount of 'negative experimental data' is directly discarded, resulting in avoidable repeated failures and creating a huge invisible waste.
3. The data is scattered and the standards are inconsistent, making it difficult to pass on knowledge. The experimental records are scattered in personal notes, and the data islands are severe, making it difficult to reuse across projects. The departure of core personnel means the loss of experience along the entire technical route, and the valuable experimental assets of the enterprise cannot be transformed into structured knowledge.
4. Highly dependent on intuitive experience of stir frying dishes, lacking in-depth scientific research. Passionate about adjusting the formula ratio, but unable to explain why it is good. Difficult to break free from conventional thinking and rationally design multi-objective conflicts (energy density, power, safety, lifespan, cost).
5. The ability to explore chemical space is extremely limited. There are tens of thousands or even millions of ways to combine materials, and trial and error methods can only explore the tip of the iceberg. The interaction between process parameters is difficult to fully explore.
6. The problem of coupling multiple scales and physical fields is difficult to solve. From atomic scale to thermal runaway spread across multiple spatiotemporal scales. Traditional reductionism separates research and cannot effectively link micro mechanisms with macro performance. It is precisely these drawbacks that have triggered a series of irreconcilable contradictions:
This is also the core reason why the industry must transform from "manual workshop style trial and error" to "data-driven rational design".
Counting the digital transformation of leading battery companies in recent years:
In 2023, CATL will establish an international research and development center focused on AI for Science in Hong Kong; In 2025, BYD Lithium Battery, ByteDance Seed and Volcano Engine will jointly build the "AI+High Throughput Joint Laboratory"; In May of the same year, Geely Automobile and Shenzhen Shenshi Technology introduced the "AI+molecular simulation" technology in the field of biomedicine into the research and development of power batteries. The pile like components all point to the same future: the digital transformation driven by "data+AI" will become the new engine for the development of the battery industry.
02. System thinking: Understanding the key to the "complex machine" of batteries
For top enterprises with sufficient investment, digital transformation has shifted from "icing on the cake" to a core means of enhancing competitiveness. The battery digitalization research and development system built by Dr. Yan has been successfully implementedShorten development cycle by over 40%, increase data processing efficiency by 8 times, and save over 30% of cyclic testing resources.
When AI for Science is deeply integrated with automation laboratories, the iteration speed of battery technology will be compressed from the scale of "year" to the scale of "month" or even "week". The ability to complete the digital transformation may become a lifeline for the development of enterprises.
How can we quickly transform?
Dr. Yan, as a veteran who has worked in the industry for over fourteen years, has provided his own practical experience. Firstly, it is necessary to recognize the limitations of traditional reductionist science and use systematic thinking to think about the research and development of battery products. Traditional reductionist science holds that the whole is composed of parts, and understanding each part can solve the whole.
But the internal system of a battery involves multidisciplinary knowledge such as physics, chemistry, electrochemistry, mechanics, and thermodynamics, and there are complex interactions between the components, making it extremely difficult to identify. Taking electrolyte formulation as an example, the quantitative impact of the interaction between components on battery performance is very complex, not to mention the interaction between electrolyte components and materials, electrode plate technology, voltage, temperature, cell form, battery operating conditions, positive and negative electrode crosstalk effects, and other factors.
Uncontrollable factors in the battery production and manufacturing process, such as personnel adjustments, temperature and humidity control, and batch consistency of incoming materials, often have a significant impact on battery performance but are easily overlooked by research and development personnel.
Yan Bo: "So, relying solely on conducting single factor variable experiments to cultivate effective battery design intuition is like daydreaming. Using full/semi factor design logic to design and verify requires resources that the vast majority of companies cannot afford. But if we look at battery research and development with a systematic mindset, we will find new solutions
What is systems thinking?
In short, it is a way of thinking that regards the research object as a whole composed of interrelated and interacting elements, and understands and grasps the essence and laws of things from the interrelationships between this whole and the external environment, whole and part, and part and part. It opposes viewing things in isolation and isolation, emphasizing that the whole is greater than the sum of its parts.
Taking battery cell research and development as an example, some engineers are accustomed to conducting independent tests on half cell single electrodes to screen positive and negative electrode active materials with excellent electrochemical performance, and then matching them with electrolyte solutions with multiple types of functional additives. Many testing resources tend to favor this top-notch combination design scheme, but the results are often not ideal. If we start from systems thinking, the approach is completely different.
Firstly, ensure potential adaptability:Emphasis is placed on examining the adaptability of the working potential of the positive and negative electrode active materials after the formation of the battery cell, ensuring that the working potential on the positive electrode side is neither too high (which can easily cause material phase transition failure) nor too low (which can easily lead to lithium deposition).
Secondly, track the dynamic evolution of potential:Pay close attention to the evolution law of the working potential of the positive and negative electrode active materials during the working process of the battery cell, and ensure the continuous adaptation of the working potential of the positive and negative electrodes through system design adjustment.
To achieve this, Dr. Yan developedDCR quantitative decomposition technologyIt can quantitatively identify the impact of adjusting various types of components (electrode materials, electrolyte components, current collectors, auxiliary materials, electrode sheet processes, etc.) on the internal impedance (R Ω, Rsei, Rct, Rd, etc.) of the battery cell. After accumulating sufficient data, algorithm models can be constructed for system optimization design.
03. DIKW Model: A Clear Path to Digitalization Implementation
Transforming thinking patterns is just the first step on the path of systematic thinking.
Systematization specifically consists of four parts, namelyStructure, Function, Process, and EnvironmentAnd the cyclic interactions between them. Therefore, understanding complex systems requires not only a holistic approach, but also iterative exploration. A significant characteristic of the implementation of digital transformation is that the business scenario is in a state of cyclic iterative optimization, constantly promoting the system to provide more rational and objective decision-making judgments. Dr. Yan's conclusion on the digital transformation of enterprises after repeated verification in multiple projects is:
The routine actions of enterprises, such as investing heavily in software systems, deploying local language models, and introducing a large number of top AI engineers, are not the most important steps. It is important for enterprises to make different layout plans based on the stage of internal business scenarios (process oriented → informatization → digitalization → digitization). In business scenarios, let data-driven decisions occur naturally according to the DIKW model.
DIKW model:Data → Information → Knowledge → Wisdom
To achieve data-driven decision-making, the following core tasks need to be completed:Clarify business scenario boundaries → streamline business processes → build information systems → develop data mining algorithm models → deploy AI agents.
The operational logic of the entire chain is as follows: when business activities occur, high-quality data is collected through manual filling, automatic collection by intelligent devices, and other methods (data quality evaluation dimensions include accuracy, precision, authenticity, effectiveness, comprehensiveness, completeness, and timeliness). These data are processed by data mining technology to generate analysis and decision-making algorithm models, which are deployed in information systems and retrieved by AI agents as needed to help achieve business scenario goals. In this system, people are liberated from mechanical repetitive labor to do creative iterative optimization tasks.
Yan Bo: "Not all data can be called data assets. There are a large number of data that have not been correctly labeled and cleaned, which can only be called off balance sheet ore or lean ore. These data are not of great economic value in the future industrial Internet ecosystem."
For enterprises that are currently unable to invest a large amount of resources in building digital systems, it is urgent to start valuing data asset construction, such as research and development of data management, standardization of business processes, and accumulation of data mining algorithm models.
In the era of AI, where is the value of human beings?
At the end of the interview, when it came to the impact of AI on young people, Dr. Yan's answer started from systems thinking and defined the scope of the question in the business workplace that everyone is more concerned about, providing a clear framework. The essence of business is the process of quickly discovering, insight into, and mining customer needs, and being able to quickly meet these needs
AI时代以前，为了实现这一目标，需要构建专业的组织团队，通过分工协作来达成目标。但在AI时代，很多商业场景，已经可以通过个人与AI Agent协作来实现目标需求。
那么，系统的架构演变会不会把“个人”也剔除掉，只留下AI Agent？
闫博士认为答案是否定的。其中的关键在于区分人之所能与AI之所不能。
斯坦福大学语言与信息研究中心创始人布莱恩·坎特韦尔·史密斯在《测算与判断》一书中强调，尽管当前AI已经表现出卓越的测算能力，但其仍缺乏道德保证、深刻的语境意识和对本体论敏感型的判断能力。
李飞飞在题为《AI教母：10年后，只会剩下两类工作者》的访谈中也反复强调：AI是工具，不是替代品。真正决定一个人会不会被替代，不是技术本身，而是有没有主动去理解它、使用它、驾驭它。未来两种类型的工作者会是主流：
前1%的顶尖专家：AI可以帮他们筛除90%的重复工作，把精力集中在最需要人类判断力的那10%上，他们的价值得以借助AI得到最大化释放。
高主动性通才：他们可以借助AI自己上手、自己造工具、重新定义工作流、不断优化迭代系统。AI技术的发展让人有更多时间和精力，去做测算之外的事情：判断、创造、共情、在模糊地带做决策。
闫博：“正如我们通过学习使用手机实现便捷沟通，学习使用智能导航实现实时路线判定，学习使用互联网打破信息壁垒，我们也需要学会借助AI工具更好地服务于人，让人摆脱机械的属性，去做具有创造力的事情。”
写在最后
关于下一代电池技术路线的讨论，行业里从未停止过。固态、钠电、硫基、空气电池……各有各的支持者，也各有各的科学或工程瓶颈。闫博士的视角有些不同：“大家都在比能量密度、比循环寿命、比安全性这些性能指标，但我觉得如何通过系统化设计，去最快满足终端客户定制化需求的能力更为重要。”在他看来，最终胜出的技术路线，未必是单项性能最拔尖的，反而可能是最先实现产业生态互联网系统的产品。
在这一产业生态内，投入资源被合理分配，处于生态系统中的企业可以更专注于自己擅长的领域，产出的数据产品可以用来做交易，产业创新积极性被激活。
“恰如消费互联网完全改变了人类生活的模式，借助AI发展的浪潮，相信工业互联网的实现会带来完全不一样的生产分配模式。”
对于钠电，闫博士持明确的乐观态度：“钠电是一个很好的技术，2026年我确实看见钠电这个技术真正走向了产业化。”数据支撑了这一判断。据伯恩斯坦研究，2025年全球钠电池出货量已达9GWh，预计2026年将增至25GWh以上。这个增速得益于整个电池行业积累的研发经验、制造工艺、数据资产，也离不开当下的AI数智化转型。
文中数据来源于公开资料及嘉宾访谈，由钠远新材团队整理撰写
