AI led battery research and development innovation

AI accelerates battery material screening and discovery

The new battery research and development technology represented by solid state batteries still faces many challenges, but with the development of AI for Science (AI4S) paradigm, more and more universities and research institutes have begun to explore AI-related technologies around battery research and development.

Specifically, AI can accelerate the screening and discovery of battery materials. The research and development of battery materials involves thousands of chemical combinations, and the time and resources for experimental verification are limited. The application of AI in high-throughput computing and machine learning allows researchers to quickly screen out potential high-performance materials through simulation and prediction.

For example, Microsoft and PNNL used AI technology to screen 32 million potential battery materials and narrowed the list to 23 within 80 hours, of which 5 were known materials. The team says the process would have taken more than 20 years to obtain the material using traditional methods.

AI optimizes battery synthesis and interface reaction

Secondly, AI also performs well in the synthesis and preparation of batteries. Specifically, the interface problem is the key bottleneck of battery performance, for example, the stability of the interface between lithium metal anode and electrolyte directly determines the safety and life of the battery to ensure long lasting battery.

Check cathode and anode for details. 

It is difficult for traditional experiments to fully understand complex reactions at the interface, but AI models can combine molecular dynamics simulation and experimental data to predict the interface reaction path and design better electrolyte materials. For example, researchers at South China University of Technology used AI models to model the interface reaction of lithium-ion batteries(solid state batteries vs lithium ion), focusing on optimizing battery components, and providing a direction for the development of more stable electrolyte materials.

AI improves battery life prediction accuracy

In the battery characterization test, AI is no less good at predicting battery life. Researchers at the Massachusetts Institute of Technology, Stanford University and the Toyota Research Institute (TRI) use AI to predict battery life. The AI algorithm developed by the team can judge the battery life according to the battery's 5 charge and discharge cycles(battery cycle), and the accuracy of the judgment results is as high as 95%, and the error between the predicted value and the actual life value of the battery is within 9%. It is worth mentioning that the dataset is open source and is the largest dataset of its kind.

Not long ago, the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences and Xi 'an Jiaotong University made new progress in the field of battery health management. Researchers have developed a new deep learning model that effectively solves the traditional method's dependence on a large number of charge test data, provides a new idea for real-time battery life prediction, and realizes the end-to-end evaluation of lithium battery life. At the same time, the model is also an important part of the core model of PBSRD Digit, the first generation of battery digital brain, which provides a solution for smart battery management.


In addition, AI also shows great potential in optimizing the production process of battery materials. Take solid-state batteries as an example, their manufacture has strict requirements for the microstructure of the electrolyte. AI technology can use computer vision and optimization algorithms to analyze parameters in the material preparation process, such as temperature, pressure, etc., to improve production consistency and reduce manufacturing costs.

For example, a joint study by several institutions at the University of Jules Verne in Picardy, France, shows how to monitor and optimize electrode manufacturing processes through machine learning technology. This method enables real-time adjustment of battery manufacturing parameters, which significantly reduces waste and improves product consistency.


It can be predicted that, driven by the AI for Science paradigm, the field of battery materials is standing on the threshold of a new technological revolution. The application of AI has not only brought new ideas and tools to the research and development of battery materials, but also is reshaping the development path of the entire battery technology.

International enterprises layout AI-driven battery research and development

The battery industry is on the crest of a wave of technological innovation, and AI is undoubtedly the core driving force leading this technological Renaissance. The in-depth application of AI technology has not only spawned the cutting-edge theory of battery science in the field of academic research, but also shown strong practical value in the industry, providing a new impetus for the commercialization, large-scale production and performance optimization of battery technology.

In the international market, a number of companies have preempted the layout of AI-driven battery research and development. Tesla optimizes its battery management system (BMS) with AI, uses deep learning and machine learning techniques to predict battery health and longevity, and uses a data-driven approach to improve supercharging and energy management.

South Korean battery manufacturer LG Energy Solution has developed an AI platform that focuses on predicting battery aging, failure modes, and energy management optimization, while providing dynamic prediction and optimization capabilities for energy storage systems (ESS).

Lithium metal battery company SES AI also announced that it will join forces with technology companies NVIDIA, Crusoe and Supermicro to accelerate the development of new battery materials, and plans to use high-performance supercomputers optimized for AI to draw small molecule databases to improve the understanding of battery chemistry systems. Accelerate the development of energy storage solutions.

In addition, NVIDIA also recently announced that the ALCHEMI NIM project is accelerating the development of sustainable energy materials such as electric vehicle batteries and solar panels through AI technology. These projects can efficiently simulate and predict the electrochemical properties of materials, not only shorten the development cycle of new materials, but also significantly reduce costs, and provide technical support for the global energy transition.

Domestic enterprises are actively exploring the application of AI in battery research and development

Returning to the domestic market, the battery research and development technology innovation of various enterprises is also a trend of contention. As a leader in the global power battery industry, Ningde Times actively applies AI technology to the modeling and optimization of battery chemistry and material properties, focusing on the research and development of high energy density batteries.

In December 2023, Ningde Times announced that it will set up an international research and development center in Hong Kong to focus on AI for Science. Zeng Yuqun, chairman of Ningde Times, has also mentioned in public many times in the past year to accelerate the introduction of AI, especially in the innovation of battery material systems.

In addition, Hive Energy (SVOLT) took the lead in creating the industry's first vehicle-level AI intelligent power battery factory in Jintan, Jiangsu, using AI to control the whole process of the battery, and launched a series of high-performance battery products, which greatly accelerated the large-scale application of new energy batteries.

The rise of new energy AI startups

At the same time, some AI battery material startups have also sprung up in foreign markets, such as QuantumScape, Inobat Auto, Mitra Chem, Aionics, etc., aiming to introduce artificial intelligence into the field of battery development. Mitra Chem is described by some battery technology industry leaders as "a Silicon Valley-based battery materials innovator powered by artificial intelligence technology."

And China's market has also emerged a number of new energy AI enterprises, such as Ouyang Minggao Academician team incubated enterprise Shengke Energy, released the world's first battery AI large model PERB2.0. This model is capable of processing and analyzing massive amounts of battery data, playing a key role in battery design, performance optimization, and intelligent decision-making.

Conclusion

Looking at the present, from material discovery to manufacturing optimization, from performance prediction to full life cycle management, AI technology is comprehensively empowering every link of battery research and development, injecting strong momentum into the new energy industry. By deeply integrating scientific research results with industrial practice, AI not only accelerates technology iteration, but also promotes the large-scale application and cost reduction of battery technology. However, the development of anything is tortuous, and the deep integration of AI and battery research and development is not achieved overnight.

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