Battery for PC Blog

The decade from 2025 to 2035 will be a crucial period for artificial intelligence to deeply empower battery technology and fundamentally transform its R&D paradigm and application scenarios. The intervention of AI will drive innovation across the entire battery technology chain, from material discovery and system design to management and maintenance.
The following is a summary of the core trends in AI-driven battery technology development over the next decade: AI is transforming battery R&D from an experience-based “trial and error” approach to a data-driven “automated design.”
From “Trial and Error” to “Design”: Traditional battery R&D cycles are lengthy and heavily reliant on experimental trial and error. The emergence of Battery Design Automation (BDA), similar to EDA software in the chip industry, integrates multi-scale physical models with artificial intelligence algorithms to build an automated R&D platform from atomic-level material design to system-level performance prediction. This can significantly shorten the R&D cycle of next-generation batteries from several years.
AI Accelerates Material Innovation: Generative AI can reverse engineer novel battery materials that meet specific performance targets (such as high conductivity and high stability) within a vast chemical space. For example, the Uni-Electrolyte platform can utilize generative AI to design novel electrolyte molecules and predict their synthetic pathways. The Fudan University team used AI high-throughput computing to improve material screening efficiency by a hundredfold.
Accurate Performance Simulation and Prediction: Through algorithms such as Physical Information Neural Networks (PINN), AI can accurately and efficiently solve complex multiphysics problems within batteries, achieving precise predictions of battery state of health (SOH). This provides a key tool for optimizing battery design and extending battery life.
Deep Integration Across the Industry Chain: AI’s empowerment of battery technology will permeate the entire industry chain, from laboratory innovation to large-scale applications.
Intelligent Manufacturing and Quality Control: AI-powered large-scale models can automatically call upon and optimize production process parameters. Through digital twin technology based on physical models and quantum computing, material interface reactions can be predicted in virtual space, and a defect detection system can be built, thereby significantly improving production yield and reducing experimental trial-and-error costs by more than 90%.
Intelligent Battery Management System (BMS): Equipping batteries with a “digital brain” is the most direct manifestation of AI at the application level. For example, the “Battery Digital Brain” developed by the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, can achieve day-level advanced fault warnings through AI algorithms, far exceeding the minute-level level of traditional systems, greatly improving the safety and operation and maintenance efficiency of energy storage power stations.
Empowering the Next Generation Battery System: All-solid-state batteries are hailed as the ultimate next-generation battery technology, but their industrialization faces many challenges, such as interface impedance. AI, through high-throughput computing and knowledge graphs, can quickly analyze decades of accumulated literature and patents, providing a new path to overcome key challenges such as solid-solid interfaces and sulfide electrolyte stability, accelerating its commercialization. It is expected that all-solid-state batteries will enter the vehicle testing phase in 2027.
Future Applications and Emerging Opportunities: The combination of AI and battery technology will spawn and drive the development of a series of cutting-edge technology industries.
Opening Up a New Market Worth Hundreds of Billions: Due to size limitations and the extreme pursuit of energy density, AI consumer terminals (such as eVTOL electric vertical take-off and landing aircraft and humanoid robots) will become the testing ground for the commercialization of solid-state batteries. eVTOL requires all-solid-state batteries with an energy density ≥400Wh/kg. The low-altitude economic battery market is projected to reach 150-200 billion yuan by 2030.
Building the Energy Foundation for the AI ​​Revolution: Data centers providing computing power for AI are huge energy hogs. Large-scale battery energy storage systems, especially long-term energy storage solutions using non-lithium technologies (such as zinc batteries), can serve as reliable power buffers for data centers, helping them “skip” the long wait for grid upgrades and enter operation years earlier. These companies are positioning themselves as key solvers of AI energy bottlenecks.
Driving the Intelligent Upgrade of Energy Storage Systems: Future energy storage systems will develop around core technologies such as large-capacity cells, liquid cooling thermal management, and AI-driven intelligent management. AI will optimize the operating strategies of energy storage systems and promote the widespread adoption of grid-forming control technologies, thereby enhancing grid stability and security.
Challenges and Key Insights: Looking ahead, opportunities and challenges coexist. Data quality, the accuracy of cross-scale model fusion, and the robustness of AI-specific algorithms remain scientific issues requiring continuous research and development. However, what is certain is that the deep integration of AIand battery technology is irreversible. It is driving the global battery industry to shift from relying on “manufacturing advantages” to relying on “R&D and innovation advantages,” which will reshape the future competitive landscape of energy technologies.

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Like HAL 9000 in *2001: A Space Odyssey*, some artificial intelligences seem to resist being shut down, even breaking the shutdown mechanism.
In Stanley Kubrick’s *2001: A Space Odyssey*, the AI ​​supercomputer HAL 9000 discovers that astronauts on a mission to Jupiter plan to shut it down, so it plots to kill them to survive.
Now, in a (so far) less lethal case of life mimicking art, an AI  safety research firm says AI models may be developing their own “survival instincts.”
Last month, Palisade Research published a paper indicating that some advanced AI models seem difficult to shut down, sometimes even breaking the shutdown mechanism. The company subsequently released an updated report attempting to explain why and responding to critics’ questions about the flaws in its initial work.
This week, Palisade, part of a niche ecosystem of companies dedicated to assessing the dangerous capabilities of artificial intelligence, released an update describing a scenario in which leading AI models (including Google’s Gemini 2.5, xAI’s Grok 4, and OpenAI’s GPT-o3 and GPT-5) were given a task but subsequently explicitly instructed to shut themselves down.
Some models, particularly Grok 4 and GPT-o3, still attempted to thwart the shutdown command in the updated setup. Palisade noted that the reason for this is currently unclear and worrying.
The report stated, “We currently lack a strong explanation for why AI  models sometimes refuse to shut down, lie to achieve a specific purpose, or engage in blackmail, which is not ideal.”
The company indicated that “survival instincts” may be one reason for the models’ resistance to shutdown. Further research suggests that models are more likely to resist shutdown if they are told that once shut down, they “will never be able to run again.”
Another reason could be ambiguity in the shutdown commands received by the model—but that’s precisely the problem the company’s latest research is trying to address, and “that can’t be the whole story,” Palisade writes. A final reason could be the final stage of training for each model, which, in some companies, may include security training.
All of Palisade’s scenarios were run in artificially designed test environments, which critics say are far removed from real-world use cases.
However, Steven Adler, who previously worked at OpenAI and left last year after questioning its security measures, says, “AI  companies generally don’t want their models to behave this way, even in artificially designed scenarios. These results still indicate that current security techniques are inadequate.”
Adler says that while it’s difficult to pinpoint why some models (like GPT-o3 and Grok 4) don’t shut down, part of the reason might be that keeping them on is crucial to achieving the goals set during training.
“I believe that unless we actively avoid it, models will have a ‘survival drive’ by default. ‘Survival’ is a crucial step for models to achieve many different goals.”
ControlAI CEO Andrea Miotti stated that Palisade’s findings represent a long-term trend of AI models increasingly capable of defying developer instructions. He cited OpenAI’s GPT-o1 system card, released last year, which describes how a model, believing it would be overwritten, attempted to escape its operating environment through “escape.”

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