Cohort: 2 

Location: Ningde, China 

Application Title: Physics-informed and agentic AI platform for EV battery cell design 

Team Members: Prof. Ni Jun, Dr. Jin Wenjing, Dr. Huang Yunlong, Dr. Jin Haizu, Song Shutao, and Ying Yuanyuan

The challenge 

Rapid growth in electric vehicle adoption has significantly shortened battery development timelines. What once took several years now often needs to be completed in roughly 18 months. This places sustained pressure on engineering teams to deliver improvements in performance and cost efficiency within increasingly tight schedules. 

Historically, battery cell design has relied on expert intuition supported by manual trial-and-error. Engineers managed thousands of interdependent parameters through long prototyping cycles, adjusting chemistry, structure and performance characteristics incrementally. The process worked, but it was slow, resource-intensive and difficult to scale. As battery systems became more complex, the limits of human-led iteration became more pronounced. 

More recently, AI-assisted models trained on historical data have shortened design cycles from weeks to days. While this marked a clear improvement over manual workflows, these approaches still depend on sequential optimization steps and tightly structured inputs. In practice, that constraint limits their ability to adapt to the complexity of real-world battery design. 

The solution 

An AI-powered cell design platform grounded in physics 

CATL has developed an AI-powered battery cell design platform that integrates physics-based electrochemical modelling with machine learning to automate and accelerate key parts of the design process. 

Physics-informed models incorporate established electrochemical principles directly into the learning process, helping ensure that predictions remain consistent with known physical constraints. These models are combined with machine-learning techniques to evaluate large design spaces efficiently. 

The platform runs on an on-premises private cloud, providing secure high-performance computing while maintaining full data sovereignty. It processes more than 50 million data records, spanning time-series measurements, text, images and graph data. Structured and unstructured inputs are combined with synthetic data generation, allowing training datasets to expand and improve over time. 

Across the organization, the platform is used by more than 4,200 employees across 15 departments, including over 1,200 core engineers. What previously took weeks of iteration can now be completed in minutes. 

How it works 

From manual optimization to autonomous design loops 

At the heart of the platform is a combination of physics-informed machine learning and agentic AI. Agentic AI refers to systems that can plan, evaluate and refine actions autonomously within defined constraints. 

Within CATL’s platform, this creates what the company describes as a “digital engineer” workflow. The system autonomously generates design candidates, evaluates them against electrochemical constraints, and refines solutions through continuous iteration. Instead of relying on human-led optimization loops, the platform operates as a closed system where data, physics and computation reinforce each other. 

By embedding domain knowledge directly into the model, CATL ensures that automation does not come at the expense of scientific rigor. 

How it differs from conventional approaches 

Reducing dependence on manual evaluation 

Conventional battery design workflows rely heavily on sequential testing and expert review. CATL’s platform reduces this dependence by enabling data-driven evaluation across a much broader design space. 

Design decisions are no longer constrained by the pace of manual evaluation. Instead, engineers can explore alternatives more quickly and focus their attention on validation and decision-making, rather than repeated parameter tuning. 

Why it matters 

Supporting faster and more reliable battery development 

By transforming battery cell design into a fully data-driven process, CATL is changing how next-generation EV batteries are developed. 

The platform shortens development timelines, improves predictive accuracy and allows innovation to scale without relying on ever-growing engineering teams. In doing so, it supports faster progress in battery performance while reducing cost and complexity, a critical step in powering the global transition to sustainable mobility.