Cohort: 1 

Locations: Beijing & Suzhou, China 

Application Title: AI-driven battery cell design and simulation democratization 

Team Members: Ke (Luke) Hu (executive sponsor), Zhe Li (executive sponsor), Lance Zhao, Weinan Zhou, Linghao Lu, Qiyu Chen, Zhixuan Wu, Liming Chang

The challenge 

Battery cells are central to electric vehicles and energy storage, yet designing them is a slow and specialised process. A single cell must balance energy density, power output, safety and lifespan, while also meeting manufacturing constraints. Small changes to materials or structure can affect performance in unexpected ways. 

Traditionally, battery design relies on a combination of physical testing and design-of-experiments methods. Engineers adjust parameters, build prototypes and test them repeatedly. This process can take several years and consumes significant material and energy. Simulation tools have improved efficiency, but they often depend on expert interpretation and are disconnected from real manufacturing constraints. As a result, optimisation remains slow and difficult to scale, particularly for smaller teams or new entrants. 

With global demand for batteries rising rapidly, these limitations have become a bottleneck for innovation. 

The solution 

A digital platform for predictive battery design 

Tsinghua University partnered with Electroder to develop an AI-driven platform that brings advanced battery design tools into a single digital environment. 

The platform integrates physical models that describe how battery materials and structures behave with AI-based optimisation methods. This allows users to explore design options virtually before committing to physical prototypes. Instead of relying on trial-and-error testing, engineers can define performance goals and constraints, then evaluate how different design choices affect outcomes. 

Electroder provides the platform infrastructure, while Tsinghua University contributes domain expertise in battery science and modelling. 

How it works 

Simulating cells before they are built 

The system uses multi-physics models to represent processes at the electrode and cell level. These models capture electrochemical behaviour and material interactions that influence real-world performance. AI algorithms then search through the design space, adjusting variables such as composition and geometry within manufacturable limits. 

Users can specify objectives such as higher energy density or longer cycle life. The platform runs simulations to assess trade-offs and identify promising designs. Sensitivity analysis highlights which parameters matter most, helping teams focus effort where it has the greatest effect. 

A built-in assistant allows users to upload scientific papers or internal documents and extract relevant design insights conversationally, lowering the barrier for teams without deep modelling experience. 

How it differs from conventional approaches 

Reducing reliance on physical trial cycles 

Conventional battery development depends on repeated physical prototyping. Even when simulations are used, they often serve as advisory tools rather than drivers of design decisions. 

The Tsinghua–Electroder platform shifts this balance by allowing most exploration to occur virtually. Physical testing is still required, but it is used to validate a smaller number of well-informed designs. This reduces material consumption and shortens development timelines. 

By embedding manufacturing constraints into optimisation, the platform also helps ensure that simulated designs can be produced in practice. 

Impact 

• Concept-to-prototype cycles accelerated by 3.6× 
• 40% reduction in material and energy waste during development 

Why it matters 

Broadening access to advanced battery R&D 

As battery demand grows, innovation needs to move faster while reducing waste. The collaboration between Tsinghua University and Electroder shows how AI-based simulation can help address this challenge by making advanced design capabilities more accessible. 

By reducing dependence on long trial cycles, the platform supports more efficient development and lowers barriers for teams working on next-generation battery technologies.