Our team recently completed a teaming secondment at Chalmers University of Technology in Gothenburg, Sweden. The visit focused on advancing physics-based and data-driven modeling for lithium-ion batteries as part of Task T3.2.
At Chalmers, we worked on enhancing the pseudo-two-dimensional (P2D) electrochemical framework by developing a lithium plating and stripping model and a data-driven parameterization approach based on electrical plating detection test data. This combined methodology improves the model’s ability to predict voltage behavior, differential voltage peaks, and capacity degradation across different temperatures and C-rates.
We also investigated cell-to-cell variance to better represent real-world battery behavior and ageing effects.
The secondment was highly valuable, both scientifically and collaboratively, offering insights into different research approaches and fostering new international connections. We thank the Chalmers team for their hospitality and productive discussions that strengthened our joint work on advanced battery modeling.
As part of our collaborative research activities, our team recently completed a teaming secondment at Chalmers University of Technology in Gothenburg, Sweden. The visit provided an excellent opportunity to work directly with a leading European battery research group, exchange expertise, and strengthen collaboration across institutions.
During the secondment, we gained valuable insight into how other groups structure their research and integrate modeling, experimental work, and validation. The open and interdisciplinary atmosphere at Chalmers fostered productive discussions and new professional connections that will support ongoing and future joint research.
Our work during the visit focused on Task T3.2, dedicated to improving physics-based electrochemical models for lithium-ion batteries. The pseudo-two-dimensional (P2D) model is a well-established framework that accurately describes internal battery processes such as voltage, temperature, and lithium concentration.
While this model provides high accuracy, it also demands significant computational resources. Existing physics-based ageing models for solid electrolyte interphase (SEI) growth and lithium plating, for example often lose precision as cells age because they cannot fully capture the complex, coupled nature of ageing mechanisms.
Our objective was to extend the P2D framework with improved descriptions of lithium plating and stripping and to develop a data-driven parameterization approach to enhance its adaptability and accuracy under different operating conditions.
We developed a detailed lithium plating and stripping model that represents the electrochemical deposition of metallic lithium on the graphite anode during charging and its subsequent removal during discharge. The model improves the ability to simulate transitions between intercalation and plating regimes, which are critical for understanding behavior at low temperatures and high C-rates. It also enables more accurate predictions of terminal voltage, capacity fade, and the effects of reversible and irreversible plating.
To further refine the model, we implemented a data-driven parameterization framework based on electrical plating detection test data. This allows the model to reproduce experimental behaviors such as differential voltage (dV/dQ) peak positions, terminal voltage curves, and capacity degradation across different ambient temperatures and cycling rates.
We also analyzed cell-to-cell variance to account for manufacturing and material differences between cells, improving the robustness of the simulations and making them more representative of real-world conditions.
The secondment at Chalmers was highly productive, both technically and collaboratively. Working closely with the Chalmers research team allowed us to exchange ideas on modeling methodologies, parameter identification, and validation techniques.
The results of this work contribute directly to the objectives of Task T3.2, strengthening the link between physics-based modeling and data-driven methods for improved prediction of battery ageing and performance.
We thank our colleagues at Chalmers University of Technology for their hospitality and constructive collaboration. The visit provided valuable technical progress and strengthened our international network, laying the foundation for continued cooperation in advancing next-generation battery models.
Jue Chen, Runyang Lian, Hanna Hoch
RWTH Aachen – Chalmers University of Technology