DIGITAL & COMPUTATIONAL CAPABILITIES FOR BATTERY DEVELOPMENT
Battery Digital Intelligence supported by multiscale modelling, simulation and Physics-Informed AI
Leveraging A*STAR’s comprehensive modelling, simulation and AI capabilities, at the Institute of High Performance Computing ( IHPC), the A*STAR Battery Centre provides a complete suite of computational techniques for developing better battery systems. The multi-scale toolsets range from ab-initio simulation tools support material discovery to large scale, CFD and FEA models provide information on the integration of discrete components within a battery pack.
Leveraging the rapid progress of Physics-informed AI, our team is able to accelerate research to achieve breakthroughs in cell and pack design that can meet industry requirements. Our state of the art models support the development of safe, robust and cost effective batteries and provide accurate predictive tools to use in next generation battery management systems.
Our Capabilities
- Atomic Scale: Chemical composition, Chemical structure, Doping effects, Reaction mechanism, AI assisted materials discovery
- Cell design and electrode performance
- Electrochemistry, Pseudo-2D, equivalent circuit models
- Module and pack thermal management
- Physics based Predictive lifetime and aging.
- High accuracy enhanced equivalent circuit models for SOC / SOH prediction.
- Battery structural reliability, structural analysis, vibration
- Battery abuse tolerance and safety simulation, response under crash, crush, short-circuit, reaction kinetics
Selected Publications
- Man-Fai Ng and Michael B. Sullivan, First-Principles Characterization of Lithium Cobalt Pyrophosphate as a Cathode Material for Solid State Li-ion Batteries, J.Phys. Chem. C 2019, 123, 29623-29629
- Hao Yuan and Yong-Wei Zhang, Role of Ferroelectric In2Se3 in Polysulfide Shuttling and Charging/Discharging Kinetics in Lithium/Sodium−Sulfur Batteries, ACS Appl. Mater. Interfaces 2022, 14, 16178−16184
- Nguyen et al, Rechargeable Magnesium Batteries Enabled by Conventional Electrolytes with Multifunctional Organic Chloride Additives, Energy Storage Mater. 45, 1120-1132 (2022).
- Foo et al. “Multiphysics modelling of Structural Battery Composites”. Composites Science and Technology 242 (29), 110178 (2023).
- Guo et al., Energy release rate for steady-state fiber debonding in structural battery composites, Composites Science and Technology 247, 110416 (2024).
- Lin, J., Khoo, E., 2024. Identifiability study of lithium-ion battery capacity fade using degradation mode sensitivity for a minimally and intuitively parametrized electrode-specific cell open-circuit voltage model. Journal of Power Sources 605, 234446.
- Smith, R.B., Khoo, E., Bazant, M.Z., 2017. Intercalation Kinetics in Multiphase-Layered Materials. J. Phys. Chem. C 121, 12505–12523.
- Khoo, E., Bazant, M.Z., 2018. Theory of voltammetry in charged porous media. Journal of Electroanalytical Chemistry 811, 105–120.
- Khoo, E., Zhao, H., Bazant, M.Z., 2019. Linear Stability Analysis of Transient Electrodeposition in Charged Porous Media: Suppression of Dendritic Growth by Surface Conduction. J. Electrochem. Soc. 166, A2280.
- Song, J., Khoo, E., Bazant, M.Z., 2019. Electrochemical impedance of electrodiffusion in charged medium under dc bias. Phys. Rev. E 100, 042204.
- Lin, J., Zhang, Y. & Khoo, E. Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis. arXiv:2110.13661 [physics] (2021). Tackling Climate Change with Machine Learning workshop at NeurIPS 2021
- Xu, Q., Wu, M., Khoo, E., Chen, Z. & Li, X. A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life. IEEE/CAA Journal of Automatica Sinica 10, 177–187 (2023).
- Riko I Made, Jing Lin, Jintao Zhang, Yu Zhang, Lionel C.H. Moh, Zhaolin Liu, Ning Ding, Sing Yang Chiam, Edwin Khoo, Xuesong Yin, Guangyuan Wesley Zheng, Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modelling. iScience, Volume 27, Issue 4, 2024
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