FULL-MAP is a pioneering initiative that seeks to redefine the limits of what is possible in battery innovation. The project focuses on creating a Materials Acceleration Platform (MAP) that amplifies human capabilities, enabling faster, more efficient development of new battery chemistries with ultra-high performances. To achieve this, FULL-MAP relies on the development of a unique R&D infrastructure and accelerated methodology that integrates inverse design of materials and computational and experimental methods with Artificial Intelligence (AI), Big Data, Autonomous Synthesis, and extensive High-Throughput characterization and testing. FULL-MAP\u2019s comprehensive and modular approach also encompasses key analyses at various levels, from material to cell assembly. It simulates the entire battery development process, from material design to final battery testing, considering environmental and economic factors, significantly advancing sustainable battery technology.<\/p>\n\n\n\n
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Siemens Software adopts smarter models and simulation to accelerate battery innovation<\/h2>\n\n\n\n
As part of our FULL-MAP research effort, SISW focuses on accelerating battery innovation, which starts with smarter models. We pursue this track by performing research along three key pillars:<\/p>\n\n\n\n
1. Materials Discovery<\/h3>\n\n\n\n
Advanced materials design requires cutting-edge technologies, which allow for the manipulation of material characteristics almost at the molecular level1<\/a><\/sup>. Smart and accurate models, combined with efficient simulation up to 3D high-fidelity2<\/a><\/sup> can lead to innovative applications; this also holds for battery research. In our FULL-MAP research, we use physics-based simulations to evaluate transport properties, mechanical behavior, and interfacial characteristics across multiple chemistries3<\/a><\/sup>. This replaces costly trial-and-error experiments with targeted computational screening, identifying high-value candidates ready for lab validation while reducing time, cost, and risk.<\/p>\n\n\n\n We build multi-physics models that accurately capture real battery behavior\u2014including cell geometry effects and realistic aging mechanisms. These high-fidelity models enable teams to evaluate design trade-offs and integration strategies virtually, aligning predictions with actual lifetime and performance before building physical prototypes4<\/a><\/sup>. This will further advance our solutions in battery cell design5<\/a><\/sup>, one of the most critical challenges in the industrial race toward electrification.<\/p>\n\n\n\n We leverage Machine Learning (ML) to make detailed simulations computationally feasible. Transfer Learning reuses knowledge across related chemistries when data is scarce, while reduced-order models cut computation time by orders of magnitude. Modular workflows integrate everything from atomistic screening to continuum simulations, enabling rapid exploration of design options and “what-if” scenarios.<\/p>\n\n\n\n The targeted result<\/strong> is to enable faster, cheaper, more predictive battery development cycles with fewer experiments and quicker iterations.<\/p>\n\n\n\n2. Cell-Level Simulation<\/h3>\n\n\n\n
3. Machine Learning Acceleration<\/h3>\n\n\n\n
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