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Aging affects most things on Earth, and batteries are no exception to this phenomenon. In a very peculiar way, batteries behave like "living" beings; their particles flow between two electrodes, there are chemical reactions, and even mechanical changes, such as an effect similar to "breathing" (expansion and contraction of the electrodes due to the intercalation and deintercalation of lithium during charge/discharge cycles). They are simply in operation, which generates natural wear and tear. You Can't Stop Aging: Electric Vehicle Fleets Will Age (and so will their batteries) However, when people consider buying an electric car, the number one criterion for them is range, followed by price and charging logistics (infrastructure and charging time). Concern about battery life only appears in 6th position. Today, this ranking may not be surprising, considering that most electric vehicles are bought new and aging seems to be a rather technical topic, with a wide variety of possible evolutions in battery life depending on the use of the electric vehicle. But, as electric vehicle fleets age (and with them their batteries), the used car markets begin to grow and, suddenly, for good resale prices, the lifespan and health of the batteries will certainly increase in importance (as predicted). Similarly, recycling battery cells that have reached the end of their useful life is still a very expensive and energy-intensive activity. And therefore, longer-lasting and more sustainable battery cells are already a competitive factor for those who design and sell electric vehicles and batteries. So, the time has come for OEMs and battery manufacturers to understand battery aging and develop cell designs that provide maximum lifespan. Engineers must understand not only when, but also where aging mechanisms occur. Using simulation with aging models can significantly help accelerate the prediction of the degradation trend of a given battery. Typically, 1D level simulations are used in this case, as they allow for very fast execution and can produce years of simulated data in a few hours. Throughout this article, it will be exemplified how this can be done in a simulation environment where physical phenomena are modeled, but first let's learn a little more about the causes of battery aging. The Toxic Ingredients That Cause Battery Cell Aging So, what triggers and affects aging? Battery aging has its root causes in several factors. First, unsurprisingly, time: whether the cell is being used or remains idle, time is at work allowing some internal chemical reaction to induce some performance degradation. Second is temperature: temperature has a significant impact on the battery lifespan degradation process. Storage and use at high temperature (high range of safe temperature limits) would accelerate aging. Low temperatures are better, but combined with fast charging can be recipes for other degradation effects. This leads to the third main criterion, the current applied to the cell. Basically, referring to the type of load applied to the battery. If it is used gently with smooth and low power demands, the current applied to the cell will be smooth and slowly affect aging. However, if the battery is used more aggressively, with more frequent fast charging, particularly under low temperature conditions, the accelerated degradation mode will be activated. A deeper analysis of battery cell degradation mechanisms What happens within the battery due to these effects is a combination of several degradation mechanisms: Solid Electrolyte Interface film growth: This is the slow growth of a thin, porous layer on the surface of the active material, which consumes Lithium atoms to grow. As it grows the inventory of available lithium, used for the cell operation, decreases, reducing the cell’s capacity. Also, the thickness of the SEI film creates a barrier to the Lithium ions and electrons trying to go in and out of the active material, which increases the cells’ overall electrical resistance Lithium plating. In this case, there is formation of lithium metal film on the surface of the active material, which also consumes the lithium inventory impacting the cell capacity. Loss of active material by dissolution: Active material responsible for storing lithium is dissolved into the electrolyte due to some undesired side reaction. The loss of this active materials further decreases the cell capacity. Loss of active material by mechanical cracking . The Lithium intercalation and de-intercalation process generates at each cycle some mechanical stress. Overtime parts of the active material can break down and be separated from the main electrode. This has the effect of losing ability to store lithium and further decreases the cell capacity. The bottom-line consequences of these effects are simple, your battery’s capacity will decrease, reducing your vehicle range compared to its brand-new range. And it will be less able to sustain aggressive power demands, reaching more rapidly the lower and maximum voltage safety limits, leading to the battery’s shut down. Aging takes time – that engineers don’t have That's why battery and vehicle manufacturers dedicate time and effort to characterizing these aging phenomena. But here's a challenge: the effects of aging can only be observed after several years of operation. Therefore, as you can understand, conducting tests to capture the correct degradation behavior requires an enormous amount of time and money to test the battery over the years of operation! Of course, there are some accelerated aging testing techniques, but the first results can only be seen after at least 6 months of accelerated aging tests. But to gain a competitive advantage, engineers analyzing these aging challenges need more detail; they need to further optimize the battery cells and understand not only when, but also where the aging mechanisms occur, so that they can better address degradation problems locally. EV battery cell formation (the initial charge) is a critical manufacturing step with respect to battery cell aging risks (Image: Chroma ATE). Inspection & Identification The first stage of the process. Battery cells are inspected and identified before entering the formation cycle. Formation This is where the initial charging of the cell happens — known as the “formation” process. A critical step that defines the cell’s electrochemical properties and directly impacts its lifespan . Ambient Aging Cells rest in ambient temperature conditions. This step allows the materials inside the cell to stabilize after formation. High Temperature Aging Cells are kept at elevated temperatures to accelerate aging and detect early defects. Ensures only stable cells move forward in the production line. OCV & ACR Testing Electrical testing to measure: OCV (Open Circuit Voltage)  – voltage when the cell is not under load. ACR (Alternating Current Resistance)  – internal resistance of the cell. These tests assess the performance and quality of each cell. Sorting Cells are classified based on results from electrical and aging tests. Cells with similar performance are grouped together to form consistent battery modules or packs. And it's not only aging that needs to be studied during operation: equally relevant is the initial charging process, known as formation, which is the critical final stage of manufacturing before the cells are shipped. It forms the crucial protective layer of the Solid Electrolyte Interface and therefore has a huge impact on the subsequent lifespan of the battery. Battery aging simulation There are several approaches to leveraging simulation to predict aging and the formation process. Firstly, our Simcenter Amesim systems solution, using 1D models, can be extremely efficient in rapidly generating years of aging simulation data under various operating conditions. The main advantage here is time acceleration. Physics-based aging models in Simcenter Amesim have been available since version 2410, in addition to the existing empirical aging models. In this type of simulation, each cell is represented by blocks that describe its electrical and thermal behavior—capacity, internal resistance, and heat exchange with the environment. By connecting multiple cells in series and parallel, it is possible to predict how performance and temperature evolve over time, simulating battery aging and allowing for design adjustments before moving on to more detailed 3D analyses in Simcenter STAR-CCM+ . Second, to address the need for spatial information, Simcenter STAR-CCM+ 's 3D Cell Design solution can predict aging evolution in a 3D cell geometry with resolved electrode layers. Of course, in this case, the execution time is much longer than in 1D simulations, but the user will have access to local information about where aging occurs and can mitigate these effects by changing the design or operating conditions. Thirdly, it is possible to combine 1D and 3D simulations. The 1D simulation is used to generate the very long-term aging simulation over years of physical time. Users can then extract from this discrete point the cell's State of Health (SOH) over the aging period, for example, every year. This SOH for each year can then be a starting point for a 3D simulation, where the cell is aged only for a short period, for example, 1 month of physical time, but long enough to generate the distribution of the various aging mechanisms, such as Solid Electrolyte Interphase (SEI) growth or lithium plating, as implemented in more recent versions of Simcenter STAR-CCM+ . Obviously, the 1D and 3D aging models are coupled with thermal models to capture the thermal effect on the evolution of degradation mechanisms. Finally, 3D simulations can be used to assist in predicting the initial Solid Electrolyte Interphase (SEI) layer during the manufacturing formation process. In fact, the SEI growth model can be used in the first charge of a battery cell and predict the growth of this critical protective layer. The 3D Cell Design feature can then help the user evaluate the uniform evolution of the SEI layer growth and determine the optimal point at which the layer is sufficiently thick and the amount of lithium consumed to generate it. This will help further refine the estimate of cyclable capacity. High fidelity battery aging simulation with Simcenter STAR-CCM+ Aging through parasitic side reactions with Sub-grid Particle Surface Film model Available since the release of Simcenter STAR-CCM+ 2406 , the “Sub-grid Particle Surface Film” model in the Battery Cell Designer allows simulating the cell's response to a duty cycle in relation to two of the main degradation mechanisms: The growth of the Solid Electrolyte Interphase (SEI) film The growth of the lithium metal plating film An Active Material Particle, presented at NordBatt Conference These are both parasitic side reactions which occur during the cell operation. Lithium plating is the deposition of Li-metal on the particle surface. And SEI is the film created from the reaction between the particle and the electrolyte. Due to the side reactions, the amount of cyclable lithium reduces, you can simply track the remaining lithium in the electrolyte and the active material. This should allow to check the effect on the capacity. The film resistance area (resistivity times thickness) is also a field function which can be tracked and contributes to the overall internal cell resistance. Mechanical-induced degradation with the Sub-Grid Particle Aging model Simcenter STAR-CCM+ includes "Sub-Grid Particle Aging," which focuses on degradation effects of a mechanical nature. In this case, the loss of active material due to mechanical stresses is characterized by alternating stresses during charging and discharging, i.e., the cyclic insertion and extraction of lithium from the active material particles, which can lead to the formation of cracks in the electrodes. This can cause loss of electrical contact and reduction of usable active material, leading to an overall loss of cell capacity and an increase in internal resistance. Active material particles undergoing surface cracking and loss of active material There are two types of cracks forming, represented with two model options under the “Sub-grid Particle Aging” model: First one is the “Loss of Active Material” model. It is characterized by the cracking of particles or electrode “blocks”, leading to an electrical contact loss of active material particles, making those particles electrochemically inert and no longer participating in the electrochemical reactions. These particles represent therefore a loss in cell’s capacity The second effect is the “Surface Crack Growth” model. The cyclic insertion and extraction generate cracks within the particles themselves. Those cracks expose a new surface for the Solid Electrolyte Interface (SEI) to grow, leading to Lithium consumption and therefore an overall capacity loss and internal resistance increase. Note that this model option is compatible with the “Sub-grid Particle Surface Film” model enabling the SEI growth effects simulation. Also note that, some publications on the topic suggest, that the tortuosity should increase when the surface cracks grow. A trustworthy battery aging simulation framework The abovementioned aging models were validated against experimental measurements generated during the EU commission funded project MODALIS² , which was focusing on developing physics-based aging models for the latest generation of Li-ion battery cells. This work was performed with key industrial partners specialists in the field of batteries, such as a cell maker, cathode supplier and electrolyte supplier. All that said, thanks to high physical modeling fidelity and the unique three-dimensional implementation of the models, these aging models offer you the ability to localize areas of the cell which most impacted by all types of aging. This is in theory. So let’s look at those models in action. Simulating aging cycles in 3D This first example was presented at the NordBatt conference in 2022 by my colleague Stefan Herberich from SIEMENS . A prototype cell used in the EU-funded MODALIS² project was used, and the cell is tested over several cycles with aggressive aging conditions to locate the weak areas where degradation is most dominant. The cell considered consists of 15 electrochemical layers. The discretized cell is shown below, along with some results. In total, there are approximately 200,000 finite volume cells. In particular, the thickness direction is discretized using 10 cells per anode and cathode layer and 2 cells for the separator and current collectors. The drive cycle consists of the following steps: first, charging is performed with constant current (CC), applied at a rate of 2C. C-rates indicate the ratio between the charging current and the battery capacity—at 1C, a fully discharged battery (0% state of charge, or SOC) is fully charged in 1 hour; at 2C, the current is doubled, and charging is completed in approximately 30 minutes. If the voltage exceeds 4.2 V, the process switches to constant voltage charging mode, remaining at 4.2 V until the state of charge reaches 95%. The 4.2 V limit is reached quickly. Then, the battery remains at rest for a little over 3 minutes and is then discharged to 60% state of charge, also at a rate of 2C. After another rest period, the complete cycle is repeated ten times. Interpretation of results The study provides insights into the effects of the two aging mechanisms that occur: SEI growth and the influence of lithium plating side reactions. The images show the average thickness of the SEI layer around the particle and the equivalent average thickness of the plated lithium on a particle, respectively. The corresponding results were observed on the anode plane and in a cross-section in the direction of the cell thickness. In addition to the analysis of the SEI, this study also provides important information about LAM (Loss of Active Material), which refers to the degradation or inactivation of the electrode material that participates in the electrochemical reactions. In the plane: the thermal boundary conditions are such that the highest temperatures are observed in the center of the battery cell. At this location, the temperature dependence of multiple material parameters leads to higher SEI growth rates. LAM is pronounced near the battery tabs, where the highest rates of voltage variation are observed. In thickness: As expected, SEI and LAM growth are greater near the separator. The operating conditions are such that the lithium metal, with an initially specified homogeneous profile, is dissolved more quickly than deposited, especially near the separator. SEI during the formation step The second study will be on SEI during the initial charge, also known as formation. Using the results presented in “Andrew Weng et al. 2023 J. Electrochem. Soc. 170 090523”, Simcenter STAR-CCM+ and the “Sub-grid Surface Film” model were used to replicate this study. The article describes the formation of SEI, i.e., the accumulation of a passivation layer on the graphite anode of a battery during the first charge cycles. The film layer is formed due to a side reaction of the solvent components S, ethylene carbonate (EC) and vinyl carbonate (VC), with Li+, which produces the film components P, lithium ethylene dicarbonate (LEDC) and lithium vinyl dicarbonate, and gaseous byproducts Q. Only the first 4 hours of the formation process were simulated, which is when the rapid dynamics occur and the transition from the kinetically limited regime to the diffusion-limited reaction regime takes place. The results reasonably correspond to the reference: The results demonstrate the ability to use Simcenter STAR-CCM+ in an approach to understand the SEI formation process, but also to be able to better control it and brings the potential to reduce its overall duration, which in some cases can last up to ~20 days. Want to understand how to predict and mitigate battery aging with high accuracy and efficiency? Schedule a meeting with CAEXPERTS and discover how Simcenter Amesim and Simcenter STAR-CCM+ solutions can revolutionize the development of longer-lasting and more sustainable cells for electric vehicles. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Build a scalable Battery Energy Storage System (BESS) and achieve high ROI This post focuses on the crucial role of energy storage in promoting corporate sustainability and profitability. By integrating BESS with renewable energy sources, companies can achieve significant cost savings, reduce their carbon footprint, and boost long-term profitability. We will explore how BESS industry leaders are creating digital plants, increasing flexibility, and building a competitive advantage in a rapidly changing market. If your company is ready to lead the transition to BESS, this is your roadmap. System Simulation Plays a Crucial Role System simulation plays a crucial role in the techno-economic evaluation of battery energy storage systems (BESS) in the energy sector, especially when integrated with renewable energy sources such as wind turbines and solar photovoltaic (PV) systems. Various use cases covered by BESS Here are some key aspects: Balancing Power Generation and Consumption : Peak Shaving and Load Shifting : By simulating different load profiles, BESS can be optimized for peak shaving (reducing peak demand) and load shifting (moving energy consumption to off-peak times), which can reduce energy costs and improve grid efficiency Grid Stability : Simulations can assess how BESS can be used to balance intermittent renewable energy generation with grid demand, enhancing grid stability and reliability Integration with Renewables : Energy Management : Advanced energy management strategies can be simulated to coordinate the operation of BESS with renewable generation, ensuring that energy is stored and dispatched in the most efficient way. Including the weather conditions. Energy Price Evolution : Forecasting and Optimization : System simulations can model future energy price scenarios, helping to optimize the operation of BESS for energy arbitrage (buying low, selling high). This ensures that the BESS is used in the most cost-effective manner OPEX/CAPEX : Cost Analysis : Simulations can provide detailed cost-benefit analyses, including capital expenditures (CAPEX) and operational expenditures (OPEX). This helps in understanding the financial viability and payback period of BESS projects Degradation Modeling : By simulating the degradation of battery cells over time, it is possible to estimate maintenance costs and replacement schedules, which are critical for long-term financial planning Overall, system simulation provides a comprehensive framework for assessing the technical and economic feasibility of BESS projects, helping stakeholders make informed investment and operational decisions. We will explore the initial phases of a Battery Energy Storage System (BESS) project together, focusing on some technical and economic assessments for success (OPEX/CAPEX, energy price trends, load balancing, return on investment), and moving through the different stages with Simcenter System Simulation : To calculate your customer's electricity bill Considering some weather forecasts From renewable energy (solar PV) Battery Electric Storage System (BESS) optimization and control strategy To typical results in operations based on realistic scenarios The use case here is a food processing plant near Lyon, France. Some effort was devoted to modeling the solar photovoltaic (PV) system integrated with the BESS and surrounding consumers. With its load and heating system represented over a one-year period (January to December), the digital twin considers solar PV BESS operations with different electricity tariff structures and PV or BESS unit costs. Therefore, it addresses the technical and economic value of adopting BESS in dynamic tariff structures. Although the case study was conducted in France, it is important to highlight that even more impressive results could be obtained in countries with higher solar incidence, such as Brazil. High irradiance throughout the year significantly increases the potential for photovoltaic generation, increasing BESS efficiency and reducing the payback period. Thus, the combination of high solar resources and the application of advanced simulation strategies makes the Brazilian scenario especially promising for energy storage and renewable energy integration projects. Better Design for Operational Excellence As the BESS economy gains unprecedented momentum, companies are racing to meet the growing demand for clean energy. However, scaling production while remaining profitable, sustainable, and resilient poses a formidable challenge. BESS producers and equipment manufacturers must overcome fragmented data systems, high energy costs, and supply chain complexities to stay ahead. Companies striving for operational excellence are already turning these challenges into opportunities. By leveraging digital twins and advanced simulations, they are optimizing processes, reducing costs, and improving scalability. The energy industry needs BESS to save money and reduce carbon emissions. Digital Twin for an Industrial Facility This is a distributed BESS digital twin to predict and optimize system performance with multiphysics. It includes consumers (food processing facility: 20°C) with the heating system and customer load, grid connection, solar PV with solar panels, stationary batteries, as well as a smart controller based on weather conditions and energy price fluctuations. BESS Digital Twin in Simcenter Amesim In the image above, the Heating System, Consumer Load, and Grid Energy blocks were modeled in Simcenter Amesim based on tables containing actual operating data from the industrial facility and predefined formulas for calculating grid energy consumption and costs. These blocks represent the system's energy behavior under different load conditions, providing a reliable basis for analyzing the performance and operational costs of BESS systems. The Solar Panel and Battery blocks use simplified physical models to represent the actual operation of these devices. Photovoltaic generation is simulated using historical solar irradiance and ambient temperature data, while the battery model was parameterized based on information from technical catalogs, allowing for the prediction of load states, efficiency, and available capacity. These models capture the system's energy dynamics. Finally, the Intelligent Control block integrates all this information to enable real-time decision-making. Thus, the control optimizes the energy flow between generation, storage, and consumption, seeking to reduce costs and improve overall system performance. By running the simulation, users can access all variables from the different subsystems. Thus, complete information is available, from consumer load [kW] to the electricity bill over time [€] (€1 to $1). The evolution of electricity prices throughout the year, with their daily fluctuations, was considered. Below, two price trends are shown on January 1 and July 19, to obtain information on minimum/maximum prices at different times of the year. Typical results obtained with the BESS digital twin The solar panel includes your GPS location, turbidity factor (the effect of particles, similar to smoke in the air), or cloud cover factor (for weather conditions). Cloud cover factor changes depending on weather conditions At the same time, the outside temperature changes are included from a known database, allowing users to assess its impact on the heating system, considering the factory indoor temperature setting. Temperature Evolution (ceiling, outdoor, indoor) and Air Conditioning Power This analysis allows you to calculate associated information, such as air conditioning power [W] or the energy consumption of all surrounding subsystems. This allows users to obtain a realistic power evolution to evaluate the balancing mechanism and optimal control strategies to implement in their BESS system. BESS Macroanalysis with Realistic Scenarios The industrial unit model allows for mass exploration. Analyses are completed in just a few minutes, opening the door to long and complex scenarios. Energy generation and consumption [kW] for all subsystems (the battery is inactive here) Users can practically evaluate the energy generation, storage, and consumption of all subsystems. Meanwhile, the intelligent control system manages the Energy Management System (EMS) to distribute the energy, store it in the BESS, or deliver it to the grid. Since all changes are intermittent or dynamic, a system simulation tool, such as Simcenter Amesim , is necessary to optimize sizing and control strategies. Finally, the user can access variations in energy flows over time. For example, you can check the energy generated by solar panels or brought in by the grid, as well as the energy supplied by the battery. This corresponds to the energy needed by the load, while some small levels of energy are taken from the grid or returned to the battery during off-peak periods when demand is low. Energy Flow Variations Over Time This is a major achievement! We can already observe good results thanks to the digital twin with Simcenter System Simulation . But it's possible to go much further, more technically and economically. See how you can save US$1 million over 20 years while simultaneously reducing a huge amount of CO₂ emissions, down to -17 tons of CO₂ equivalent. Save US$1 million and tons of CO₂ equivalent We will now address the business and decarbonization aspects, with the goal of demonstrating how it is possible to create a scalable forecast for BESS systems, in order to measure and replicate significant successes. The digital twin of the food processing unit is equipped with metadata to produce the relevant economic KPIs (key performance indicators) to ensure its monetization, return on investment (ROI), or payback through CAPEX (capital expenditures) or OPEX (operating expenses). Operating Costs [$k] during the Scenario The reference is the electricity bill without solar panels or BESS. It amounts to US$103,000 paid over one year. By installing the solar panels and BESS, the new electricity bill, which is now US$33,000 after one year, can be captured, with an investment of US$625,000 for the photovoltaic system and US$77,000 for the BESS. This corresponds to a savings of US$70,000 per year in OPEX, thanks to the installation. Discounting CAPEX costs, a benefit of US$698,000 is obtained after 20 years of operation. CAPEX costs are reimbursed after a 10-year payback period. Knowing that the value doubles every 15 years due to the interest rate, it can be assumed that the actual savings will reach US$1 million after 20 years. Please note that this is a preliminary calculation that shows the potential, while things like inflation and maintenance costs are not covered, which is good for a first estimate. Profitability [$k] including payback [year] Now is the right time to optimize sizing and extract maximum value from the new installation. Discover the maximum benefits and best returns in just a few clicks. A batch study was configured to vary selected parameters, defined as the number of solar panels (366, 488, 610) and the number of battery racks (0, 100, 150). It was observed that the payback period can be reduced to approximately 9 years (-11%) in the most favorable configurations, while other options can extend it to up to 12 years (+20%). Comparison of return on investment [year] depending on the number of solar panels and the number of racks Finally, for the Earth's good health in relation to climate change, it is also essential to consider the reduction of carbon emissions thanks to renewable sources, the BESS system, and smart control strategies. CO₂ emissions from the grid, load, and heating, as well as the total CO₂ reduction per year A significant reduction of 17 tons of CO₂ equivalent per year was achieved. This result represents a significant contribution to the sustainability process through decarbonization. All these achievements were achieved using the digital twin through Simcenter System Simulation . Going Further You can even go a step further, introducing a new paradigm with grid supervisory control. The latest and most innovative technologies allow the combination of artificial intelligence (AI), weather forecasting, and streamed data. This offline digital twin is converted into an executable digital twin that connects real-time performance data with accurate and well-orchestrated plant information and simulation tools, allowing you to troubleshoot critical system situations (surges during switching, etc.) or benefit even more from price and CO₂ reductions. What a great prospect! Grid Supervisory Control, Combining AI, Weather Forecasting, and Streaming Data In short, owner-operators in the global BESS business have a historic opportunity to expand their business and market share in the coming decades. The companies that will emerge as leaders in the delivery sector will be those that can overcome the complexity of BESS and turn it into a competitive advantage. System simulation definitely helps you succeed in your BESS journey thanks to digitalization, system integration, and intelligent controls. Schedule a meeting with CAEXPERTS and discover how to transform the potential of your BESS project into real results. Our experts will show you how the use of digital twins and system simulation can optimize sizing, reduce OPEX, and accelerate return on investment (ROI)—all while your company advances in the energy transition and decarbonization. Take the next step toward efficiency and sustainability: contact us today. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Challenge: achieve fast and accurate virtual prototyping The optimal multi-attribute design of transmissions remains a critical challenge for drivetrain engineers, gaining even greater significance in the era of electric vehicles. Today’s automotive industry demands powertrains that excel in multiple aspects: minimizing noise and vibrations, maximizing power density and efficiency, while ensuring unprecedented durability performance. For this purpose, transmission engineers need to design innovative transmissions that meet the multi-attribute performance criteria. Computer-Aided Engineering (CAE) allows engineers in industry to virtually prototype and optimize their next product optimization. Simulations are essential to accurately predict and optimize component behavior and the system-level transmission performance throughout the development cycle. Powerful physics-based models and simulation capabilities are available, which accurately model real-life products, perform predictive simulations and optimize the product’s performance for statics, dynamics, aerodynamics, acoustics, durability, etc. These physics-based models, for example Finite Element (FE) models, are successfully adopted in industrial development workflows. For high-fidelity simulations (e.g. detailed FE contact simulations), the model fidelity must increase to allow accurate predictions, which unfortunately also increases the model’s computation time and cost. In CAE, Artificial Intelligence (AI) and Machine Learning (ML) technologies are revolutionizing physics simulations. Among the advancements, surrogate modeling for physics simulations experiences rapid growth thanks to advanced AI techniques and architectures. Surrogate models, or reduced-order models (ROMs), provide efficient alternatives to computationally expensive high-fidelity simulations (such as detailed FE contact simulations). These AI/ML-based surrogate models, capable of providing fast and accurate predictions while maintaining high fidelity, unlock the potential for extensive and automated design space exploration, enabling engineers to efficiently evaluate thousands of design variants in a fraction of the time required by traditional simulation methods. Gears, as critical components in transmission systems, particularly benefit from such advanced simulation approaches, as their design requires careful consideration of deformation, contact and bending stresses, and durability. This blog post introduces a novel gear design analysis method3 that combines the strengths of FE and AI/ML models to achieve efficient and accurate gear stress prediction, demonstrating how this technology can be practically implemented in industrial workflows. Solution: combine powerful high-fidelity models with AI/ML The innovative gear stress analysis method3 exploits the powerful predictive simulation capabilities of FE models to generate accurate simulation data, which is then used to train an AI/ML surrogate model. Once trained, the efficient AI/ML surrogate model can accurately predict gear stresses for unseen combinations of design parameters. This combines the strengths of FE and AI/ML into a novel AI/ML-accelerated gear stress analysis capability that provides accurate predictions quickly. The simulation workflow for comprehensive gear analysis integrates the capabilities of Simcenter 3D and Simcenter Nastran software solutions, while Simcenter HEEDS serves the dual purpose of automating data generation for AI/ML model training and orchestrating multi-objective optimization studies that make use of the AI/ML model calculations. Figure 1 illustrates the automated data generation workflow, in which Simcenter HEEDS orchestrates the physics-based simulation process. The workflow streamlines the creation of gear datasets, while incorporating elements from Simcenter 3D Motion Gear Design Optimization methodologies. For each design, first the macrogeometry and microgeometry of the gear pair are evaluated before the nonlinear FE analysis (NLFEA) module generates the FE model and uses Simcenter Nastran for automated FE-based gear contact. Figure 1: Automated gear data generation workflow, involving Simcenter HEEDS as orchestrator. This workflow adopts high-fidelity physics-based simulation models, bringing several advantages to design engineers: highly accurate predictions of real-world behavior through detailed modeling of physical phenomena (such as the nonlinear contact mechanics in gear mesh), and a deep understanding of complex interactions and phenomena that could be missed in simpler models. These detailed models provide a reliable source of training data for AI models and AI model validation. To accelerate gear stress and durability analysis, our approach replaces the traditional NLFEA-based contact analysis by an AI/ML surrogate model, trained on NLFEA contact analysis results, to predict the altering gear blank and tooth root stress throughout the gear meshing cycle. The resulting AI/ML-based ROM offers significantly reduced computation times compared to the full order NLFEA-based simulations, enabling rapid design iterations. Embedding the AI/ML model within the standard gear design simulation workflows creates an AI/ML-accelerated process that remains highly accurate, while opening doors to multi-objective optimization studies (typically unachievable via traditional, resource intensive, FE models). Two additional solution elements are introduced to realize the AI/ML accelerated workflows: Simcenter Reduced Order Modeling8 that enables the creation and deployment of ROMs from simulation and test data. An upcoming Simcenter AI technology for 3D surrogate modelling that leverages operator learning techniques. Results: realize fast and accurate gear stress analysis A solution workflow has been introduced, aimed at combining the power of high-fidelity models with AI/ML models to achieve AI/ML-accelerated gear stress analysis. This section introduces a gear use case to verify the workflow vs. the objective to achieve efficient yet accurate gear stress analysis. The gear design use case is described in detail below and has four design parameters: the normal pressure angle, in range [18°-22°], the addendum coefficient of the Pinion, in range [1.00-1.30], the addendum coefficient of the Wheel, in range [1.00-1.30], and the profile shift coefficient of the Pinion, in range [0.35-0.70]. Using Simcenter HEEDS and Simcenter Nastran , 81 gear designs were created, varying the four design parameters through a level 3 full factorial DOE. The data set is split randomly into 64 designs as training set and 17 designs as test set. A transformer-based operator learning AI/ML model is trained to predict gear surface stresses, given the surface geometry and contact forces as model inputs. In this first study, we chose to have the AI/ML model learn the full 3D signed von Mises stress field, which captures both compressive and tensile behavior. The signed von Mises stress is created via postprocessing of the full 3D tensor at each node, which is stored in the NLFEA-based contact results. The model focuses on surface nodes where failures typically initiate (tooth root and contact regions), optimizing computational efficiency while maintaining accuracy for critical stress predictions3. Three approaches are evaluated for the gear design use case: FE Reference: NLFEA-based contact simulations (Reference), ML with FE Forces: AI/ML predictions using NLFEA contact forces, ML with Motion Forces: AI/ML prediction using Simcenter 3D Motion contact forces. Multibody-based gear contact forces (computed via Simcenter 3D Motion ) are used in this study as they provide a computationally efficient alternative to expensive nonlinear finite element analyses, while still maintaining sufficient accuracy in predicting contact force distributions – an aspect that will be crucial for future design optimization workflows. Figure 2 presents an analysis of the full stress field using the three approaches. The stress field predicted by the AI/ML surrogate model with FE-based contact forces as input (Fig. 2b), shows to be near-identical to that of the reference FE-based results (Fig. 2a), while the stress field predicted by the AI/ML surrogate model with Motion-based contact forces as input (Fig. 2c) shows only minor deviations from the reference results. These findings are confirmed by Figure 3, which presents the results for the tooth root stress during five mesh cycles for a point located along the middle of the face width and in the middle of the tooth root arc. Figure 2: Von Mises (signed) stress fields, computed from a) NLFEA-based contact simulations, b) ML model with FE-based forces (input), c) ML model with Motion-based forces (input), for a pinion gear example case. Figure 3: Comparison of tooth root stress for a point (middle flank, middle root arc) over the course of 5 mesh cycles (created based on the stress of 5 teeth during 1 mesh cycle) for a pinion gear example case Figure 3. Though data generation and AI/ML model training require an initial time investment, these are one-time offline processes. The resulting trained model enables rapid predictions, ideal for efficient design space exploration and optimization. The AI/ML model that was used in this study requires about 0.1 seconds to compute the full stress field for one point in the mesh cycle, while one NLFEA contact simulation requires on average about 5 minutes. Knowing that a full mesh cycle requires about 20 to 30 angular configurations per gear pair and that an industrial transmission has multiple gear pairs for which hundreds of variants are explored, the inclusion of the AI/ML surrogate models can truly accelerate gear design optimization. Conclusion and outlook This study demonstrates the successful integration of AI/ML surrogate models into gear design workflows, achieving both speed and accuracy in stress prediction. The AI/ML approach delivers results that closely match traditional nonlinear finite element analyses while being multiple orders of magnitude faster. This dramatic speed improvement, combined with maintained accuracy, opens new possibilities for comprehensive design space exploration and optimization of transmission systems. The developed workflow, supported by Simcenter tools , and validated through practical case studies, effectively demonstrates the industrial viability of AI/ML-accelerated gear and transmission design. References D. Park, A. Rezayat and Y. Gwen, “Gear design optimization for multi-mesh and multi-power flow transmissions under a broad torque range incorporated with multibody simulations”, in VDI International Conference on Gears 2022, Munich, Germany, 2022. M. Vivet, J. Melvin, S. Donders, “Advancing bevel gear contact simulation towards quiet transmissions”, Simcenter Blog, August 19, 2024. M. Vivet, D. Park, A. Scheuer, “AI/ML-accelerated gear durability analysis within gear design optimization”, in VDI International Conference on Gears 2025, Garching near Munich, Germany, September 10-12, 2025. Schedule a meeting with CAEXPERTS and learn how to accelerate gear stress analysis with solutions that combine high-fidelity simulation and Artificial Intelligence. By combining high-fidelity models and AI/ML, we help your company reduce development time, increase analysis accuracy, and accelerate innovation in transmission systems. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter SPEED Design and analyze engines and generators analytically. Industry's most used rotating machine design software. Permanent magnet and synchronous, induction, reluctance, DC with brushes, commutated with field winding and axial flux machines, among others. Simcenter SPEED Design and analyze motors and generators analytically in Simcenter SPEED, which provides access to theoretical and physical models of most major classes of electrical machines (for example, electrically excited synchronous and permanent magnet machines, induction, reluctance, DC with brushes, switched with field and axial flux winding), along with their drives. In addition, Simcenter SPEED writes a predefined set of specific parameters and maps that can be imported into Simcenter Amesim, supporting system-level simulation of the electronic machine integrated into its environment. Contact an Expert Electric machine rapid design software Simcenter SPEED features Industry's most used rotary machine design software Link with Multiphysics software Automated Design Space Exploration and Optimization Simcenter SPEED software supports engineers in virtually validating design choices through detailed analytical simulation, fast and intelligent use of 2D finite element magnetostatic analysis. It includes all the theoretical and physical models needed for rapid electrical machine design with a flexible approach and an interface with links to even more accurate and detailed analyzes and simulations such as 2D and 3D Multiphysics Finite Element/Finite Volume (FE/FV) magnetostatic or magnetotransient, thermal, mechanical or vibroacoustic. Electric machine model: set up an electric machine model quickly; Multiphysics software link : model export to finite element software ; Design Exploration: Evaluate the influence of parameters or optimize machine performance for one or more objectives; System-Level Simulation: Export model data for a system-level simulation in Simcenter Amesim. Simcenter SPEED supports the most common machine types including motor, generator and also inverters. The user can benefit from predefined templates for the following machines: Synchronous machines (PC-BDC) Induction machines (PC-IMD) Switched reluctance machines (PC-SRD) Brushed PM-DC machines (PC-DCM) Wound Field Switched Machines (PC-WFC) Axial flow machines (PC-AXM) To improve simulation accuracy, Simcenter SPEED provides links to several general purpose electromagnetic finite element solvers such as Simcenter STAR-CCM+, Simcenter MAGNET or to Simcenter SPEED's dedicated tool, SPEED FEA Solver. They make it possible to model and study the electrical machine more accurately and under specific conditions, with saturation and the occurrence of faults. In general, users can connect Simcenter SPEED with other tools needed for complete electrical machine solution using various scripts or programming languages. More specifically, automation makes use of scripting capabilities , driving Simcenter SPEED alone, or in conjunction with other programs such as STAR-CCM+. This automated workflow follows the scripting approach and uses STAR-CCM+ and its multiphysics solvers for electromagnetic, thermal (full 3D conjugated heat transfer) and mechanical stress analysis, along with Java scripts to provide and provide additional information. Vibroacoustics can also be studied by combining FE models of the stator and frame subsystem with a BE model of the surrounding free space to assess the sound quality of the electrical machine. The objective is to eliminate noise through simulation in Simcenter 3D Acoustics. What is the expected end result of this Model-Based Systems Engineering approach? Answer: a support for making the best design choice , and by “best” I mean the optimized viable choice, again through an efficient and continuous workflow. As mentioned, Simcenter SPEED delivers results almost instantly thanks to its analytical approach, which makes it very suitable for Design Space Exploration programs, supporting clients with “What if” studies and optimization runs. HEEDS is a powerful software package in the Simcenter portfolio that automates this process of exploring the design space, and Simcenter SPEED provides an integrated graphical user interface for accessing HEEDS. ⇐ Back to Tools

Simcenter 3D and Madymo Pre-collision and collision avoidance scenarios. Design, analyze and optimize vehicle safety for occupants; Reduces the cost of building and testing prototypes; Multibody (MB), finite element (FE) and computational fluid dynamics (CFD) in a single solver; Mannequin models; Simcenter 3D Security Simcenter Madymo™ software has been used extensively for automotive safety simulation and provides almost everything an engineer needs to create advanced, integrated safety systems. It provides a dedicated software environment to develop occupant and pedestrian safety; offers fast and accurate simulations, allowing extensive design of experiments (DOE) and optimization studies; offers a comprehensive package including solver , dummy , and human model pre- and post-processing tools, and provides token -based licensing . Simcenter Madymo is an excellent computer-aided engineering (CAE) solution for the occupant safety market. Pre-collision and collision avoidance scenarios are multiplying and the duration of these simulated events is increasing. With its accurate and computationally efficient solver , dummy and human models, built-in sensors, control functionality and interfaces for co-simulation with other software , Simcenter Madymo is an excellent solution. Solution Benefits Flexibility in modeling Impact dummy and human body models Simcenter Madymo workspace Easy access with licensing Features Flexibility with interface Provides world-standard software to design, analyze and optimize vehicle safety for occupants and vulnerable road users Reduces prototype building and testing costs, which speeds time to market Minimizes the risk of making design changes late in the development phase Correlates precisely with crash test results Allows security engineers to apply experiment designs, methods, and run multiple scenarios simultaneously Offers a complete package including solver , dummy and human model, pre-processing and post-processing tools Simcenter Madymo allows the user to integrate multibody (MB), finite element (FE) and computational fluid dynamics (CFD) technology into a single solver, giving the engineer the flexibility to model safety systems with the right balance between accuracy and velocity. Simcenter Madymo input syntax allows for hierarchy indeck input. This allows engineers to take a modular approach to theirdeck input, in which submodels can be easily exchanged. Simcenter Madymo includes a database of validated crash dummies and human body models. Simcenter Madymo occupant models are widely used in the automotive industry for occupant safety engineering and human biomechanics (impact) research. The Simcenter Madymo suite of products includes Workspace, which consists of several pre-processing and post-processing modules. Users can easily configure Simcenter Madymo models and also view, present and report simulations and test results. Token -based licensing allows direct access to all Simcenter Madymo tools and templates using just one token set . This means you can directly access less frequently used tools and templates without having to obtain additional licenses and costs. Explicit Multibody Dynamics Solver Explicit finite element solver CFD solver for airbag gas dynamics Full seat belt and airbag modeling Built-in detection and control functionality Dedicated vehicle safety output options (SAE filters, injury criteria, ISO-MME format) Simcenter Madymo can be used to interface and run in co-simulation with other explicit FE solvers . This allows engineers to use Simcenter Madymo occupant models and restraint systems in any FE vehicle structure model. Simcenter Madymo can also run in co-simulation with the MATLAB® environment and the Simulink® environment, allowing the user to include the most advanced control algorithms in the security systems modeled by Simcenter Madymo. ⇐ Back to Simcenter

The high degree of automation of SIEMENS DIGITAL INDUSTRIES tools ensures that, even while the engineering team rests, your company continues to generate value, products and innovative solutions. Structural, thermal, acoustic, electrical design and whatever else is needed. HEEDS; Topological CAD and CAE. Project Optimization In optimization, one can look for values minimizing/maximizing a mathematical function through the systematic choice of values that allows the comparison between different configurations and a detailed study of the models in different physics. Contact an Expert Keep designing, even after shifts Structural, thermal, acoustic, electrical design and whatever else is needed The high degree of automation of SIEMENS DIGITAL INDUSTRIES tools ensures that, even while the engineering team rests, your company continues to generate value, products and innovative solutions. This feature ensures that the engineering team can dedicate their time to the innovation and product development processes, while the software takes care of testing the solutions, delivering the best possible option. Optimization software from Siemens Digital Industries has the ability to deal with different physics together, integrating calculation routines already validated by companies with the most popular CAE applications on the market . This allows the complete integration of the entire production and design cycle, integrating the engineering areas, making it possible to optimize products and projects with a focus on reducing raw material costs, production time, efficiency and product robustness. All this in the same software , in an integrated and automated way. HEEDS Software specialized in optimization, capable of evaluating data from different sources in search of the best design alternatives using CAD/CAE parameters. ⇐ Voltar para Serviços