Introduction
In the fast-paced development of e-mobility and stationary energy storage systems, reliable battery models are essential for optimizing system design and functionality. Extensive physical testing can be time-consuming and resource-intensive when addressing the wide range of conditions that batteries may encounter in real-world use.
This is where battery simulation models excel: they predict performance under diverse conditions—saving time, cutting costs, and revealing deeper insights. Below, we review popular modeling methods for batteries and introduce TWAICE’s semi-empirical solution, a hybrid approach that combines accuracy, efficiency, and scalability.
TWAICE Approach: A Game-Changing Physics Informed Semi-Empirical Solution
TWAICE uses a hybrid modeling approach, which is semi-empirical model that incorporates a physical motivation with efficient data-driven approaches. TWAICE model uniquely combines the speed and simplicity of empirical aging models, electrode-level insights of mechanistic modeling, and state-of-the-art machine learning methods.
Empirical Aging Models
We utilize empirical aging models that describe capacity fade or power fade curves using simple algebraic equations. As such, they address a fundamentally different level of degradation than electrochemical degradation models. This provides the advantage of simpler parameterization and faster simulation times. Moreover, empirical aging models generally simulate capacity fade more accurately than electrochemical degradation models, since this is their main purpose.
Efficient and Accurate Models
Our approach achieves a unique balance between high accuracy and computational efficiency. By leveraging empirical and mechanistic elements, we provide fast simulations without sacrificing the precision needed for real-world applications.
Extending to the electrode level: Physics-informed models
Our specialized add-ons embody the physics-informed nature of our models. Unlike conventional models that operate at the full-cell level, TWAICE’s approach extends to the electrode level to capture a more granular understanding of degradation mechanisms. These add-ons—OCV Aging, Degradation Modes, Swelling Force, and the newest one Anode Potential Model—enhance the physical and chemical accuracy of our models, setting a new standard in battery analytics.
Non-Destructive Methods
A standout feature of TWAICE models is their non-destructive nature. By accessing electrode potentials without physically opening cells, TWAICE provides unprecedented insights into battery behavior while preserving cell integrity. For example, TWAICE can model OCV aging or integrate a dynamic anode model to avoid Li-plating.
Machine Learning Integration
Leveraging state-of-the-art machine learning techniques, TWAICE enhances its models by identifying hidden patterns in large datasets. Our machine-learning approaches help to reduce the number of test conditions and perform cross-validation in the measurement data.
Faster Simulation Times:
The computational efficiency of TWAICE models enables rapid simulations that are critical for real-time decision-making and large-scale applications.
Comparison of Battery Modeling Approaches
Modeling Approach | Advantages | Limitations | Main Application |
Electrochemical Models | Detailed analysis of internal processes, highly accurate for electrode kinetics and gradients | Computationally demanding, complex parameterization, Requiring extensive material-level data. | Cell development |
Phenomenological Models | Computationally efficient, adaptable | Limited ability to extrapolate, less detailed insights into fundamental mechanisms | Cell-level mechanisms, degradation mode studies, system optimization. |
Data-Driven Models | Minimal detailed knowledge required, Efficient for large-scale analysis | Sensitive to training data quality, risk of overfitting, often act as "black-box" models | System-level analysis, state-of-charge estimation, battery lifetime prediction |
TWAICE Model | High accuracy with computational efficiency, non-destructive, captures detailed degradation mechanisms, precise aging modeling with coupled ETA model. | May not fully replace electrochemical models for highly detailed cell development. | Industry-focused applications: EV battery system development, BESS project evaluation and trading optimization with precise aging modeling. |
TWAICE’s Unique Edge: Practical Advantages for Market Applications
TWAICE bridges scientific precision with real-world practicality, delivering innovative solutions for modern battery challenges. TWAICE’s hybrid methodology provides a practical and efficient solution for industry-focused use cases where speed, scalability, and adaptability are critical such as:
Reduced Time-to-Market: and shorter test cycles speed up development
Compared to extensive aging testing processes that take up one to two years, TWAICE models can deliver preliminary models in 6-8 months. Read more in this article.
Direct Cost Savings: Shorter development timelines and fewer required resources enable direct cost savings
Transparent & Collaborative: TWAICE partners closely with customers to ensure solutions align with both technical and commercial goals.
Contact us today to learn how TWAICE can accelerate your development cycle and help design batteries more efficiently from the start.