Navigating the Complexity of Battery Health | Monolith

Engineers are continually seeking solutions that enhance efficiency and reliability. One area where this drive is particularly evident is in battery testing. As the demand for energy storage solutions grows, so does the need for accurate, efficient testing methodologies. 

 

While traditional simulation techniques have played a crucial role in accelerating product development, they are not without their limitations, especially when it comes to the complexities of battery systems. Because batteries are so difficult to accurately simulate, battery developers have to rely on more costly and time-consuming testing to validate new designs.   

  

The Rise and Limitations of Simulation 

 

Engineering design and simulation software began impacting product development in the mainstream in the 1980s and 1990s. With better simulation tools, engineers could predict how new designs would perform over different conditions, reducing reliance on physical testing and expediting time-to-market. 

 

However, when it comes to battery simulation, traditional physics-based models cannot accurately represent a battery’s combination of chemical, electrical, and thermal characteristics to make reliable predictions of battery performance and health. 

 

Watch Battery Testing Webinar

 

One aspect of battery performance that is particularly challenging to model and simulate is battery degradation or aging. Battery degradation is a multifaceted phenomenon, influenced by various factors such as charging mechanisms, usage profiles, thermal variation, cathode material degradation, metallic lithium plating, and more. 

 

These degradation mechanisms operate across different scales within lithium-ion battery (LIB) technology, from the atomic level of active materials to the macroscopic level of battery packs. Predicting the long-term health of batteries requires models that can account for electrochemical processes and degradation mechanisms accurately. 

 

The Complexity of Battery Degradation 

Thermal runaway events, characterized by uncontrolled increases in temperature, pose significant safety hazards and can compromise battery stability. Cathode material degradation, essential for ion movement, can lead to performance degradation over time. 

 

Additionally, issues such as metallic lithium plating, anode solid electrolyte interphase (SEI) growth, gas evolution, cycle fatigue, particle cracking in electrodes, and electrolyte dilution further contribute to the degradation process. 

 

exploring sensitivity analyses of batteries and specific dependent variable types with monolith

 

While physics-based models like the pseudo-two-dimensional (P2D) model offer detailed insights into electrochemical behaviour, their computational complexity and numerous parameters make them challenging to implement. Moreover, these models often struggle to predict long-term degradation accurately due to poorly understood degradation modes and limitations in model identifiability. 

 

Navigating Simulation Challenges with AI-Powered Solutions 

Engineers are turning to AI-powered solutions to revolutionize battery testing methodologies. By utilising the power of machine learning algorithms, engineers can analyse vast amounts of battery test data to uncover valuable insights and predict performance with unprecedented accuracy. 

 

 

One approach gaining traction is the use of AI to develop predictive models that can forecast battery health based on real-world test data. By training algorithms on historical test data, AI models can identify patterns and correlations that traditional simulation techniques may overlook. 

 

Using this data-driven approach, engineers can make informed decisions about battery design, optimization, and maintenance, ultimately leading to more reliable and efficient energy storage solutions. 

 

Furthermore, AI-powered solutions offer a more efficient alternative to traditional simulation techniques. While physics-based models like the P2D model excel in capturing electrochemical behaviour, they often require significant computational resources and expertise to develop and implement. 

 

In contrast, AI algorithms can quickly process large datasets and generate predictive models in a fraction of the time, streamlining the testing process and accelerating product development cycles. 

 

Striking a Balance with Hybrid Approaches 

 

To address the trade-off between accuracy and computational efficiency, researchers are exploring hybrid approaches that combine the strengths of different modelling techniques. By integrating statistical models with physics-based simulations, engineers can strike a balance between predictive power and computational efficiency. 

 

Equivalent Circuit Models (ECMs) offer simplicity and ease of implementation but may lack the accuracy required for precise performance predictions. On the other hand, physics-based detailed models like the P2D model provide high accuracy but are complex and time-consuming to develop and utilise. 

 

Hybrid approaches aim to leverage the flexibility of statistical models while incorporating the physical insights offered by physics-based simulations, providing a more practical solution for many battery testing applications. 

 

Conclusion 

In the landscape of battery testing, AI solutions offer a pathway to overcome the limitations of traditional simulation techniques. 

 

White Paper: AI-Guided Test for EV Battery Development

 

Engineers can utilise valuable insights from battery test data, predict performance with accuracy, and accelerate the development of reliable energy storage solutions. Reach out if you want to learn more about our solution. 

 

Share this post

Request a Demo

Ready to get started?

BAE Systems
Jota
Aptar
Mercedes Benz
Honda
Siemes