How can you predict lithium-ion battery degradation over time using modeling?

1 What is battery degradation?

Battery degradation is the process of losing capacity and power over time due to chemical and physical changes in the battery cells. These changes can result from internal and external factors such as electrode reactions, electrolyte decomposition, solid electrolyte interphase (SEI) formation, parasitic currents, mechanical stress, thermal stress, and environmental conditions. Battery degradation can reduce the energy density, power density, efficiency, and safety of lithium-ion batteries, and increase the risk of failure and fire.

2 Why is modeling important?

Modeling is a powerful tool for predicting battery degradation over time and evaluating the impact of different operating strategies and environmental conditions on the battery performance and lifespan. This method can be used to estimate the state of health (SOH) and state of charge (SOC) of the battery based on the measured voltage, current, and temperature. Moreover, it can simulate degradation mechanisms and their effects on the battery capacity and power over multiple cycles and calendar years. Additionally, modeling can help optimize the battery design, operation, and management to maximize performance, reliability, and safety. Finally, it can analyze the trade-offs between different battery parameters such as energy, power, cost, and lifetime.

3 What are the main types of models?

When predicting battery degradation over time, there are various types of models that can be used, depending on the level of detail, accuracy, and complexity required. Empirical models are simple and easy to implement, but they lack physical meaning and cannot capture underlying degradation mechanisms or the effect of different variables. Physics-based models are more accurate and realistic, but also more complex and require more parameters and data. Machine learning models are flexible, adaptive, and scalable; however, they require a lot of data, training, and validation, and may not be interpretable or explainable.

4 How can you choose the best model?

Choosing the best model to predict battery degradation over time is dependent on various factors. These include the purpose and objective of the prediction, the availability and quality of the data, and the computational resources and time. For instance, if you want to estimate the remaining useful life (RUL) of a battery for maintenance planning, you may need a more accurate and reliable model than if you just wanted to compare different battery types. On the other hand, if you have limited or noisy data, you may prefer a simpler or more robust model that can avoid overfitting or extrapolation errors. Additionally, if you need a fast and real-time prediction, you may opt for a less detailed and more efficient model that can run on a low-power device. However, if you have more resources available, you may use a more comprehensive and sophisticated model that can account for more factors and interactions.

5 What are some examples of modeling tools?

Software tools and platforms can be utilized to model and predict battery degradation over time through various approaches and methods. Battery Design Studio is one such commercial tool which allows for the design, simulation, and optimization of lithium-ion batteries with the integration of empirical or machine learning models. COMSOL Multiphysics is a general-purpose software tool that enables the creation and solving of multiphysics models, including those related to lithium-ion batteries. Moreover, TensorFlow is an open-source software platform that provides a comprehensive framework for developing and deploying machine learning models, including neural networks, with pre-built models and libraries available.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?