Battery Performance Modelling
Working alongside a leading organisation specialising in battery performance modelling, our goal was to improve the accuracy and efficiency of predicting battery performance for industries reliant on energy storage, such as electric vehicles (EVs), portable electronics, and grid energy systems.
The Challenge
Accurately predicting battery performance and longevity under varying conditions such as temperature changes, charge cycles, and usage patterns is a critical challenge for industries reliant on energy storage. Traditional testing methods are resource-intensive and costly, requiring extensive physical trials. The industry needed a more efficient solution to streamline these processes while ensuring reliability and precision.
The solution
We developed a comprehensive solution combining advanced simulations and machine learning. Our models simulate battery behaviour under various conditions, including temperature and charge/discharge cycles, providing insights into degradation and longevity. By applying machine learning and neural networks, we identified key performance factors and enhanced prediction accuracy, allowing for more precise forecasts of battery health and lifespan without the need for exhaustive testing.
The features
Leveraging PyBaMM, a robust Python tool, we developed sophisticated models that simulated a wide range of scenarios, reducing the need for extensive testing and providing valuable insights without lengthy experimentation.
Leveraging PyBaMM, a robust Python tool, we developed sophisticated models that simulated a wide range of scenarios, reducing the need for extensive testing and providing valuable insights without lengthy experimentation.
Using innovative feature selection techniques, such as genetic algorithms, we identified the most relevant factors affecting battery performance, ensuring our models were built on the most relevant data available whilst eliminating unnecessary variables.
We applied a range of machine learning techniques, with a strong emphasis on deep learning and neural networks, to analyse our curated dataset. This approach enabled us to uncover complex patterns and relationships within the data, significantly enhancing the accuracy and efficiency of our predictions.
The result
Our approach provided the client with a powerful, scalable tool for modelling battery performance, significantly reducing testing costs and improving battery designs. By delivering highly accurate predictions of battery behaviour, we helped the client to enhance product reliability and accelerate their product development process, enabling them to bring products to market faster and boosting overall efficiency.
More Case Studies
Solutions
01
Advanced Simulations
We design cutting-edge simulations that accurately capture the behaviours of battery materials and physics. Our models help in understanding and predicting battery performance under various conditions.
02
Detailed Cycle Analysis
Our comprehensive simulations study battery cycles in-depth, providing insights into performance and longevity under different scenarios.
03
Smart Feature Selection
We implement advanced algorithms to pinpoint the key factors that affect battery performance, ensuring precise and reliable predictions.
04
Machine Learning Integration
By applying sophisticated neural network models to our simulated data, we enhance the accuracy and efficiency of battery performance predictions, including metrics like longevity and health.
