PRIDEBAT Project
Intelligent prediction of battery degradation in electric vehicles
PRIDEBAT aims to develop an advanced battery degradation prediction tool for current LiFePo-based storage technologies to provide detailed information on their cyclability, efficiency and lifetime.
- Start date: 01/01/2025.
- End date: 31/12/2027.
Battery degradation is one of the challenges that the industry needs to address in the transition to electric vehicles. It is critical to ensure that the battery will provide a defined lifetime in years and/or cycles for the specific application.
However, manufacturers provide limited information on the cyclability and degradability of batteries under actual usage conditions. The available data is based on ideal conditions, such as constant current and consumption curves, suitable for stationary applications or applications with repetitive and stable consumption patterns.
PRIDEBAT aims to contribute to the challenge of battery sizing in terms of autonomy and traction performance, as well as critical aspects such as warranty and after-sales service.
To this end, it will develop an advanced battery degradation prediction tool for current LiFePo-based storage technologies, with the aim of providing detailed information on their cyclability, efficiency and lifetime. The tool will provide accurate and reliable predictions under real operating conditions.
Objectives
- Collect comprehensive data on battery performance and degradation under real-life conditions based on current, temperature, depth of discharge (DOD) and average cycle state of charge. This will include obtaining experimental data in different operating scenarios, considering the above variables, for the elaboration of performance curves as a function of cycling.
- Create digital twins of the longitudinal vehicle and powertrain dynamics, for different vehicle configurations, scenarios and operating conditions. These digital twins will be used to replicate real operating conditions and study their impact on battery degradation.
- Create digital twins of electric vehicle batteries to represent with maximum reliability and level of detail the global behaviour of a battery.
- Design and validate battery degradation prediction models. These models will integrate both standard characterisation curves of energy storage systems, provided by manufacturers, and degradation curves derived from experimental data. By applying advanced artificial intelligence and machine learning techniques, these models will be able to predict with high accuracy the lifetime, cyclability and efficiency of batteries under real-life conditions.
- Develop Vehicle to Grid (V2G) strategies optimising the ratio between economic benefits and reduced battery life.
NAITEC's role
It is responsible for the tasks associated with battery pack analysis and data acquisition, the development of digital twins, vehicle dynamics analysis and simulation due to its capabilities in urban vehicle battery characterisation. It will also develop a battery life prediction model specific to EVs.
Partners
Funding
