BIG LEAP Project: Advancing Battery Management Systems and Second-Life Battery Integration

22.04.2024 The new Horizon Europe project BIG LEAP aims to achieve the next generation of Battery Management Systems (BMS) to improve the interoperability between battery chemistries and architectures and to enhance the operation reliability of second life batteries.

Batteries are identified as a key technology in guiding the clean-energy transition, especially in automotive and energy storage. However, there are still some challenges, like the lack of interoperability between battery chemistries and architectures, or the non-standardized process of battery refurbishment. The project BIG LEAP wants to address these challenges by developing solutions for Second-Life Battery Energy Storage Systems. Under the leadership of Brussels Research and Innovation Center for Green Technologies (BRING), BIG LEAP brings together a consortium of 16 international partners.

Positive impact throughout the battery value chain

The technological breakthroughs planned for the Battery Management Systems include a three-layer architecture to ensure interoperability, safety, and reliability. This will be complemented by an adaptable Energy Storage System design, facilitating BMS integration and expanding SLB's potential applications. Moreover, the project aims to optimize the battery refurbishment process by making it cost-effective, faster, and standardized.

The development methodology involves collecting data from Electric Vehicles (EV), maritime E-Vessels, and Energy Storage System batteries. The testing will take place at three demonstration locations. The aim is to validate the effectiveness and compatibility of the innovative BMS and ESS, facilitating their upscale in the market. This solution is expected to have a positive impact on the European economy throughout the battery value chain, emphasizing sustainable benefits.

BFH's tasks involve focusing on novel battery impedance analysis to estimate Power/Capacity fades for the development of State of Health (SOH) and State of Power (SOP) algorithm. BFH will also develop an algorithm based on ML applied to a reduced data format (known as the BFH-statistical dataframe) to estimate SOH.

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