AMG LIVA Power Management Systems GmbH Acquires the Redox Flow Battery Activities from J.M. VOITH SE & CO. KG.

 

Amsterdam, 27 December 2023 --- AMG Critical Materials N.V. (“AMG”, EURONEXT AMSTERDAM: “AMG”) is pleased to announce that LIVA Power Management Systems GmbH (“LIVA”), a wholly owned subsidiary of AMG Critical Materials N.V. has agreed to acquire the Vanadium Redox Flow Battery (“VRFB”) activities from J.M. VOITH SE & CO. KG (“VOITH”).

VOITH has developed a unique technology for controlling and balancing large-scale high-voltage VRFB energy storage systems. The technology compliments LIVA´s large scale VRFB systems. LIVA will integrate this technology into its large-scale energy storage systems.

LIVA and VOITH will cooperate in their activities in the field of industrial and grid-scale energy storage systems in the future.

Volker Kölln, CEO of AMG LIVA, commented, “This acquisition is an important building block for LIVA to further expand the attractiveness of the VRFB technology. By integrating the VOITH IP, we can improve the energy efficiency of electricity storage while reducing system costs. Highly efficient and reliable mass energy storage systems are the key to the energy transition in industry and for grid management applications.”

About LIVA

LIVA Power Management Systems GmbH (‘LIVA’) as a subsidiary of AMG Critical Materials NV is active in the B2B market for industrial and large-scale energy storage systems (4.0-100+MWh) for demand & supply side power management to reduce energy costs and CO2 emissions. LIVA builds custom-tailored hybrid energy storage systems (Hybrid-ESS) for realizing the industrial energy transition.

LIVA combines Li-Ion- and Vanadium Redox Flow battery technologies with sophisticated operating software to create a virtual Hybrid-ESS with enhanced properties for heavy duty industrial applications. The LIVA ecosystem simulates and operates stationary large scale as well as further energy assets like Power-to-Gas or Power-to-heat facilities with artificial intelligent (AI) routines and self-learning algorithms. Besides maximizing efficiency, safety and lifetime of the batteries, the system enables the economic integration of sector coupling strategies with renewable energies and green hydrogen.