"Hispacold has announced the start of serial production of HVAC systems using natural refrigerant R290 for public transport vehicles. The development, according to the supplier, marks a first in the sector and aligns with the European F-Gas Regulation (EU 517/2014), which promotes the adoption of sustainable, low-impact refrigerants. The first systems are planned to be deployed in Germany, with production beginning in Q4 2025."
"Key features of the R290 heat pump systems include the use of a natural refrigerant with a Global Warming Potential of just 3, compliance with A3 refrigerant safety standards, a modular design with the same footprint as conventional systems, and advanced control software for energy efficiency and passenger comfort. The solution also aims to lower Total Cost of Ownership through simplified maintenance and standardized components."
"Alongside the new product, Hispacold has introduced Signum, an AI-powered predictive maintenance platform tailored to HVAC systems in public transport. The tool applies machine learning to each vehicle's operating profile, enabling early fault detection, accurate diagnostics, and actionable maintenance insights. This approach is designed to reduce unexpected failures, improve efficiency, extend component lifespan, and deliver operational savings."
Hispacold began serial production of HVAC systems using natural refrigerant R290 for public transport, with initial deployment planned in Germany from Q4 2025. The range, for electric buses and rail vehicles, resulted from a €6 million R&D program launched in Sevilla in 2023 focused on sustainability, energy efficiency and digital transformation. The R290 heat pump offers a Global Warming Potential of 3, meets A3 safety standards, uses a modular design with the same footprint as conventional systems, and includes advanced control software for efficiency and passenger comfort. The solution targets lower Total Cost of Ownership via simplified maintenance and standardized components. Hispacold also launched Signum, an AI predictive maintenance platform that uses machine learning on vehicle operating profiles to enable early fault detection, accurate diagnostics, extended component life and operational savings.
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