Innovative Zinc-Air Battery Technology Poised to Transform D…

Sthyr Energy, a deep-tech startup developing long-duration energy storage systems, successfully closed its seed-funding round led by Speciale Invest and Antares Ventures. 

The company is revolutionising battery technology to tackle a significant challenge in the global shift to renewable energy: the long-term storage of clean energy. It’s creating a mechanically rechargeable zinc-air battery system designed specifically for applications like seasonal energy storage and replacing fossil fuel peaker plants.

The technology, ideated in 2018, has been under development at IIT Madras since 2019, with government grants and support from industry players such as Hindustan Zinc and Ashok Leyland. The core differentiator of Sthyr Energy’s technology is its ability to use mechanically rechargeable zinc-air batteries, which have a long lifespan and are environmentally friendly.

Their innovation targets the limitations of traditional energy storage by focusing on safety, scalability, and cost-efficiency. Using non-flammable, water-based electrolytes enhances safety, while its design separates energy and power components for flexible, modular applications. Importantly, it is expected to provide a competitive levelized cost of energy for long-duration storage, making it a viable alternative to lithium-ion technologies.

In an exclusive conversation with AIM, Gunjan Kapadia, founder of Sthyr Energy, said that the startup’s technology addresses two critical challenges in battery performance: lifespan and cost. While one electrode is made of zinc, the other is a gas-deficient electrode involving oxygen, which improves battery lifespan. Their performance rivals or exceeds that of international competitors, making this a significant differentiator.

Kapadia added that they use mechanically rechargeable zinc batteries instead of conventional electrical charging, separating charging and discharging into two units. This innovative setup allows energy storage by generating and stacking zinc plates, efficiently increasing charge capacity.

“We have tested these batteries, which are a 3.2 kilowatt-hour module, tabletop size, and now we are scaling it up with the funding. When I say scaling it up, [it means] we will improve the R&D side,” Kapadia explained. 

AI Integration in Battery Technology

Sthyr Energy is working on developing its own AI-driven battery management system (BMS). This system will leverage AI and machine learning models built on real-world data collected from the company’s test sites. AI could also help improve battery lifecycle management, optimise the charging and discharging process, and improve efficiency.

“We collect data every 500 milliseconds. It’s a complete operation of a microgrid, which is 100% off-grid, and we don’t take power from any. That is where the AI/ML comes in.”

The company will utilise AI to evaluate performance information gathered from various testing locations, such as the microgrid at IIT Madras, which uses solar energy, lithium-ion batteries, and zinc-air batteries.

While the team is building the system, implementation is not expected soon. These features, like water-based batteries, are essential, but the lifespan is also a key consideration. The founder added that integrating intelligent algorithms within a battery management system is necessary. “These systems will be designed to be smart and efficient,” he said. 

“As of now, we are building physical models, which once we have put in and gotten a lot of data, we’ll try to have a hybrid model between AI and physical models. This would help us improve the lifetime and operation of the battery, besides optimising those operations,” Kapadia explained. 

AI algorithms also assist in optimising energy storage and distribution and forecasting the necessary size of energy storage systems for different situations (e.g., solar panel dimensions, and energy backup needs).

As reported by The Economic Times, artificial intelligence algorithms can evaluate real-time data regarding energy demand, renewable energy production, and grid status to enhance battery charging and discharging cycles. This guarantees that batteries are utilised efficiently, prolonging their lifespan and maximising their role in maintaining grid stability.

AI can enhance manufacturing by analysing test data and improving the design and efficiency of battery systems. By using AI, Sthyr Energy can ensure quality control and improve the performance of its zinc-air battery systems at scale.

Sthyr Energy intends to use AI platforms to gain insights into the performance of its batteries in various climatic conditions (e.g., in Ladakh, Chennai, and Gujarat). This knowledge will enhance system designs tailored to regional energy storage requirements, ensuring that long-duration storage systems can respond effectively to diverse environmental challenges.

Long-Duration Energy Storage Systems and Their Impact on Data Centres

Long-duration energy storage involves two main aspects: storage duration and discharge duration. Kapadia said that for data centres requiring power backup, discharge duration can range from two to four hours for homes to up to 24 hours for data centres, depending on their needs. 

Reports suggest that AI can significantly enhance the integration of batteries into India’s national grid, improving resilience and efficiency. AI algorithms analyse data to predict energy demand, optimise battery cycles, and strengthen grid stability, leading to better renewables management, reduced energy waste, and a more reliable power supply.

“You have to look into multiple energy storage options where they would fit and have multiple applications, just from a larger perspective. Zinc-air batteries would fit into grid storage when the energy percentage has reached 30-40% in India, which would happen by 2035. But we have a lot of other applications in India today, such as rural microgrids and data centres,” Kapadia added. 

He suggests that data centres could replace diesel generators with zinc plates and battery systems for efficient energy. Integrating renewable energy into the grid will take three to five years, and achieving the goal of becoming a grid-scale, long-duration energy storage operator, he said. 

He added that data centres manage vast amounts of data and require significant energy, with about 50% used for cooling and the rest for operating systems. 

Utilising AI models can help improve efficiency, particularly in optimising reasonably well-managed cooling systems. Kapadia concluded that by addressing these challenges through better engineering and design, data centres can gain valuable insights and enhance their overall performance.

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