| Energy storage
Full dashboard for a typical battery energy storage system. (Image: Socomec)
services and revenue streams from energy trading to frequency regulation and grid flexibility services. This is due to being unable to predict how each of these use-cases might affect battery performance and lifetimes. In this way, inaccurate or incomplete data is reducing the potential profitability of batteries, creating risks and uncertainties that could deter investors.
AI-powered batteries
The predictive power of AI has helped other industries, from telecoms to retail, create smarter product development, pricing, and maintenance. Innovations in AI are similarly being used to turbocharge battery safety, performance, and lifespans.
Data analytics innovations can now quality- check battery data automatically across its lifetime for everything from missing files to incorrect timestamps, transforming raw data into refined material for AI algorithms. Battery data can be processed and visualised with digital dashboards displaying everything from battery operating status to monitoring parameters. The data can be used to define the optimal patterns of usage balancing organisational goals such as discharging power during peak periods with the need to preserve battery health and extend its lifespan. AI algorithms can then harness this quality- assured data to help predict and prevent degradation and safety hazards, enabling predictive maintenance and smarter battery management.
For example, AI insights are helping optimise battery use to significantly extend battery lifetimes. AI can now predict at installation stage how many cycles of charging and discharging batteries can tolerate without experiencing degradation. These algorithms can generate flexible lifetime forecasts
that change based on the latest state of health updates or patterns of use, ensuring operators can plan for upgrades or replacements. Similarly, machine learning is yielding new insights into the causes of degradation and deterioration in battery safety, such as cell imbalances or lithium plating causing thermal runaway. Whereas conventional systems could only detect thermal runaway minutes to hours before the incident, these AI systems can predict potential thermal runaway months in advance. As a result, AI is opening a new window into battery performance, helping estimate the true state of health and state of charge with 98% accuracy. This could help optimise future arbitrage strategies, ancillary services, or off-take agreements to maximise profits while maintaining battery capacity, and life expectancy. AI data could also help operators demonstrate that batteries can provide reliable future capacity, attracting fairer prices for battery storage in the capacity markets and incentivising more investments in battery storage. For example, it’s more difficult to estimate the extent to which batteries can provide reliable power than other flexible generating capacity such as gas fuelled power plants, which makes it harder to give a fair capacity value to batteries in the capacity markets. AI-driven data on long-term battery capacity could enable battery operators to prove their long-term capacity to regulators. This could even enable battery lifespans to be predicted at installation stage and help optimise charging methods to increase performance and extend lifecycles. Ultimately, the data could be used to refine battery designs, chemistries and materials to boost performance or provide specific services such as energy trading. The data could even help find substitute materials
that reduce supply chain costs. By improving end-to-end processes from design to operations, AI algorithms can drive a 30% increase in overall battery system lifetime value.
With the clean energy transition increasingly hinging on the health of batteries, we will need to understand the complex factors governing battery health, performance, and lifetimes with greater accuracy than ever before. AI innovations have the potential to bring unprecedented transparency to battery lifetimes and performance, so that everything from design to operations is powered by smart data. This could enable operators to predict the lifespan and optimal usage for each type of battery at the design stage to create and operate more long- lasting, safe, and high-performance batteries. For example, battery AI could predictively optimise arbitrage strategies requiring rapid discharging during peak demand, to improve battery capacity and reduce maintenance costs. Utilities or EV chargepoint operators could predict the impact of surges in demand on battery lifespans and create smarter demand management and battery storage. Ultimately, this could unblock the energy transition by enabling batteries to provide safe, sustainable, and dependable backup power across society.
Battery cell temperature measurements. (Image: Socomec)
www.modernpowersystems.com | November/December 2024 | 31
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