AI and Energy Storage: Opportunities and Challenges in a New Energy Landscape
Date: March 15, 2025
Summary: Since the introduction of DeepSeek, over half of the central enterprises in the energy and power sector, as well as energy storage companies, have integrated this model. The combination of AI and energy storage has shown significant advantages in various fields, including intelligent operations and maintenance, but also highlights concerns such as technological bottlenecks and cost pressures. The industry must address these challenges to reshape the energy landscape.
Since the launch of DeepSeek, more than half of the central enterprises in the energy and power sector, including five major energy groups and two major power grids, have adopted this model. Companies like Day Energy and others in the energy storage sector have also joined in. The integration of AI and energy storage is accelerating the global energy transition.
As reported, major players like CATL, BYD, and LG Energy are beginning to leverage AI for battery production. As the reality of AI-assisted battery manufacturing unfolds, the potential of AI and energy storage is becoming increasingly apparent. In 2025, the National Energy Administration issued guidelines emphasizing that by 2030, a preliminary digital and intelligent innovation application system for energy systems should be established, fully activating the potential of data elements.
On March 12, Zhuoyang Digital Energy launched a new AI assistant called “Zhuo Xiao Yang.” This intelligent agent is specifically designed for the renewable energy industry, aiming to enhance industry efficiency and optimize decision-making support through large model empowerment. Key applications of “Zhuo Xiao Yang” include industry knowledge Q&A, scenario solution outputs, asset revenue combination analysis, and energy plant investment analysis.
Previously, based on the privatized deployment of DeepSeek under the “Ronghe·Baize” system, over 20 million battery cells were monitored daily, processing information in the terabyte range, resulting in over a 50% efficiency improvement; with millisecond-level fault detection and scheduling response, operational and maintenance costs were further reduced by over 30%. This deep integration of AI and energy storage has redefined operational paradigms amid the current fluctuations in the energy storage industry.
In fact, since the introduction of DeepSeek, more than half of the central enterprises in the energy and power sector, including major energy groups and power grids, have adopted this model. Companies such as CATL, Zhuoyang Digital Energy, Ronghe Yuanshu, Sunshine Power, BYD, and Haibosichuang are harnessing AI and energy storage to showcase their capabilities in battery innovation, operational optimization, and intelligent maintenance.
What Opportunities Will AI + Energy Storage Bring?
The opportunities and challenges associated with AI and energy storage are two sides of the same coin. As noted by CATL’s CEO Zeng Yuqun, “Without disruptive technological breakthroughs, there will be no real energy revolution.” With DeepSeek reducing AI training costs by 90% and Huawei achieving full lifecycle digitalization of energy storage systems, this transformation is becoming irreversible.
1. Intelligent Operations and Maintenance
Operational and maintenance costs for energy storage stations account for 30% of total lifecycle costs, and AI is poised to rewrite this economic model. Sunshine Power has launched the iSolarBPS system, which integrates power electronics, electrochemistry, and AI algorithms (GeneSafe algorithm cluster) to monitor battery cell health in real-time. It can provide up to a seven-day advance warning for consistency anomalies, identify internal short-circuit risks 100 hours in advance, and predict thermal runaway an hour ahead, forming a three-tiered proactive defense mechanism. This system covers over 50 indicators through five-dimensional diagnostics and generates a diagnostic report for a 100 MW station in just one minute, pinpointing failures down to the cell level and enhancing operational efficiency by 30%.
The Risen Cloud system developed by Dongfang Risen analyzes real-time data from over 100,000 battery cells, achieving a fault warning accuracy rate of 98%. This capability is driven by deep learning algorithms that dynamically model over 200 parameters such as battery internal resistance and temperature differentials, enabling cell cycles to exceed 10,000 times.
2. Power Trading
As photovoltaic output curves intersect with fluctuations in the electricity spot market, AI becomes a crucial factor in maximizing revenue. One leading energy storage company disclosed that its reinforcement learning-based trading strategy system improved energy arbitrage opportunities in the Shandong electricity market from 0.25 RMB/kWh to 0.38 RMB/kWh. This translates to a 4.2 percentage point increase in the IRR for projects over a 20-year operational period, fundamentally altering investment logic for energy storage projects.
3. Adaptation to Extreme Environments
In response to extreme conditions such as high temperatures and humidity, AI can create multi-physical field coupling models that simulate battery cell failure under high stress. For example, Tsinghua University has developed a thermal runaway model offering high-precision predictions for 15 different battery systems within a temperature range exceeding 500°C. In the Saudi desert, where temperatures reach 50°C, BYD’s MC Cube-T system achieved a zero-failure record with its CTS integration technology. Its success lies in an AI-driven dynamic thermal management system that utilizes 384 temperature sensors to adjust liquid cooling flow rates, maintaining a temperature differential within ±1.5°C.
4. Data Center Energy Storage
The world is rapidly transitioning into an economy centered around artificial intelligence, blockchain, and the Internet of Things, which is driving a massive demand for computing power. Over the past few years, this demand has grown by more than 400% annually, significantly exceeding the growth rate predicted by Moore’s Law. Traditionally, data centers relied on lithium batteries as part of their uninterruptible power supply (UPS) systems. However, as data centers shift towards renewable energy sources, the role of lithium batteries is evolving from backup to a primary energy source. GGII forecasts that global shipments of lithium batteries for data center energy storage will exceed 69 GWh by 2027, with this figure expected to grow to 300 GWh by 2030, representing a compound annual growth rate of over 80%.
5. AI Disrupting Battery Innovation
Traditionally, the development of lithium battery materials required iterative “trial and error” methods, but AI is compressing this process significantly. The team from Tsinghua University has utilized explainable machine learning techniques to identify the key factors influencing the reduction stability of electrolytes, further creating a dual-driven framework for predicting molecular properties of electrolytes. They have identified 29 potential molecules that could be suitable for high-performance electrolytes in wide temperature ranges and high safety scenarios. CATL is utilizing AI to discover the next generation of revolutionary materials beyond lithium-ion systems. As early as September 2024, CATL’s chairman mentioned that the company has over 20,000 engineers focused on research into fundamental material structures, simulation analysis, and material interactions.
Concerns Amidst the AI + Energy Storage Boom
According to incomplete statistics, global financing in the energy storage sector exceeded $50 billion in 2023, with over 30% of this funding directed towards AI and energy storage-related companies. Technology giants worldwide, including Tesla’s Autobidder platform and CATL’s AI energy storage management system, have further heightened market enthusiasm. However, underlying concerns about AI and energy storage remain evident.
1. Technological Bottlenecks
The deep integration of AI and energy storage still requires breakthroughs. The accuracy of AI models heavily relies on data quality, and the data collection and standardization processes in energy storage systems remain inadequate. Moreover, existing algorithms have limited adaptability in complex scenarios. The application of AI in energy storage is still in its infancy, with many technologies yet to undergo large-scale validation, leaving their effectiveness uncertain.
2. Cost Pressures
The high costs associated with AI integration and the lengthy return on investment pose significant challenges for small and medium-sized energy storage enterprises. In the short term, the cost advantages of AI and energy storage may not be evident, which could impact the profitability of these companies.
3. Cybersecurity Challenges
The cybersecurity of AI systems cannot be overlooked. A cyber attack could lead to the loss of control over energy storage systems, resulting in safety incidents. Additionally, protecting the privacy of energy storage data is increasingly important, necessitating a balance between data sharing and privacy protection that the industry must address.
4. Lack of Policies and Standards
Currently, the AI and energy storage sector lacks unified technical standards and industry regulations, which may lead to market confusion and technical barriers. While policy support is substantial, the implementation details and regulatory mechanisms still need to be refined. An industry insider raised an interesting question: how to define legal responsibilities when AI autonomously decides the charging and discharging strategies of energy storage systems and makes algorithmic errors? The European Union’s recent “Ethical Guidelines for Energy AI” emphasizes the importance of retaining human intervention interfaces for key decisions, which may provide insights for related legislation.
Reflecting on the current state of AI’s transformation of the energy storage industry, we find ourselves in the “tool empowerment” phase. However, looking toward 2035, revolutionary trends may be brewing. As Xu Jian, president of Kehua Shuneng, stated, “We are not just transforming energy storage; we are reshaping the way humans interact with energy.”
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