Battery management systems key to deploying batteries in data centers

<p class="p1"><span class="s1">A new study highlights the critical role of advanced AI-integrated battery management system technologies in monitoring, optimizing, and predicting battery performance for reliable and sustainable data center operations.</span></p><p>Powering data centers with renewable energy and storage presents a range of <a href="https://www.pv-magazine.com/2026/02/02/solar-plus-storage-for-data-centers-not-a-simple-switch/" rel="noopener" target="_blank">technical and economic challenges</a> that independent power producers, EPC contractors, and investors have only recently begun to address. In this context, large-scale batteries are expected to play a key role, supplying the active power needed during demand spikes and coordinating energy flows between generation, storage, and data center loads.</p>
<p>Yet it remains unclear whether current battery technologies can perform these tasks efficiently, raising doubts about the short-term ability of the solar-plus-storage industry to meet the growing electricity demand from data centers.</p>
<p>To explore this, an international research team investigated how batteries could effectively support data center power and found that the development of advanced battery management systems will be crucial. &#8220;Energy storage, battery technologies, artificial intelligence (AI) integration, and thermal regulations can be considered as the top four sectors requiring more investigation and research to have reliable data centers, with the lowest costs and highest revenues,&#8221; the group stated.</p>
<p><span><div class="callout alignnone "><div class="callout-body"> </span>Want to learn more about <strong>matching renewables with data center demand</strong>?</p>
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<p>The event will spotlight how solar and energy storage solutions are driving sustainable and reliable infrastructure, with a particular focus on powering the country’s rapidly growing data center sector.</div></div>
<p>In the paper &#8220;<span class="title-text"><a href="https://www.sciencedirect.com/science/article/pii/S2352152X26000502#f0020" rel="noopener" target="_blank">A research-industry perspective of battery systems technology for next-generation data centers</a>,&#8221; published in the <em>Journal of Energy Storage</em>, the </span>researchers noted that most existing reviews focus on demand response, load modeling, energy management systems, airflow control, resource allocation, and energy-efficient architecture, but rarely address batteries or their integration with standards, industry white papers, financed projects, and practical applications.</p>
<p>The study fills this gap by providing a comprehensive overview of battery technologies, their components, and control methodologies, while linking them to broader energy efficiency strategies in data centers. This holistic approach covers both batteries for uninterrupted power supply (UPS) inside data centers and battery energy storage systems (BESSs) deployed outside the facilities.</p>
<p>The researchers reviewed all battery technology systems for data centers, outlining power architectures and recommended topologies, examining batteries and battery management systems (BMSs) integrated with AI control strategies, summarizing net-zero initiatives and industry standards, and analyzing recent industry white papers, proofs-of-concept, and financed projects. By detailing UPS topologies and the types of BESS in use, they highlighted that advanced BMS technologies will be essential for reliable, efficient, and sustainable data center operations.</p>
<p>“BMS technology is critical for ensuring the safety, remaining useful life (RUL), and efficiency of lithium-ion batteries by monitoring parameters such as temperature, voltage, current, and battery states,” the team explained. “It prevents overcharging, overheating, and deep discharging, and enables strategies like cell balancing, thermal management, and predictive maintenance, which optimize battery performance.”</p>
<p>The academics explained that, in modern data centers, BMSs continuously monitor key states including state of charge (SoC), state of health (SoH), state of energy (SoE), and state of temperature (SoT). Cell monitoring collects voltage, temperature, and current data from individual cells, enabling real-time adjustments to optimize performance and extend battery life.</p>
<p>The hierarchical BMS architecture comprises three layers &#8211; data, computation, and application &#8211; allowing the integration of measurement, modeling, and real-time control. The computation layer estimates internal battery states, while the application layer manages security, aging, thermal control, balancing, and fault diagnosis. SoC and SoH estimation uses conventional, AI, observer-based, adaptive filter, and hybrid methods, often integrated into multi-state estimation frameworks that predict RUL and performance across real-time, intermediate, and long-term scales.</p>
<p>Moreover, the team emphasized that advanced AI-based models such as neural networks, neuro-fuzzy systems, reinforcement learning, BiLSTM, LSTM, random forests, and generative adversarial networks (GANs) further enhance state estimation, fault detection, and predictive control. Activation functions support these AI models by mapping inputs effectively, enabling accurate SoC and SoH predictions and optimizing BMS performance for high-performance data centers.</p>
<p>The researchers concluded that AI-driven BMSs will be able to provide real-time monitoring, predictive control, and optimized energy management, laying the foundation for intelligent, reliable, and sustainable battery operation in data centers.</p>
<p>“The integration of AI into BMS improves state estimations, reduces failures, and extends RUL,” they stated. “However, challenges remain in scaling AI-driven solutions, achieving net-zero data centers, and meeting diverse industry-specific requirements.”</p>
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