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The Battery Energy Storage Systems (BESS) industry is experiencing unprecedented growth. Driven by the demand for cleaner energy sources, BESS installations have rapidly increased across the globe. But with this boom comes challenges, as new technologies and teams with limited experience in battery storage systems push the boundaries of energy storage deployment. Predictive analytics is emerging as a critical tool to address these challenges, ensuring safety, reliability, and efficiency in BESS operations.
The rise of BESS has been nothing short of remarkable. Today, over half of global battery production comes from factories that were built in the past two years. Similarly, a significant portion of construction crews and other specialists in the sector are also new, carrying less than two years of experience. Although batteries are a largely safe and reliable technology, the rare incidents that do occur often draw significant attention and scrutiny, highlighting the need for proactive risk management. While the industry is maturing and failure rates are on the decline, batteries remain a largely safe and reliable technology; however, the rare incidents that do occur often draw significant attention, underscoring the importance of proactive risk management.
When batteries fail, the stakes are high. Beyond the immediate financial losses, such incidents can damage public confidence in energy storage solutions, spark community resistance to new projects, and lead to hesitancy from banks and insurance providers. Addressing the causes of these failures is therefore essential for the continued growth and success of the industry.
Battery-related failures can emerge from several root causes, some of which have already had significant impacts on the energy storage and tech industries. Common failures include:
Battery Management System (BMS) Issues
A prominent example is the 2006 recall of over 4 million Dell laptop batteries due to BMS malfunctions.
Water Damage
The 2021 Victorian Big Battery incident in Australia, caused by water ingress in Tesla batteries, highlights how environmental factors can trigger failures.
Cell Production Defects
Automakers were forced to recall electric vehicles in 2020 because of cell manufacturing flaws.
Overheating
High temperatures can lead to thermal runaway.
Predictive analytics is transforming how operators handle battery failures, enabling a proactive stance for identifying and mitigating risks before they escalate. By analyzing operational data—such as voltage, temperature, and current—through advanced algorithms, machine learning, and cloud computing, predictive analytics offers early warnings about potential issues. These alerts can often arrive days or even weeks in advance, giving operators critical time to prevent accidents, reduce downtime, and uphold public confidence. However, it is important to note that environmental factors, such as water ingress, remain outside the scope of predictive analytics, as they cannot be predicted and avoided through data analysis.
Safety monitoring is at the core of predictive analytics, but understanding battery behaviors and their implications can be complex. Data patterns can reveal potential safety issues, such as weak cells, connection problems, dendrite growth, electrode potential anomalies, or rapid capacity loss. However, not all anomalies are severe, and distinguishing between critical and non-critical events is key.
The introduction of safety score systems represents a significant innovation in the battery storage industry. By assessing the impact of various performance and safety indicators, these systems provide a clear, quantifiable measure of a BESS's overall safety status. This actionable insight enables operators to make data-driven decisions, prioritizing maintenance and operational efforts effectively. Systems with increased safety scores can be identified and addressed well in advance of potential failures, allowing for timely, focused interventions to enhance reliability and safety.
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Predictive analytics has established itself as a reliable and widely utilized tool, with numerous real-world successes demonstrating its ability to detect and address critical issues before they escalate. Here are some noteworthy examples:
Case 1: A BESS utilizing lithium iron phosphate (LFP) batteries faced repeated issues where the BMS ignored preset voltage limits. Without intervention, this could have caused irreversible damage to the battery cells. Predictive analytics flagged the issue in time, enabling corrective measures.
Case 2: Another system flagged voltage anomalies. Upon analysis, predictive tools differentiated the root cause—a faulty sensor in this case—from actual battery imbalances, preventing unnecessary replacements and focusing efforts on the real problem.
Case 3: Using Incremental Capacity Analysis (ICA), a predictive approach identified irregular patterns in an NMC-based battery rack. The abnormal behavior pointed to lithium plating and internal short circuits, allowing swift maintenance before these issues led to operational failure.
The power of predictive analytics depends heavily on the quality and availability of data. To enable effective monitoring, certain types of data are essential:
Having access to real-time, high-resolution data ensures predictive models can accurately track and diagnose system performance, making it easier to identify early warning signs.
The rapid adoption of BESS technology shows no signs of slowing, which means understanding and mitigating risks has never been more important. With predictive analytics, operators are equipped with tools to stay ahead of potential failures, safeguarding both investments and public trust. From detecting minor sensor malfunctions to identifying critical cell defects, predictive analytics represents a significant leap forward for the industry.
For stakeholders in BESS—from energy operators to insurers and banks—investing in predictive monitoring isn’t just about reducing risks. It’s about ensuring the industry remains reliable, sustainable, and prepared for the challenges of tomorrow.
ACCURE helps companies reduce risk, improve performance, and maximize the business value of battery energy storage. Our predictive analytics solution simplifies the complexity of battery data to make batteries safer, more reliable, and more sustainable. By combining cutting-edge artificial intelligence with deep expert knowledge of batteries, we bring a new level of clarity to energy storage. Today, we support customers worldwide, helping optimize the performance and safety of their battery systems. Visit us at accure.net.
Tania joined ACCURE in 2021 as a Senior Marketing Manager to build media relationships, coordinate marketing initiatives from content creation to trade shows, and shape the company’s brand. She closely monitors developments in the renewable energy sector and supports customers by gathering industry insights and lessons learned. When not immersed in marketing, Tania cheers for her favorite soccer team and upcycles furniture.