As the demand for high-capacity, high-performance batteries continues to grow, the battery manufacturing industry is striving to keep pace. However, there is one challenge that persistently hampers productivity and profitability: high battery scrap rates.
Scrap rates are a key driver of business success across the battery manufacturing lifecycle. Ramping up a new production line, whether at a new factory or existing facility, can require hundreds of millions or even billions in up-front investment. Achieving payback on that investment, and eventual profitability, is fundamentally an exercise in increasing production throughput while reducing battery scrap rates as quickly as possible. The longer this process takes, the more likely you are to see upside-down economics on your new production capacity.
For a typical battery factory producing millions of cells a day in a steady state, reducing scrap rates by a single percentage point relative to total production can mean tens of millions in additional profit annually. On the flip side, every cell that you make but can’t sell represents wasted costs and lost revenue. Moreover, if those substandard cells aren’t discovered until the end of a weeks-long formation and aging process, they can tie up significant working capital.
Sophisticated industry leaders have managed to achieve extremely low scrap rates, below 2% – but this took years and required significant investment in metrology and analytics. For most industry players, a scrap rate of around 5% is normal, even after a couple of years of optimizing. And for lower-quality producers, the scrap rate can climb well above 10%!
Why are battery scrap rates often so high? And how can Enterprise Battery Intelligence help? Let’s dive in.
Battery manufacturing is a complex process, and high battery scrap rates have long been a significant challenge for the industry. High scrap rates can stem from a variety of causes, ranging from inconsistencies in raw materials, variations in manufacturing processes, or even slight fluctuations in the ambient environment. There are literally thousands of materials and processing parameters that must be optimized in order to attain profitable levels of yield and throughput for your production line. Each of these individual factors can lead to defective or substandard batteries that must be discarded, contributing to high scrap rates and reducing overall yield.
When ramping up production, the challenge is to iterate quickly and learn as much as possible with each early production run, fine-tuning your processing parameters and driving down scrap rates to a level that supports profitability. Once running at full scale, the challenge shifts to identifying production problems at the earliest possible stage, performing root-cause analysis, and remediating the issue as fast as you can.
To tackle these issues, however, it’s essential to understand that addressing high battery scrap rates isn’t just about rectifying problems as they arise. It’s about identifying and understanding the underlying causes and then implementing a systematic approach to remediation. This requires thorough data collection and analysis to determine exactly where and why failures are occurring – ahead of time. This continuous improvement process, powered by robust data analysis and actionable insights, enables manufacturers to improve their yields and reduce scrap rates on an ongoing basis.
The potential to transform battery manufacturing processes and alleviate high battery scrap rates lies in leveraging advanced data science and Artificial Intelligence (AI) and using software tools developed specifically for the battery sector. This combination provides a robust framework for comprehending the intricacies of battery manufacturing, driving efficiency, and ensuring quality.
Data science and AI
The bedrock of optimizing any modern manufacturing process is data. By collecting, analyzing, and interpreting vast amounts of information, manufacturers can uncover patterns, correlations, and causal relationships, otherwise invisible through manual inspection. This is where data science and AI come into play. Through machine learning algorithms and predictive models, it is possible to pinpoint potential quality issues early, quickly identify root causes upstream, and refine production processes for efficiency and reliability.
However, these advanced techniques come with their own set of challenges. The critical first steps towards effectively employing data science and AI — at scale and in production — are data gathering, organizing, formatting, and feature extraction (tasks that often constitute 80-90% of a data science project’s workload!). It is also vitally important that your solution can operate continuously in your production environment — otherwise, you’ll be limited to a one-off demonstration. This is where the importance of a scalable underlying data platform is evident. With a platform that organizes data in a common, clean, labeled format, and extracts relevant features, manufacturers can unlock the full potential of data science and AI from the outset.
Battery-specific tools
Another valuable ally in the quest for reducing scrap rates is the use of battery-specific software tools. For instance, integrating processing parameter data with specialized battery performance data analysis can result in a holistic and responsive solution for learning and improvement during battery manufacturing. Teams can also create environments that enable the exploration and experimentation needed to develop models, statistical analyses, and the prototyping of machine learning and AI tools within the same platform.
As the battery manufacturing space evolves and matures, these types of industry-targeted solutions are becoming increasingly available, and have the potential to boost yields and enhance cell quality considerably.
At each of the vast multitude of steps in the manufacturing process, there exists the potential for issues to arise that can lead to the production of faulty, sub-standard batteries, or ‘scrap.’ Here is where Enterprise Battery Intelligence (EBI) emerges as a game-changer.
The backbone of EBI is the unification of upstream and downstream data, providing a holistic view of the manufacturing process. Specifically, an EBI solution can directly analyze formation cycling data in situ, when the battery generates its first electrochemical charge-discharge “heartbeat,” and flag potential problems long before the full (weeks-long) formation and aging process would otherwise run to completion. Automatic correlation to upstream equipment settings and materials parameters enables rapid root-cause determination and remediation to keep a production line humming.
One of the most compelling benefits of EBI is how it facilitates the application of advanced data science, AI, and machine learning to battery applications. With an organized, clean, and feature-rich data platform, manufacturers can leverage EBI for predictive modeling and early fault detection. By predicting potential issues before they manifest as defective cells, manufacturers can address them preemptively, significantly reducing scrap rates.
Historically, the inability to correlate downstream Quality Assurance (QA) data with upstream process materials and recipe data has been a significant contributor to high scrap rates. Isolated data systems can result in long timelines for issue identification and correction, causing a cascade of defective cells through the production line. However, EBI enables manufacturers to break down these data silos, identify issues more accurately, and intervene proactively to prevent scrap generation.
Moreover, EBI provides flexibility in the face of evolving manufacturing challenges. As new materials, chemistries, and techniques enter the scene, manufacturers need to adapt quickly to maintain quality and yield. An EBI platform can absorb these changes and integrate them into the workflow seamlessly, preventing potential production disruptions that could lead to increased scrap.
In essence, EBI provides manufacturers with the tools they need to stay ahead of issues that cause scrap. By harnessing data integration, scalability, and advanced analytics, manufacturers can significantly reduce battery scrap rates, save costs, and contribute to a more efficient and sustainable battery industry.
Here’s a real-world example.
One of our customers was dealing with persistently low yield rates which translated into high scrap rates, amounting to millions of dollars in costs and lost revenues. The problem arose from cells failing QA due to high resistance observed during the formation step, a dilemma they spent months trying to resolve.
Through the analysis provided by our platform, we were able to identify a correlation between high resistance and specific fixture positions within the formation chamber. The issue stemmed from temperature variations across the formation racks, highlighting the significance of environmental controls like HVAC in battery manufacturing. This factor is often overlooked but plays a vital role in maintaining a consistent and quality-controlled environment.
We ultimately determined that the problem was more pronounced towards the edges of the formation chambers due to temperature disparities. While upgrading the HVAC system would be a longer-term process, we took immediate action to adjust the resistance limits for these edge positions. This enabled the customer to increase yield by several percentage points and save millions of dollars annually, significantly reducing scrap rates.
We love a success story like this – but this is just one example of how EBI can help drive down scrap rates. It underscores the unique insight and value that EBI can bring to battery manufacturing, addressing issues that might otherwise remain unresolved.
The difference between a scrap rate of 2% and a scrap rate of 10% is huge. It equates to a variance of tens of millions of dollars in annual profit.
As such, tackling high battery scrap rates is a crucial imperative for battery manufacturers, and Enterprise Battery Intelligence provides the tools and strategies to make significant headway. By integrating data, leveraging advanced analytics, and providing scalability, EBI enables manufacturers to not only reduce scrap rates but also improve overall productivity and profitability.
To learn more about how Voltaiq can help you take control of your manufacturing process and significantly reduce scrap, don’t hesitate to get in touch with us today. Let’s work together to shape the future of the battery industry.