Batteries power many of the modern era’s greatest innovations. However, batteries are also complex pieces of technology that carry risks. It’s essential for businesses to comprehend these risks to ensure the safety and integrity of their products. This eBook provides a comprehensive overview of seven distinctive techniques, and lays out how Enterprise Battery Intelligence (EBI) can help.
In this eBook, you can expect to:
Learn how to leverage seven strategic techniques that automate and enhance the extraction of insights from your battery data, leading to more efficient workflows, reduced time to market, and minimized risk of defects.
Plotting statistics provides an immediate and objective baseline. See Figure 4 here, which displays the discharge capacity of 20 cells, a standard method for evaluating a battery’s cycle life. This plot, along with mean performance and standard deviations, can be enhanced with upper and lower spec limits to create an SPC chart and calculate CPK for process capability.
This statistical baseline is crucial for various comparisons, such as cell-to-cell, to determine quality control parameters like rejection rates, and batch-to-batch, to ascertain adherence to original specs and detect any alterations in the supplier’s manufacturing process. These comparisons are vital for proactive manufacturing, allowing for the detection and response to trends before they become uncontrollable.
Differential capacity analysis is a pivotal battery fingerprinting technique that condenses extensive battery data into a manageable and visual format, allowing for the swift identification of defective batteries by comparing them to a predefined fingerprint. This method is not just limited to detecting defective cells but also uncovers alterations in vendor supplies, providing tangible proof of any changes in production.
This technique, illustrated in Figures 6 and 7, reveals anomalies and low-resistance cells that warrant immediate investigation. Unlike traditional methods requiring extensive cycles and months of testing, this analysis is swift and proactive, allowing for the immediate addressing of issues and optimal resource utilization.
This is pivotal for discerning the impacts of alterations in recipes, processes, or operational variables, effectively answering “how does this affect that” questions – crucial in an industry constantly experiencing subtle changes, such as lithium shortages necessitating substitutions and manufacturing adjustments.
This technique, exemplified in Figure 8, offers insights into the performance of different battery chemistries, aiding in straightforward decision-making, provided the analysis is supported by well-labeled and organized data. It’s a versatile tool for addressing specific design and manufacturing problems, preventing futile pursuits of linkages between unrelated variables and battery performance.
To mitigate risk, automating defect detection is crucial– especially in the battery industry, where a 1% defect rate can have catastrophic consequences, such as potential explosions leading to large fires in electric vehicle battery packs.
Figure 11 exemplifies the application of machine learning in identifying normal and anomalous battery performance data points over several hundred cycles, allowing for the comparison and analysis of vast amounts of data, beyond human capability, to detect subtle anomalies that might be overlooked in manual inspections. This technique is particularly potent in real-world applications, where it can predict battery hazards and fires by analyzing electrochemical data and computing various parameters to determine risk levels, thereby alerting end users to imminent dangers hours or even days in advance.
Implementing a sophisticated, battery-specific traceability system is crucial, especially with impending “Battery Passport” regulations. This system, surpassing traditional spreadsheet methods, is essential during audits for identifying affected products and mitigating recall repercussions. An optimal, automated traceability system should include detailed elements like serial numbers and dates, ensuring comprehensive traceability of every battery component. Proactive and detailed record-keeping is vital for managing extensive metadata and is foundational for conducting advanced analyses like Metadata-to-Performance, thereby maintaining product quality and safety in an industry where the urgency to market is prevalent.
Automating frequent analyses is a pivotal technique for engineering teams, especially when battery-specific tasks are not their core competency. This automation encapsulates the specialized knowledge of a battery expert, allowing for its perpetual retention and utilization within the organization.
While manual analysis is time-intensive, automation significantly accelerates the process, enabling the efficient analysis of multiple cells and vendor qualifications. For instance, the automotive industry employs the Hybrid Pulse Power Characterization (HPPC) protocol, a standard yet intricate analysis depicted in Figure 12, to assess a battery’s power delivery and internal resistance.
Maintaining a comprehensive data library of all qualified cells offers immense value by enabling the selection of the most suitable cells for specific applications and facilitating supply chain agility. Having immediate access to this data mitigates the delays associated with requalifying and retesting cells, a significant advantage amidst prevalent supply chain uncertainties that can extend lead times exponentially.
This practice of keeping a benchmark of tested cells not only preserves options but also expedites the qualification and development processes, ensuring adaptability and efficiency in unpredictable circumstances.
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We do an intensive test simulating accelerated consumer usage scenarios.
(Delta Open Circuit Voltage) We check for any change in voltage throughout the manufacturing process from component level to assembled device.
This eBook will equip you with strategic insights for leveraging battery complexity as a competitive advantage. By enhancing productivity through advanced analytics, mitigating risks with automated quality assurance, and saving time with streamlined processes, your team will be better positioned to thrive. Implement these transformative techniques to not only excel in battery management but also to drive your products to market with efficiency and confidence.