In the rapidly evolving field of battery manufacturing, data-driven decision making is proving to be a competitive differentiator. Consider a simple scenario where a battery manufacturer identifies a recurring defect in their production line, but they don’t know precisely where and have to discard defective cells as they come off the line. By leveraging real-time and offline data analytics, original equipment manufacturers (OEMs) can pinpoint the exact stage where the defect occurs, analyze the root cause, and implement corrective steps with, most importantly, measurable impacts. This not only improves the quality of the batteries but also reduces waste and enhances overall efficiency. In this month’s article, we’ll take a look at five ways that data-driven decision making can positively impact battery manufacturing.
The Importance of Data-Driven Decision Making
Battery manufacturing is a complex process that can involve sourcing material, slurry mixing, coating, drying, calendering, slitting, vacuum drying, fabrication (jelly roll winding or stacking), welding, packaging, electrolyte filling, formation, and aging. Each stage generates a vast amount of data that, if harnessed correctly, can provide valuable insights into production efficiency, quality control, and cost management. Data-driven decision making enables manufacturers to make informed choices based on real-time and offline data analytics, leading to improved product quality, reduced production costs, and faster time-to-market.
The impact of data-driven decision making in battery manufacturing is supported by various studies and industry reports. For instance, a study by Siemens and Accenture found that implementing data-driven practices in battery manufacturing can lead to a 10.3% reduction in material scrap rates and a 7.2% increase in machine uptime[1]. Additionally, predictive maintenance enabled by data analytics can reduce energy consumption by 9.3%, translating into significant cost savings and environmental benefits[2].
A typical manufacturing process for lithium ion batteries, including electrode preparation, cell assembly, cell formation and finishing, can produce enormous amounts of data that can only be practically managed with a data analytics solution. Source
Five Challenges Faced by OEMs
OEMs in the battery industry face the following five challenges that can be mitigated through data-driven decision making:
- Quality Control: Variations in raw materials, manufacturing processes, and environmental and ambient conditions can all lead to defects. With common problems such as lack of calibration, welding defects or electrolyte leakage, data analytics can help monitor critical variables in real-time, allowing for immediate adjustments to maintain quality standards.
- Scalability: As the demand for batteries for electric vehicles (EVs) and energy storage continues to grow, OEMs must scale up their production capabilities by either expanding existing facilities or building new gigafactories. Data-driven insights can optimize increasingly complex production processes and harness the wealth of data produced, ensuring scalability without compromising on quality.
- Cost Management: Battery production is capital intensive, with significant costs associated with raw materials, labor, and energy consumption. By analyzing production data, OEMs can identify areas where costs can be reduced, particularly with minimizing material waste and improving energy efficiency.
- Supply Chain Management: The battery supply chain is complex and global, involving multiple suppliers and logistics providers. Data analytics can enhance supply chain visibility, enabling OEMs to track raw materials, manage inventory levels, and predict potential disruptions.
- Regulatory Compliance: OEM’s must adhere to stringent environmental and safety regulations, for example with the Battery Passport. This digital tool, mandated by the EU Battery Regulation (2023/1542), provides a comprehensive digital ledger that tracks a battery’s lifecycle from raw material sourcing to recycling (4). Data analytics plays a critical role in enabling this technology.
Addressing the Challenges with Peaxy Build
Peaxy Build is a comprehensive software platform designed to tackle the multifaceted challenges faced by battery manufacturers. One of its standout features is the “manufacturing traveler” capability, which is a data threading solution that captures critical data at each step of the manufacturing process and ensures that every aspect of production is meticulously documented and analyzed, thus providing a robust foundation for data-driven decision making. Here’s how it can specifically address the five key challenges battery OEM’s face.
Enhanced Quality Control: Peaxy Build’s advanced analytics capabilities allow manufacturers to monitor many different production variables. They can also track critical quality KPIs such as production yield, battery cycle life, battery charge time, energy density per cell, manufacturing cost per unit and others.
By capturing data from each step of the manufacturing process comparing as specified to as built (i.e. metrology including AI enabled scanning electron microscope (SEM) analytics) and performance (i.e. performance changes during formation cycles) through the manufacturing traveler, Peaxy Build not only anticipates potential issues but also empowers battery manufacturers with the ability to address them proactively through real-time insights. These viewpoints would be otherwise impossible with valuable data trapped in silos, proprietary or outdated systems, and even handwritten forms. Continuous real-time monitoring is the key to consistent quality, ultimately leading to a reduced scrap rate and fewer warranty claims.
Optimized Scalability: As the demand for batteries grows, manufacturers need to scale up their operations efficiently. Peaxy Build provides data-driven insights by collecting and threading the manufacturing data. A major benefit of this is the ability to monitor in-line manufacturing metrics as a lead indicator of a cell’s characteristics. By leveraging the significant amount of historic data collected at key processes such as during formation, and utilizing a machine learning tool such as Peaxy Predict, algorithms can be created and updated to provide an early indication of the cell’s quality and performance – critical in scaling up any operation.
Cost Efficiency: Reducing production costs is a key priority for any manufacturer. Peaxy Build helps achieve this by identifying inefficiencies and areas for improvement. For example, an analytics solution can capture data on material usage, energy consumption, and labor efficiency, providing a comprehensive view of the costs involved in the entire span of the production process. By analyzing this data, manufacturers can pinpoint areas where costs can be reduced, including material and process optimization, energy efficiency, and yield improvement. An added benefit is more sustainable production practices in line with net zero initiatives.
Supply Chain Visibility: An analytics solution like Peaxy Build integrated with an enterprise resource planning (ERP) system enhances supply chain management by providing real-time visibility into raw material availability, inventory levels, and logistics. Imagine a battery manufacturer that sources raw materials like lithium and cobalt from multiple suppliers worldwide. By leveraging an ERP integrated with a data analytics platform like Peaxy Build, the manufacturer can gain real-time visibility into the status of these materials as they move through the supply chain and record data at each step, from raw material procurement to final product assembly.
If a shipment of lithium is delayed, the data analytics tool can immediately alert supply chain managers, allowing them to take proactive measures, such as sourcing lithium from an alternative supplier or adjusting production schedules to minimize downtime. Additionally, predictive analytics can forecast potential disruptions based on historical data and current trends, enabling the manufacturer to plan ahead and mitigate risks.
Regulatory Compliance: Compliance with environmental and safety regulations is critical in battery manufacturing. Factors include properly disposing of hazardous materials, managing overheating risks, avoiding fines and penalties, meeting sustainability goals and ensuring access to target markets by meeting local standards. Monitoring and alerting capabilities ensure that production processes adhere to specific regulations and compliance goals by capturing the relevant data. For example, if a batch of batteries shows higher-than-allowed levels of a particular contaminant, data analytics can pinpoint the exact stage of production where the issue occurred, allowing for immediate rectification and/or remediation.
With data analytics, manufacturers can also ensure that key steps of the production process adheres to stringent environmental and safety regulations. For instance, the Battery Passport mentioned above can track the origin and processing of critical minerals like cobalt and lithium, ensuring they are ethically sourced and processed without environmental violations [5]. This level of traceability not only meets regulatory demands but also promotes transparency and sustainability in the supply chain.
Statistical Process Control
In addition to the above areas, Statistical Process Control (SPC) is becoming an increasing area of focus for OEM’s looking to systematically maintain quality control and compliance. This is a capability that we recently introduced in Peaxy Build. SPC uses statistical techniques to monitor and control the production process, ensuring that it operates at its maximum potential. By employing control charts, manufacturers can track variations in key production metrics, such as electrode coating thickness or electrolyte concentration.
Statistical Process Control (SPC) is crucial in battery manufacturing, using control charts, for example, to monitor production quality in real-time. By identifying variations and ensuring processes stay within control limits, SPC helps maintain high standards and reduce defects.
For example, a control chart might use formulas like the mean and standard deviation to establish upper and lower control limits (UCL and LCL). These limits help identify when a process is deviating from its expected performance, allowing for timely interventions. In addition to ensuring a process is stable, control charts can also determine whether improvements should target non-routine events or the underlying process itself. Process capability indices like Cp and Cpk measure how well a process can produce output within specified limits, providing insights into overall process stability. In summary, implementing SPC is a controlled, systematic method to determine the difference between normal process variability and anomalies that need immediate attention.
Conclusion
Peaxy Build addresses the key challenges faced by battery manufacturers through its advanced data analytics and manufacturing traveler capabilities. By capturing critical data at each step of the manufacturing process, Peaxy Build provides the insights needed to enhance quality control, optimize scalability, improve cost efficiency, ensure supply chain visibility, and maintain regulatory compliance. This comprehensive approach enables battery manufacturers to stay competitive and meet the growing demand for high-quality batteries in a rapidly evolving market.
References
[1] Siemens and Accenture: Data-driven approach for modern battery …
[2] The Power of Digitalization in Battery Cell Manufacturing
[4] How Digital Solutions are Propelling Battery Manufacturing – METTLER TOLEDO
[5] Tata Elxsi – Battery passport: Revolutionizing lifecycle management …
