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Digitalizing battery design and manufacturing

Establishing a digital ecosystem around cutting-edge manufacturing processes yields a powerful new approach to deliver higher quality battery products, improve efficiencies and innovate for an electrified future.

We have earlier outlined the opportunities provided through the digitalization of battery systems in order to enhance battery performance and extend operational lifetimes. Altogether, these technologies deliver better business cases for battery system owners; enabling them to maximize their use of battery-powered assets whilst at the same time optimize battery usage and management.

 

(See, Part 1: Setting a new standard in digitalization of battery assets)

 

The scope and significance of digitalizing battery ecosystems does not end there, however. Valuable data is available from the earliest stages of the lifecycle of a battery cell, including that relating to materials and manufacturing processes.

 

Capturing this data with traceability technologies which tag data to components and materials (in either serialized or unitary manner) serves valuable purposes in its own rights in terms of improving manufacturing processes. However, by evaluating this in the context of telemetry and other data streams from battery assets deployed in the field we can consider further opportunities still.

 

This is not the convention. Most battery producers collect only batch-level data up until the relatively late step of cell assembly and formation; data which cannot be precisely associated to individual cells. Almost entirely absent from these manufacturers is data collected from deployed batteries in the field.

 

Nevertheless, with both approaches in play, we are advancing a future which will deliver higher performing batteries built for purpose, more efficient manufacturing lines and streamlined innovation in R&D.

 

As Landon Mossburg, Northvolt Chief Automation Officer, noted: “Collecting high definition manufacturing data about an individual cell is kind of like decoding a person’s DNA. We combine this with connected pack data which tells us a lot about the cell’s environment, how it is used, and how well it performs. Combining these two sets of data – cell “DNA” and cell usage – allows us to make much better predictions about how a given cell will perform in the future.”

 

Incorporating a digital approach into design

Leveraging the strengths of multiple technologies applied in concert with one another, Northvolt is working towards the application of high resolution insights into design and manufacturing of battery products. These insights will be informed through collection and analysis of a blend of real-world usage, R&D and manufacturing data.

 

Landon explained: “We collect, store, and analyze not only what goes into each battery we make, but also process and quality test data we measure against every cell. We also do this much earlier in the process of cell manufacturing than other manufacturers, which required us to develop new technology to trace huge amounts of work in progress material through high speed processes.”

 

Here, we can highlight the application of cloud data management, machine learning and artificial intelligence as being key to unlocking novel insights. These digital tools will take responsibility for handling the extremely large volumes of data involved, parsing out meaningful correlations and identifying actionable insights. At the same time, novel printing technologies and machine vision are also required to support traceability.

 

Landon continued: “Once we have this data, and we correlate it with the performance of end-products, both at end-of-line testing and in-field performance, we can use it to develop better cells and packs, but we can also use it to improve those we have in the field and to bring new production online much cheaper and faster than before.”

 

 

Manufacturing process improvement

A wide range of applications present themselves with this digital ecosystem, however several examples serve for illustrative purposes.

 

Through enabling identification of process changes which result in greater process efficiency (or, overall equipment effectiveness), both better quality products and lower costs may be attained.

 

Taking this one step further, because machines can be automated, these intelligent systems may, over time, begin to take a proactive role in tweaking ongoing processes in response to real-time evaluation.

 

It can also be highlighted that establishing a digital ecosystem around manufacturing lines will support quality assurance practices. A salient example of this presents itself in considering the utility of being able to retrospectively identify the makeup and origins of a particular battery system. Since all constituent materials and components will be tagged, any anomalous battery event can be evaluated in relation to its manufacturing. Not only does this mean that root causes may be identified, but also that other products, featuring components or materials from the same batch or manufactured in the same manner, may be flagged for action.

 

 

Optimizing battery performance

A data-driven approach combining comprehensive collection, smart analytics and traceability, will also support the iterative improvement that is essential to the future of Li-ion battery technology.

 

“One example we are excited about is repurposing neural networks used for image classification to instead use cell traceability data to predict cell quality earlier in the manufacturing process. This is especially interesting as a strategy to reduce aging time after formation and to identify earlier on where quality problems are in the manufacturing line,” said Landon.

 

“Another good example is the identification of variations in production processes which lead to greater or worse cell performance in specific use cases; for instance, tracking how cell formation protocols influence performance and reacting accordingly.”

 

Advantages also emerge in considering the critical matter of battery degradation. Landon explained: “If we track how degradation features and other performance outliers arise, and draw correlations between them based on usage and component and/or material origins, we’re in a far better position to optimize our design and manufacturing methods.”

 

 

The introduction of these approaches is expected to dramatically impact the manner in which manufacturers are able to deliver battery solutions to the market. Moreover, by incorporating all of these practices in-house, the industry will gain a significant edge in terms of its capacity to continue to research, develop, manufacture and support operation of Li-ion batteries.