Archives for Industry 4.0

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.

Setting a new standard in digitalization of battery assets

The digital frontier

All battery customers are rightfully concerned for loss in battery power and energy through life and usage. Performance degradation is inherently par for the course with batteries, but with new approaches on the horizon the status quo isn’t something we are bound to.

 

By leveraging tools that define the state of the art in modern industry, including machine learning and artificial intelligence (AI), a digital infrastructure can be established that enhances battery performance, curtails degradation and extends operational lifespans.

 

Considered in its fuller sense, this digital approach goes further still – setting manufacturers up to work in a wholly new landscape, with a data-driven foundation enabling the fine-tuning and tailoring of future products from cell chemistry to system design.

 

Oscar Fors, Northvolt President, Battery Systems comments: “Batteries are often thought of as passive systems – we plug them in, and they provide power. But we see batteries as a far more dynamic asset. If you can properly understand them and develop the right tools to work with all the insights on offer, we can tap into batteries in a way never seen before.”

 

“It is here where we see substantial opportunity for improving the operational performance and lifetimes of batteries, and it’s driving an approach we’re calling Connected Batteries.”

 

Bringing Industry 4.0 to batteries

With electrification of industries where batteries are a new asset in play, users are not necessarily familiar with intricacies of operating and managing batteries. Since poor battery management is a sure road to battery degradation, the issue represents a challenge which must be overcome if we’re to fully exploit all that battery technology has to offer.

 

Fortunately, the situation is one that may be improved upon through a combination of intelligent data analytics, enhanced traceability and automation. Carefully applied, these technologies may yield far better lifetime management of battery assets than otherwise possible.

 

As is characteristic of Industry 4.0, the key to securing this goal rests in harnessing data. To this end, Northvolt is building telemetry and data collection into every aspect of its business and products.

 

Landon Mossburg, Northvolt Chief Automation Officer, explains: “Recognizing the dynamic nature of batteries and that increasing number of data points leads to far better basis for management and performance.”

 

“We’re moving beyond simply collecting current and temperature measurements. We want to know everything we can about batteries, from design and manufacture right through to operations and the ambient environment during deployment.”

 

Data collection at Northvolt begins with manufacturing, where virtually every process will be tracked. Subsequent to this, battery materials and components will be tagged with metadata so that their origins can be traced with specificity.

 

Once batteries are deployed, core parameters over which Northvolt is gathering battery performance data include temperature, state of health (SOH), state of charge (SOC), cooling system performance, electrical measurements, and usage metrics. This data is also supplemented with contextual information on where the asset is situated and how it’s being used.

 

At Northvolt, battery telemetry will be streamed to a secure facility where data will be evaluated by self-learning algorithms and intelligent systems. Customers will own their battery data, but in sharing it with Northvolt, substantial untapped value will be unlocked for them.

 

These systems will analyze battery telemetry data alongside all other data, for instance environmental and contextual information, and use the results to inform a range of diagnostics and subsequent operations to ensure that batteries deployed around the world are being used, charged, and treated as well as possible.


On the customer end, operators will have access to a Northvolt-built API app providing immediate, real-time insights. Here, simply scanning a QR code with a smartphone will allow for components and whole battery systems to be quickly identified. The data provided through the app will facilitate O&M, asset management, logistics and much more.

 

“Knowledge on how asset use influences the long-term nature of a battery and battery cell consumption lifespan will open up significant new ways for customers to work much more cost-effectively with batteries,” says Landon.

 

Inner workings of Connected Batteries

A core aspect to the Connected Batteries solution is machine learning enabled pattern detection. Once patterns are identified as being causally related to some aspect of battery performance, they can be used to develop optimized solutions and reactive measures. These can be pushed out over the wire to batteries and implemented through software/firmware.

 

Solutions could be implemented on individual batteries which are flagged for action, or across a relevant segment of all globally deployed batteries.

 

“This is not simply about collecting data but taking a proactive approach to implementing new protocols that enhance battery performance,” says Oscar.

 

“You can consider it a rule-based system: ‘If A and B, then execute C’. For instance, once a pattern is learnt, its subsequent detection can trigger a particular protocol to engage. That protocol, executed through the battery management system (BMS), may be a particular cooling pattern, or other adjustment.”

 

With this digital ecosystem of connected batteries, there is an envelope of some 10-20% in typical lifetime battery degradation in power and energy which Northvolt seek to reduce.

 

Applications

There are numerous circumstances where digitalization of batteries in ways outlined above will yield considerable advantages. At Northvolt, applications are considered across three timescales: immediate/operational, tactical and long-term strategic.

 

In the immediate context, systems will identify significant, potentially problematic, deviations from the norm or ideal envelop within which batteries should be operated. Alerting technicians to this, remedial action may be taken in real-time, beginning with contacting the battery owner/operator. The beauty of this is that diagnosis (and solutions) can be prepared in advance of dispatched technicians reaching the battery in question, thereby reducing asset downtime.

 

In the tactical timescale, Northvolt will evaluate patterns that will enable it to determine new, refined practices to optimize battery performance, for example adjusting BMS parameters in response to use profiles.

 

A short, simplified use-case illuminates how the system will function:

 

Imagine a mining vehicle, operating a hot-swap battery protocol (where a depleted battery is exchanged for a fresh, fully-charged one). Northvolt detects a pattern of repeated overcharging events and flags the battery. Subsequent analysis reveals the problem: the exchange of batteries is taking place at the top of the mine and precedes the vehicle’s descent down into the mine during which regenerative breaking is leading to over-charge of battery. The solution is a simple one: hot-swap at the bottom of mine, avoid over-charge and prolong the life of the battery.

 

Many more scenarios can be imagined too. For instance, ones relating to seasonal or weather-dependent charging considerations and the delivery of solutions involving compensating across appropriate parameters. Or solutions building off the idea that although optimal charge may typically be between 10-90%, situation-specific circumstances may prompt that being adjusted to 20-80%.

 

Across the long-term strategic scale, new insights on performance coupled with traceability (bringing fresh perspective on otherwise unknown manufacturing process variables) is envisioned to empower Northvolt with perspective to work at a whole new level of battery cell and system development and manufacture. (A topic dealt with in part 2.)

 

“This is a truly new area for battery R&D,” said Oscar. “With this kind of intelligence, we can tune operating parameters, adjust firmware, design cooling solutions customized to certain circumstances or better charging management software in response to particular charge profiles…the options are endless.”

 

Predictive maintenance & novel business models

Beyond improving battery performance, novel business cases and beneficial commercial practices emerge with the digitalization of batteries.

 

For instance, digital architecture for battery systems will enable Northvolt to predict with pinpoint accuracy when assets need to be serviced or replaced. There is every reason to expect that so-called predictive maintenance of this sort will be met with the same kinds of success as can be seen within other industries that have adopted the Industry 4.0 approach.

 

In turn, a consequence of these solutions taken together is new flexibility in how battery products are purchased. The doors open on the introduction of usage-based dynamic warranties which work in the favor of battery owners, and purchase agreements which recognize that customers will be operating within the best possible bounds of battery usage and care.

 

As Oscar says: “By providing owners with the tools to get the most from batteries we can substantially improve the value proposition of every business case – that’s good for us as a manufacturer concerned with encouraging battery-based electrification, and for our customers.”

 

These advantages exist irrespective of the use-case for battery systems, and most certainly extend to stationary battery storage system performance. With these systems, understanding how the delivery of particularly grid services is precisely impacting the health and longevity of a battery system asset will be key to owners determining the most cost-effective deployment strategy for their investments.

 

Towards an evolution in battery technology

Altogether, Northvolt’s approach represents a significant departure from that taken by traditional battery cell manufacturers which, historically, have not engaged with data analytics in the manner envisioned by Northvolt. Indeed, Northvolt expects that its adoption of this new methodology will bring about a significant competitive edge.

 

That being the case, the implementation of these technologies will deliver strategic gains that extend well beyond optimizing battery usage and the associated benefits of this.

 

Earlier, Oscar noted the long-term applications of digitalization – a context where enhanced battery data insights will drive new innovation in battery manufacturing itself.

 

As Landon Mossburg, concludes: “Manufacturing data coupled with telemetry leads to unrivalled product intelligence with which we can fine-tune operations. But beyond this, we’re talking about the DNA of battery packs, and with that we’re able to begin manufacturing batteries with a whole new set of data-driven priorities.”

 

This is a topic to be picked up in part 2.