energy storage

Invention Boosts State-of-Health Estimation Accuracy

Skeleton has submitted a patent application for a novel method designed to determine the State-of-Health (SOH) of supercapacitors. Application Software Engineering Expert Bertram Schemel, the innovator behind this ground-breaking approach, provides backstage insights.

Why is State-of-Health important? 

Energy storage system cells share a similarity with human beings: they experience aging over time, which is accelerated significantly when subjected to demanding workloads and challenging environmental conditions.

State-of-health (SOH) serves as an indicator of both age and condition. At the outset of a cell's, module's, or system's lifespan, its value stands at 100%. However, as it gradually depletes to 0%, the corresponding component can no longer maintain its intended performance, necessitating bypass, replacement, or shutdown.

Consequently, it is crucial for our customers to possess accurate insights into the current SOH and the timeframe leading up to 0%. Armed with this information, they can proactively schedule maintenance, mitigate downtime, and ultimately achieve cost savings.

What are the conventional methods for estimating SOH?

There are two prevalent methods. The first approach involves utilizing a computational aging model that draws from an extensive repository of recorded laboratory data. This model serves as a foundation for predicting how the SOH evolves under the prevailing operating conditions, encompassing factors such as temperature, voltage, and current.

The second method centers around the use of an observer or filter mechanism to estimate capacitance and/or resistance (ESR). Subsequently, the SOH is deduced from these indicators.

Why did we need to come up with something new?

The first method outlined earlier involves an extensive collection of test data, which demands weeks or even months for measurement. During this period, it occupies valuable test benches, resulting in associated costs. Additionally, the time window between finalizing cell design and initiating module production is often insufficient for accommodating these lengthy tests.

Furthermore, a highly accurate SOH estimation is essential for offering tangible benefits to our customers. Regrettably, the techniques mentioned in the second method can’t provide the required level of accuracy, except for our new proprietary method.

What can you tell us about your invention?

Essentially, I have adapted the mathematical modelling of supercapacitors to facilitate the utilization of a method that's already established within the battery domain. Directly applying this method from batteries to supercapacitors was impossible due to their different electrochemical behaviour. 

Is it also applicable to Skeleton's latest product, the SuperBattery?


What advantages does the improved State-of-Health algorithm offer to our customers?

Primarily, this is a distinctive competitive advantage over all other players who lack this capability. Additionally, looking ahead, it holds the potential to unlock new functionalities. By combining this feature with the standard computation aging model mentioned earlier, intriguing opportunities arise. For instance, when the SOH approaches a critically low point, the system could proactively recommend adjusted operational parameters such as temperature and voltage to the user. This approach could effectively extend the system's operational timeframe by several weeks or even months.

This approach empowers customers to make informed decisions. They can opt to undertake extra maintenance promptly or choose to wait for a pre-scheduled time window, thereby achieving higher operational flexibility. 

Tell us a little bit about your background. 

I pursued a degree in "Engineering Science" at TU Berlin, with a specific focus on "Numerics and Simulation" and "Vibration/Control Technology." After that, I dedicated several years to battery models and algorithms, including State-of-Health, within the automotive sector. I find it fascinating how you can extract otherwise invisible insights from a small set of measurement signals and even predict the future with them.

From a broader perspective, I've always wanted to be part of a profession that contributes to environmental sustainability and plays a role in preserving our planet. This aspiration sounds less cliché and takes on a more meaningful context when considering the global events that have unfolded in recent years.

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