Can we know when the battery “dies”?

Lithium-ion batteries, on which countless devices depend today, from our phones to electric vehicles, are over 30 years old and have become an important component of the technologies of today and tomorrow.

Knowing its availability, duration, benefits and capabilities is already vital for users. But it is difficult to know for sure these significant variables.

Given this, the question arises: should we trust the information that some devices, such as the iPhone or any other mobile phone – or electric car – give us about the state of the battery? Or, in other words, are we sure that the battery will not leave us stranded just when we need it most?

“Life” and “death” in a chain

Lithium-ion batteries are made up of cells, each containing a positive and a negative electrode. They are immersed in an electrolyte which acts as a conductor for the transport of ions. Thus, the electrons pass through an external circuit that powers electrical devices and makes them work.

In this process, the batteries are discharged and need to be re-energized. This is called a cycle, and like any other battery, the more cycles they go through, the sooner they “die”.

Research measures the number of battery life cycles under certain electrical conditions. Unfortunately, changing factors such as operating temperature, charge and discharge rates, and usage time result in varying durations, making it very difficult to determine the condition of batteries over time.

Will it be possible to estimate when the battery will fail? Is it possible to find out what functionality they can have after their useful life?

digital twins

Industry 4.0 has been working on virtual simulation technologies since mid-2010, in so-called digital twins (digital twin in English). These are sets of virtual information that fully describe the physical product.

It is in this area that significant progress has been made in the development of simulation programs both in the design of industrial enterprises and in the virtual reconstruction of their processes.

These twins are designed to analyze, optimize, and improve enterprise performance in real time, reduce development time, and detect failures early.

WITH software suitable ones can be modeled from industrial plants to devices such as batteries. And thus have an accurate digital reality in which one can compare the information recorded in the digital twin with the information implemented in the battery management system.

This makes it easier for them to operate at peak efficiency and provide greater longevity, as well as allowing them to explore their benefits at certain points, avoid crashes, and even tackle possible optimizations.

Too simple models

The problem is that batteries are very complex systems to model accurately.

Usually indicators are used that sometimes cannot be measured directly, for example, the level of charge (State of charge in English SOC), which is the amount of charge that the battery has relative to the maximum possible, and the state of health (health status in English SOH), a parameter that evaluates the performance of a battery compared to its ideal conditions.

Thus, the models are still too simple and their characteristics depend on different types of batteries, their construction and the type of their manufacture.

Therefore, the accuracy of the indicators described above decreases and not quite linearly, which must be taken into account when operating energy storage systems.

BEST project

From the IMDEA Energy Institute and the University of Alcala de Henares, we entered the field of digital twins for batteries with the Battery Energy Storage Digital Twin (BEST) project funded by the Ministry of Science and Innovation.

In this initiative, we propose the use of digital twins of batteries by integrating mathematical models and health assessment tools, as well as analyzing performance data using artificial intelligence methods.

This will provide greater knowledge and control over the actual state of the battery systems throughout their lifetime, reducing the differences that may exist between the model definition and the actual system.

These differences usually appear either when the passage of time affects the characteristics of the batteries, or when it is impossible to make an accurate model, or when working with a detailed model is impossible or inefficient.

As a strategy, we have chosen a twin that combines different methods and allows us to achieve this goal by creating a dynamic model with two approaches:

• The first is to reproduce the health status of batteries based on an assessment of the state of the most significant cell indicators (the SOC and SOH mentioned above).

• Secondly, it is the development of battery degradation models obtained by adequate characterization of cells with different chemical composition and types of electrochemistry (power and capacity of lithium-ion batteries, including other types of batteries, such as redox flow batteries).

This, combined with the analysis of operational data using artificial intelligence methods, will provide much more complete and useful information about the real state of battery systems.

With this structure, one could, for example, calculate when they reach the end of their life cycle and determine their health status, either find an efficient recycling method, or give them a second life in another, less demanding application.

If it worked, we would have found nothing less than a way to avoid a sudden battery drain in a mobile phone and, secondly, get an excuse to blame the battery for being late for a meeting or not responding to a message for a while.

Enrique García-Quismondo Hernais, Renewable Energy Researcher, IMDEA ENERGÍA

This article was originally published on The Conversation. Read the original.

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