Can we know when a battery is going to “die”?

Can we know when a battery is going to “die”?

The lithium-ion batteries on which countless devices, from our phones to electric vehicles, depend today are more than 30 years mature and have become an essential component for present and future technologies.

Knowing their availability, longevity, performance and capabilities is already vital for users. But it is difficult to know with certainty these essential variables.

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

There are cell phones that provide information about the health status of the battery. Omar Tursic / Shutterstock

“Life” and “death” on a circuit.

Lithium ion batteries are composed of cells, each containing a positive and a negative electrode. These are immersed in an electrolyte which acts as a conductor to transport the ions. In this way the electrons travel through the external circuit that energizes the electrical devices and makes them work.

In that process, the batteries are discharged and have to be re-powered again. That is called a cycle, and, like any other battery, the more cycles they experience, the sooner they “die”.

Studies measure how many cycles the battery lasts under given electrical conditions. Unfortunately, changing factors such as operating temperature, charge/discharge rate, and usage time lead to different duration, making it really difficult to establish the health conditions of batteries over time.

Will it be possible to estimate when a battery will cease to be operational? Is it possible to know what functionality they may have once they have reached the end of their useful life?

Digital twins

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

In this area specifically, much progress has been made in the development of simulation programs both in the design of industrial plants and in the virtual recreation of their processes.

These “twins” aim to analyze, optimize and improve the productivity of a plant in real time, reducing development times and detecting failures early.

With a software suitable can be simulated from industrial plants to devices such as batteries. And thus have an accurate digital reality in which to contrast the information recorded in the digital twin with that implemented in the battery management system.

This makes it easier for them to operate with maximum efficiency and ensure greater durability, in addition to exploring their performance at specific times, avoiding failures and even addressing possible optimizations.

Models that are too simple

The problem is that batteries are very difficult systems to model faithfully.

Indicators are generally used that sometimes cannot be measured directly, such as state of charge (State of Charge (SOC), which represents the amount of charge the battery has with respect to the maximum possible, and the state of health (State of Health SOH), a parameter that assesses the performance of a battery compared to its ideal conditions.

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

Therefore, the accuracy of the indicators described above decreases and not precisely linearly, so this should be considered in the operation of energy storage systems.

The BEST project

From Institute IMDEA Energía and the University of Alcalá de Henares we enter 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 battery twins through the integration of mathematical models and health state estimators, as well as the analysis of performance data using artificial intelligence techniques.

From this we will obtain greater knowledge and control over the real conditions of the battery systems throughout their operating life, thus reducing the differences that may exist between the definition of the model and the real system.

These differences generally appear either when the passage of time affects the characteristics of the batteries, or when an accurate model cannot be made, or when working with a detailed model is not possible or is ineffective.

As a strategy, we have chosen a twin that brings together different techniques and allows to achieve this purpose by creating a dynamic model with two approaches:

  • The first one is the reproduction of the battery health state based on the estimation of the state of the most relevant indicators of the cells (the already mentioned SOC and SOH).

  • The second is the development of battery degradation models, obtained by proper characterization of cells of different chemistries and type of electrochemistry (power and capacity lithium-ion batteries, even other types of batteries such as redox flow batteries).

This, integrated with an analysis of the operation data by artificial intelligence techniques, will allow to have much more complete and useful information about the real conditions of the battery systems.

With this framework it would become possible, for example, to calculate when they will reach the end of their life cycle and determine the state of health they are in, either to find an efficient recycling method or to give them a second life in another less demanding application.

If it works, we would have hit on nothing less than a way to avoid suddenly running out of battery on your cell phone and, secondarily, end up with the excuse of blaming the battery for being late for a meeting or not answering a message on time.The Conversation

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

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

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Daniel Chapman