Big Data:

Big Data, a term widely recognized across various industries, has now made its presence felt in battery engineering. To appreciate its significance, it is essential to consider Big Data through the lens of the five V´s:

• Volume: Big Data encompasses datasets of vast size, ranging from Terabytes (1 TB equals 1,000 Gigabytes) to even Pettabytes (1 PB equals 1,000 Terabytes).
• Variety: Having a high volume of data is challenging enough, but having variety within that data increases complexity. Battery data exhibits a range of variables depending on the application, from current, voltage, and power in home storage systems to velocity, requested power, voltage, and current in electric buses. Furthermore, data resolution can vary significantly, with home storage systems offering data at 1–5-minute intervals, while electric vehicle (EV) applications demand data at the 1-second level or even less.
• Velocity: In battery research, speed is of the essence. Real-time or near-real-time analytics have become crucial; particularly, battery safety algorithms have become more computationally intensive, in order to maintain velocity and prevent critical failures.
• Value: it is the underlying reason for tackling all other technical challenges Big Data brings with it. After all, having data is worthless if it cannot be used to generate value. Value is created in the battery industry by analyzing collected data to reduce safety risks and related costs, inform battery supply chain decisions, and extend battery lifetimes.
• Veracity: Ensuring data accuracy and validity is paramount, but this is often challenging due to the sheer volume and variety of data. An outlier voltage reading in one system might be the norm in another.

Nowadays, there are available different databases that provides many materials structure information. Some of them are experimentals, such as the Inorganic Cristal Structure Database (ICSD), and other theoretical, such as the Material Project (MP). But the general-purpose database usually cannot meet the special needs of battery materials. For the development of battery materials, it is necessary to consider specific properties such as energy density, ion transport properties, charge and discharge rates, and so on.

In the battery space, data volume is generated by the battery management systems (BMS). The volume of data generated by a single BMS is small and doesn’t fit into the scope of Big Data. However, when we start collecting historic BMS data, we easily get into the Terabyte range of data volume.

Internet of things (IoT):

Today, the use of sensors connected to devices that measure various magnitudes has become commonplace. In the context of energy storage, Battery Management Systems (BMS) serve as a prime example of IoT integration.

In the domain of batteries, these sensors measure crucial performance metrics such as voltage, current, and temperature. Continuous monitoring and supervision of these metrics are facilitated through software, and battery state predictions, including State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL), are computed with the assistance of Machine Learning (ML).

One notable deficiency is the absence of alert mechanisms related to the behavior of Lithium-ion Batteries (LiB). Alerts are of great importance in monitoring tasks for batteries, even more when managing complex and sophisticated equipment. For example, if no alert is generated when the temperature increases above a certain value, the battery can have great problems, being necessary to replace.

In the framework of the IoT, the goal is to achieve power autonomy through batteries that can sustainably recharge themselves. For that, it would be interesting to harvest ambient energies such as light, heat and vibration and convert them to electricity. In fact, it has to be taken into account that most devices have an operational life of over 10 years, while the batteries that power them last 2 years or less.

Machine Learning in Battery Research:

In the realm of battery research, various paradigms have been employed. Traditional trial-and-error experimental methods rely on prior experience and are often time-consuming, costly, and inefficient.

On the other hand, some limitations of the traditional multi-physics-based materials simulations, which are prominent of the model-based theoretical science and computational simulation paradigms, are that the computational simulation methods take less account of the realistic experimental conditions, and that the hypothetical structures may not be thermodynamically stable or even do not exist in practical applications. In addition, the high computational cost and the quantitative errors of the simulations are the main drawbacks of these paradigms.

Machine learning, a prominent subset of Artificial Intelligence (AI), has assumed a central role in contemporary battery research, alongside Big Data. Machine learning offers several advantages:

• Rapid Screening: Machine learning facilitates the swift screening of extensive material databases. Unlike computationally demanding multi-physics simulation methods, machine learning accelerates the evaluation of numerous materials, reducing development costs and enhancing material discovery efficiency.
• Quantitative Structure-Property Relationships (QSPR)/Quantitative Structure-Activity Relationships (QSAR): Machine learning aids in the discovery of complex structure-property and structure-activity relationships in material systems with minimal expertise required.
• Atomic Potentials and Force Fields: Machine learning can be leveraged to derive atomic potentials or force fields from sets of quantum chemical calculations. This approach enables faster calculations by considering interatomic potentials, rather than explicitly modeling electrons for each element.

High-Performance Computing (HPC) for Battery Modeling:

High-Performance Computing (HPC) stands as a cornerstone in modern battery research, offering the computational muscle needed to tackle the intricacies of battery systems. One of the key advantages of HPC is its capacity to handle complex multi-physics simulations that consider a multitude of variables, including thermal effects, electrochemical reactions, and materials behavior. This capability is particularly crucial when exploring novel materials and designs, optimizing electrode structures, or assessing the performance of advanced energy storage concepts.

Furthermore, HPC enables researchers to delve into the fundamental science of batteries, exploring phenomena such as ion diffusion, charge transfer processes, and solid-state electrolyte behavior with remarkable detail. This level of granularity is vital for not only understanding the inner workings of batteries but also for engineering next-generation energy storage solutions.

By harnessing the computational prowess of HPC, battery researchers can accelerate the development of safer, more efficient, and longer-lasting energy storage systems. 

Quantum Computing in Battery Research:

Quantum computers harness the principles of quantum mechanics to perform calculations that were previously beyond the capabilities of classical computers. In the context of battery research, this opens doors to a realm of possibilities.

One of the most significant advantages of quantum computing in battery research is its ability to handle complex quantum mechanical calculations with unparalleled efficiency. Classical computers often struggle to simulate the behavior of electrons and atoms within materials accurately. Quantum computers, however, excel in solving these quantum mechanical equations, providing insights into material properties and behaviors that were once elusive.

This newfound computational power allows researchers to explore battery materials and designs at the atomic and molecular levels with unprecedented precision. It enables the discovery of materials that exhibit exceptional energy density, ion transport properties, and charge-discharge rates. Additionally, quantum computing aids in identifying thermodynamically stable structures, ensuring that materials are not only theoretically viable but also practical for real-world battery applications.

As quantum computing technology matures, battery researchers will be able to simulate and optimize entire battery systems with exceptional detail. This includes understanding the behavior of electrolytes, interfaces, and electrode materials at quantum scales.

In summary, the integration of Big Data, IoT, machine learning, high-performance computing, and quantum computing into battery research is poised to revolutionize the field. These technologies are not only enhancing battery performance, safety, and sustainability but also streamlining research efforts, reducing costs, and accelerating the discovery of innovative materials and designs for future batteries.