Like many other industries, new big data and data mining solutions open up a new range of opportunities for the battery industry. Especially in terms of accelerating its technological development and optimization in order to meet the challenges facing the energy storage industry.

The use of this type of solutions makes sense especially due to the wide variety of key parameters and critical indicators associated with batteries, which require, by themselves, tracking and monitoring to achieve an improvement in their performance. Aspects such as energy density, c-rate, cyclability, temperature or geometry (to name a few) are elements to be analyzed throughout the battery life cycle in order to optimize them both from an individual and global perspective.

This is where a key component for the future of batteries comes in: the so-called "Battery Management Systems" or "BMS". These devices consist of an electronic system that, together with the appropriate software, is capable of collecting all the key information on the operation and life of the battery. This not only makes it possible to manage the correct operation of the device during use (the main application for those in use today), but also provides a data bank to be exploited in order to optimize its operation in terms of performance, longevity and safety.

Advanced BMS and Data Analytics for improving battery performance. From manufacture to the end of its lifespan


As mentioned above, the analysis and use of all this information is an important lever to optimize and evolve the current state of the art of batteries. By developing this type of information exploitation approaches, the aim is to achieve an increasingly digitized and analytical model that not only serves to monitor battery performance, but also to continuously improve it.

Firstly, the use of this type of solution enables traceability of the entire battery cycle. The collection of data throughout its life enables a complete monitoring of results for all the key indicators identified, as well as for all the components that make up a battery. All this, moreover, from the perspective of the different scenarios in which the device may have been used, allowing comparisons to be made according to specific applications and situations.

This makes it possible to know everything that happens in a battery from its manufacture to the end of its life, thus being able to understand the "what", "when" and "where" of the product in order to deepen the knowledge of the devices and carry out the necessary adjustments that allow the optimization of these solutions both throughout their life and for the future.

This last point is another of the great values provided using this type of tools. By increasing the knowledge, we have of batteries and the keys to their operation and results, we can begin to predict and model their configuration and composition with a view to new generations to be manufactured. In other words, this type of solutions will allow us to have a theoretical formulation capacity of the batteries from the exploitation of the information we have, thus being able to define "a priori" the solutions that can give us the best results considering their final application. All this, moreover, without the need for preliminary tests or trials, which reduces both the production time of these devices and the cost associated with them.

In addition, this "prediction" capability is complemented by a real-time "modeling" capability. The use of this type of solution takes battery management during operation one step further. By establishing a "bidirectional" communication system between the battery (specifically, between the BMS and its software) and the data analysis and exploitation system, corrections and adjustments can be made in real time to optimize battery use and operation in accordance with the parameters and needs observed at any given time.

Finally, there is another great advantage offered by the use of solutions based on data analytics, in this case associated with an incipient sub-industry within the large industry that is beginning to be the world of batteries: the second life.

Greater knowledge of the life of batteries will not only help to optimize their present life or the manufacture of future generations, but also to understand the keys of these devices to boost their use in second life (a market that is expected to grow exponentially, especially from the end of this decade). Understanding what has worked and what needs to be improved in the use of a device that has already fulfilled its first life is very valuable information to give rise to a greater residual value of its application in a second life or use.

How can analytics and advanced bms systems help during batteries lifecycle? While designing it, during its lifespan, and during its second life.


In view of these perspectives and advantages, the industry is working to respond to two of the major challenges posed by the widespread implementation of this model within the industry.

The first of these, and perhaps the most pressing, is to develop BMS systems that have the appropriate software to capture and exploit information.

It must be taken into account that the BMS itself is a "base" device that exists in all batteries to ensure their correct operation in terms of safety and performance. Hence, it needs the incorporation of new software solutions that allow it to go beyond the current state of the art, making this system a real "brain" that not only manages the information, but is able to "understand" it and get, through the system, the most out of it.

In addition, the other major challenge for the coming years is the development of advanced BMS capable of adapting to any generation of batteries. In other words, this type of solution should advance at the same pace as the energy storage technology itself, being able to adapt to different configurations, chemistries or approaches (such as those based on solid state).

In this sense, there are already different agents in the market that are working to promote this type of solutions. Large companies such as Bosch, Intel, Panasonic or Continental are working to develop new advanced BMS technologies based on data analytics, as are start-ups like Brill Power and Novo.

These are only international examples, but there is no need to go that far to find companies that are committed to the development of this type of key solutions to boost the energy storage industry. An example of this is Bcare (first spin-off of CIC energiGUNE) that offers today in the market different solutions and products aimed at maximizing the life of the batteries, anticipating the various incidents and problems that may present any storage system.

All this, with the support of CIC energiGUNE itself, which is also working on the development of new solutions and approaches to deepen the knowledge on battery management and its modeling and simulation based on new technologies.


In conclusion, we can see once again how key sectors for the future (such as batteries) can also boost the application and use of new digital innovations (such as big data and data analytics), taking advantage of the range of opportunities they offer. Hence, as we have previously analyzed in our blog, it is said that the commitment to the energy transition goes beyond sustainability, and is also a way to accelerate technological and digital development that will have a positive impact on our environment and society.

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