The AI Evolution of Battery Management Systems

Did you ever think there would be a time when wind and solar generation produced more electricity than coal-fired power plants? That’s what happened last year and many experts say this event represents critical breakthroughs for wind and solar technologies, but it shouldn’t be surprising. In 2023 a report from Energy Innovation found that 99% of the U.S. legacy coal-fired plants were more expensive to keep operating than replacing them with new wind or solar generation. That also includes the cost of battery storage systems necessary to make this clean energy dispatchable.

Developers of large wind and solar facilities have been utilizing large-scale battery storage systems for many years and so have utilities and grid operators. They’re all taking advantage of battery storage systems to make solar resources more dependable, but they also need battery management systems (BMS). Even when they’re found behind-the-meter (BTM) they need these BMSs. This technology has become critical as more wind, solar, and storage takes up the slack in power generation.

Gigawatts Get Noticed

To put this in perspective, the figures from 2024 have been compiled and are starting to be digested by a number of authorities. Several research group websites announced that last year 93% of all the new energy capacity coming online in the US. was solar, wind, and storage. It really got interesting as they broke that down into more tangible terms. This 93% equated to about 49 gigawatts. It also brought about the overall capacity of clean energy to a record of over 300 gigawatts connected to the power grid.

SEIA (Solar Energy Industries Association)/Mackenzie Power & Renewables publication “U.S. Solar Market Insight 2024 Year in Review” included some facts and figures that got attention. SEIA reported that 4.7 gigawatts of residential solar was installed last year along with about 3.8 gigawatts of solar in the commercial and community solar segment. That may not seem like much when compared with the overall gird-scale solar capacities making headlines, but consider its location. It’s on the BTM segment, which is essential when it comes to grid decentralization.

One other point the Review made was, “Homeowners and businesses are increasingly demanding solar systems that are paired with battery storage. Over 28% of all new residential solar capacity was paired with storage in 2024.” All of the solar-plus-storage has an economic value to its owner and it needs an efficient BMS. Imagine if developers, utilities, and BTM customers had an application that tells them when to sell their power at the exact time that would get the highest dollar per kilowatt.

Speaking of the BTM customers, aggregators have identified this segment as a lucrative resource of revenue with the huge amount of solar-plus-storage gigawatts available here. Still, utilizing all of these solar-plus-storage resources is challenging. Combining individual systems into larger amounts has been the subject of past “Charging Ahead” articles concerning AI-driven microgrids and VPPs (virtual power plants). It’s the ultimate non-wire, grid-enhancing technology and it’s available today.

Needed Active Not Passive

Today’s BMSs can no longer be passive like they were initially. When storage was first added to solar energy systems it was a simple and passive device, but as technologies moved forward, more is expected from them. The basic tasks of limited monitoring aren’t sufficient any longer. A BMS can no longer only observe, it has to interact and take an active role in the battery’s health and operation. Once again, it’s the old “what if” syndrome popping up.

If you remember, we discussed that a few months ago with maturing technologies. Users started asking those loaded questions and suppliers valued the feedback. Questions like, “What if BMSs could be less reactive and more proactive” came up. Some users asked if BMSs could be more adaptive and optimize on the fly? Utilities questioned if these systems could interact directly with the power grid’s needs? The manufacturers had their own lists of “what ifs” too.

They were interested in the BMS transitioning toward a more autonomous approach. The smart grid’s technology has shown the value in being smarter and more efficient in its operation. The introduction of the power of cloud computing to grid resulted in the addition of sophisticated software and cutting-edge communications, which expanded applications. Somewhere along the way BMSs evolved into BMS platforms. These upgrades were superior to earlier versions, and there was no turning back.

Wanted - Smarter Systems

Once BMSs moved from being passive to a more active mode, it seemed there was interest in moving from proactive to predictive. Visualize being able to make intelligent decisions about how the BMSs interacted with the power grid in real-time. Sounds more like science fiction than scientific fact, but it’s happening. The combination of artificial intelligence (AI) technology and BMS applications has changed the BMS’s operational efficiency. It’s able to manage larger and more complex storage systems than ever before. Once more AI has enabled a technology to leapfrog the status quo. In this case it’s BMSs platforms.

There is a variety of AI groupings available, but let’s concentrate on the AI-driven application integrated into the BMS platform. An AI-driven application uses the AI component as the dominant driving force of the system’s functionality. It’s part of the AI spectrum that has proven very adept at taking applications like BMS to its next level. It not only makes BMS platforms smarter, but they’re more adaptable. The AI-driven BMS takes advantage of all the AI tools like advanced algorithms, machine learning, and predictive analytics. AI has made the BMS platform an intelligent tool that learns from the data being fed to it and makes informed decisions, which enhances its capabilities.

One advantage of an AI-driven BMS platform is its ability to perform real-time monitoring in conjunction with data analysis to determine what is taking place within the battery. It also predicts what is going to happen next. Predictive algorithms classify and organize the flood of big-data produced by the BMSs. It is classified into well-defined groupings. Then the data could be processed by conditional analytics, which makes predictions based on sets of probabilities.

AI-Driven Advantages

Case in point, there are many factors that impact a battery’s health and service life like battery degradation and component failure. Generative AI identifies these patterns and makes predictions. Those predictions are used by many of the AI-driven BMS platforms for predictive diagnostics, adaptive control, and predictive maintenance. Without taking too deep a dive on generative algorithms, machine learning, and pattern recognition analysis. Let’s look at some generalities.

Reducing maintenance is a much sought after goal by budget-strapped utilities and grid operators around the world. AI-driven BMS platforms are heavyweights when it comes to reducing maintenance costs through predictive diagnostics applied to predictive maintenance. The platforms excel at identifying potential issue before they become problems or catastrophic faults. In many cases it’s a simple autonomous adjustment that corrects the situation.

Wouldn’t it be handy to be able to differentiate between regular aging and an emerging failure? Predictive diagnostics can do that, which in turn reduces downtimes and maintenance costs. Another use of AI pattern recognition is adaptive management, which can be combined with external databases like environmental conditions, demand forecast, operational grid constraints, and energy trading. This gives providers of ancillary services an advantage when it comes to the question of getting the most money for the sale of their services to the power grid.

Making a Difference

These are only a few of the advantages AI-driven BMS platforms provide. As they mature, they’re making a difference for their owners. Some suppliers of BMSs using AI their offerings are able to free up 10% of additional capacity from a typical battery storage system. There are also claims of being able to double or triple the battery’s life with their technology. Others are focused on improving the numbers of charge and discharge cycles and the list goes on.

With all of these options, potential customers need to do their homework. The technology offers many benefits and features, but it’s easy to get them confused. It’s also easy to get on the wrong side of technological assumptions. That’s why it’s a good idea to understand both what is needed from the application and what the particular AI variation can do. It’s easy to get mixed up when talking about AI-enhanced, AI-powered, and AI-driven, which was discussed a few months ago in a previous “Charging Ahead” article. These grid-enhancing technology are used on both sides of the meter and they offers substantial dividends to well-informed users. The 2025 power demand predictions show high demand, but supplying electricity to our customers is going to be challenging with the Washington chaos. AI-driven BMSs, however, offer a technical end-around to the political flip flopping that’s trying to derail clean energy deployments. Remember the BTM side of the grid has lots of gigawatts that AI-Driven BMSs can quickly supply for growing demand!