Supercharge your BMS with EBI
BMS: The battery’s nanny
In this rapidly electrifying economy, an ever increasing number of devices, products, and systems are turning to battery power. As we’ve discussed extensively in this series, batteries are complicated and need to be carefully controlled in order to ensure that they operate safely and reliably, and last throughout the warranty period once your product is in customer hands. For any product powered by a lithium ion battery, from tablet computers to electric school buses and everything in between, this job falls to the battery management system (BMS).
The role of the BMS is to ensure that the battery operates safely throughout its application life (an increasingly daunting challenge as EV battery warranties are standardizing around 8 years / 150,000 miles). To do so, the BMS must carefully monitor and control the battery, keeping within narrow limits of voltage, current, and temperature under every application scenario. In a small consumer electronic device the BMS may consist of a power management chip paired with a few passive electrical components. In high-power applications (like vehicles) the BMS is considerably larger, comprising extensive sensing, computing, and control circuitry to do things like monitor voltages and temperatures throughout the pack, operate the battery thermal management system, and compute the battery’s state of charge (SOC, which determines remaining vehicle range) and state of health (SOH; remaining battery service life).
For any company designing a BMS or building a BMS into your product, it is important to note the ways in which data analytics, offered through a new category of software known as Enterprise Battery Intelligence (EBI), can optimize your BMS across the full product lifecycle. Namely:
- Product engineering: Accelerate and improve BMS development, and improve the product’s battery performance
- Post-sale: Monitor your product in situ to enhance performance and safety
Supercharge your BMS design, and your products with EBI
Broadly speaking, the task of designing a BMS breaks down into two primary jobs:
- Designing the BMS hardware
- Developing the algorithms that control the battery.
BMS algorithm development first requires extensive testing of the battery that will be “managed” (controlled), in order to thoroughly characterize its behavior across the full range of expected application scenarios. To do this, many dozens or even hundreds of battery cells (and modules, and packs for larger systems, like vehicles) are tested on specialized equipment, charging and discharging them at different rates, operating them at different temperatures, applying a variety of pulse loads that simulate real-world operation, and “cycling” them over and over again to see how their performance changes as they age over the product’s expected lifetime. All of this testing generates huge volumes of data that must be processed and analyzed in order to develop the full map of how the battery should be operated across its service life. Data volumes are further compounded by the additional testing performed with the BMS in place, to verify that the full system is working properly.
Historically, this data processing and analysis work has been performed manually by teams of engineers combing through these mountains of data using spreadsheets and other desktop computing tools. Some estimate that over 90% of a BMS engineer’s “algorithm development” time is actually spent just manually cleaning and formatting all of this data. Data volumes presented such a challenge that BMS developers would actually limit the amount of battery testing they’d perform, for fear that they wouldn’t be able to process all of the data in a timely fashion. And it was typical to use only the most basic metrics of battery performance to inform BMS algorithms — really just scratching the surface of battery insight — because doing anything more was simply too much work. But those were the bad old days.
Today, a new class of Enterprise Battery Intelligence software solutions has emerged to accelerate, and optimize BMS development. An EBI platform completely automates the work of aggregating all of the battery test data generated during BMS development, and processes the data to extract the key performance indicators (KPIs) that power BMS control algorithms (and not just the basics — EBI will compute dozens of additional metrics that offer deeper insight into battery behavior). An EBI solution is literally thousands of times faster performing this analysis relative to a typical manual, spreadsheet-based workflow.
By using software to automate away the drudgery of manual data processing, BMS engineers can focus on the high-value tasks around developing and validating battery control algorithms. Freed from limitations around data volumes, testing can be expanded to evaluate a larger set of application scenarios, or to test to higher resolution in pursuit of a more precise SOH calculation for regulatory compliance. Tangible outcomes of this work can include developing ways to speed up charging or extend vehicle range, as both are directly impacted by the BMS. Inefficient legacy data analysis practices will not deliver these results, but an EBI-powered BMS development program can enable a better product and ultimately improve the customer experience.
With EBI the BMS enables additional post-sale value
Once your product is in customer hands, the BMS can form a vital link to powerful remote computing resources that provide further value to your customers across a number of dimensions. While the BMS typically keeps some log of the battery’s performance, onboard data storage capacity usually limits the practical length or resolution of the log that can be stored locally. Internet-enabled devices, however, can send high-resolution data to a cloud-based EBI platform for further analysis and value creation. (And today just about everything is an internet-enabled device!) When aggregated and analyzed alongside data from the full fleet of customer devices, an EBI system can:
- Monitor the battery for any safety issues not anticipated in the original design
- Anticipate necessary maintenance
- Better understand product and user behavior for future product improvement
- Improve battery (and product) performance in-situ, or proactively avoid safety incidents through an over-the-air (OTA) update
These use cases become even more valuable as your fleet of battery-powered devices, systems, and vehicles ages through its service life. Even under normal usage, batteries experience significant internal wear and tear, simply through the chemical and physical changes that accumulate with each charge-discharge cycle. Fast charging, extreme temperatures, repeated full discharges, and a host of other usage patterns can accelerate this degradation. A cloud-connected BMS paired with an EBI solution can empower you with the visibility to ensure your battery stays healthy over the full (and variable!) lifetime of your product.
In this capacity, the BMS serves as a pass-through for battery performance data headed for the cloud. While BMS computing power will surely increase over time, the real next-level value comes from aggregating data from across your customer fleet, and leveraging an EBI capability to drive continuous improvement.
As battery-powered products and systems evolve and improve, the role of the BMS will remain as vital as ever. Advanced data analytics, delivered through an Enterprise Battery Intelligence solution, will ensure that your BMS delivers best-in-class performance and ultimately the best possible customer experience.
