24th February 2022

Improving Battery Safety Using Predictive Maintenance

Battery Energy Storage System

The demand for renewable and distributed generation is leading to rapid evolution in the electric grid. These changes are beginning to considerably strain the transmission and distribution infrastructure. Over the past few years, as the number of Battery Energy Storage Systems (BESS) has increased, system integrators, government bodies, and organizations have put considerable effort into developing safety standards and best practices for engineering and commissioning. 

It can be tough to know when a failure is imminent when it comes to batteries. But what if you can predict the future? Imagine preventing failures and potential accidents by predicting system defects and fixing them ahead of time. Imagine replacing your batteries at the exact time when their lifecycles would end.

With predictive maintenance systems, you don’t have to imagine. You can make this a reality.

What is Predictive Maintenance?

Predictive maintenance involves monitoring tools and data analytics to pinpoint the exact moment when your equipment fails. It’s an industry practice that has been around since the 1990s. Today, it is gaining widespread popularity due to IoT applications and improved cloud and machine learning developments. 

It follows the condition monitoring principles, where you continuously monitor an asset to ensure optimal performance. Using various data points, a Battery Management System analyzes these inputs to make precise predictions for maintenance requirements. 

Predictive maintenance aims to determine the most cost-efficient and convenient moment when maintenance has to be performed. This ensures the battery’s life lasts longer and is used to its fullest capacity without compromising performance. 

Predictive vs Preventive

The key difference between predictive and preventive maintenance is the time when maintenance work is performed. During preventive maintenance, you inspect and perform maintenance regardless of whether the machinery needs it. Maintenance work is done based on the length of time you’ve used the equipment. On the other hand, predictive maintenance involves performing maintenance work only when necessary. 

Another key difference is the kind of data analyzed to determine the extent of maintenance work. Preventive maintenance uses data averages, historical data, and life expectancy statistics. Predictive maintenance relies on the batteries’ actual and real-time condition as monitored by data analytics systems.

Preventive maintenance is typically easier and cheaper to implement, but its recurring costs can add up. Predictive maintenance requires more upfront investments but is proven to be 8% to 12% less costly in the long run.

In preventive maintenance, you usually have to follow the manufacturer’s guide on changing critical parts and replacing parts even when completely unnecessary. With predictive maintenance, you replace parts only as needed. 

Advantages of Predictive Maintenance

Batteries are prone to be overcharged or discharged. This creates undue stress on the battery, leading to a reduced lifespan, cell failure, and rapid degradation. 

With predictive maintenance, you know exactly when a battery unit is near its end cycle and needs to be replaced. It offers the following benefits:

  • Extending battery life by up to 40%
  • Optimizing battery use until the actual end-of-life
  • Reducing the number of faulty cells
  • Improving overall battery prognosis

By using intelligent battery management systems, you can rely on your actual battery usage and determine if you need to repair or replace parts sooner to prevent failure or later to preserve unwanted costs. You are no longer left guessing or blindly following the manufacturer’s advice on when to perform maintenance for your energy storage systems.

Further, predictive maintenance provides the following advantages:

  • Reduced downtime for the equipment to be under maintenance
  • Reduced productive hours lost due to maintenance operations
  • Reduced costs for unnecessary spare parts and maintenance procedures

Predictive maintenance has been reported to produce 30% to 40% savings and a tenfold return on investment. This is because, with predictive maintenance, productivity is increased as downtimes are significantly decreased. A study has shown that predictive maintenance improves productivity by as much as 25%! 

A 2019 report by GlobalData, “Predictive Maintenance in Power,” noted several successful implementations of this approach in the utility sector:

  • The monitoring and diagnostics centre at the utility American Electric Power identified warning signs of failure and initiated repair work of a gas turbine blade before breakdown. This resulted in savings of about $19 million.
  • Duke Energy used predictive analytics for the early detection of a crack in a turbine rotor. This resulted in savings of over $7.5 million.
  • Southern Company applied predictive analytics to power station models to decrease unexpected maintenance and maintain data quality reliability. This resulted in savings of approximately $4.5 million.
  • Many wind turbine operators now use predictive analytics to monitor the health of the gearboxes. The cost of gearbox failure can be upwards of $350,000 per incident. 

Considering the rising costs of energy storage systems, cost savings incurred through efficient maintenance programs would be welcome news.

Predictive maintenance may require retraining personnel, added investments, and a shift in perspectives, but the merits outweigh these inconveniences. Predictive maintenance is the thing of the future, and if you can predict the future, why shouldn’t you?

Implementing Predictive Maintenance

Preventive maintenance remains to be the preferred program by 80% of maintenance personnel. However, the demonstrated advantages of predictive maintenance have made it an increasingly popular maintenance scheme.

Before starting a predictive maintenance (PdM) program, you should carefully consider the following steps:

  • Determine which critical assets would require predictive maintenance
  • Communicate the need for PdM to major stakeholders and get their buy-in
  • Establish the historical database of critical assets
  • Assess existing maintenance programs
  • Identify failure modes and make failure predictions
  • Decide which predictive maintenance technology to use

The key to predictive maintenance is the successful use of IoT solutions, which demands a learning curve. Therefore, personnel training is also a huge consideration.


With Altergo’s predictive maintenance, you are able to detect abnormal situations in your operation and potential problems with your assets and their components using data analysis techniques, so you can address them before they go wrong. In practice, this provides for the smallest possible maintenance frequency so as to avoid unexpected reactive repairs while avoiding schedule maintenance expenses.

Altergo anticipates issues by studying historical and real-time data from many aspects of your operations and sensors.

Altergo Impacts:

  • Identifying and fixing potential problems makes your Storage system more reliable and trustworthy.
  • With a better understanding of the condition of batteries, there are fewer unscheduled outages.
  • By continuous system optimization, battery performance is improved and battery life is increased.


Altergo is a Modern Asset Management Platform for Energy that empowers new energy companies to create an accurate Digital Twin, a virtual replica of physical components, assets and their contracts. By unifying the internal and external data and leveraging advanced data science, Altergo significantly impacts a business’ ROI and enables companies to make data-driven decisions that improves asset life and performance.

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