From the Road to the Rack: Using Physics-Based Simulation to Integrate Second-Life Batteries into Data Centers

Today’s data center industry is facing a massive power crunch.  Driven by the explosive growth of AI workloads and a strained electrical grid, the industry is desperate for solutions that can provide firm power solutions and resilience.

Second-life (2nd life) batteries, repurposed modules from retired EVs, are a tempting solution to fill this gap at a lower CAPEX.  But there is a massive sourcing hurdle: in the second-life market, you can’t buy trust in bulk.  Unlike primary batteries that come with a clean slate and a manufacturer’s warranty, 2nd life batteries arrive as a “black box.”  Without a transparent record of a battery’s first life, incorporating these modules into a mission-critical BESS (Battery Energy Storage System) feels less like a design choice and more like a gamble on system reliability.

To bridge this data gap, this blog will apply the same high-fidelity physics models used in predicting battery degradation in a battery’s first life that we explored in previous blogs (Predicting Aging for Real-World Applications and Predicting System Performance with Aged Batteries) to the stationary storage world and the 2^nd life battery opportunity.

The Challenges of Buying 2^nd Life Batteries:

The biggest barrier to the widespread adoption of second-life batteries in critical infrastructure like data centers is uncertainty.

1. Non-linear Aging: Batteries don’t age at a constant rate. Toward the end of their life, degradation mechanisms like lithium-plating and electrolyte dry out can accelerate the capacity fade, leading to a sudden “aging cliff” where performance drops off precipitously.
2. Unknown History: Unlike a new “primary life” battery with a clean slate, a second-life battery arrives with a history. It has endured thousands of miles of highway and city driving, fast-charging events, and varying thermal environments. Buyers of 2^nd life batteries cannot know how close or far from the aging cliff their batteries are.
3. Increased Resistance: Not only does the capacity of a battery decrease with age, but the resistance of the battery increases as there’s more SEI layer growth, electrolyte breakdown, and loss of active material. This increase in battery resistance can increase the cooling requirements of 2^nd life energy storage systems.
4. BMS Complexity: Developing a Battery Management System (BMS) that can interpret pack health and balance loads evenly is already a heavy engineering lift. Scaling this up to a BESS where every battery pack has its own unique, “un-clean” history makes load balancing exponentially more complex.

GT Solutions:

With the power of GT-AutoLion and GT-SUITE, you can build physics-based digital shadows of 2^nd life batteries.

Electrochemistry with GT-AutoLion

By modeling the internal electrochemical processes, such as SEI layer growth, electrolyte dry-out, Lithium-plating, and more, engineers can:

• Predict the “Knee”: Predict when a second-life battery will transition from stable operation to rapid failure.
• Assess Health Without History: Use a “synthetic” first-life simulation to estimate the internal state of a battery even if the original vehicle data is missing.
• Optimize for Stationary Use: Data centers and microgrids have vastly different load profiles than vehicles. We can simulate how an “aged” battery will respond to grid outages or peak shaving demands of a microgrid, ensuring the system meets its 10- or 15-year ROI targets.

From EV Packs to Microgrid Resilience

In a data center application, reliability is non-negotiable. Using GT-SUITE, you can integrate these aged battery models into a larger system-level simulation that includes:

• Thermal Management: Ensure that an aged battery’s increased internal resistance is accounted for when sizing the cooling system for your BESS. (Check out Dig Vijay’s blog on experimenting with varying battery cooling concepts with GT-SUITE)
• Safety: As batteries are closer to their end of life, the risk of thermal runaway events can be elevated, and ensuring that one cell or pack entering thermal runaway doesn’t cause a chain event across the entire BESS becomes critical (See Alireza Kondori’s blog on mitigating the domino effect of battery thermal runaway).
• Control: Validate BMS algorithms to ensure that data centers can maximize the potential of each battery pack (See Nikil Biju’s first entry into a blog series on Battery Management Systems).
• Techno-economic Modeling: Predicting the Remaining Useful Life (RUL) with high confidence to confidently purchase second-life systems over buying new primary batteries (See Ujjwal Chopra’s webinar on tecno-economic microgrid modeling using GT-SUITE).
  

  Bridging the Gap

  By leveraging the same high-fidelity physics models that designed the vehicle’s first life, we can provide the certainty needed to power the next generation of sustainable data centers.

  Are you looking to incorporate second-life batteries into your energy storage projects? Contact us today to see how GT-AutoLion and GT-SUITE can help you de-risk your transition to the circular economy.