The global energy transition faces a paradox. As electric vehicles scale into the millions and renewable energy expands across the grid, we are producing batteries at an unprecedented rate. However, when these batteries reach 70-80% of their original capacity and are retired from their first application, we struggle to extract their remaining value. A battery removed from an EV still holds tremendous potential for less demanding applications like grid storage or backup power, but our ability to capitalize on this “waste” as an energy asset has been limited by our ability to truly understand them.
This is where AI is fundamentally changing the equation for climate tech. For the first time, we can manage repurposed batteries with the precision needed to make them a viable, scalable climate solution. AI is not just improving battery management, it is a crucial step to bringing circular economy models from theory to practice.
Solving for Imperfect Battery Information
Battery management relies on the answer to a deceptively simple question: what is actually happening inside a cell over time?
Traditional battery management systems use measurements like voltage, current, and temperature to make educated guesses about battery state, including state of charge and state of health of the battery, to understand how much energy is available and how much the battery has degraded from its original capacity. The problem is that these guesses are fuzzy and become increasingly unreliable over time, like trying to navigate with a map that slowly loses accuracy as distance travelled increases.
For new batteries in controlled environments, this approach works reasonably well. For repurposed batteries with unknown histories and varying degradation patterns, it is a major challenge.
The consequences ripple through the entire system. Discharge a cell too aggressively based on poor state estimates, and you accelerate degradation. Make overly conservative assumptions to stay safe, and you leave massive amounts of usable capacity on the table. For years, the industry has accepted these trade-offs as unavoidable, limiting the viability of second-life battery systems for climate applications.
AI as the Focusing Lens
Machine learning approaches are fundamentally different because they learn patterns directly from massive datasets rather than relying on predetermined formulas. When you expose these algorithms to thousands of charge-discharge cycles across diverse battery chemistries and usage patterns, they begin to recognize subtle signatures that inform battery state with greater reliability and precision.
AI-powered battery state estimators can now predict current capacity and health with accuracy that approaches direct physical testing. This matters enormously for second-life applications, as you are managing a population of batteries with significantly different histories, ages, and health levels. Some cells might retain 85% of their original capacity while others sit at 65%. Traditional systems treat this diversity as a liability, but AI-powered systems can now treat it as manageable complexity.
The breakthrough is particularly important for newer battery chemistries that are becoming central to the energy transition. Lithium iron phosphate (LFP) batteries have gained popularity for their safety profile and cost advantages, but they present a unique challenge. Their voltage curve is remarkably flat across most of their charge range, making traditional voltage-based estimation techniques nearly useless. AI models can integrate dozens of other signals including current flow patterns, thermal characteristics, and historical performance data to estimate state accurately where conventional methods fail completely.
More importantly, AI can predict how battery health will evolve over time under different usage scenarios. This is game-changing, because it equips repurposers with enough information to provide warranties and performance guarantees for repurposed battery systems with a degree of confidence that did not exist previously.
Active Balancing and Lifetime Optimization
The most powerful implication of AI in battery management is not just understanding current state, but using that understanding to actively extend battery life and maximize the climate impact of every cell produced.
Modern AI-driven battery management systems can dynamically adjust how individual cells are used within a larger system based on their predicted longevity. Consider a practical example. You have two battery modules in a grid storage system, both currently showing adequate performance. Traditional systems would use them roughly equally, wearing them down at the same rate.
An AI-driven control algorithm, however, may glean from its predictive model that one module is weaker and more susceptible to stress-induced degradation. Instead of a simple 50/50 split, it will dynamically adjust the power flow, drawing perhaps 60% from the healthier module and only 40% from the other to fulfil the load requirement while protecting the weaker asset. This granular control extends the weaker module’s life and maximizes the total energy the system can deliver before any component needs replacement.
This becomes exponentially more valuable when managing hundreds or thousands of cells in large-scale energy storage installations. The AI system continuously balances multiple competing objectives. It must meet immediate power demands while keeping all cells in safe operating ranges and optimizing for maximum system longevity. These trade-offs change moment to moment based on usage patterns, temperature, and the evolving health of each cell.
Data-driven control algorithms make split-second decisions that maximize the useful life extracted from every cell in the system. For second-life batteries powering renewable energy systems, where margins are tighter and every percentage point of additional capacity matters economically, this optimization represents the difference between viable and unviable climate solutions.
Making Clean Energy More Accessible
The broader implications extend well beyond battery management as an engineering problem. Second-life batteries represent a crucial piece of the climate puzzle. They offer a path to significantly more affordable energy storage, making renewable energy viable in applications and geographies where new batteries remain economically out of reach.
AI transforms the uncertainty and variability of repurposed batteries from an insurmountable obstacle into a manageable challenge. When integrated in combination with compliance to industry certification and safety standards, AI provides the deep, predictive insight needed to guarantee performance and manage ongoing health, while certification validates a battery’s baseline safety and viability. This powerful pairing enables business models where organizations can confidently deploy second-life systems, creating circular economy markets that align climate goals with economic reality.
As these AI models continue to improve with more data and more sophisticated architectures, they will unlock progressively more value from every battery produced. They will help us squeeze every possible kilowatt-hour from cells before they truly reach end of life. Batteries are the linchpin of a sustainable future, and AI will be a crucial step in making clean, affordable, reliable power a more tangible reality for communities worldwide.
