As the global demand for reliable electricity grows, University of Houston researchers are deploying artificial intelligence to solve a critical vulnerability in the power grid: the unpredictable aging and decay of large-scale batteries.
The latest research from Xingpeng Li, associate professor at the Cullen College of Engineering, aims to make power grid batteries more efficient as solar and wind power expand.
These sources increasingly depend on battery energy storage systems to balance swings in energy supply, which is stored when energy is high and released when output drops. The problem is batteries degrade over time, reducing their performance and efficiency, which makes it difficult for grid operators to know when to use them.
“The growing use of alternative energy and battery storage in bulk power systems and small-scale microgrid systems, where solar and wind fluctuations make reliable scheduling more challenging,” Li said. “A key motivation was that batteries degrade over time that may affect the system performance especially in later years when battery capacity reduced greatly, so we developed a smarter way to include battery aging in energy planning without making the optimization too slow.”
How AI Comes into Play
Li and his team addressed this problem by developing a specialized AI-based model that predicts how batteries degrade under real-world conditions.
The model accounts for various factors that affect battery health, which includes temperature, charge rates and overall usage patterns. Traditionally, simple battery-aging assumptions have been used widely.
Li’s method offers a more accurate picture of how quickly a battery will wear down; however, while the models can improve predictions, they are often too complex to use in real-time energy planning.
“Charging and discharging at the right times helps store excess renewable energy and release it when demand is high or renewable output is low,” Li said. “It also reduces unnecessary battery wear, which can extend battery life and lower system costs. It will also better enhance grid overall efficiency and reliability.”
To address the issue, the team helped design a streamlined version that removes less important connections within the neural network to create a simplified system that maintains accuracy while reducing demands.
That allows the system to incorporate battery degradation predictions into energy scheduling, resulting in faster, more informed decisions about when to charge or discharge batteries.
“AI can help predict battery degradation based on how batteries are discharged and charged, allowing operators to make faster and more informed scheduling decisions,” Li said. “By using sparse AI neural network, the model keeps much of the accuracy of deep learning while reducing computational burden, making it more practical for day-ahead and real-time operations.”
“Smarter battery scheduling can also reduce congestion and improve the stability of renewable-heavy power systems.”
—Xingpeng Li, associate professor at the Cullen College of Engineering, University of Houston
Li said this process could potentially lead to lower prices for electricity.
“For consumers, this could mean a more reliable grid, better use of alternative energy and lower electricity costs over time,” he said. “Smarter battery scheduling can also reduce congestion and improve the stability of renewable-heavy power systems.”
Battery Improvement Plan
Scientists at UH have long-pursued techniques that could improve battery life, the latest being when engineering Professor Yan Yao exposed structural weaknesses that drive lithium-ion battery failures.
And prior to that, Yao and his team uncovered what caused solid-state batteries to break down and how that process could be slowed. Unlocking that secret was the first step to improving battery life for everything from cell phones and laptops to electric vehicles.
Adding to that potential, Bo Zhao, an award-winning and internationally recognized engineering professor at the Cullen College of Engineering, discovered a technique to control the flow of heat in electronics, which would ultimately help prolong battery endurance.
Part of a Growing AI Research Portfolio
Li’s research is part of a robust and expanding body of work at the University of Houston, where AI is being treated as a primary engine for industrial progress. More than 140 faculty researchers are leading over $70 million in AI-related funded research.
This portfolio spans critical sectors including healthcare, resilient infrastructure, cybersecurity and advanced mathematics — advancing innovations that are essential to both the Houston economy and national competitiveness.