Hybrid Regression Technique Using Gray Wolf Optimization Enhances State of Health Estimation for Bipolar Lead-Acid Batteries

Researchers have developed a novel hybrid regression technique, optimized using the Gray Wolf Optimization algorithm, to enhance the accuracy of state of health (SOH) estimation for bipolar lead-acid batteries. This advancement addresses critical challenges in monitoring and maintaining battery performance, particularly in energy storage systems. Bipolar lead-acid batteries, which differ from traditional valve-regulated lead-acid (VRLA) batteries through their unique architectural design, have gained attention for their potential to improve energy efficiency and reliability.

The innovative approach leverages the Gray Wolf Optimization algorithm to fine-tune regression models used in SOH estimation. By placing the cathode and anode on opposite sides of a bipolar substrate, these batteries allow electrons to flow directly between adjacent cells without requiring external connections. This design reduces internal resistance and enhances overall performance. The new technique aims to provide more precise assessments of battery health by integrating advanced computational methods with this improved battery architecture. Researchers anticipate that this method could play a significant role in optimizing the operation and lifespan of bipolar lead-acid batteries across various applications.

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Source: GO-AI-ne1

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Date: April 8, 2026

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GOAI