The battery chips help devices run longer, show charge accurately, and save space on the board. Useful for laptops, e-bikes, and medical tools.
Texas Instruments (TI) has introduced two new single-chip battery fuel gauges—BQ41Z90 and BQ41Z50—that feature TI’s unique adaptive Dynamic Z-Track™ technology. These new gauges deliver more efficient and reliable performance in battery-powered devices by using predictive modeling algorithms that achieve state-of-charge and state-of-health accuracy within 1% error. This improvement can extend battery run time by up to 30% compared to traditional methods.
As electronic devices like laptops, e-bikes, and medical equipment demand more power, accurate real-time battery monitoring becomes critical. TI’s new fuel gauges enable precise battery capacity readings even during unpredictable power usage. This helps engineers design systems with the right battery size—avoiding the common practice of oversizing—while ensuring better performance and longer run times.
The BQ41Z90 is the industry’s first single-chip solution that combines a fuel gauge, battery monitor, and protection functions for 3- to 16-series lithium-ion (Li-ion) cells. By integrating these features, it helps engineers reduce design complexity and shrink board size by up to 25% compared to traditional multi-chip implementations. For 2- to 4-cell configurations, the BQ41Z50 offers similar benefits in a compact form.
“As battery-powered devices grow more complex, efficient use of board space becomes increasingly important,” said Yevgen Barsukov, Ph.D., TI Fellow and head of BMS algorithm development. “Whether you’re finishing a project on your laptop or riding home on an e-bike, accurate battery capacity estimates and reliability are critical. Traditional battery monitoring methods often struggle with accuracy under erratic use conditions, leading to unreliable predictions. However, our new Dynamic Z-Track technology is a predictive battery model that can self-update across dynamic load conditions, like those created by AI applications, ensuring the most accurate run-time prediction. Evolving from 20 years of reactive monitoring, this innovation enables users to experience dependable function, safer operation, and precise tracking of battery age and run time.”
