Texas Instruments Introduces Predictive Battery Management Tech for Electronics, Laptops, and E-Bikes

Texas Instruments introduced a predictive battery management technology called Dynamic Z-Track, which can extend battery life by up to 30% in electronics, laptops, and e-bikes. The technology uses AI to self-update across dynamic load conditions, providing accurate run-time predictions. This innovation is expected to enable users to experience dependable function, safer operation, and precise tracking of battery age and run time.

Texas Instruments (TI) has introduced a groundbreaking predictive battery management technology, Dynamic Z-Track, designed to extend battery life by up to 30% in various electronic devices, including laptops and e-bikes [1]. This innovation leverages artificial intelligence (AI) to self-update across dynamic load conditions, ensuring accurate runtime predictions.

The Dynamic Z-Track technology is integrated into TI's new single-chip battery fuel gauges, the BQ41Z90 and BQ41Z50. These gauges offer state-of-charge and state-of-health accuracy within 1% error, significantly improving upon traditional battery monitoring methods [1]. This enhanced accuracy allows engineers to design devices with precise battery capacity readings, even under unpredictable loads, thereby eliminating the need for oversized batteries.

The technology's ability to self-update across dynamic load conditions makes it particularly valuable in applications where battery performance can vary greatly, such as AI-driven applications. According to 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 ensures the most accurate runtime prediction" [1].

The BQ41Z90 is the industry's first highly integrated fuel gauge, monitor, and protector for 3-to-16 lithium-ion (Li-ion) battery cells in series, while the BQ41Z50 supports 2-to-4 cells in series. These single-chip solutions enable engineers to reduce complexity and save as much as 25% of board space compared to traditional discrete solutions [1]. This integration not only enhances efficiency but also contributes to cost savings, making it an attractive option for manufacturers.

The introduction of Dynamic Z-Track technology aligns with the growing demand for reliable and efficient battery management systems (BMSs) in electric vehicles and energy storage systems. Machine learning (ML) has emerged as a powerful tool for battery state of health (SOH) estimation, offering timeliness, strong adaptability, and the ability to handle complex nonlinear relationships [2]. However, the effective implementation of ML in battery management requires a comprehensive understanding of data collection, preprocessing, feature extraction, and model training.

The new TI technology addresses several challenges in battery management, including the need for accurate and real-time monitoring, especially under unpredictable conditions. By providing precise battery capacity readings and extending battery life, Dynamic Z-Track technology can significantly impact the performance and longevity of electronic devices, making it a valuable addition to the field of battery management.

References:
[1] https://cxotoday.com/press-release/predictive-battery-management-technology-from-ti-delivers-up-to-30-longer-run-time-in-battery-powered-electronics1/
[2] https://www.sciencedirect.com/science/article/abs/pii/S1364032125007981