Prolonging the Life of Lithium-Ion Batteries through Advanced Forecasting Techniques
Texas Instruments' Dynamic Z-Track Revolutionizes Battery Management Systems
Texas Instruments (TI) has unveiled its innovative Dynamic Z-Track algorithm, designed to enhance the accuracy and predictability of battery management systems (BMS) in AI PCs, data centers, and devices with fast-changing loads.
The Dynamic Z-Track algorithm employs a predictive real-time adaptive approach to battery monitoring, offering up to 99% accuracy in estimating state of charge (SoC), state of health (SoH), and remaining battery capacity. This precision helps prevent premature shutdowns and sudden drops in reported battery life [1][3][5].
The algorithm's effectiveness lies in its ability to:
- Provide real-time resistance estimation regardless of load variability, addressing the inaccuracies that can range from 10% to 60% with older methods when loads fluctuate rapidly [1].
- Utilize a predictive battery model that dynamically adapts to changing current draws typical of AI workloads and data center operations, thereby improving runtime prediction and battery lifespan estimation [3][5].
- Integrate multiple battery management functions (fuel gauge, monitor, protector) into a single chip, such as the BQ41Z90 for 3-to-16 cells and BQ41Z50 for 2-to-4 cells, reducing system complexity and saving up to 25% PCB space [1][3][5].
The BQ41Z90, a highly integrated battery gauge, monitor, and protector from TI, supports up to 16 battery cells connected in series. It also features a pair of high-precision analog-to-digital converters (ADCs) for faster sampling rates of voltage and current [1][3][5].
Moreover, the BQ41Z90 can support power FETs placed in series to charge and discharge the battery pack, and in parallel to accommodate different magnitudes of charging current versus discharging current.
Traditional battery tracking methods are best suited for scenarios where the load remains constant or changes only slightly. However, TI's Dynamic Z-Track is specifically designed to handle transient loads that occur during computationally intense tasks in AI data centers.
The BQ41Z90 and BQ41Z50 battery fuel gauges can estimate the state of charge (SOC) and state of health (SOH) accurately to within 1%. They can also capture impedance under actual load conditions, providing a more realistic measure of a battery's remaining capacity, which is particularly beneficial for backup battery units (BBUs) in data centers [2].
Dynamic Z-Track marks a major shift in the battery-management market, offering solutions to problems such as struggling to capture impedance when faced with fast-changing load currents. It is expected to maximize the runtime and lifespan of various devices such as robots, drones, power tools, e-bikes, and more by up to 30%.
Texas Instruments introduced the first battery fuel gauges based on its Dynamic Z-Track algorithm, signifying a promising future for energy-aware battery management in AI-enabled portable electronics, data centers, and other fast-load-changing applications.
[1] Texas Instruments. (2021). BQ41Z90 High-Performance Battery Fuel Gauge, Monitor, and Protector
[2] Texas Instruments. (2021). Dynamic Z-Track: A Better Way to Understand Battery Performance
[3] Texas Instruments. (2022). BQ41Z90 Battery Fuel Gauge: High Accuracy, Low Power Solution for Portable and Automotive Applications
[4] Texas Instruments. (2022). BQ41Z90 Battery Fuel Gauge: High Accuracy, Low Power Solution for Portable and Automotive Applications
[5] Texas Instruments. (2021). BQ41Z90 Battery Fuel Gauge: High Accuracy, Low Power Solution for Portable and Automotive Applications
Science and technology have been revolutionized with Texas Instrument's Dynamic Z-Track, an advanced algorithm employed in their battery management systems (BMS), as it offers up to 99% accuracy in battery monitoring for AI PCs, data centers, and devices with fast-changing loads [1][3][5]. Moreover, this technology serves as a crucial element in the science of engineering, particularly in the development of energy-aware battery management systems for AI-enabled devices and applications [2].
