Battery Power for Consumer Electronics

Battery life and performance are constantly traded against each other, with power-saving modes dimming screens and throttling performance even when the battery would never run low. This talk presents BAIO Boost, a deep-learning approach that predicts each user's next-day battery demand from individual usage history and flags at-risk days when the state of charge is expected to fall below a defined threshold. Validated at scale against real-world usage, it shows how anticipating demand can end the battery-versus-performance compromise. Join this session to see what user data reveals about how we really work, and where AI-driven power management goes next.

State-of-charge indication relies on correlation of open circuit voltage with depth of discharge (DOD). Established chemistries have a stable OCV vs. DOD relationship over the age of the battery. However, new chemistries with higher energy density but less stable OCV are appearing. One example is Si-anode, with larger capacity, but rapid change of cells' OCV. We will discuss the TI solution for this issue and analyze its accuracy.

This presentation explores innovative battery designs tailored for augmented reality glasses. We examine the unique power demands and spatial constraints of these devices, proposing solutions that enhance energy efficiency, reduce weight, and extend operational lifespan. By integrating advanced materials and smart battery management systems, our research aims to improve user experience and device performance in immersive applications, paving the way for future developments in augmented technologies.

Battery swelling is a critical reliability and product integration challenge for high-energy-density Li-ion batteries used in consumer electronics. Excessive thickness growth can impact mechanical design, device lifetime, and customer experience. Despite its importance, swelling prediction remains significantly less mature than electrochemical performance and degradation modeling, and product development often relies on empirical testing and simplified acceleration models. Ongoing battery modeling efforts at Microsoft have identified predictive swelling modeling as a key capability for improving reliability assessment and battery optimization. 

This work presents an ongoing effort to develop a physics-based swelling modeling framework using PyBaMM and cell characterization data generated from lab testing. The objective is to connect lab measurements with predictive simulations capable of evaluating a wide range of operating conditions, including temperature, voltage window, depth of discharge, and charge/discharge rate.The proposed framework seeks to capture these multiple degradation pathways within a single model. 

This work aims to establish a scalable pathway from cell characterization to predictive swelling simulations that support battery design, reliability assessment, and future usage-based battery digital twins.

Among solid-state electrolytes, halide electrolytes offer high ionic conductivity, anodic stability, and room temperature processability, yet stable cycling is typically achieved under stack pressures of 10-100 MPa, for reasons that remain unclear. Here we identify the unusually high susceptibility of brittle halide electrolytes to fracture as the underlying mechanism and show that stack pressure—conventionally applied to suppress mechanical damage—can instead promote it by enhancing strain transfer from the active materials to the surrounding electrolyte. Guided by this insight, we demonstrate an anode-free halide pouch cell that retains 85% of its capacity over 200 cycles at C/3 under only 2 MPa.

AI-Enabled medical and consumer electronics are creating demand for smaller, safer, and higher-performance batteries. Hearing aids, hearables, and connected devices increasingly integrate advanced sensing, edge processing, wireless connectivity, and AI features such as real-time language translation within highly constrained form factors. This presentation explores evolving battery requirements, limitations of incumbent technologies, and the opportunity for solid-state batteries to deliver greater energy density, power, safety, and design flexibility. Using Ensurge Micropower’s solid-state lithium microbatteries as a case study, attendees will learn why these markets offer attractive commercialization opportunities and how emerging microbattery technologies and manufacturing advances can enable next-generation devices.

This presentation explores how AI is reshaping notebook PC battery requirements, from increasing power demands and energy efficiency, to battery management and optimization. Drawing on Lenovo’s perspective, it will examine the challenges and opportunities AI-enabled computing creates for battery performance, runtime, thermal management, and future notebook design.

Medical device applications use lithium primary and lithium-ion batteries in miniature implantable products, portable devices, and large hospital capital equipment. Delivering the highest levels of safety and reliability requires a strong understanding of use conditions and clear requirements. This abstract examines how these factors shape battery design and use and reviews test methods used to verify that requirements are met.

In this presentation, the design of batteries used in Personal Protective Equipment (PPE) is examined in relation to real-world usage patterns. By tailoring battery design to factors such as the current profile during operation, recharge behavior, and expected temperature range, designers may enhance the performance of the equipment and provide users with more reliable and comfortable experiences on the job.