Microsoft Technology Licensing, LLC

Julia Louise Meinershagen

Ranveer Chandra

Stephen Edward Hodges

Thomas Moscibroda

Nissanka Arachchige Bodhi Priyantha

Anirudh Badam

Anthony John Ferrese

Bojun Huang

Pan Hu

Evangelia Skiani

Jonathan Alan Dutra

David Douglas Bohn

Stephen G Latta

Rod G Fleck

James William Scott

Jeffrey Alan Kohler

Eric I Horvitz

Brian C McDowell

Vladimir Holostov

Sebastian Sylvan

Jeff Cole

Austin Tyler Czarnecki

Christopher Dekmezian

Techniques for dynamically changing internal state of a battery are described herein. Generally, different battery configurations are described that enable transitions between different battery power states, such as to accommodate different battery charge and/or discharge scenarios.

Dynamically changing internal state of a battery

Various embodiments provide techniques and devices for scheduling power loads in devices having multiple batteries. Loads are characterized based on the power required to serve them. Loads are then assigned to batteries in response to the type of load and relative monitored characteristics of the batteries. The monitored battery characteristics can change over time. In some embodiments, stored profile information of the batteries can also be used in scheduling loads. In further embodiments, estimated workloads can also be used to schedule loads.

Load scheduling in multi-battery devices

This document describes techniques and apparatuses of load allocation for multi-battery devices. In some embodiments, these techniques and apparatuses determine an amount of load power that a multi-battery device consumes to operate. Respective efficiencies at which the device's multiple batteries are capable of providing power are also determined. A respective portion of load power is then drawn from each of the batteries based on their respective efficiencies.

Load allocation for multi-battery devices

This document describes techniques and apparatuses for suppressing power spikes. In some embodiments, these techniques and apparatuses determine an available amount of power that a battery is capable of providing while maintaining a particular voltage level and a requisite amount of power that components will consume to perform a task. When the requisite amount of power exceeds the available amount of power, execution of the task or other tasks can be rescheduled effective to enable the battery to maintain the particular voltage level.

Battery parameter-based power management for suppressing power spikes

Schedule-based energy storage device selection is described for a device having an energy storage device system with heterogeneous energy storage devices, such as heterogeneous battery cells. The techniques discussed herein use information regarding a user's schedule (e.g., the user's calendar) to predict future workload patterns for a computing device and reserve energy storage device capacities across multiple heterogeneous energy storage devices to improve efficiency of the energy storage devices. For example, if a user is expected to attend a video conference call later in the day (e.g., due to the video conference call being on the user's calendar), then energy in an energy storage device that is better capable of handling such a workload (providing power during the video conference call) more efficiently is preserved so that the energy is available when the video conference call occurs.

Schedule-based energy storage device selection

Latency-based selections of energy storage devices are described herein. In implementations, latency behavior of computing tasks performed by a computing device is predicted for a period of time. Based on the predicted latency behavior of the computing device over the period of time, an assessment is made regarding which of multiple heterogeneous energy storage devices are most appropriate to service the system workload. For example, high energy density devices may be favored for latency sensitive tasks whereas high energy density devices may be favored when latency sensitivity is not a concern. A combination of energy storage devices to service the current workload is selected based upon the latency considerations and then power supply settings are adjusted to cause supply of power from the selected combination of energy storage devices during the time period.

Latency-based energy storage device selection

The description relates to controllable device marking. One example can be manifest as a device that has a housing and a marking apparatus integrated into the housing. The marking apparatus can include a display and a disablement mechanism. While the disablement mechanism is in a first state the display is controllable to allow content presented on the display to be defined and when the disablement mechanism is transitioned to a second state the content is persisted and unchangeable on the display.

Controllable marking

A shared electrode battery includes multiple electrodes of one type (e.g., two or more cathodes) that share an electrode of another type (e.g., a shared anode). The multiple electrodes of the same type (e.g., the multiple cathodes) can have different characteristics, such as different chemistries, particle sizes and distributions, capacities, and so forth that are designed to provide particular features such as high energy density, high power density, high cycle life, fast charge, safety, and so forth. Multiple cathode-anode pairings of one of the multiple electrodes of the same type with the shared electrode are possible. Switching hardware is operable to select one of the multiple pairings at any given time, allowing the battery to provide power using the cathode having the desired characteristics at that given time. A single battery is thus able to provide these multiple different features.

Shared electrode battery

This document describes techniques and apparatuses for suppressing power spikes. In some embodiments, these techniques and apparatuses determine an available amount of power that a battery is capable of providing while maintaining a particular voltage level and a requisite amount of power that components will consume to perform a task. When the requisite amount of power exceeds the available amount of power, power states of the components are altered effective to enable the battery to maintain the particular voltage level.

Methods for optimizing battery use for a known future load are described. In an embodiment, one or more battery cells are selected from a set of battery cells to provide power to a computing-based device. The battery cells are selected based on discharge profile data for each battery cell and both the current power requirement and a known future power requirement of the computing-based device. The known future power requirement is calculated based on information available to the operating system running on the computing-based device. In some examples, one or more battery cells may also be selected for charging when a power source is available and these cells may be selected based on charge profile data and the known future power requirement. The selection of the battery cells may also be made in order to satisfy a battery optimization goal, which may be defined by a user.

Optimizing battery use for known future load

A blocking image generating system and related methods include a head-mounted display device having an opacity layer. A method may include receiving a virtual image to be presented by display optics in the head-mounted display device. Lighting information and an eye-position parameter may be received from an optical sensor system in the head-mounted display device. A blocking image may be generated in the opacity layer of the head-mounted display device based on the lighting information and the virtual image. The location of the blocking image in the opacity layer may be adjusted based on the eye-position parameter.

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Seattle, WA, United States of America

Julia Louise Meinershagen