Veritone Alpha, Inc.

Microsoft Technology Licensing, LLC

Vcritonc Alpha, Inc.

Yanfang Shen

Wolf Kohn

Jordan Makansi

Michael Victor Ehrenberg

Rekha Nanda

Xiaofeng Zhang

Zelda B Zabinsky

Malvika Malge

Techniques are described for implementing automated control systems that each control or otherwise manipulate, for a target system having one or more battery cells each having internal components that include one or more internal supercapacitor components in parallel with at least one battery component, usage operations for one of the internal components of one of the battery cells, with the usage operations for the internal components of a particular battery cell being synchronized or otherwise coordinated to protect the battery component(s) of the battery cell while satisfying other criteria (e.g., to increase battery cell life and/or reduce power dissipation). In at least some situations, the target system is an electric vehicle, and the automated control systems control the electric vehicle's battery cells to provide electrical power to the motor during acceleration and constant speed driving, and to store electrical power in the battery cells during braking or other deceleration.

Controlling ongoing usage of a battery cell having one or more internal supercapacitors and an internal battery

Techniques are described for implementing automated control systems to control operations of target physical systems including batteries, such as based at least in part on models of the batteries' dynamic behaviors that are generated by gathering and analyzing information about the batteries' operations under varying conditions. The techniques may include, for each of multiple charge levels of a battery and for each of multiple resistive loads, injecting multi-frequency microvolt pulses into the battery, and using sensors to collect time changes of the responses to these pulses. Information about the inputs and the responses is then analyzed and used to generate an incremental parametric state model representing the internal dynamics of the battery, which is further used to control additional ongoing battery operations (e.g., to control whether and how much power is supplied to and/or extracted from the battery in a current or future time period).

Using battery state excitation to model and control battery operations

An automated control system to control at least some operations of one or more target physical systems that each includes one or more batteries. The described techniques may include determining whether and how much power to supply for each of a series of time periods, and implementing the determined power amount for a time period by determining and setting one of multiple impedance level control values of an associated actuator component. Repeated automated operations of this type may include using parametric linear approximation to determine one of multiple enumerated control values that best satisfies one or more defined goals at a given time in light of current state information (e.g., current output from the battery, voltage from the battery, battery temperature, etc.), such as by repeatedly determining an improved distribution function over the control values, and propagating it over multiple future time periods.

Controlling ongoing battery system usage via parametric linear approximation

Techniques are described for implementing automated control systems that repeatedly perform automated modifications to control system actuator components' ongoing operations to improve functionality for target battery systems, such as to reduce power dissipation while performing other battery power use activities to maximize battery life. Controlling a battery's usage may include using a DC-to-DC amplifier, and the repeated automated modifications may include modifying the state of the DC-to-DC amplifier actuator to adjust a level of resistance and/or an amount of time during which power is supplied. The repeated automated modifications may be performed to repeatedly reduce the distance between the current battery performance and an idealized version of the battery performance (e.g., a version with no power dissipation).

Controlling ongoing battery system usage while repeatedly reducing power dissipation

According to examples, an apparatus may include a processor and a memory on which are stored machine readable instructions that when executed by the processor, cause the processor to identify a plurality of tasks, identify a plurality of resources configured to execute the tasks, and decompose the plurality of tasks into multiple groups of tasks based on a plurality of rules applicable to the multiple groups of tasks. The instructions may also cause the processor to, for each group in the multiple groups of tasks, model the group of tasks and a subset of the plurality of resources as a respective resource allocation problem and assign a respective node of a plurality of nodes to solve the resource allocation problem.

Rules based decomposition of tasks for resource allocation

Techniques are described for implementing automated control systems that repeatedly perform automated modifications to control system actuator components' ongoing operations to improve functionality for electrical devices in target systems, such as to reduce power dissipation while using the electrical devices. The described techniques further include synchronizing a particular control system's state improvements with corresponding control system state improvements being performed for one or more other control systems that are each controlling one or more distinct electrical devices in the target system(s), so as to improve the collective control system functionality according to one or more criteria (e.g., to reduce power dissipation)&#x2014;such synchronizing may include, for example, generating a mean field representation of the overall control system state for the target system(s) and using the mean field representation to improve the overall control system state.

Managing coordinated improvement of control operations for multiple electrical devices to reduce power dissipation

Techniques are described for implementing automated control systems for target battery systems based at least in part on battery state information gathered from active excitation of the batteries, such as to maximize battery life while performing other battery power use activities. The excitation of a target battery system may occur while it is in use, by repeatedly introducing small defined variations as input to the battery system while the battery system is otherwise used to supply or receive electricity. Corresponding small variations in output of the battery system from the excitation activities are then measured by hardware sensors, aggregated and analyzed to generate a current model of the internal state of the one or more batteries, and then used to assist in controlling further operations of the battery system, including in some cases to update a previously existing model of the battery system.

Using battery state excitation to control battery operations

A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster.

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Bellevue, WA, United States of America

Yanfang Shen