Description:FIELD OF INVENTION
Power management for integrated electric devices focuses on optimizing energy usage through techniques like Dynamic Voltage and Frequency Scaling (DVFS), power gating, and energy-efficient circuit design. This field aims to extend battery life, reduce energy costs, and enhance device performance by dynamically adjusting power consumption based on operational demands and device activity.
BACKGROUND OF INVENTION
Power management for integrated electric devices and methods of operation are critical in ensuring efficient and reliable performance in a wide range of applications, from consumer electronics to industrial systems. The background of this invention lies in the increasing complexity and power demands of modern electronic devices. As devices become more integrated and multifunctional, they require sophisticated power management solutions to optimize energy consumption, prolong battery life, and maintain operational stability. Historically, power management in electric devices was straightforward, focusing primarily on simple on/off mechanisms and basic voltage regulation. However, the evolution of technology has introduced numerous challenges. Today's integrated devices feature multiple components, each with unique power requirements and operational states. These components include processors, memory units, sensors, communication modules, and more, all of which must be managed cohesively to prevent power wastage and overheating. The invention of advanced power management techniques addresses these challenges through dynamic power allocation, real-time monitoring, and adaptive control mechanisms. Techniques such as dynamic voltage and frequency scaling (DVFS), power gating, and adaptive power control allow devices to adjust power consumption based on workload demands, thereby enhancing energy efficiency and performance. Additionally, sophisticated algorithms and control systems are employed to predict power usage patterns and optimize power distribution accordingly. In essence, power management for integrated electric devices and methods of operation is about creating a harmonious balance between performance and energy efficiency. By implementing advanced power management strategies, devices can achieve longer battery life, reduced energy costs, and improved overall reliability. This is crucial in today's world, where energy efficiency and sustainability are paramount considerations in the design and operation of electronic systems.
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SUMMARY
Power management for integrated electric devices and methods of operation encompasses techniques and strategies aimed at optimizing the use and distribution of electrical power within various devices and systems. This field addresses the need for efficient energy utilization, reduced power consumption, and enhanced performance of integrated circuits and electronic devices. Key components of power management include power supply regulation, energy harvesting, dynamic power scaling, and thermal management. Power supply regulation ensures that devices receive stable and precise voltage levels, critical for their proper operation. Techniques like low-dropout regulators (LDOs) and switching regulators are employed to maintain voltage stability and efficiency. Energy harvesting involves capturing and converting ambient energy sources, such as solar, thermal, or kinetic energy, into electrical power, which can be used to supplement or replace traditional power sources. Dynamic power scaling, including dynamic voltage and frequency scaling (DVFS), adjusts the power usage of a device based on its current workload, thereby reducing energy consumption during periods of low activity. This approach is particularly effective in battery-operated devices, extending their operational life. Thermal management is another crucial aspect, as excessive heat can degrade performance and shorten the lifespan of electronic components. Methods such as heat sinks, thermal interface materials, and advanced cooling techniques are used to dissipate heat and maintain optimal operating temperatures. Advanced power management techniques also integrate smart control algorithms and real-time monitoring systems to dynamically adapt power usage, ensuring devices operate efficiently while meeting performance requirements. These methods are essential in the development of sustainable, high-performance electronic systems in various applications, from consumer electronics to industrial automation and beyond.
DETAILED DESCRIPTION OF INVENTION
Four decades of continuous scaling and the success of Moore’s law have enabled a myriad of low-power computation and communication devices. This has ushered in a new generation of complex and compact devices that are influencing human civilization on a broad scale. These devices have now permeated our daily lives, manifesting in hand-held, wearable, and implanted devices, cyber-physical systems, and distributed sensor nodes that interact and share information. Due to the mobile and sometimes standalone nature of these devices, powering them poses a new paradigm in power delivery solutions.
The ever-increasing demand for features in these devices leads to a workload that demonstrates significant variation in terms of both voltage and current. Additionally, the drive to improve battery life and advancements in ambient energy harvesting make opportunistic energy harvesters a reality. This adds another dimension to the challenge, as energy harvesters are variable and sporadic sources of power. Thus, there is a need for a platform and interface circuits that provide optimum power transfer with minimal losses.
Traditional power delivery networks designed for servers, desktops, and high-end mobile phones are based on worst-case load conditions. This approach, targeted at performance, is inadequate in the IoT world where power efficiency is paramount. Worst-case designs are agnostic to the wide variations in both load circuits (digital, analog, and RF) and energy sources. Therefore, it is critical to re-evaluate and modify the strategy for designing power delivery networks for IoTs. Adaptive and reconfigurable designs for components close to both the source and the load can provide a viable and energy-efficient solution.
In this work, we will discuss such adaptive designs and control strategies that can efficiently power the next generation of IoTs amidst all the variations and dynamic conditions.
The Source and the Load
Figure 1 shows a typical power flow architecture for an IoT device, consisting of three important stages: the source, the power delivery network (PDN), and the load. Generally, the source is a rechargeable battery. In recent years, energy harvesting devices have resurged, facilitated by two factors: (1) increasing energy conversion efficiency of harvesting transducers and (2) lower power demand of IoT load circuits, narrowing the gap between supply (harvesters) and demand (load). Important energy harvesters include photovoltaic, vibrational, thermoelectric, and wireless energy scavengers.

Figure 1: power flow architecture for an IoT device
The load consists of various circuits and components depending on the application. Digital circuits may include CPU, GPU, memory, accelerators, audio, and video processing blocks. There are also numerous analog and RF blocks essential for a connected world. Between the energy sources and the loads is the PDN, whose primary task is to provide supply voltage stability, high power efficiency, excellent load and line regulation, and maximum power transfer from the source to the load in a dynamic environment.
The Dynamic Source
The last decade has seen continuous development in low-power devices and significant improvements in the efficiency and power output of energy harvesters. As the gap between energy produced by harvesters and demanded by load circuits narrows, self-powered IoTs are becoming a reality. Although fully self-powered systems may remain impractical in certain applications, opportunistic energy harvesting, where secondary batteries are supplemented with harvested energy, is becoming feasible. This is particularly useful for distributed sensor nodes, which may be physically inaccessible.
Energy harvesters, by nature, are sporadic and exhibit extreme variations in power output, depending on natural conditions. The Thevenin model for each harvester generally demonstrates extreme variations in open circuit voltages, short circuit current, and internal resistances.
Table 1 summarizes typical ranges of open circuit voltages and short circuit currents for different harvesters.

Combining energy from multiple harvesters could improve battery life and reliability of IoT devices, provided the PDN can deliver maximum power transfer under varying source conditions.
PDN (on the Source Side) Challenges
The PDN interfaces with energy sources and plays two crucial roles. It deals with a range of impedances of the energy harvesters with varying output powers. In some harvesters, like thermoelectric and vibrational transducers, the output impedance remains within a narrow range. For photovoltaics, the driving point impedance exhibits a large dynamic range. According to the Maximum Power Transfer (MPT) theory, to obtain maximum power, the load resistance should equal the source's internal resistance. In this case, the load resistance is the effective resistance offered by the PDN’s interfacing component.

Figure 2: Boost converter as interface circuit for an Energy harvester
The second role is to step up or boost the voltage produced by the harvesters. Energy harvester output voltage can be extremely low, a few hundred mVs, while load circuits may require higher voltages, typically ~1V. These roles could be played by an off-chip boost converter design. For certain harvesters, like vibration energy harvesters, the voltage produced may be higher than required, necessitating a buck-boost converter.
Optimal Impedance for Maximum Power Transfer and Boosting Ratio
To achieve maximum power transfer from an energy harvester to a load using a boost converter, the input resistance of the boost converter, RIN, must match the internal resistance of the energy harvester. This impedance matching ensures optimal energy extraction.
Discontinuous Conduction Mode (DCM)
In DCM, the boost converter operates with the inductor charging to a maximum current ILmax during the first switching phase ϕ1 for a time t1. In the second phase ϕ2, the inductor discharges to zero current in time t2. After this, there is a dead period where the inductor does not conduct any current. The input resistance RIN in DCM is given by:

This approximation holds true when t2≪t1, which is typically the case when a high boosting ratio is required.
Boosting Ratio
The boosting ratio for the boost converter can be expressed as:

Adapting to Dynamic Environments
A static design with fixed switching parameters may not perform optimally under varying conditions. An adaptive Power Delivery Network (PDN) can modify the switching frequency or the time interval t1 to maintain optimal impedance matching. This dynamic adjustment is crucial for handling different energy harvesters with varying output impedances and for maximizing system efficiency.
Impact on Power Efficiency
If the boost converter operates under static conditions, any extra voltage generated must be dropped across a Low Dropout Regulator (LDO), leading to significant energy losses. An adaptive design can adjust the boosting ratio dynamically to minimize these losses, maintaining higher power efficiency across a wide range of operating conditions.
The efficiency of the LDO is given by:

Addressing Inductor Degradation
Inductor degradation over time can reduce the efficiency of the boost converter. An adaptive PDN can compensate for this by modifying the switching frequency or t1 to maintain optimal impedance matching and power extraction.
Maximum Power Point Tracking (MPPT) for PV Sources
For photovoltaic (PV) energy harvesters, which have non-linear I-V characteristics, a one-time calibration of internal resistance is insufficient. MPPT algorithms dynamically adjust control parameters to maximize power extraction. In DCM, the extracted power increases with t2:

Dynamic Load Adaptation
Modern load circuits employ dynamic voltage and frequency scaling, clock and power gating to optimize power consumption. This requires the PDN to supply stable voltages and handle large current variations. Adaptive control in LDOs can adjust the compensation network and control loops to maintain stability and efficiency across a wide range of load conditions.
To maximize power transfer from an energy harvester using a boost converter, it is essential to dynamically match the input impedance of the converter with the harvester's internal resistance. An adaptive PDN can adjust the switching frequency and time intervals to maintain this impedance matching, ensuring high power efficiency across varying conditions and compensating for component degradation over time.
DETAILED DESCRIPTION OF DIAGRAM
Figure 1: power flow architecture for an IoT device
Figure 2: Boost converter as interface circuit for an Energy harvester , Claims:1. Power Management for Integrated Electric Devices and Methods of Operation claims that adaptive Power Control is a method for dynamically adjusting the power levels of integrated electric devices based on real-time usage patterns and performance requirements to optimize energy consumption.
2. Incorporation of thermal management techniques to maintain optimal operating temperatures of integrated electric devices, thereby enhancing efficiency and longevity.
3. Utilization of energy harvesting mechanisms, such as solar or kinetic energy, to supplement the power supply of integrated electric devices, reducing reliance on external power sources.
4. Implementation of multiple low-power operating modes that can be activated during periods of inactivity or low usage to conserve energy without compromising device functionality.
5. Design and operation of integrated electric devices that can interface with smart grid systems to manage power distribution and consumption more efficiently.
6. Advanced battery management techniques that monitor and regulate the charging and discharging cycles of batteries used in integrated electric devices to maximize battery life and performance.
7. Development of intelligent load-balancing algorithms that distribute power consumption evenly across multiple devices to prevent overloading and improve overall system efficiency.
8. Implementation of predictive maintenance systems that monitor the health and performance of integrated electric devices, allowing for timely interventions that prevent power-related failures.
9. Use of components and materials specifically chosen for their low power consumption and high efficiency, contributing to the overall energy efficiency of the integrated device.
10. Provision of user-configurable power management settings that allow end-users to customize the power consumption profiles of their integrated electric devices according to their specific needs and preferences.