Idleness-Aware Dynamic Power Mode Selection on the i.MX 7ULP IoT Edge Processor

Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs. The dynamic tuning of power consumption to the application requirements is indeed a viable approach to save energy. In this paper, we propose the implementation of a power management strategy for a novel low-cost low-power heterogeneous dual-core SoC for IoT edge fabricated in 28 nm FD-SOI technology. Ss with more complex power management policies implemented on high-end application processors, we propose a power management strategy where the power mode is dynamically selected to ensure user-specified target idleness. We demonstrate that the dynamic power mode selection introduced by our power manager allows achieving more than 43% power consumption reduction with respect to static worst-case power mode selection, without any significant penalty in the performance of a running application.

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Dynamic power management strategy based on weighted Markov model

Information and Communication Technology for Education ◽

10.2495/icte130731 ◽

2014 ◽

Author(s):

Zujin Yu ◽

Youmeng Li ◽

Yikui Zhang

Keyword(s):

Markov Model ◽

Power Management ◽

Management Strategy ◽

Dynamic Power Management ◽

Dynamic Power ◽

Power Management Strategy

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Management and Distribution Strategies for Dynamic Power in a Ship’s Micro-Grid System Based on Photovoltaic Cell, Diesel Generator, and Lithium Battery

Energies ◽

10.3390/en12234505 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4505 ◽

Cited By ~ 3

Author(s):

Wanneng Yu ◽

Suwen Li ◽

Yonghuai Zhu ◽

Cheng-Fu Yang

Keyword(s):

Power Management ◽

Working Conditions ◽

Management Strategy ◽

Lithium Battery ◽

Grid System ◽

Diesel Generator ◽

Dynamic Power ◽

Power Management Strategy ◽

Cruise Ships ◽

Micro Grid

Combining new energy technology with electric propulsion technology is an effective way to decrease the pollution of water resources caused by cruise ships. This study examines the stable parallel operation of a ship’s micro-grid system through a dynamic power management strategy involving a step change in load. With cruise ships in mind, we construct a micro-grid system consisting of photovoltaics (PV), a diesel generator (DG), and a lithium battery and establish a corresponding simulation model. We then analyze the system’s operating characteristics under different working conditions and present the mechanisms that influence the power quality of the ship’s micro-grid system. Based on an analysis of the power distribution requirements under different working conditions, we design a power allocation strategy for the micro-grid system. We then propose an optimization allocation strategy for dynamic power based on fuzzy control and a load current feed-forward method, and finally, we simulate the whole system. Through this study we prove that the proposed power management strategy not only verifies the feasibility and correctness of the ship’s micro-grid structure and control strategy, but also greatly improves the reliability and stability of the ship’s operation.

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State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.783 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Ahmed Mohamed Ali ◽

Bedatri Moulik ◽

Nejra Beganovic ◽

Dirk Söffker

Keyword(s):

Fuel Cell ◽

Power Consumption ◽

Power Management ◽

Management Strategy ◽

Health Management ◽

Power Management Strategy ◽

Performance Requirements ◽

Driving Cycles ◽

Weighted Fusion ◽

Power Handling

This paper presents a novel situation-based power and battery health management strategy for fuel cell vehicles. In such hybrid powertrains, the synergy role of batteries is essential to minimize overall power consumption and maintain higher electric efficiency of the fuel cell. On the other side, lifetime degradation of the battery is associated to the recurrent charging/discharging cycles. The proposed power management strategy addresses the trade-off between these contradictive objectives. Vehicle states in each situation are defined in terms of driver-related identification parameters (power demand and speed) corporately with powertrain related ones (on-board battery’s state of charge). Optimal power handling solution for each situation is searched offline considering different optimizations criteria: range extension, lifetime maximization, or power consumption minimization. A weighted fusion of these optimized solutions can be implemented online based on desired driving strategy, leading to situation-based optimized solution. This contribution aims to provide flexible power handling options meeting performance requirements (energy efficiency and driveability) without scarifying battery’s lifetime. Simulation tests using different driving cycles are conducted for evaluation purpose.

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