Dual-Current Pump Module for Transient Improvement of Step-Down DC–DC Converters

Liu, Pang-Jung; Lo, Yu‐Kang; Chiu, Huang-Jen; Chen, Yi-Jan Emery

doi:10.1109/tpel.2008.2010322

Cited by 29 publications

(4 citation statements)

References 15 publications

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“…However, if IP(t) is turned off instantly, a large current difference between IL(t) and IP(t) will be generated, which has the same effect as the change of IR(t). As observed, multiple undershoot/overshoot effect will be induced [36], [37], deteriorating the transient response and the system stability. This problem can be solved by turning off IP(t) slowly such that the change of IP(t) change can be compensated by IL(t).…”

Section: E Hold Period Delayed Ramping Generatormentioning

confidence: 84%

“…Transient improvement of the auxiliary current is degraded by the compensated bandwidth of the error amplifier control loop. Besides, several voltage-triggering current pump sources inject auxiliary current by detecting the output voltage directly, supporting fast auxiliary current injection [36]- [38]. However, without output tracked turning-off control, multiple undershoot/overshoot may be induced when the current pump sources are turned off instantly, thus deteriorating the expected transient performance and the system stability.…”

Section: Introductionmentioning

confidence: 99%

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A 40nm CMOS Hysteretic Buck DC-DC Converter With Digital-Controlled Power-Driving-Tracked-Duration Current Pump

2020

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Section: E Hold Period Delayed Ramping Generatormentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A 40nm CMOS Hysteretic Buck DC-DC Converter With Digital-Controlled Power-Driving-Tracked-Duration Current Pump

2020

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“…The CPU frequency of the remote cloud server is f c q = 3.6 × 10 10 Hz [34] and the CPU frequency of the mobile device is f l m = 1.0 × 10 9 Hz [35]. The amount of power for transmission between edge and cloud is P 0 = 0.1W [36].…”

Section: A Evaluation For the Sep Problem 1) Settings For Sepmentioning

confidence: 99%

Dependency-Aware Computation Offloading for Mobile Edge Computing With Edge-Cloud Cooperation

Chen

Zhang

et al. 2022

IEEE Trans. Cloud Comput.

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Most of existing Multi-access edge computing (MEC) studies consider the remote cloud server as a special edge server, the opportunity of edge-cloud collaboration has not been well exploited. We propose a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints. Each mobile device has a limited budget and has to determine which sub-task should be computed locally or should be sent to the edge or remote cloud. To address this issue, we divide the offloading problem into two application finishing time minimization sub-problems with two different cooperation modes, both of which are proved to be NP-hard. We then devise one greedy algorithm with approximation ratio of 1 + for the first mode with edge-cloud cooperation but no edge-edge cooperation. Then we design an efficient greedy algorithm for the second mode, considering both edge-cloud and edge-edge co-operations. Extensive simulation results show that for the first mode, the proposed greedy algorithm achieves near optimal performance for typical task topologies. On average, it outperforms the modified Hermes benchmark algorithm by about 23% ∼ 43.6% in terms of application finishing time with given budgets. By further exploiting collaborations among edge servers in the second cooperation mode, the proposed algorithm helps to achieve over 20.3% average performance gain on the application finishing time over the first mode under various scenarios. Realworld experiments comply with simulation results.

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“…As a result, interest in developing EVs powered by clean and renewable energy sources to replace fossil-fuel vehicles has grown steadily. Electric vehicles (EVs) are a promising alternative for transportation-related applications because they can help the environment by using renewable energy sources [1], [2]. In the case of electric vehicles, the solar PV system is used as a clean energy source.…”

Section: Introductionmentioning

confidence: 99%

A Single Inductor Multi-Port Power Converter for Electric Vehicle Applications

2023

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This research presents a non-isolated multi-port power converter feasible to hybridize energy alternatives in electric vehicles. Due to the hybridization of the various input sources, there are several advantages in load power distribution in the system. Flexible control of discharging as well as the charging process concerning the energy sources can be achieved. The developed converter can able to boost the voltage levels with dual inputs such as a renewable solar PV and the other input as a battery and provides dual outputs with various voltage levels, which can able to suit the converter fed for several loads like motor drive and the low-rated loads like lighting and other auxiliary supplies in electric vehicles. Also, as the various voltages appear at the output, this converter can be interfaced with multilevel inverters fed electric vehicle drivetrain. The utilization of multilevel inverters reduces the total harmonic distortion and torque ripples in motor drives in electric vehicles. The proposed converter consists of less number of components making the circuit simple and cost-effective. With one inductor, two various modes are obtained for charging and discharging states concerning the energy storage units. A state-space analysis is designed for all the converter operating modes along with its control design. The proposed multi-port DC-DC converter is designed in MATLAB/Simulink and tested in a laboratory environment with a hardware setup.INDEX TERMS Multi-port DC-DC converters, hybrid energy storage system, state-space modeling, electric vehicles.

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Dual-Current Pump Module for Transient Improvement of Step-Down DC–DC Converters

Cited by 29 publications

References 15 publications

A 40nm CMOS Hysteretic Buck DC-DC Converter With Digital-Controlled Power-Driving-Tracked-Duration Current Pump

A 40nm CMOS Hysteretic Buck DC-DC Converter With Digital-Controlled Power-Driving-Tracked-Duration Current Pump

Dependency-Aware Computation Offloading for Mobile Edge Computing With Edge-Cloud Cooperation

A Single Inductor Multi-Port Power Converter for Electric Vehicle Applications

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