Adaptive energy management strategy for hybrid batteries/supercapacitors electrical vehicle based on model prediction control

Fu, Zhumu; Li, Zhenhui; Tao, Fazhan

doi:10.1002/asjc.2180

Cited by 5 publications

(3 citation statements)

References 32 publications

(64 reference statements)

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“…By doing so, the complexity and the cost of AC‐DC converters can be reduced [4]. The DC microgrids have many successful applications in the field of solar‐powered vehicles [5]. Project Loon provided a wireless network to remote areas via a set of balloons, which captured sunlight and stored the energy in rechargeable batteries [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

Zhang

Zuo

Wang

2022

Asian Journal of Control

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In this paper, the issue of state-of-charge balance and load voltage regulation of DC microgrids used in solar-powered vehicles is investigated. The system consists of multiple distributed generation units, each of which is equipped with photovoltaic cells and energy storage systems. Based on the information shared among the neighbors, a single-layered distributed control strategy is proposed to solve two aspects of the problem simultaneously. Rigorous analysis of the closed-loop system ensures stability and the effectiveness of the control strategy.The power constraints of each distributed generation unit are taken into consideration so that safety and applicability are guaranteed. To avoid the cycling of the batteries, the state-of-charge balance is achieved by regulating the output power feeding a common load, promoting efficiency and battery life. The proposed controller operates without any knowledge about load, line impedance, and global information of communication network, and consequently possesses the capability of plug-and-play. Finally, the effectiveness and applicability are illustrated by an example from the Sky-Sailor project.

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Section: Introductionmentioning

confidence: 99%

Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

Zhang

Zuo

Wang

2022

Asian Journal of Control

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“…The MPC method is adopted for battery management in hybrid electric vehicles (HEV) in [25,26], in which the MPC controller attempts to minimize the vehicle equivalent fuel consumption and the battery capacity fade at the same time. To enhance the battery lifespan and power performance, references [27,28] propose an MPC approach to reduce the battery power variation and maintain supercapacitors' SOC at their desired values. In [29], the battery capacity fading is described by an online least-squares identification method, and the degradation cost function is used in the MPC scheme to increase the battery lifetime.…”

Section: Introductionmentioning

confidence: 99%

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

et al. 2021

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Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

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“…The MPC method has also been adopted for battery management in Hybrid Electric Vehicles (HEV) in [25], [26], in which the MPC controller attempts to minimize the vehicle equivalent fuel consumption and the battery capacity fade at the same time. To enhance the battery lifespan and power performance, [27] and [28] propose an MPC approach to reduce the battery power variation and maintain supercapacitors' SOC at their desired values. In [29], the battery capacity fading is described by an online leastsquares identification method, and the degradation cost function is used in the MPC scheme to increase the battery lifetime.…”

Section: Introductionmentioning

confidence: 99%

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Wang¹,

Atkin²,

Bazmohammadi³

et al. 2021

Preprint

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Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

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Adaptive energy management strategy for hybrid batteries/supercapacitors electrical vehicle based on model prediction control

Cited by 5 publications

References 32 publications

Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

Distributed control for state‐of‐charge balance and load voltage regulation in DC microgrids with clustered generations

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

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