Sliding Mode Control Enabled Hybrid Energy Storage System for Islanded DC Microgrids with Pulsing Loads

Abianeh, Ali Jafarian; Ferdowsi, Farzad

doi:10.1016/j.scs.2021.103117

Cited by 46 publications

(14 citation statements)

References 28 publications

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“…SC gained attention in the present energy scenario, where a battery must get supported in a micro-grid application when sudden load variation occurs. 43 SC's importance in electric vehicle applications is much higher than grid applications since EV load demands are highly fluctuating even for similar routes at different traffic, driving behaviors, and environments. In hybrid source EVs, SC plays a crucial role in handling sudden or high-frequency power demands and ensures a smoother battery current profile.…”

Section: Sc Modulesmentioning

confidence: 99%

Adaptive intelligent hybrid energy management strategy for electric vehicles

Kashyap

Castelino

2023

Energy Storage

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Electric vehicles (EVs) utilizing hybrid energy sources is a significant step toward a sustainable future in the transportation industry. The electric three‐wheeler (3W) considered in the proposed work includes three sources—battery, supercapacitor (SC), and photo‐voltaic (PV) panels. In battery electric vehicles (BEV), battery life cycle, energy efficiency, and performance are affected by variations in driving conditions that inhibit their wider adoption. The main focus of the proposed intelligent hybrid energy management strategy (IHEMS) is to enable the vehicle to adaptively manage and diminish the effects of load fluctuations due to varying conditions. IHEMS diverts the load fluctuations to the SC bank by ensuring an effective absolute energy sharing among the sources with a fuzzy logic control algorithm. PV energy is utilized to assist the battery during sunny days. Performance of the EMS in hybrid source EV is analyzed in MATLAB/SIMULINK environment with a combination of three different standard real‐time driving profiles (NYCC, Artemis Urban, WLTP class‐1). Proposed EMS reduces peak battery power by 20% and 14.35% and improves battery life by 16.4% and 11.4% compared to BEV and conventional EMS, respectively. This proves that the proposed EMS exhibits adaptive energy management irrespective of the driving conditions and ensures improved battery performance and longevity.

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Section: Sc Modulesmentioning

confidence: 99%

Adaptive intelligent hybrid energy management strategy for electric vehicles

Kashyap

Castelino

2023

Energy Storage

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“…Calculate the controller gains K i and P j by LMIs ( 15)-( 17) and ( 31)-(32) 3. Calculate u n (t) from (4b) Example II: The TS fuzzy model of a flexible joint robot by considering both the external disturbances is as follows (Banks et al, 2005)…”

Section: Example Imentioning

confidence: 99%

“…Although, the limitations on the control signal and the optimality performance have not been considered in the design process. Some other studies such as reaching law-based SMC (Zhao et al, 2021), recursive adaptive SMC (Shao et al, 2021), adaptive second-order SMC (Ding et al, 2021), and SMC design for islanded DC microgrid (Abianeh and Ferdowsi, 2021) have been done which are either not optimal and constrained performance, or can be used for a specific dynamic system.…”

Section: Introductionmentioning

confidence: 99%

Optimal constrained integral sliding mode control design for fuzzy-based nonlinear systems

Daneshian

Forouzanfar

Moosavi

et al. 2023

Transactions of the Institute of Measurement and Control

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This study introduces a novel H∞ optimal constrained integral sliding mode control (OCISMC) for nonlinear systems due to the matched/unmatched external disturbances based on Takagi–Sugeno (TS) fuzzy models. Based on the Lyapunov function, the appropriate conditions are provided to reach the sliding mode from any initial condition and robustness against the matched disturbances is guaranteed. The optimal nominal part of the proposed OCISMC is designed based on an online optimization problem. To reach robustness against the unmatched disturbances and constrained controller, the H∞ performance and the limitations of the control signal are included in the design procedure of the suggested OCISMC. Furthermore, by considering a non-quadratic Lyapunov function (NQLF), a new linear matrix inequality problem with less conservatism is derived. Finally, two examples are simulated and compared with the existing works to illustrate the capability of the proposed OCISMC.

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“…For environmental considerations and the depletion of fossil fuel, variable renewable energy (VRE) share is increasing, which also led to an increase in microgrids (MGs). MGs are small-scale power grids, composed of distributed energy resources (whether renewable or not), loads, and energy storage, which can be connected to the utility-grid and trading energy with it or isolated depending on local generation and storage [1,2]. However, the uncertain and intermittent characteristics of VRE cause technical challenges to the operation and control of MG [3].…”

Section: Introductionmentioning

confidence: 99%

Battery Scheduling Control of a Microgrid Trading with Utility Grid Using Deep Reinforcement Learning

Mahmoud

Tsuji

2023

IEEJ Transactions Elec Engng

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Managing microgrids (MGs) with variable renewable energy (VRE) is challenging because of uncertainties of electricity production, loads, and energy price, so we need flexible control strategies for battery energy storage system (BESS) to handle those challenges. Model-based approaches require precise models of the MG to give accurate results but having an accurate model can be difficult in continually changing environments. We introduced a new day-ahead optimization method to control BESS scheduling and power exchange between the utility grid and an MG with a load, photovoltaics, and BESS with the aim of energy cost minimization. Deep reinforcement learning (DRL) was used for the optimization of sequential actions of BESS over a time horizon. A theoretical optimum scheduling was derived using a linear programming optimization to be compared with the DRL agent. Both no-battery and greedy control algorithms were used as baselines. It was shown that the results of the proposed technique were better than the baselines through numerical simulations using whole year's data.

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Sliding Mode Control Enabled Hybrid Energy Storage System for Islanded DC Microgrids with Pulsing Loads

Cited by 46 publications

References 28 publications

Adaptive intelligent hybrid energy management strategy for electric vehicles

Adaptive intelligent hybrid energy management strategy for electric vehicles

Optimal constrained integral sliding mode control design for fuzzy-based nonlinear systems

Battery Scheduling Control of a Microgrid Trading with Utility Grid Using Deep Reinforcement Learning

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