A Unified Control of Grid-Interactive Off-Board EV Battery Charger With Improved Power Quality

Lenka, Rajesh Kumar; Panda, Anup Kumar; Dash, Ashish Ranjan; Senapati, Laxmidhar; Tiwary, Nishit

doi:10.1109/tte.2022.3172354

Cited by 26 publications

(4 citation statements)

References 37 publications

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“…Additionally, the study finds that Lithium-Ion batteries have the highest short circuit current, followed by Nickel-Cadmium and then Lead-Acid batteries, with a difference of approximately 6% between them. A Sliding Mode Control (SMC)-DC-link voltage controller (SMC-DLVC) that aims to mitigate excessive dc-link voltage overshoot during grid disturbances in all operation modes of an EV charger is introduced in [43]. The effectiveness of the proposed control algorithm is verified using a laboratory prototype of a 12.6-kVA off-board charger.…”

Section: A Review Of Control and Fault Detection Techniquesmentioning

confidence: 99%

Impact of Grid Unbalances on Electric Vehicle Chargers

Alwahaibi,

Wheeler,

Rivera

et al. 2023

Energies

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There is a global trend to reduce emissions from cars through the adoption of other alternatives, such as electric vehicles (EVs). The increasing popularity of EVs has led to a growing demand for electric vehicle chargers. EV chargers are essential for charging the batteries of EVs. Since the EV charger stays connected to the grid for long periods of time to charge the EV battery, it must be able to handle disturbances in the power grid. The goal of this paper is to present an overview of the impact of grid events on EV battery chargers. As well as the impact of grid unbalances on EV chargers, this paper also provides an overview of the impact of grid faults on other, similar power electronics interfaced resources such as PV and energy storage systems.

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Section: A Review Of Control and Fault Detection Techniquesmentioning

confidence: 99%

Impact of Grid Unbalances on Electric Vehicle Chargers

Alwahaibi,

Wheeler,

Rivera

et al. 2023

Energies

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show abstract

“…Because the unit battery cost varies widely in the market, it is vital to investigate the effect of battery capacity and unit battery cost on the energy cost [52]. The charging costs with various battery capacity specifications are tested by the delayed charge strategy as shown in Figure 7.…”

Section: ) Sensitivity Analysismentioning

confidence: 99%

En-Route Charging Strategy for Wirelessly Charged Electric Bus Considering Time-of-Use Price

et al. 2022

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To mitigate the range anxiety problem of electric bus system, wireless power transfer is regarded as one of the emerging technologies for long-term range extension. Previous studies have discussed the optimization problem of the power track deployment. However, the en-route charging strategy also significantly influences the operation cost besides the power track, which is yet to be investigated sufficiently. To fill this gap, a new wireless charging model for optimizing the energy cost is proposed. In particular, the cost of battery and the time-of-use electricity price are taken into account. Firstly, a microscopic power consumption model considering passenger flows and automobile dynamics is developed to estimate the charging cost. Then, a relaxation approach based on penalty function and grey wolf optimization (GWO) algorithm is developed to solve the non-deterministic polynomial-hard (NP-hard) problem with complex multidimensional variables and multiple inequality constraints. And the performance of the proposed charging strategy is verified in a real-world bus line via numerical simulation. A sensitivity analysis is conducted to quantify the marginal impact of the unit cost of battery capacity on the total energy cost. Finally, the computational performance of the proposed algorithm (GWO) is validated by comparing other outstanding methods such as genetic algorithm (GA), particle swarm optimization (PSO) and CPLEX solvers. INDEX TERMSElectric bus; En-route charging strategy; Power consumption model; Time-of-use price; Battery capacity.

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“…Moreover, the significance of the improved grid stability and quality provided by three-phase VSIs should not be underestimated [4]. Three-phase VSIs demonstrate proficiency in managing both active and reactive power [5], serving as a crucial component in regulating voltage and frequency [6], mitigating harmonics [7], and enhancing power factors [8].…”

Section: Introductionmentioning

confidence: 99%

Empowering Dynamic Active and Reactive Power Control: A Deep Reinforcement Learning Controller for Three-Phase Grid-Connected Electric Vehicles

Ahmadian,

Sedghisigarchi,

Gadh

2024

IEEE Access

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Advancing bidirectional voltage source inverters in the on-board chargers of electric vehicles is vital for smart grid electric power management. These electric vehicle inverters play a crucial role in actively contributing to active/reactive power flow control in vehicle-to-grid and grid-to-vehicle applications. In this research, a proof-of-concept, model-free reinforcement learning-based agent/controller is introduced that eliminates the need for system identification or linearization techniques for control design. The goal is to dynamically control user-specified active and reactive electric vehicle on-board charger power outputs in all four quadrants for three-phase grid-connected electric vehicle voltage source inverters. A Twin-Delayed Deep Deterministic Policy Gradient deep-reinforcement learning-based controller was designed (1) to smoothly learn continuous control variables and to generate deterministic actions from the learned control policy, (2) to avoid exhaustive system identification techniques associated with nonlinearities involved in the on-board charger of electric vehicles, and (3) to ultimately track continuous active and reactive powers across all four-quadrant operation modes, ensuring safe and minimal steady-state errors. In this paper, a Markov Decision Process framework was established, encompassing the environment, actions, states, and a unique reward function. The minimal steady state error achieved in active and reactive power reference tracking across all four quadrant operation modes substantiates the trained agent's capacity to sustain tracking accuracy and stability across a variety of operational settings. This research could potentially pave the way for any type of electric vehicle to actively play a role in smart grid management, especially through vehicle-to-grid and grid-to-vehicle capabilities.INDEX TERMS EV (Electric Vehicle), OBC (On-Board Charger), VSI (Voltage Source Inverter), RL (Reinforcement Learning).

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A Unified Control of Grid-Interactive Off-Board EV Battery Charger With Improved Power Quality

Cited by 26 publications

References 37 publications

Impact of Grid Unbalances on Electric Vehicle Chargers

Impact of Grid Unbalances on Electric Vehicle Chargers

En-Route Charging Strategy for Wirelessly Charged Electric Bus Considering Time-of-Use Price

Empowering Dynamic Active and Reactive Power Control: A Deep Reinforcement Learning Controller for Three-Phase Grid-Connected Electric Vehicles

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