Parameter Estimation of Vehicle Batteries in V2G Systems: An Exogenous Function-Based Approach

Khalid, Haris M.; Flitti, Farid; Muyeen, S. M.; Elmoursi, Mohamed Shawky; Sweidan, Tha’er O.; Yu, Xinghuo

doi:10.1109/tie.2021.3112980

Cited by 42 publications

(16 citation statements)

References 31 publications

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“…The amount of loss is calculated using the following equations for power systems. If the total power injected into the busbar i is known, the total system loss will be equal to the complex power of all machines [31][32][33][34][35][36][37].…”

Section: First Scenariomentioning

confidence: 99%

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Makahleh¹,

Amer²,

Manasrah³

et al. 2023

Computers, Materials &Amp; Continua

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In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

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Section: First Scenariomentioning

confidence: 99%

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Makahleh¹,

Amer²,

Manasrah³

et al. 2023

Computers, Materials &Amp; Continua

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“…Batteries also require a very careful operation as they are very sensitive to several parameters like voltage, current, and temperature [35]. Power depletion during charging and discharging cycles could seriously affect the aging of batteries [36]. Hence, batteries must be protected from being over-charged or discharged to extend its service life [37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The search agents in PO consist of n political parties as given by Equation (36), where each party (pr i ) has n members as expressed in Equation (37). pr j i is the j th member of ith party, which is considered a candidate solution where pr j i is a vector of length d as given by Equation (38), where d is the number of decision variables of the optimization problem.…”

Section: Party Formation and Constituency Allocationmentioning

confidence: 99%

Energy management system for islanded multi‐microgrids using a two‐stage optimization scheme based on political optimizer

Mahmoud

Hasanien

Turky

et al. 2023

IET Renewable Power Gen

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An energy management system (EMS) is proposed in this paper to minimize the amount of load shedding in islanded multi‐microgrids (MMGs) systems in a cost‐effective manner. The suggested EMS adopts a two‐stage hierarchical optimization model. The first stage is called microgrid (MG) optimization, in which the load shedding in each MG is minimized. The day‐ahead schedule of the diesel generator, the charging/discharging power of the battery energy storage system (BESS), the reshaped load profile after applying the demand response programs (DRP), and the shortage/surplus power in each MG are determined in the first stage of optimization. The second stage is MMG optimization, in which the amount of energy shortage in the MMG system is further minimized by energy exchange between individual MGs. The remaining shortage can be reduced with the aid of the community MG resources. The proposed optimization model is formulated as a nonlinear complex optimization problem that can be handled more efficiently by the meta‐heuristic techniques compared with the conventional analytic methods. The political optimizer is used in this paper. To prove the effectiveness of this optimizer in handling the proposed model, we also solved the optimization problem using the particle swarm optimization algorithm, and results are compared.

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“…To enable intelligent vehicles to monitor and manage vehicle states more comprehensively, analyzing battery state parameters has become particularly important, in addition to estimating dynamic parameters. Khalid et al [19] introduced a technique using exogenous functions to determine the parameters of vehicle batteries in V2G systems. This approach integrates real-time estimation with actual measurement data, thus improving the system's robustness and accuracy.…”

Section: Introductionmentioning

confidence: 99%

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

Chen,

Zhang,

et al. 2024

Applied Sciences

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The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.

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Parameter Estimation of Vehicle Batteries in V2G Systems: An Exogenous Function-Based Approach

Cited by 42 publications

References 31 publications

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Energy management system for islanded multi‐microgrids using a two‐stage optimization scheme based on political optimizer

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

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