Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Huang, Kai; Guo, Yongfang; Li, Zhigang; Hsiung-Cheng, Lin

doi:10.1155/2018/3793492

Cited by 10 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bio-inspired algorithms have also been extensively researched in recent years owing to their versatility and ease of implementation [87]. To model a battery, the internal circuit parameters need to be estimated.…”

Section: Battery Parameters Extraction Techniques Using Grey Box Modelling Data-driven Approachmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

133

View full text Add to dashboard Cite

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

show abstract

Section: Battery Parameters Extraction Techniques Using Grey Box Modelling Data-driven Approachmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

133

View full text Add to dashboard Cite

show abstract

“…The second term that is associated with a local search is proportional to the vector ( − ) and points from the current position of the particle to its best personal position. The third term that is associated with a global search is proportional to ( − ) and points to the position of the best global particle [29][30][31].…”

Section: Pso Algorithm Descriptionmentioning

confidence: 99%

“…The significance of these two constants is that they determine to what extent pbest and gbest affect the movement of the particles. The recommended value for these two constants is approximately 2 [30][31][32].…”

Section: Pso Algorithm Descriptionmentioning

confidence: 99%

Diagnosis of a battery energy storage system based on principal component analysis

et al. 2020

View full text Add to dashboard Cite

This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS.The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions.

show abstract

“…Other authors have proposed multiple operative models to coordinate the daily operation of the batteries; some of these approaches are: mixed-integer linear programming [20][21][22]; second order cone optimization [23][24][25], semidefinite programming [26]; genetic algorithms [27][28][29], particle swarm optimization [30,31]; nonlinear programming [32][33][34][35][36], and reinforcement learning for energy system optimization [37,38]. The main characteristic of those researches is that the batteries are modeled through a linear relation between the state-of-charge and the amount of power injected/absorbed into the grid [11]; this linear representation allows solving efficiently the problem of the optimal dispatch of these batteries in AC and/or DC grids where these are previously located to the network.…”

Section: Introductionmentioning

confidence: 99%

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

et al. 2021

View full text Add to dashboard Cite

This paper deals with the problem of the optimal selection and location of batteries in DC distribution grids by proposing a new mixed-integer convex model. The exact mixed-integer nonlinear model is transformed into a mixed-integer quadratic convex model (MIQC) by approximating the product among voltages in the power balance equations as a hyperplane. The most important characteristic of our proposal is that the MIQC formulations ensure the global optimum reaching via branch & bound methods and quadratic programming since each combination of the binary variables generates a node with a convex optimization subproblem. The formulation of the objective function is associated with the minimization of the energy losses for a daily operation scenario considering high renewable energy penetration. Numerical simulations show the effectiveness of the proposed MIQC model to reach the global optimum of the optimization model when compared with the exact optimization model in a 21-node test feeder. All the validations are carried out in the GAMS optimization software.

show abstract

Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Cited by 10 publications

References 31 publications

A Review on Battery Modelling Techniques

A Review on Battery Modelling Techniques

Diagnosis of a battery energy storage system based on principal component analysis

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

Contact Info

Product

Resources

About