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Journal ArticleDOI

Genetic Algorithm Driven Generic Estimation Model of Lithium-Ion Battery for Energy Balance Calculation in Spacecraft

TL;DR: The proposed genetic algorithm driven generic estimation - energy balance (GAGE-EB) model uses cell open circuit voltage characteristics, internal resistance ($r_{\text{int}}$) characteristics, and coulomb counting to estimate deliverable capacity and internal resistance in lithium-ion battery.
Abstract: Lithium-ion battery is generally used as energy storage device in spacecraft and other applications. Spacecraftbattery modeling and energy balance estimation is critical. The proposed genetic algorithm driven generic estimation - energy balance (GAGE-EB) model uses cell open circuit voltage characteristics, internal resistance ( $r_{\text{int}}$ ) characteristics, and coulomb counting. The cell characteristics are obtained by offline tests. For series-parallel or parallel-series configured battery, the model estimates deliverable capacity and internal resistance by accurately matching the terminal voltage. Estimated parameters are used to check the energy balance for new load and generation profiles. The GAGE-EB model is validated for electrical performance with various cell types and battery configurations with respect to experimental data and is within 1% and 2% accuracy for voltage and capacity estimation, respectively.
Citations
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Journal ArticleDOI
TL;DR: In this article , the authors proposed a GA-based strategy for minimizing active and reactive power losses through optimal location and size of DGs and quantified and tallies the total network power losses for the cases with random as well as optimal allocation of generators.
Abstract: Due to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature‐inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

4 citations

Journal ArticleDOI
TL;DR: In this article , the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced was explored. But the authors did not consider the impact of enhancement in the number of DG on different aspects of power system performance.
Abstract: Owing to the acute shortage of electric power in the majority of countries, short-term measures such as installation of Distributed Generators (DGs) have attracted much attention in recent decades. Employment of DGs can provide numerous advantages for the power systems through reduction of losses, escalation of the voltage profile, as well as mitigation of pollutant emissions. However, in case they are not optimally allotted, they may even lead to aggravation of the network operation from different aspects. The aim of this paper is to explore the optimal size and location of DGs using metaheuristic optimization algorithms so that the network performance is enhanced. The salient feature of the proposed strategy compared to the previous works is that it contemplates optimal allotment of DGs under various objectives, i.e. minimization of total network active and reactive power losses, and Cumulative Voltage Deviation (CVD), with different weight values. Furthermore, the impact of enhancement in the number of DGs on different aspects of power system performance is investigated. Finally, to increase the accuracy of the results, three different nature-inspired optimization algorithms, i.e. Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are deployed, and their speed in approaching the global optimum is compared with each other. The simulation results on IEEE 14-bus system indicate that the proposed strategy not only can reinforce the overall network performance through reduction of active and reactive power losses, and voltage deviation but also lead to the improvement of network voltage profile.

1 citations

References
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Journal ArticleDOI
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.

7,098 citations

Journal ArticleDOI
TL;DR: New strategies are needed for batteries that go beyond powering hand-held devices, such as using electrode hosts with two-electron redox centers; replacing the cathode hosts by materials that undergo displacement reactions; and developing a Li(+) solid electrolyte separator membrane that allows an organic and aqueous liquid electrolyte on the anode and cathode sides, respectively.
Abstract: Each cell of a battery stores electrical energy as chemical energy in two electrodes, a reductant (anode) and an oxidant (cathode), separated by an electrolyte that transfers the ionic component of the chemical reaction inside the cell and forces the electronic component outside the battery. The output on discharge is an external electronic current I at a voltage V for a time Δt. The chemical reaction of a rechargeable battery must be reversible on the application of a charging I and V. Critical parameters of a rechargeable battery are safety, density of energy that can be stored at a specific power input and retrieved at a specific power output, cycle and shelf life, storage efficiency, and cost of fabrication. Conventional ambient-temperature rechargeable batteries have solid electrodes and a liquid electrolyte. The positive electrode (cathode) consists of a host framework into which the mobile (working) cation is inserted reversibly over a finite solid–solution range. The solid–solution range, which is...

6,950 citations

Journal ArticleDOI
TL;DR: An accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment that accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response.
Abstract: Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.

1,986 citations


Additional excerpts

  • ...It has given an accuracy of 2% for battery run time and 30 mV for voltage [6]....

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Proceedings ArticleDOI
01 Sep 2007
TL;DR: In this paper, an easy-to-use battery model applied to dynamic simulation software is presented, which uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem.
Abstract: This paper presents an easy-to-use battery model applied to dynamic simulation software. The simulation model uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem. It is shown that this model, composed of a controlled voltage source in series with a resistance, can accurately represent four types of battery chemistries. The model's parameters can easily be extracted from the manufacturer's discharge curve, which allows for an easy use of the model. A method is described to extract the model's parameters and to approximate the internal resistance. The model is validated by superimposing the results with the manufacturer's discharge curves. Finally, the battery model is included in the SimPowerSystems (SPS) simulation software and is used in the Hybrid Electric Vehicle (HEV) demo. The results for the battery and for the DC-DC converter are analysed and they show that the model can accurately represent the general behaviour of the battery.

1,102 citations


"Genetic Algorithm Driven Generic Es..." refers methods in this paper

  • ...This model is implemented for four different cell chemistry and used in hybrid electric vehicle application [5]....

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Journal ArticleDOI
TL;DR: In this article, a machine learning method was used to predict battery lifetime before the onset of capacity degradation with high accuracy. But, the prediction often cannot be made unless a battery has already degraded significantly.
Abstract: Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

1,029 citations


"Genetic Algorithm Driven Generic Es..." refers background or methods in this paper

  • ...Chemistry agnostic approaches are tried for modeling [44], [45]....

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  • ...Both the aspects are electrically represented by increase in internal resistance and capacity fade [15], [21], [45]....

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