Design and analysis of capacity models for Lithium-ion battery

Garg, Akhil; Peng, Xiongbin; Le, My Loan Phung; Pareek, Kapil; Chin, Christina

doi:10.1016/j.measurement.2018.02.003

Cited by 56 publications

(37 citation statements)

References 33 publications

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“…The findings and analysis from the proposed study can pave the way for assisting experts in making decisions on which battery to be reused, re‐manufactured, or recycled. Authors may consider using advanced optimization algorithms such as those based on evolutionary principles (genetic algorithms and particle swarm optimization) involving uncertainties in the design variables (stress, resistance, microstructure properties, etc) and compare findings with respect to the present work.…”

Section: Discussionmentioning

confidence: 99%

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Garg

Liu

et al. 2019

Int J Energy Res

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Summary The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.

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Section: Discussionmentioning

confidence: 99%

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Garg

Liu

et al. 2019

Int J Energy Res

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“…Thus, to obtain the maximum fitness and minimizing error, the GA was used to find the optimal input parameters to optimize the SVR. GA is a heuristic search and optimization technology inspired by natural evolution, which solves problems by mimicking the survival of the fittest individuals for successive generations . Advantages of GA include parallelism, robustness, stochastic, and supporting multi‐objective optimization.…”

Section: Proposed Integrated Finite Element‐meta‐optimization Frameworkmentioning

confidence: 99%

An integrated framework for minimization of inter lithium‐ion cell temperature differences and the total volume of the cell of battery pack for electric vehicles

et al. 2019

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There is not much research carried out on factors such as temperature differences in different regions of battery pack, which is highly important and poses a great challenge. Temperature changes across batteries affect the battery pack performance and its life span largely. Thus, the present work proposes the Integrated Finite Element‐Meta‐Optimization Framework for solving this problem. The integrated framework includes the formulation of Finite Element Model of a battery pack and Meta‐Optimization Framework for multi‐objective minimization of three objectives such as the inter‐cell temperature differences, temperature standard deviation and the total volume of the cell. In this framework, firstly Support Vector Regression (SVR) model is formulated based on the data and the SVR parameters are optimized using a genetic algorithm. It can conclude that the resulting model has a coefficient of correlation metric of 99%. The simulated annealing algorithm is then applied to find the optimal values of inter‐cell and cell‐enclosure distances for minimization of inter‐cell temperature differences and the total volume of the cell. Optimization analysis concluded that the volume of the battery pack was decreased by 29%, temperature difference (K) by 42% and SD of the temperature over the entire pack decreased by 55%.

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“…When there are several parameters to evaluate, numerical methods are helpful tools to analyze parameter interactions to reduce number of experiments. Soft computing methods and statistical experiment design techniques such as artificial neural network (ANN), fuzzy logic, genetic programming (GP), and design of experiment (DoE) are commonly used for evaluation, modeling, and optimization in various fields . Generally, ANNs, GP, and similar advanced numerical methods are chosen for the complex and nonlinear processes.…”

Section: Introductionmentioning

confidence: 99%

“…Soft computing methods and statistical experiment design techniques such as artificial neural network (ANN), fuzzy logic, genetic programming (GP), and design of experiment (DoE) are commonly used for evaluation, modeling, and optimization in various fields. [17][18][19][20][21][22][23] Generally, ANNs, GP, and similar advanced numerical methods are chosen for the complex and nonlinear processes. An ANN is a learning system depending upon a computational procedure to simulate the neurological processing ability of the human brain, and it has the ability to capture a high degree of nonlinearity between the process parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the PEMFC operating parameters for cathode in the presence of PtCo/CVD graphene using factorial design

2019

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Summary Chemical vapor deposition (CVD) grown graphene‐supported PtCo catalyst was developed as a polymer electrolyte membrane fuel cell (PEMFC) cathode. The effects of cell temperature, back pressure, relative humidity, anode, and cathode flow rates on the fuel cell performance were investigated utilizing Design Expert software for statistical analysis. Cell temperature, back pressure, and relative humidity were shown to be the most critical factors to improve fuel cell performance in the presence of PtCo on CVD grown graphene. The maximum current density of 1779.5 mAcm−2 and power density of 785.38 mWcm−2 are obtained at cell temperature of 79.99°C, back pressure of 4.99 psi, relative humidity of 50%, anode flow rate of 0.156 dm3min−1, and cathode flow rate of 0.199 dm3min−1.

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Design and analysis of capacity models for Lithium-ion battery

Cited by 56 publications

References 33 publications

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

An integrated framework for minimization of inter lithium‐ion cell temperature differences and the total volume of the cell of battery pack for electric vehicles

Optimization of the PEMFC operating parameters for cathode in the presence of PtCo/CVD graphene using factorial design

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