A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Li, Wei; Chen, Siqi; Peng, Xiongbin; Xiao, Mi; Gao, Liang; Garg, Akhil; Bao, Nengsheng

doi:10.1016/j.eng.2019.07.005

Cited by 64 publications

(30 citation statements)

References 39 publications

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“…In addition, the discard of a battery pack only implied that the single battery is aged and the overall performance of the battery pack fails to meet the requirements. After disassembling the single battery, the batteries with low battery capacity and open circuit voltage attenuation can be screened out and recombined into new batteries for reuse through the use of advanced estimation [18][19][20][21] and probabilistic-based methods including clustering analysis. [22][23][24][25] The rescreening and combination of waste batteries cannot only give full play to the residual value of waste batteries but also extend the life cycle and utilization time of lithium batteries and alleviate the pressure of battery recycling to a certain extent.…”

Section: Application Of Cluster Analysis In Battery Recyclingmentioning

confidence: 99%

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Garg

Singh

et al. 2020

Int J Energy Res

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The development of fault diagnosis of Li-ion batteries used in electric vehicles is vital. In this perspective, the present work conducted a comprehensive study for the evaluation of coupled and interactive influence of charging ratio, number of cycles, and voltage on the discharge capacity of Li-ion batteries to predict the life of battery. The charging-discharging experimental tests on Li-ion batteries have been performed. The data such as charging ratio, number of cycles, voltage, and discharge capacity of Li-ion batteries are measured. Machine learning approach of neural networks is then applied on the obtained data to compute the effects, normal distribution, parametric analysis, and sensitivity analysis of the input parameters on the capacity of battery. It can be noticed that discharge capacity increased with an increase in full voltage. Further, it has been observed from the sensitivity analysis that the full voltage is most relevant parameters to the capacity of the battery. Additionally, scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) of the electrodes before and after experiments have been performed, to investigate the elemental dissolution due to the charging/discharging cycles. The findings and analysis from the proposed study shall facilitate experts in making decisions on the remaining life and charging capacity of the battery.

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Section: Application Of Cluster Analysis In Battery Recyclingmentioning

confidence: 99%

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Garg

Singh

et al. 2020

Int J Energy Res

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“…An IBMS integrated with battery aging models can be developed to improve the performance of battery pack. For this purpose, intelligent modelling 69,70 and optimisation methods [71][72][73][74] can be integrated in the mobile APP interface. Thus, each cell's performance (voltage, temperature, capacity, etc.)…”

Section: Future Research Directionsmentioning

confidence: 99%

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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Summary In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.

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“…2,3 Inconsistency between single cells limits the actual available capacity, voltage, and other performance parameters, and the overheating of one cell also speeds up the other cells' temperature rising. 4 To solve this problem, the temperature deviation of a battery module needs to be controlled within 5 C. [5][6][7] Thermal runaway causes the most severe safety problem, even catastrophic damage in large lithium-ion energy storage devices because of mismanagement, such as fire and combustion, 8,9 which results from drastic or successive reaction. The mechanism of these reactions includes decomposition of solid electrolyte interface (SEI), decomposition of cathode material and electrolyte, and reactions between anode and electrolyte.…”

Section: Introductionmentioning

confidence: 99%

“…The above three objectives are reached through the candidate design selection. In addition, studies based on multiphysics modeling and advanced evolutionary algorithms [38][39][40] have been applied for energy storage systems design. [41][42][43] However, the heat generation rate and the temperature rising speed are not the same during the discharging and charging process.…”

Section: Introductionmentioning

confidence: 99%

An experimental investigation of liquid cooling scheduling for a battery module

et al. 2020

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Summary The thermal safety of electric vehicle battery modules attracts public concern; controlling the severe temperature rise and ensuring uniform temperature distribution are essential to addressing this problem. In this research, a liquid cooling‐based cooling structure equipped with minichannels is proposed to prevent a battery module's overheating. A novel cooling scheduling study is proposed to arrange the coolant flow rates at different cooling stages. The temperature rise, temperature difference, and energy consumption of all the cooling schedules are measured in experiments. Experimental findings indicate that appropriate cooling scheduling achieves the thermal objectives and reduces energy consumption through scheduling the coolant flow rate in the cooling process. A comprehensive cooling schedule selection is carried out to select the optimal cooling schedule with the highest cooling efficiency through evaluating both the thermal and energy consumption objective parameters under different discharging current rates (0.5C, 1C, and 1.5C). The optimal cooling schedule maintains the maximum temperature of the battery module within 26°C, 32°C, and 40°C under 0.5C, 1C, and 1.5C discharging current rates, respectively. Moreover, the temperature SD and the energy consumption of the liquid cooling‐based battery pack can be controlled within 3.5°C and 40 J, respectively.

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A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Cited by 64 publications

References 39 publications

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

An experimental investigation of liquid cooling scheduling for a battery module

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