Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

Zhang, Yun; Shang, Yang; Cui, Naxin; Zhang, Chenghui

doi:10.3390/en11010019

Cited by 20 publications

(12 citation statements)

References 41 publications

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“…In the result, the ohmic resistance at −10°C is significantly higher than those at higher temperatures. According to the findings in the literature, low temperatures also greatly enhance the effect of the current on the ohmic resistance of lithium batteries. This phenomenon explains why the models have relatively large errors at low temperatures.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 89%

Modeling and comparative analysis of a lithium‐ion hybrid capacitor under different temperature conditions

Huang

Tian

et al. 2020

Int J Energy Res

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Summary As a combination of the battery‐type material and the supercapacitor‐type material, lithium‐ion hybrid capacitors (LIHCs) can achieve high energy density and high power density. An optimal model is essential to manage the LIHCs effectively for electric vehicle applications. However, modeling of LIHCs has not been systematically investigated. In this paper, the basic dynamic performance of a commercial LIHC is comprehensively analyzed. Two battery‐supercapacitor hybrid (BSH) models are proposed on the basis of physical structure and dynamic performance of the LIHC. Meanwhile, nine battery models and two supercapacitor models are systematically compared to determine the optimal model for the LIHC under different temperature conditions. All the model parameters are calibrated with the dynamic response analysis in the time domain. The applicability of the 13 equivalent circuit models (ECMs) under three different temperature conditions are evaluated. According to the evaluation results, the BSH models with highest accuracy and moderate computational complexity are more appropriate for the LIHC behavior description in the electric vehicle operating conditions. This research can well guide the modeling of LIHCs and their state estimator design.

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Section: Comparative Analysis and Discussionmentioning

confidence: 89%

Modeling and comparative analysis of a lithium‐ion hybrid capacitor under different temperature conditions

Huang

Tian

et al. 2020

Int J Energy Res

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“…It consists of an anode and a cathode (i.e., electrodes) immersed in an electrolyte solution. An electrical potential is created in the battery due to the difference in electric charge between the two electrodes in a cell, which is determined by the product of the reaction and the reactants (i.e., standard Gibbs Free energies) [29]. The data has batteries whose OCV reading is observed at 297.15 K and 277.15 K, and at 1 atmospheric pressure.…”

Section: Background On Battery Characteristicsmentioning

confidence: 99%

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

et al. 2020

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Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

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“…The performance parameters such as lifecycle, capacity and charging time are strongly affected by its operating conditions especially temperature and state of charge (SOC) 3 . Pesaran pointed out that the maximum inter‐cells temperature difference is 5°C that confined the operating temperature range of LIB 4 .…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary optimal design of prismatic lithium‐ion battery with an improved thermal management system for electric vehicles

Tak

Chin

et al. 2021

Energy Storage

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The reliability and performance of lithium‐ion battery implemented in electric vehicles is greatly influenced by temperature. However, there is yet to have a systematic battery thermal strategy due to the poor understanding of battery thermal behavior. Although several heat management systems have been proven to be functional in regulating the operating temperature effectively, its capability would be overtaken by the escalating internal resistance due to ageing after a certain period. Hence, an integration of finite element method (FEM) with particle swarm optimization (PSO) approach is proposed, more specifically for phase change material‐based cooling system, by performing multiobjective optimization of battery pack volume and temperature effects. As such, the multidisciplinary design optimization is considered level by level for the battery pack in order to prevent premature ageing. After justifying the lithium‐ion composition, the prismatic battery pack is modeled parametrically and its thermal responses assessed using FEM. The dimension and arrangement of battery cells are assigned as design variables and constrained in a fixed range, which will then be iterated through online optimization whereby the temperature difference, SD and volume are objectives and minimized by using PSO algorithm. The optimization results are summarized: maximum temperature difference is 0.8208383 K, SD is 0.17239 K, and area is 53 082 cm3. The results also imply that the numbers and columns for cells have a significant impact on the effectiveness of heat transfer which may be correlated to its cell dimension.

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Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

Cited by 20 publications

References 41 publications

Modeling and comparative analysis of a lithium‐ion hybrid capacitor under different temperature conditions

Modeling and comparative analysis of a lithium‐ion hybrid capacitor under different temperature conditions

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

Multidisciplinary optimal design of prismatic lithium‐ion battery with an improved thermal management system for electric vehicles

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