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Advanced Application Technology of Lithium-Ion Batteries

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 10918

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Special Issue Editors

Dr. Linjing Zhang

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Guest Editor

National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
Interests: capacity degradation mechanism, optimal charging strategy of lithum-ion batteries

Prof. Dr. Ning Li

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Guest Editor

1. School of Materials Science & Engineering, Beijing Institute of Technology, Beijing 100811, China
2. Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
Interests: energy materials design and synthesis; synchrotron-based material diagnostics

Dr. Zhaoxia Peng

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Guest Editor

School of Transportation Science and Engineering, Beihang University, Beijing, China
Interests: electric vehicle

Special Issue Information

Dear Colleagues,

Since their advent in the late 1980s, lithium-ion batteries have been successfully commercialized and widely applied in electronic portable devices, hybrid electric vehicles, electric vehicles, electric tools, energy storage for renewable energy, and so on. This dramatically increasing application of lithium-ion batteries inevitably triggers much more requirements for lithium-ion batteries, such as high energy density, low cost, long cycling life, high safety, and environmental benignity. Therefore, advanced application technologies are essential to lithium-ion batteries at different application scenarios.

This Special Issue aims to present and disseminate the most recent advances related to the application technologies for lithium-ion batteries.

Topics of interest for publication include, but are not limited to:

Advanced battery modeling technology;
Estimation methods for state of charge, state of health, and cycling life;
Fast charging and charging safety strategies;
Advance battery failure diagnosis and identification;
Machine-learning- and artificial-intelligence-assisted battery simulation;
Battery low-temperature heating method and heat-dissipation design;
Advance sensors and smart batteries;
Design and synthesis of advanced battery materials;
Advanced characterizations for battery and related materials.

Dr. Linjing Zhang
Prof. Dr. Ning Li
Dr. Zhaoxia Peng
Guest Editors

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Keywords

lithium ion batteries
battery modeling and simulation
state of charge and health
life prediction
battery failure
machine learning
low-temperature heating
battery sensor
smart batteries
batteries materials

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Published Papers (5 papers)

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Research

18 pages, 4280 KiB

Open AccessArticle

Improved State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Parameter Estimation and Multi-Innovation Adaptive Robust Unscented Kalman Filter

by Cheng Li and Gi-Woo Kim

Energies 2024, 17(1), 272; https://doi.org/10.3390/en17010272 - 4 Jan 2024

Cited by 9 | Viewed by 1948

Abstract

In this study, an improved adaptive robust unscented Kalman Filter (ARUKF) is proposed for an accurate state-of-charge (SOC) estimation of battery management system (BMS) in electric vehicles (EV). The extended Kalman Filter (EKF) algorithm is first used to achieve online identification of the model parameters. Subsequently, the identified parameters obtained from the EKF are processed to obtain the SOC of the batteries using a multi-innovation adaptive robust unscented Kalman filter (MIARUKF), developed by the ARUKF based on the principle of multi-innovation. Co-estimation of parameters and SOC is ultimately achieved. The co-estimation algorithm EKF-MIARUKF uses a multi-timescale framework with model parameters estimated on a slow timescale and the SOC estimated on a fast timescale. The EKF-MIARUKF integrates the advantages of multiple Kalman filters and eliminates the disadvantages of a single Kalman filter. The proposed algorithm outperforms other algorithms in terms of accuracy because the average root mean square error (RMSE) and the mean absolute error (MAE) of the SOC estimation were the smallest under three dynamic conditions. Full article

(This article belongs to the Special Issue Advanced Application Technology of Lithium-Ion Batteries)

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23 pages, 5425 KiB

Open AccessArticle

A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

by Haochen Qin, Xuexin Fan, Yaxiang Fan, Ruitian Wang, Qianyi Shang and Dong Zhang

Energies 2023, 16(14), 5414; https://doi.org/10.3390/en16145414 - 16 Jul 2023

Cited by 4 | Viewed by 1865

Abstract

High maintenance costs and safety risks due to lithium-ion battery degeneration have significantly and seriously restricted the application potential of batteries. Thus, this paper proposes an efficient calculation approach for state of health (SOH) estimation in lithium-ion batteries that can be implemented in battery management system (BMS) hardware. First, from the variables of the charge profile, only the complete voltage data is taken as the input to represent the complete aging characteristics of the batteries while limiting the computational complexity. Then, this paper combines the light gradient boosting machine (LightGBM) and weighted quantile regression (WQR) methods to learn a nonlinear mapping between the measurable characteristics and the SOH. A confidence interval is applied to quantify the uncertainty of the SOH estimate, and the model is called LightGBM-WQR. Finally, two public datasets are employed to verify the proposed approach. The proposed LightGBM-WQR model achieves high accuracy in its SOH estimation, and the average absolute error (MAE) of all cells is limited to 1.57%. In addition, the average computation time of the model is less than 0.8 ms for ten runs. This work shows that the model is effective and rapid in its SOH estimation. The SOH estimation model has also been tested on the edge computing module as a possible innovation to replace the BMS bearer computing function, which provides tentative solutions for online practical applications such as energy storage systems and electric vehicles. Full article

(This article belongs to the Special Issue Advanced Application Technology of Lithium-Ion Batteries)

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14 pages, 2321 KiB

Open AccessArticle

Joint Prediction of the State of Charge and the State of Health of Lithium-Ion Batteries Based on the PSO-XGBoost Algorithm

by Jiakun An, Wei Guo, Tingyan Lv, Ziheng Zhao, Chunguang He and Hongshan Zhao

Energies 2023, 16(10), 4243; https://doi.org/10.3390/en16104243 - 22 May 2023

Cited by 6 | Viewed by 1896

Abstract

Lithium-ion batteries are widely used in power grids as a common form of energy storage in power stations. The state of charge (SOC) and state of health (SOH) reflect the capacity and lifetime variation in the Li-ion batteries, and they are important state parameters of Li-ion batteries. Therefore, the establishment of accurate SOC and SOH prediction models is an essential prerequisite for the correct assessment of the status of lithium batteries, the improvement of the operational accuracy of energy-storage stations, and the development of maintenance plans for energy-storage stations. This paper first analyzes the correlation between SOC and SOH, and then proposes a joint SOC and SOH prediction model using the particle swarm optimization (PSO) algorithm to optimize the extreme gradient boosting algorithm (XGBoost), which takes into account the dynamic correlation between SOC and SOH dynamics, thus enabling more accurate SOC and SOH prediction. Finally, the prediction model is validated using the Oxford battery aging dataset. The correlation between SOC and SOH is verified by comparing the joint prediction results with the SOC individual prediction results. Then, the prediction results of the PSO-XGBoost model, the traditional XGBoost model, and the long short-term memory neural network are compared to verify the effectiveness and accuracy of the PSO-XGBoost model. Full article

(This article belongs to the Special Issue Advanced Application Technology of Lithium-Ion Batteries)

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31 pages, 9584 KiB

Open AccessArticle

Cooling Optimization Strategy for a 6s4p Lithium-Ion Battery Pack Based on Triple-Step Nonlinear Method

by Hongya Zhang, Hao Chen and Haisheng Fang

Energies 2023, 16(1), 460; https://doi.org/10.3390/en16010460 - 31 Dec 2022

Cited by 2 | Viewed by 2169

Abstract

In a battery cooling system, by adopting a cooling optimization control strategy, the battery temperature under different external environments and load currents can be adjusted to ensure performance and safety. In this study, two modes of the thermal management system are established for the 6s4p (six serial and four parallel batteries in a stage) battery pack. A single particle model, considering battery aging, is adopted for the battery. Furthermore, a cooling optimization control strategy for the battery is proposed based on the triple-step nonlinear method, and then the optimization effect is validated under two C-rate charge–discharge cycles, NEDC cycles, and US06 cycles. Moreover, an extended PID control strategy is constructed and compared with the triple-step nonlinear method. A comparison of pump power, thermal behavior, and aging performance indicate parallel cooling is more advantageous. This verifies the validity of the triple-step nonlinear method and shows its advantages over the extended PID method. The present study provides a method to investigate the thermal behavior and aging performance of a battery pack in a BTM system, and fills in the research gaps in the cooling optimization control strategy for battery packs. Full article

(This article belongs to the Special Issue Advanced Application Technology of Lithium-Ion Batteries)

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19 pages, 4579 KiB

Open AccessArticle

Study on the Homogeneity of Large-Size Blade Lithium-Ion Batteries Based on Thermoelectric Coupling Model Simulation

by Fei Chen, Wenkuan Zhu, Xiangdong Kong, Yunfeng Huang, Yu Wang, Yuejiu Zheng and Dongsheng Ren

Energies 2022, 15(24), 9556; https://doi.org/10.3390/en15249556 - 16 Dec 2022

Cited by 7 | Viewed by 2175

Abstract

To improve the energy density of lithium-ion battery packs, lithium-ion batteries are gradually advancing towards large-size structures, which has become one of the dominant development trends in the battery industry. With large-size blade lithium-ion batteries as the research object, this paper develops a high-precision electro-thermal coupling model based on the relevant parameters obtained through basic performance experiments, explores the mechanism of battery inhomogeneity from a simulation perspective, and further proposes a design management method. First of all, the optimal intervals of capacity and temperature, as well as the characteristics of the inhomogeneity distribution for large-size cells, are determined by essential performance and inhomogeneity tests; subsequently, the electrochemical and thermal characteristics of the large-size battery are described precisely through a 3D thermoelectric coupling mechanism model, and the inhomogeneity of the temperature distribution is obtained through simulation; eventually, the optimized cell connection method and thermal management strategy are proposed based on the validated model. As indicated by the findings, the above solutions effectively ease the inhomogeneity of large-size cells and significantly boost the performance of large-size cells under different operating conditions. Full article

(This article belongs to the Special Issue Advanced Application Technology of Lithium-Ion Batteries)

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