Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

Min, Hao; Sun, Weiyi; Li, Xinyong; Guo, Dongni; Yu, Yuanbin; Zhu, Tao; Zhao, Zhen

doi:10.3390/en10050709

Cited by 37 publications

(13 citation statements)

References 30 publications

(31 reference statements)

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“…The MCC reduces the charging time and controls the temperature rise, but it becomes problematic, when to fix the constant current value for each charging step. To overcome the issue, some soft computing algorithms like Taguchi method, 143 ant-colony algorithm, 144 particle swarm optimization, [145][146][147] genetic algorithm, 137,148,149 dynamic programming algorithm, 150 and multi-objective biogeography-based optimization 141,151 have been used to find the optimal values for each step of MCC. However, capacity loss due to electrolyte decomposition may occur when switching at different current rates.…”

Section: Charging and Discharging Of Batterymentioning

confidence: 99%

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Adaikkappan

Nageswari

2021

Intl J of Energy Research

104

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Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium-ion (Li-ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well-parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well-known battery models such as the electrochemical model, equivalent circuit model, and data-driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model-based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real-time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized.

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Section: Charging and Discharging Of Batterymentioning

confidence: 99%

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Adaikkappan

Nageswari

2021

Intl J of Energy Research

104

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“…Thomas and Newman's research [18] on the heat generation mechanism of the battery shows that the heat generation of the battery in the process of charging and discharging is mainly generated by Joule heat generation and entropy heat generation. In this paper, the charging current of the cylindrical battery is small, so the entropy heat generation is ignored, andonly Joule heat generation is considered, as shown in the following formula [19]:…”

Section: Mathematical Model Of Low Temperature Charging and Heatingmentioning

confidence: 99%

Research on the Combined Control Strategy of Low Temperature Charging and Heating of Lithium-Ion Power Battery Based on Adaptive Fuzzy Control

et al. 2020

Self Cite

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A low temperature environment will lead to the decrease of chemistry reaction rate and increase of the internal resistance of the lithium battery. In addition, the excessive charging current will cause the lithium to separate out and even the permanent attenuation of battery capacity. In order to solve these problems, this paper proposes a low-temperature charging heating combined control strategy, which takes the temperature acceptable charging current of the battery at low temperature as the charging current constraint and the maximum output power of the system as the power constraint. Firstly, a scheme of combined charging and heating control system is put forward. Secondly, the low temperature charging control strategy based on adaptive fuzzy control is established and then the model is simulated and analyzed in MATLAB software. At last, a Chroma 72,001 charge and discharge tester is used to conduct a low temperature test on 18,650 lithium iron phosphate battery monomers. The results show that the low-temperature charging control strategy proposed in this paper has a more stable temperature control effect on the battery, the constant current charging time of the battery is reduced by 14% compared with the traditional threshold control method, and the overall charging energy consumption is reduced by 5.6%.

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“…At the same time, the magnitude of the current can be increased for individual steps by using multiple optimization methods [8][9][10][11][12][13][14]. Although the temperature rise in MCC is very close to CC-CV charging [15], this method can result in a significant temperature rise unless sufficient cooling is provided.…”

Section: Introductionmentioning

confidence: 99%

Feedback PID Controller-Based Closed-Loop Fast Charging of Lithium-Ion Batteries Using Constant-Temperature–Constant-Voltage Method

Kaleem¹,

Khalil²,

Aslam³

et al. 2021

Electronics

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Lithium-ion batteries are the most used technology in portable electronic devices. High energy density and high power per mass battery unit make it preferable over other batteries. The existing constant-temperature and constant-voltage charging technique (CT–CV), with a closed loop, lacks a detailed design of control circuits, which can increase charging speed. This article addresses this research gap in a novel way by implementing a simpler feedback proportional integral and differential (PID) control to a closed-loop CT–CV charging circuit. Voltage-mode control (VMC) and average current-mode control (ACM) methods were implemented to maintain the battery voltage, current, and temperature at safe limits. As per simulation results, 23% faster charging is achieved by implementing VMC and almost 50% faster charging is attained by employing the ACM technique in the PID controller. Our proposed control strategy is validated experimentally, which yields up to 25% faster charging of a battery than the reference battery.

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Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

Cited by 37 publications

References 30 publications

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Research on the Combined Control Strategy of Low Temperature Charging and Heating of Lithium-Ion Power Battery Based on Adaptive Fuzzy Control

Feedback PID Controller-Based Closed-Loop Fast Charging of Lithium-Ion Batteries Using Constant-Temperature–Constant-Voltage Method

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