Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging

Zou, Changfu; Hu, Xiaosong; Wei, Zhongbao; Wik, Torsten; Egardt, Bo

doi:10.1109/tie.2017.2772154

Cited by 187 publications

(108 citation statements)

References 36 publications

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“…However, often a large quantity of information needs to be stored by using DP, which leads to large computation cost especially in high dimension charging problems. The MPC-based charging approaches also need to design a suitable battery model firstly [134][135][136]. Then the charging behaviours such as current, voltage, and even temperature can be predicted directly by the designed electric model or thermal model.…”

Section: Optimization Of Battery Charging Approachmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Optimization Of Battery Charging Approachmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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“…According to Hu et al 34 and Zou et al, 35 electrical models typically encompass a set of parameters in terms of electrical resistances and capacitances, and all these parameters are temperature dependent. According to Hu et al 34 and Zou et al, 35 electrical models typically encompass a set of parameters in terms of electrical resistances and capacitances, and all these parameters are temperature dependent.…”

Section: Heat Generation Modelmentioning

confidence: 99%

A novel resistance-based thermal model for lithium-ion batteries

Xie

Yang

et al. 2018

Int J Energy Res

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Summary A thermal model considering effects of the state of charge (SOC) and temperature on heat generation is developed for lithium‐ion (Li‐ion) batteries, which models the ohmic resistance, polarization resistance, and entropy change. This model describes a coupling relationship between heat generation and temperature distribution and can be implemented in real time. Then, the thermal model is applied to predict thermal evolutions of an NCR 18650B Li‐ion battery at different ambient temperatures and discharge rates. Extensive experiments are conducted and illustrate that the proposed model can accurately capture the thermal behavior at different ambient temperatures and discharge rates, with an average error of 1.18°C. In addition, comparative studies show that this model significantly outperforms its two counterparts, namely a constant resistance‐based thermal model and a model whose heat generation depends on the temperature but not the SOC.

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“…Currently, a variety of lithium‐ion battery charging strategies have been proposed, such as the typical constant current (CC), constant voltage (CV), pulse current (PC), and intelligent charging strategies . The CC strategy charges a battery with a constant current until the battery voltage attains a preset level.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, a variety of lithium-ion battery charging strategies have been proposed, such as the typical constant current (CC), constant voltage (CV), pulse current (PC), and intelligent charging strategies. 20,21 The CC strategy charges a battery with a constant current until the battery voltage attains a preset level. Various algorithms have been proposed to improve its efficiency, [22][23][24][25][26][27] but most of them were tested within a narrow temperature range, meaning the temperature effect was not fully taken into account.…”

Section: Introductionmentioning

confidence: 99%

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

et al. 2019

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Summary Battery charging techniques are critical to enhance battery operation performance. Charging temperature rise, energy loss, and charging time are three key indicators to evaluate charging performance. It is imperative to decrease temperature rise and energy loss without extending the charging time during the charging process. To this end, an equivalent circuit electrical model, a power loss model, and a thermal model are built in this study for lithium‐ion batteries. Then, an integrated objective function is formulated to minimize energy loss and temperature increment during battery charging. To further validate the generality and feasibility of the proposed charging strategy, experiments are conducted with respect to different current, operating temperatures, battery types, and aging status. Comparison results demonstrate that the devised charging strategy is capable of achieving the intended effect under any operating temperature and with different aging status.

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Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging

Cited by 187 publications

References 36 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

A novel resistance-based thermal model for lithium-ion batteries

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

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