Battery optimal charging strategy based on a coupled thermoelectric model

Liu, Kailong; Li, Kang; Yang, Zhile; Zhang, Cheng; Deng, Jiankang

doi:10.1109/cec.2016.7748334

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…In CC-CV charging process, CC stage and CV stage can be complementary in some way [108], the capacity loss caused by the large electrochemical polarization in CC stage will be effectively compensated by CV stage. Hence the CC-CV charging approach is superior to the sole CC as well as sole CV charging in the applications of EVs, and has been selected as a benchmark to compare with the performance of other newly developed battery charging approaches [109,110]. Although standard CC-CV charging approach is easy to apply, the challenging issue is to set the appropriate constant current rate at the CC stage and constant voltage value at the CV stage.…”

Section: Traditional 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: Traditional 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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“…The first dataset was collected from SONYVTC6 batteries, with a rated capacity of 3 Ah. For the first battery in this dataset, a cyclic aging profile of constant-current-constantvoltage (CCCV) charging [41,42] and constant-current (CC) discharging is carried out. The current rate of this cyclic profile was 1C in the CC phase, and the cutoff conditions were set as 4.2V, 2.75V and 0.05C for the upper voltage limit, lower voltage limit, and current limit, respectively.…”

Section: Description Of the Datasetsmentioning

confidence: 99%

Model Migration Neural Network for Predicting Battery Aging Trajectories

Tang

Liu

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

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144

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“…Assuming that the battery surface and internal temperatures are both uniform, the battery has a uniform distribution of heat generation. Heat conduction is the only heat transfer form between the ambience and the battery surface and also between the battery interior and the surface, and a two-stage battery cell thermal model is then described as [37,38]…”

Section: Principle Of Teaching-learning-based Optimizationmentioning

confidence: 99%

Data‐Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management

Liu

Pan

et al. 2018

Complexity

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Temperature is a crucial state to guarantee the reliability and safety of a battery during operation. The ability to estimate battery temperature, especially the internal temperature, is of paramount importance to the battery management system for monitoring and thermal control purposes. In this paper, a data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) is proposed to estimate the internal temperature for lithium-ion battery thermal management. To be specific, the suitable input terms and the number of hidden nodes for the RBF NN are first optimized by a two-stage stepwise identification algorithm (TSIA). Then, the teaching-learning-based optimization (TLBO) algorithm is developed to optimize the centres and widths in every neuron of basis function. After optimizing the RBF NN model, a battery lumped thermal model is adopted as the state function with the EKF to filter out the outliers of the RBF model and reduce the estimation error. This data-driven approach is validated under four different conditions in comparison with the linear NN models. The experimental results demonstrate that the proposed RBF data-driven approach outperforms the other approaches and can be extended to other types of batteries for thermal monitoring and management.

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Battery optimal charging strategy based on a coupled thermoelectric model

Cited by 15 publications

References 12 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

Model Migration Neural Network for Predicting Battery Aging Trajectories

Data‐Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management

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