A Design of a Grey-Predicted Li-Ion Battery Charge System

Chen, Liang-Rui; Hsu, Roy Chaoming; Liu, Chuansheng

doi:10.1109/tie.2008.928106

Cited by 126 publications

(57 citation statements)

References 23 publications

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“…Some of the approaches involve computational intelligence techniques including neural networks [4], grey prediction [5], fuzzy control [6], and ant-colony algorithm [7]. Some other strategies take the battery charging behaviors as an explicit optimization problem which is then solved using an optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Battery optimal charging strategy based on a coupled thermoelectric model

Liu

Yang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Section: Introductionmentioning

confidence: 99%

Battery optimal charging strategy based on a coupled thermoelectric model

Liu

Yang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…The performance of the GA was dependent on the system accuracy; the robustness of the GA decreased because of the noise and time variance properties of the battery. Chen et al [42] used the grey prediction technique for battery charging without taking the temperature into consideration. The advantages of the proposed methodology over the previous techniques are that it is easy to implement and that all battery states are considered during the charging state.…”

Section: Resultsmentioning

confidence: 99%

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

et al. 2018

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Abstract:The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current-constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLAB TM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

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“…Suffering from system noise and the time-variance properties of batteries, the robustness of these methods is weak. In [18], to increase the charging speed, a grey prediction technique is utilized to develop a grey-predicted control system. In that study, the control system does not take increase in temperature into consideration and only applies a one-step prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Model Predictive Control-Based Fast Charging for Vehicular Batteries

Yan

Qian

et al. 2011

Energies

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Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

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A Design of a Grey-Predicted Li-Ion Battery Charge System

Cited by 126 publications

References 23 publications

Battery optimal charging strategy based on a coupled thermoelectric model

Battery optimal charging strategy based on a coupled thermoelectric model

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

Model Predictive Control-Based Fast Charging for Vehicular Batteries

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