Searching for the optimal current pattern based on grey wolf optimizer and equivalent circuit model of Li-ion batteries

Chen, Guan‐Jhu; Liu, Yihua; Wang, Shun‐Chung; Luo, Yi‐Feng; Yang, Zong‐Zhen

doi:10.1016/j.est.2020.101933

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…The multistage constant current charging (MSCC) is an amelioration to the CCCV standard charging protocol, where the constant voltage phase is replaced by multiple constant current values that are decreasing [16], [17]. Zhang et al [18] used a model of 26650 LiFePO4 battery, applied constant current charging at different C-rate, the multi stage charging was segmented according to the SOC of the cell and the charging current at each interval decreases by a calculated amount, the maximum reduction in charging time was 3.42%, or 2.13 min reduced.…”

Section: Introductionmentioning

confidence: 99%

A fast charge algorithm for Li-ion battery for electric vehicles

Bouzaid,

El Mehdi,

El Ballouti

2024

IJECE

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The renewable solar energy industry and electric vehicle industry are today seeking for fast battery pack recharging methods to achieve higher performances, and fast energy recovery for energy storage systems (ESS) and for electric vehicles. The charge rate of batteries impacts directly the temperature which in turn impacts the capacity fade, thus it should be kept low to prevent the cells from warming up. This not only limits the charging rate but also puts us on a trade-off, a long lifetime or a fast recharge. In this study, we tried to achieve fast charging using a new charging method that combine two charging methods, without much deterring the capacity of the battery, in order to be able to maintain a long battery lifetime. Charging time of around 82 min was achieved for a 1.8 Ah battery. We compared our findings with the literature with known charging profiles.

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Section: Introductionmentioning

confidence: 99%

A fast charge algorithm for Li-ion battery for electric vehicles

Bouzaid,

El Mehdi,

El Ballouti

2024

IJECE

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“…42 The highspeed temperature sensor is also introduced to monitor the working state of batteries with low supply sensitivity, which benefits the present application and nonfunctional requirements. 43 Markov-based state estimation is conducted to characterize the charging process, and modular-fault tolerant processing is investigated for the mathematical state-space Equation. 44 Various operating strategies are analyzed for the battery's redox performance with structural optimization.…”

Section: Introductionmentioning

confidence: 99%

“…The optimized state estimation is supposed to realize the automotive tests by the adaptive management of the battery safety protection 42 . The high‐speed temperature sensor is also introduced to monitor the working state of batteries with low supply sensitivity, which benefits the present application and non‐functional requirements 43 . Markov‐based state estimation is conducted to characterize the charging process, and modular‐fault tolerant processing is investigated for the mathematical state‐space Equation 44 .…”

Section: Introductionmentioning

confidence: 99%

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

Wang

Fan

et al. 2022

Intl J of Energy Research

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Summary Accurate state of charge (SOC) estimation is essential for the whole‐life‐cycle safety guarantee and protection of lithium‐ion batteries, which is quite difficult to realize. In this study, a novel weighting factor‐adaptive Kalman filtering (WF‐AKF) method is proposed for the accurate estimation of SOC with a collaborative model for parameter identification. An improved bipartite electrical equivalent circuit (BEEC) model is constructed to describe the dynamic characteristics combined with the mathematical correction of the time‐varying factors. The model parameters are identified online, corresponding to various SOC levels and temperature conditions. Considering the internal resistances, ambient temperature, and complex current rate variations, an adaptive multi‐time scale iterative calculation model is constructed and combined with the real‐time estimation and correction strategies. The maximum closed‐circuit voltage (CCV) traction error is 0.36% and 0.24% for the main pulse‐current charging and discharging processes, respectively. The proposed WF‐AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under the complex working condition. The SOC estimation is accurate and robust to the time‐varying characteristics and working conditions even when the initial error is large, providing a safety protection theory for lithium‐ion batteries.

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“…Chen, G.J. et al [16] proposed a gray wolf optimization method to find the optimal charging mode with five-stage constant current considering both charging time and charging loss. The results show that the proposed charging method is comparable to the optimization method that only considers charging time.…”

Section: Introductionmentioning

confidence: 99%

Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model

Wei

Sun

et al. 2022

Energies

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New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system.

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Searching for the optimal current pattern based on grey wolf optimizer and equivalent circuit model of Li-ion batteries

Cited by 25 publications

References 26 publications

A fast charge algorithm for Li-ion battery for electric vehicles

A fast charge algorithm for Li-ion battery for electric vehicles

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model

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