A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles

Wang, Limei; Yong, Cheng; Zhao, Xinbing

doi:10.1016/j.apenergy.2014.12.081

Cited by 61 publications

(24 citation statements)

References 24 publications

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“…Their results indicate that the model responds accurately in a given battery voltage fluctuation. Wang et al [27]. Their results indicate that the model responds accurately in a given battery voltage fluctuation.…”

Section: Simscape Battery Cell Modelmentioning

confidence: 81%

“…Similarly, the SOH of the battery pack is the minimum of the series cells' SOHs. Wang et al [27]. Their results indicate that the model responds accurately in a given battery voltage fluctuation.…”

Section: Battery Pack Modelmentioning

confidence: 81%

“…This is a practical method. Moreover, the Simscape model has been tested by Huria et al [20] and Wang et al [27]. Their results indicate that the model responds accurately in a given battery voltage fluctuation.…”

Section: Simscape Battery Cell Modelmentioning

confidence: 99%

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Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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Abstract:To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

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Section: Simscape Battery Cell Modelmentioning

confidence: 81%

Section: Battery Pack Modelmentioning

confidence: 81%

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Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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“…A battery pack capacity estimation method using the measured voltage-to-capacity curve transformation to overlap the standard one provided by the manufactory is proposed. 16,17 Cell life is much longer than battery module life. In the series connection of non-uniform cells, the total capacity of the battery is governed by a cell with the smallest capacity.…”

Section: Introductionmentioning

confidence: 99%

Battery module capacity fade model based on cell voltage inconsistency and probability distribution

Zhang

Yang

et al. 2017

Advances in Mechanical Engineering

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Cell voltage inconsistency of battery module is correlated with cell capacity fading inconsistency caused by uneven temperature or improper charge/discharge rate, so it is essential to study on cell voltage inconsistency when establishing a battery module capacity fade model. An accelerated life experiment is conducted on 12-series (12S) LiMn 2 O 4 battery. The evaluation index of the voltage inconsistency is given, and the evolution of the voltage of the 12S battery module is obtained. Furthermore, a model of capacity fade of this 12S battery is established based on cell voltage inconsistency, which is extended to any-series battery by means of probability distribution. Based on this extended model, the relationship between the number of cells and the life of the battery is obtained.

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“…On the other hand, the amount of cells connected in parallel can affect the current and the whole pack resistance. More cells connected in parallel will certainly produce higher efficiency, while contributing a heavier weight and more expensive cost [6,19,20]. Thus, a global optimization should be applied to determine these two topologies to minimize the battery lifetime cost and the total amount of all-life cost considering overall foregoing influences.…”

Section: Introductionmentioning

confidence: 99%

Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

Chen

Guo

et al. 2017

Energies

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This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs). Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA) is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO). The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method.

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A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles

Cited by 61 publications

References 24 publications

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

Battery module capacity fade model based on cell voltage inconsistency and probability distribution

Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

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