Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

Gao, Zhiqiang; Chin, Cheng Siong; Chiew, J; Jia, Jing; Zhang, Caizhi

doi:10.3390/en10101503

Cited by 41 publications

(43 citation statements)

References 28 publications

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“…The demand for them is expected to rise continuously in the coming years [5,6]. However, safety concerns related to the lithium-ion batteries still remain [7][8][9] because hazardous incidents such as fire accidents in the Boeing 787-8 aircraft [10] and battery failures in the Samsung Note7 [11] have been frequently reported by media [12]. The main cause of these two events is internal short circuit (ISCr) in the lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

et al. 2018

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Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it describes self-discharge phenomenon caused by the internal short circuit clearly. However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal voltage of the pack, leading to difficulty in estimating accurate internal short circuit resistance. In this paper, estimating the resistance with the whole terminal voltages and the load currents of the pack, a detection method for the soft internal short circuit in the pack is proposed. Open circuit voltage of a faulted cell in the pack is extracted to reflect the self-discharge phenomenon obviously; this process yields accurate estimates of the resistance. The proposed method is verified with various soft short conditions in both simulations and experiments. The error of estimated resistance does not exceed 31.2% in the experiment, thereby enabling the battery management system to detect the internal short circuit early.

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Section: Introductionmentioning

confidence: 99%

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

et al. 2018

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“…[6][7][8][9][10][11][12]. In battery management, the accurate estimation of various states of the battery, including state-of-charge (SOC), state-of-health (SOH), and state-of-power (SOP), is a priority [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Shun

Huang

et al. 2018

Energies

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Abstract:The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery's peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

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“…To further validate the proposed ambient temperature model and SOC estimation algorithm under the practical and dynamic situation, the New European Driving Cycle load profile [49] was applied to the 12-cell series battery pack prototype to simulate the electric vehicle applications under varying the ambient temperature. The ambient temperature variation can be seen in Figure 13(a).…”

Section: Soc Estimation Under Realistic Load Profile and Varying Ambimentioning

confidence: 99%

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

Gao

Chin

Toh

et al. 2017

Journal of Advanced Transportation

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This paper presents an integrated state-of-charge (SOC) estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4) battery power system. The strong tracking cubature extended Kalman filter (STCEKF) gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

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Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

Cited by 41 publications

References 28 publications

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

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