Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Sidhu, Amardeep; Izadian, Afshin; Anwar, Sohel

doi:10.1109/tie.2014.2336599

Cited by 239 publications

(95 citation statements)

References 39 publications

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“…In addition to SOC estimation, an active cell balancing (AB) circuit [43][44][45] is another important element in the battery management system. Most batteries are made up of multiple cells that required AB to prevent each cell from overcharge or discharge after the SOC was determined.…”

Section: Introductionmentioning

confidence: 99%

“…There exist different cell balancing methods using a resistor, capacitor, inductor/transformer, and energy converter balancing method [44,45]. For example, the shuttling capacitors cell balancing method named "charge shuttling cells equalization" used capacitors as external energy storage for alternating the energy between the cells for charge balancing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Bai et al [21] applied a combined power generation system (CPGS) to achieve a reliable evaluation of a distribution network with microgrids combined with fault duration. In addition, many model-based diagnostic algorithms such as extended kalman were presented that diagnoses thermal faults in Lithium-ion batteries [22][23][24][25]. The simulation and experimental studies were also presented to illustrate the effectiveness of the proposed schemes.…”

Section: Introductionmentioning

confidence: 99%

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Hong¹,

Wang²,

Liu³

2017

Preprint

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This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

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“…As an important part of a BMS, battery fault diagnosis techniques have attracted much more attention in recent years [2][3][4][5]. For battery cells, as the basic unit of battery packs, the faults of the cell are difficult to detect directly.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

Zheng

Chen

et al. 2017

Energies

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Abstract:The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

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Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Cited by 239 publications

References 39 publications

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

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

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

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