A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations

Lipu, Molla Shahadat Hossain; Hannan, M. A.; Hussain, Aini; Hoque, Mohammed Mozammel; Ker, Pin Jern; Saad, Mohamad Hanif Md; Ayob, Afida

doi:10.1016/j.jclepro.2018.09.065

Cited by 537 publications

(228 citation statements)

References 95 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, an efficient and effective evaluation of residual energy left in the batteries shall be of great importance for determining the reuse or remanufacturing or recovery of materials so as to ensure the growth and sustainability of the EVs market . Evaluation and accurate estimation of the battery condition have been of great interest for battery manufacturers and for road safety purpose because an early detection of short circuit or cycle life of battery based on state of health (SOH) value unforeseen occurrence of dire events . This is important for safety design of EVs and prevents unforeseen accidents such as those based on thermal failure, short circuit, and external impact from happening .…”

Section: Introductionmentioning

confidence: 99%

“…6 Evaluation and accurate estimation of the battery condition have been of great interest for battery manufacturers and for road safety purpose because an early detection of short circuit or cycle life of battery based on state of health (SOH) value unforeseen occurrence of dire events. 7 This is important for safety design of EVs and prevents unforeseen accidents such as those based on thermal failure, short circuit, and external impact from happening. [8][9][10] Therefore, developing the novel strategy for an effective evaluation of residual energy in the battery pack shall pave the way for an efficient time-based recycling for use of spend batteries for either reuse, remanufacturing, or recovery of materials.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Garg

Liu

et al. 2019

Int J Energy Res

View full text Add to dashboard Cite

Summary The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Garg

Liu

et al. 2019

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Different intelligent techniques such as support vector regression [15], [16], Bayesian prediction [17], [18], and artificial neural network [19]- [21] have been successfully applied to build data-driven models for battery cyclic aging prediction. On the one hand, some review papers have summarised these state-of-the-art applications [22], [23], concluding that several limitations still exist as: 1) data-driven approaches are mainly used to capture battery cyclic aging states but very few attempts have been done for calendar aging diagnosis. 2) most publications fit the model on aging data obtained under constant operating conditions, ignoring various cases of temperature and SOC.…”

mentioning

confidence: 99%

Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

Liu

et al. 2020

IEEE Trans. Ind. Inf.

275

119

View full text Add to dashboard Cite

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and SOC. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multi-step prediction test and accelerated aging training test, the proposed ARDbased GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis.Index Terms-Lithium-ion batteries, calendar aging prediction, battery health, Gaussian process regression, datadriven model.

show abstract

“…46 The load-responsive model switching SoC estimation was investigated on the LIBs, and an event trigger procedure was developed to detect the estimation performance by leveraging the high-pass filter and coulomb counting algorithms. 51 An adaptive SoC estimation was conducted by using the separation battery model, 52 which is also realized by the adaptive Extended Kalman filter (EKF) and wavelet transform matrix. 48 An equivalent model was constructed by the physical property analysis that is easy to parameterize.…”

mentioning

confidence: 99%

“…50 A model-based SoC observer was constructed, and the error analysis was performed as well. 51 An adaptive SoC estimation was conducted by using the separation battery model, 52 which is also realized by the adaptive Extended Kalman filter (EKF) and wavelet transform matrix. 53 The multitimescale observer was designed for the SoC estimation of the LIBs, in which the design parameter sets could be tuned in different timescales.…”

mentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

show abstract

A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations

Cited by 537 publications

References 95 publications

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Contact Info

Product

Resources

About