Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

Arachchige, Buddhi; Perinpanayagam, Suresh; Jaras, Raul

doi:10.3390/app7111172

Cited by 19 publications

(11 citation statements)

References 23 publications

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“…Furthermore, in order to test the accuracy of the prediction of the PF and proposed SPF algorithm, different cycle 'starting points' were applied, such as 20, 50 and 80 cycles. Again, the two algorithms were used here for online estimation of the parameters in (16). A second order degradation model has been developed based on the fitting results explained earlier in Section 3.…”

Section: Experimental Validation and Discussionmentioning

confidence: 99%

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Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

et al. 2021

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Accurate prediction of the remaining useful life (RUL) in Lithium-ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)-based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non-linear characteristics and non-Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Experimental Validation and Discussionmentioning

confidence: 99%

“…As shown in Figure 1, battery capacity decreases dramatically with an increase in the number of cycles used. Thus, the degradation curves are well fitted using an exponential growth model as presents in (16).…”

Section: Capacity Degradation Modellingmentioning

confidence: 99%

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

et al. 2021

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“…LIB failure, however, occurs when the capacity drops below a normal capacity value or failure threshold value [75]. To perform this task for safe and reliable use of LIBs [76], there are basically three methods classified as physical methods, data driven methods, and hybrid methods that are used to realize an accurate BMS. The summary of these methods is illustrated in Table 4.…”

Section: Prognostic Methodsmentioning

confidence: 99%

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

2018

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The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

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“…Considering the demands of engineering application, the dynamic characteristics of a lithium battery should be characterized by uncomplicated equivalent model [45,46], so the Thevenin model of lithium battery equivalent circuit model is selected in this paper. The Thevenin model is composed of a resistance capacitance (RC) circuit in series with ohm internal resistance [47][48][49]. Its basic idea is to use ohm internal resistance to characterize the instantaneous change in terminal voltage in the charging and discharging process [50][51][52], and an RC parallel circuit to characterize the polarization effect of the battery in the using process [53,54].…”

Section: Dynamic Thevenin Modementioning

confidence: 99%

An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries

et al. 2020

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An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model.

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Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

Cited by 19 publications

References 23 publications

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries

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