Hybrid optimization strategy for lithium-ion battery's State of Charge/Health using joint of dual Kalman filter and Modified Sine-cosine Algorithm

Qian, KF; Xt, Liu

doi:10.1016/j.est.2021.103319

Cited by 41 publications

(16 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such models are commonly referred to as lumped-parameter models, which typically entail a limited number of parameters and facilitate the derivation of state space equations. 30 In order to simulate and analyze the fuel cell system at the system level, a first-order RC equivalent circuit model is frequently utilized. For example, the PEMFC equivalent circuit is established based on Randles' first-order model, 31 as exemplified in Fig.…”

Section: Z F V T E Dt I T E Dtmentioning

confidence: 99%

“…9,10 EIS has become a widely utilized technique for measuring internal resistance and assessing the health status of PEMFC. 11,12 In the impedance calculation method, a single-frequency sinusoidal disturbance signal can be applied, and the amplitude and phase of the resulting current and voltage signals can be obtained using a lock-in amplifier to achieve impedance calculation. Additionally, for multifrequency superimposed periodic harmonic signals, the fast Fourier transform (FFT) can be used to obtain the amplitude and phase of multiple frequencies, enabling the realization of multi-frequency impedance calculation.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Fast Fuel Cell EIS Measurement Method Based on Square Wave Excitation and Modified Wavelets

Dong

Zhou

Lian

et al. 2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

Electrochemical impedance spectroscopy (EIS) is an effective technique for real-time fault detection of vehicle-mounted proton exchange membrane fuel cell (PEMFC). In this study, a method to enable fast online measurements of the EIS for PEMFC is presented based on square excitation and modified Morlet wavelet transform (WT), which is validated experimentally. The Morlet wavelet function has been optimized to be more suitable for WT applications in impedance calculations. With this improvement, WT is able to cover the analysis frequency range of 1 Hz to 4 kHz by using a very short time-domain data, precisely at the moment of a square-wave signal jump. This results in a significant reduction in computational cost and allows efficient impedance calculations. The calculated impedance based on the modified Morlet WT has a maximum calculation error of 2.57%, as confirmed by comparing the theoretical impedance at different frequency points. Compared to the general fast Fourier transform EIS calculation method, the proposed method demonstrates significant advantages in terms of both calculation accuracy and test implementation.

show abstract

Section: Z F V T E Dt I T E Dtmentioning

confidence: 99%

mentioning

confidence: 99%

A Fast Fuel Cell EIS Measurement Method Based on Square Wave Excitation and Modified Wavelets

Dong

Zhou

Lian

et al. 2023

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…The author proposed a battery health assessment framework based on fuzzy comprehensive evaluation (FCE) and improved multiple grey model (IMGM) to optimize multiple health indicators (MHIs) system to reduce the influence of health indicator (HI) error on the overall prediction results 35 and data driven and support vector machine 36–38 . The author proposes an improved sine‐cosine algorithm based on the two‐order RC model 39 . The author takes machine learning as the starting point, analyzes and compares five popular intelligent algorithms, and provides some enlightenment for maximum likelihood method to estimate state of health 40 …”

Section: Introductionmentioning

confidence: 99%

“…[36][37][38] The author proposes an improved sine-cosine algorithm based on the two-order RC model. 39 The author takes machine learning as the starting point, analyzes and compares five popular intelligent algorithms, and provides some enlightenment for maximum likelihood method to estimate state of health. 40 With the increasing accuracy requirements of the algorithm, this paper proposes a multi-time scale low-pass filter forgetting factor recursive least squares method -double extended Kalman filter (FFRLS-DEKF) co-estimation algorithm for state of charge and state of health.…”

Section: Introductionmentioning

confidence: 99%

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Long

Wang

Cao

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

For the lithium battery management system and real-time safety monitoring, two issues are of great significance, namely, the ability to accurately update the model parameters in real time and to accurately estimate the state of charge and health. In this context, this thesis adopts the second-order RC equivalent circuit model and the forgetting factor recursive least squares -double extended Kalman filtering (FFRLS-DEKF) algorithm with multi-time scales and low-pass filter. Forgetting factor recursive least squares is applied to conduct online parameter identification, and the traditional double extended Kalman filtering algorithm is optimized to evaluate the state of charge and model parameters in the micro-scale and macro-scale. In this way, the error caused by two different characteristics is reduced, and a low-pass filter is added to optimize the fluctuation problem of the estimated value of the model parameters. According to the experiment results, the maximum error between the model simulation value and the actual value of the terminal voltage is 0.0459 V. If the initial value of the state of charge deviates from the actual value, the maximum errors under BBDST and HPPC conditions record 0.0235 and 0.0048, respectively, the forgetting factor recursive least squares -double extended Kalman filtering algorithm with multi-time scales and low-pass filter is able to track the true value within 40 s. Furthermore, the lithium-ion battery state of health both reaches 98% under the two conditions. In summary, the experimental analysis shows that the algorithm helps reduce the influence of initial values on the results, thereby reducing error accumulation and improving the robustness.

show abstract

“…The battery accounts for about 50% of the total cost in the entire vehicle cost of electric vehicles [18], the limitations of lower energy density, lower capacity, and lower voltage can be overcome by a large number of series-parallel links [19].…”

Section: Introductionmentioning

confidence: 99%

Structure optimization of liquid-cooled lithium-ion batteries based on particle swarm algorithm

Song

Liu

Qian

2022

Preprint

View full text Add to dashboard Cite

If lithium-ion batteries are used under high temperature conditions for a long time, it will accelerate the aging of the battery, and the excessive temperature difference will also affect the consistency of the battery. The cooling system of the battery can make the battery work more safely, and it is important to optimize the heat dissipation of the Lithium-ion battery. It is difficult for batteries to improve the overall performance by optimizing only a single factor. Based on orthogonal analysis and response surface, the thermal performance of the cell was analyzed with five factors, such as runner thickness, runner width, inlet temperature, inlet flow rate, and ambient temperature. A particle swarm algorithm was used to optimize the parameters of the variables, and the best cooling performance can be obtained with the optimized parameters. Using this optimization method, the overall temperature of the cell can be reduced and the uniformity of temperature distribution can be improved. The strategy can be widely applied to improve the structure of the cell and reduce the analysis time.

show abstract

Hybrid optimization strategy for lithium-ion battery's State of Charge/Health using joint of dual Kalman filter and Modified Sine-cosine Algorithm

Cited by 41 publications

References 31 publications

A Fast Fuel Cell EIS Measurement Method Based on Square Wave Excitation and Modified Wavelets

A Fast Fuel Cell EIS Measurement Method Based on Square Wave Excitation and Modified Wavelets

Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Structure optimization of liquid-cooled lithium-ion batteries based on particle swarm algorithm

Contact Info

Product

Resources

About