Modeling and state of charge estimation of lithium-ion battery

Chen, Xikun; Sun, Dalin

doi:10.1007/s40436-015-0116-3

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…These algorithms range from modelbased approaches to advanced non-model-based techniques that can identify issues like lithium plating-a common cause of battery degradation [8]. Such diagnostic capabilities are critical for maintaining battery health and ensuring safety, especially under the stress of rapid charging conditions [9,10].…”

Section: Recent Advances In Computational Intelligence For Bmsmentioning

confidence: 99%

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

Tapaskar,

Revankar,

Ganachari

2024

WEVJ

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As electric vehicles (EVs) gain momentum in the shift towards sustainable transportation, the efficiency and reliability of energy storage systems become paramount. Lithium-ion batteries stand at the forefront of this transition, necessitating sophisticated battery management systems (BMS) to enhance their performance and lifespan. This research presents an innovative simulation of a 4S3P lithium-ion battery pack using MATLAB R2023b, designed to refine BMS capabilities by employing advanced mathematical modelling and computational intelligence. The simulation meticulously analyses critical operational metrics such as state of charge (SOC), state of health (SOH), temperature variations, and electrical behaviour under diverse load scenarios, offering deep insights into the intricate dynamics of lithium-ion batteries in EV applications. The results corroborate the simulation model’s accuracy in reflecting actual battery pack performance and underscore significant improvements in BMS strategies, especially concerning predictive maintenance and adaptive charging techniques. By seamlessly integrating computational intelligence into BMS, this study lays the groundwork for more durable, efficient, and intelligent energy storage systems in electric vehicles, marking a significant stride in e-mobility technology.

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Section: Recent Advances In Computational Intelligence For Bmsmentioning

confidence: 99%

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

Tapaskar,

Revankar,

Ganachari

2024

WEVJ

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“…Various filter algorithms like EKF, UKF, and CKF were employed in the aforementioned studies, each with some algorithmic enhancements. However, Kalman filters exhibit limitations in certain aspects [21][22][23][24][25], particularly their sensitivity to inaccurate initial conditions and unknown perturbations, resulting in substantial estimation errors in the model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang,

Li,

Sun

et al. 2024

Batteries

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This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

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“…UKF utilizes UT transformation in order to avoid solving differentiation, while its filtering depends on the selection of parameters, resulting in insufficient reliability 32 . CKF (Cubature Kalman Filter) uses radial rules based on the third‐order spherical surface, simplifying its implementation and making it reliable with high filtering accuracy, which makes up for the shortcomings of UKF 33 . However, after CKF has been filtered many times, the accumulated rounding error causes the state covariance matrix to gradually lose its non‐negative qualitativeness; therefore, the filtering accuracy decreases or diverges 34 .…”

Section: Introductionmentioning

confidence: 99%

“…32 CKF (Cubature Kalman Filter) uses radial rules based on the third-order spherical surface, simplifying its implementation and making it reliable with high filtering accuracy, which makes up for the shortcomings of UKF. 33 However, after CKF has been filtered many times, the accumulated rounding error causes the state covariance matrix to gradually lose its non-negative qualitativeness; therefore, the filtering accuracy decreases or diverges. 34 In an attempt to resolve this issue in CKF, Liu et al 35 combined CKF with singular value decomposition (SVD) to estimate the battery SOC and verified the effectiveness of this method under various operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Wang

Meng

Chang

et al. 2021

Intl J of Energy Research

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Summary Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium‐ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF‐AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞‐CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium‐ion battery SOC. FF‐AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD‐CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF‐AGLS and H∞‐SVD‐CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

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Modeling and state of charge estimation of lithium-ion battery

Cited by 11 publications

References 25 publications

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

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