An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency

Yang, Chaofan; Wang, Xueyuan; Fang, Qiaohua; Dai, Haifeng; Cao, Yanhua; Wei, Xuezhe

doi:10.1016/j.est.2020.101250

Cited by 90 publications

(26 citation statements)

References 28 publications

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“…Following this, Sun et al [165] focus on serial connected Lithium Nickel Manganese Cobalt Oxide (NMC) cells and emphasized the need for efficient algorithms for large battery systems. The approach of Yang et al [166] is similar and focuses on serial strings only.…”

Section: Online Identification Of Core Temperaturementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Section: Online Identification Of Core Temperaturementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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“…Obviously, it is impertinent to treat a pack as a simple cell when estimating the pack SOC. To now, the widely applied approach for pack SOC estimation is the mean-plus-difference model, which adopts a mean model to identify the mean condition of battery pack and a difference model to assess the diversity of cell SOC and internal resistance [29]. In [30], the second-order ECM and simple resistance model are respectively established as the cell mean model and difference model, and then the EKF is introduced to estimate the 5 of 30 mean and difference SOC.…”

Section: Of 30mentioning

confidence: 99%

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

Shu

Zhang

et al. 2021

IEEE Trans. Transp. Electrific.

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Accurate estimation of state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, firstly, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate battery cell's SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation value from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited within 2% after convergence.

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“…A deep learning method for lithium-ion battery capacity prediction based on long short-term memory (LSTM) recurrent neural network is studied in the study by Chen et al (2020), which is used to capture the potential long-term correlation of capacity degradation. A multi-timescale extended Kalman filter (EKF) algorithm is proposed in the study by Yang et al (2020) to estimate the state of charge and capacity of each battery in the battery pack. A linear aging model based on the capacity data in the sliding window is used to predict the RUL of the battery by combining Monte Carlo simulation to generate the prediction uncertainty (Xiong et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

2022

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Battery health prediction is very important for the safety of lithium batteries. Due to the factors such as capacity regeneration and random fluctuation in the use of lithium ion battery, the accuracy and generalization ability are poor when using a single scale feature to predict the health state of lithium ion battery. To solve these problems, we propose a comprehensive prediction method based on variational mode decomposition, integrated particle filter, and long short-term memory network with self-attention mechanism. Firstly, the capacity data of lithium ion battery is decomposed by variational mode decomposition to obtain the residual component which can reflect the global degradation trend of lithium ion battery and intrinsic mode functions component that can reflect the local random fluctuation. Then, the particle filter algorithm is employed to predict the residual component, and the long short-term memory network with self-attention mechanism is proposed to predict the intrinsic mode functions component. Finally, the prediction results of each subcomponent are reconstructed to obtain the final prediction value of lithium ion battery health state. The experimental results show that the prediction method proposed in this article has good prediction accuracy and stability.

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An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency

Cited by 90 publications

References 28 publications

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

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