Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Tan, Xiaojun; Liu, Xiaoxi; Wang, Huanyu; Fan, Yuqian; Guo, Feng

doi:10.3389/fenrg.2022.844985

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…During discharging, lithium ions are released from the cathode material, flow through the electrolyte, and are embedded into the anode material via the separator. At the same time, an equal amount of electrons with the same charge are transferred from the cathode to the anode through the external circuit (Luo MY et al, 2022;Tan et al, 2022;Yang et al, 2022).…”

Section: Parameters Graphite Coppermentioning

confidence: 99%

A novel multilayer composite structure based battery thermal management system

et al. 2023

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The battery thermal management system (BTMS) utilizing phase change materials (PCM) has shown promising performance in high heat flux heat dissipation. However, conventional PCM systems do not fully exploit the latent thermal properties of paraffin wax to enhance battery cooling efficiency. To address this issue, this paper proposes a novel multilayer composite material for BTMS, aiming to improve the thermal performance of the battery and overcome the low thermal conductivity of paraffin wax. The preparation process involves positioning the battery at the center of a triangular container, melting paraffin wax and pouring it into a 100 mm high container to form a 20 mm paraffin layer, placing copper foils and graphite layers on the paraffin surface, and repeating this step once. Finally, pour the 40 mm paraffin wax into the container, resulting in a sandwich-like structure with two layers of graphite. The cooling performance of the multilayer composite structure was experimentally tested at different ambient temperatures (15°C and 20°C) and discharge rates, and compared with a conventional BTMS based on pure paraffin wax. The results demonstrate that the multilayer composite structure exhibits superior heat dissipation compared to the pure paraffin structure, significantly reducing battery temperature rise, particularly at higher discharge rates. At an ambient temperature of 20°C and a discharge rate of 5°C, the battery temperature rise is only 14.97°C, with a remarkable cooling effect of 32.6%. Moreover, optimization of the number and thickness of graphite layers in the composite structure reveals that the 6-layer graphite structure outperforms the 2-layer, 4-layer, 8-layer, and 10-layer graphite structures. Additionally, a relatively lower battery surface temperature is observed with a graphite thickness of 0.5 mm on the basis of the 6-layer graphite structure. These findings indicate that the proposed novel layout structure exhibits excellent thermal performance, effectively addressing the low thermal conductivity limitation of traditional paraffin cooling systems, and providing a new approach for thermal management of lithium batteries.

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Section: Parameters Graphite Coppermentioning

confidence: 99%

A novel multilayer composite structure based battery thermal management system

et al. 2023

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“…It can only be indirectly estimated by other means. Battery capacity degradation is commonly used to characterize the aging process of a battery, so extracting features from directly measurable data that can characterize battery aging at different scales is critical for estimating battery health [32,33]. In this study, taking the CS35 battery as an example, three features related to its capacity degradation were extracted from the lithium battery charge/discharge dataset.…”

Section: Feature Extractionmentioning

confidence: 99%

Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network

Guo,

Huang,

Zhang

et al. 2023

Energies

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Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is restricted. In this regard, this study proposes a method for estimating the SOH of lithium batteries based on sample data generation and a temporal convolutional neural network. First, we analyzed the charge/discharge curves of the batteries, from which we extracted features that were highly correlated with the SOH decay. Then, we used a Variational Auto-Encoder (VAE) to learn the features and distributions of the sample data to generate highly similar data and enrich the number of samples. Finally, a temporal convolutional neural network (TCN) was built to mine the nonlinear relationship between features and SOH by combining the source and extended domain data to realize SOH estimation. The experimental results show that the proposed method in this study has less than 2% error in SOH estimation, which improves the accuracy by 64.9% based on its baseline model. The feasibility of using data-driven models for battery health management in data-constrained application scenarios is demonstrated.

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“…As one of the core parameters of BMSs, the main methods for SOH estimation can be divided into three categories: direct measurement methods, model-based methods and data-driven methods (Xiong et al, 2018;Tan et al, 2022). Each of the three categories contain multiple specific methods as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method

Zha²,

Huang³

et al. 2023

Front. Energy Res.

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With the advantages of high energy density, long cycle life and high stability, lithium-ion batteries have been used in a large number of fields such as electric vehicles and grid scale energy storage. To ensure the safe and reliable operation of battery systems, it is important to make an accurate and rapid estimation of the state of health (SOH) of Li-ion cells. A Li-ion cell is a complex nonlinear dynamic system. The SOH of a Li-ion can not be measured directly in actual working conditions; it can only be estimated indirectly by external characteristic parameters that reflects the extent of cell aging. It is difficult to ensure the reliability of method based on a single aging feature or model. Therefore, this paper proposes a multi-feature SOH estimation method that combines data-driven XGBoost and a Kalman filter. Firstly, a principal component analysis algorithm to reconstruct multiple battery aging features based on data is used, and an XGBoost online estimation model incorporating multiple features based on the reconstructed feature data is constructed. Finally, the joint optimal estimation of SOH of Li-ion cells by introducing a time-domain Kalman filter based on the real-time correction of the XGBoost model is achieved in this method. The results show that the method improves the accuracy and robustness of the estimation model and achieves a high-precision joint estimation of SOH for Li-ion cells.

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Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Cited by 12 publications

References 32 publications

A novel multilayer composite structure based battery thermal management system

A novel multilayer composite structure based battery thermal management system

Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network

Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method

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