Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

Lin, Mingqiang; Yan, Chenhao; Meng, Jinhao; Wang, Wei; Wu, J.

doi:10.1016/j.energy.2022.123829

Cited by 69 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the analysis in Section 2.2, it can be seen that the hysteresis voltage gradually decreases with the increase of temperature. For the convenience of discussion of the relationship between the hysteresis voltages at different temperatures, the hysteresis voltage at 0 °C is set as the standard value, and the hysteresis voltage ratio at different temperatures is calculated using Equation (6). The obtained data are plotted as curves as shown in Figure 8.…”

Section: Construction Of Hysteresis Submodelmentioning

confidence: 99%

“…The results are shown in Table 7. Based on this, the equation for the hysteresis voltage at different temperatures can be obtained from Equation (6), as shown in Equation (11).…”

Section: %-75% Soc Intervalmentioning

confidence: 99%

“…The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

View full text Add to dashboard Cite

With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback

show abstract

Section: Construction Of Hysteresis Submodelmentioning

confidence: 99%

“…The results are shown in Table 7. Based on this, the equation for the hysteresis voltage at different temperatures can be obtained from Equation (6), as shown in Equation (11).…”

Section: %-75% Soc Intervalmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Moreover, the methods of extracting geometric characteristics from images by transforming the original measurement data have also fascinated growing predilection. Furthermore, incremental capacity (IC) peak value [10], differential thermal voltammetry (DTV) [11], and differential thermal capacity (DTC) [12] features have been demonstrated to be capable of capturing SOH aging characteristics preferably.…”

Section: Introductionmentioning

confidence: 99%

“…Classical machine learning regression algorithms such as support vector regression (SVR) [12,13], Gaussian process regression (GPR) [14], and neural networks and their variants [15][16][17] are also commonly used to mine the mapping relationship between features and SOH. Wang et al [18] adopted broad learning system (BLS) to effectively reconstruct the model through incremental learning, shortening the training process and avoiding catastrophic forgetting.…”

Section: Introductionmentioning

confidence: 99%

State of Health Estimation Method for Lithium-Ion Batteries via Generalized Additivity Model and Transfer Component Analysis

Lin

Yan

Zeng

2023

WEVJ

Self Cite

View full text Add to dashboard Cite

Battery state of health (SOH) is a momentous indicator for aging severity recognition of lithium-ion batteries and is also an indispensable parameter of the battery management system. In this paper, an innovative SOH estimation algorithm based on feature transfer is proposed for lithium-ion batteries. Firstly, sequence features with battery aging information are sufficiently extracted based on the capacity increment curve. Secondly, transfer component analysis is employed to obtain the mapping that minimizes the data distribution difference between the training set and the test set in the shared feature space. Finally, the generalized additive model is investigated to estimate the battery health status. The experimental results demonstrate that the proposed algorithm is capable of forecasting the SOH for lithium-ion batteries, and the results are more outstanding than those of several comparison algorithms. The predictive error evaluation indicators for each battery are both less than 2.5%. In addition, satisfactory SOH estimation results can also be obtained by only relying on a small amount of data as the training set. The comparative experiments using traditional features and different machine learning methods also testify to the superiority of the proposed algorithm.

show abstract

A Temperature and Voltage Coupling Equivalent Electrical Behavior Model of Lithium‐Ion Battery Pack for Electric Unmanned Aerial Vehicle under Variable Load Conditions

Chen

Liu

Yang³

et al. 2023

Energy Tech

View full text Add to dashboard Cite

It is a valuable and challenge problem to reveal the coupling mechanism between heat and electricity in lithium‐ion battery under variable load conditions. A temperature–voltage coupling equivalent electrical behavior model for lithium‐ion battery under variable load conditions is established to describe the electrical behavior of the battery by analyzing the effect of temperature on particle motion, the effect of particle motion on charge distribution, and the effect of charge distribution on the electrical behavior of the battery and to accurately portray the temperature and voltage coupling mechanism. In the model, heat is taken as the state variable of lithium‐ion battery, and the relationship between the external temperature and voltage is described. The model is verified by simulation and measured data of lithium‐ion battery used in electric unmanned aerial vehicles. The results indicate that the established temperature–voltage coupling model can accurately describe the electrical behavior of the battery and can accurately reveal the coupling relationship between temperature and voltage, and the effect of temperature on the electrical behavior of the battery reflected by the model is basically consistent (voltage and temperature mean absolute percentage error is 5.50% and 2.02%, respectively) with the measured data.

show abstract

Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression

Cited by 69 publications

References 20 publications

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

State of Health Estimation Method for Lithium-Ion Batteries via Generalized Additivity Model and Transfer Component Analysis

A Temperature and Voltage Coupling Equivalent Electrical Behavior Model of Lithium‐Ion Battery Pack for Electric Unmanned Aerial Vehicle under Variable Load Conditions

Contact Info

Product

Resources

About