Research

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21 pages, 7171 KiB

Open AccessArticle

A Lithium-Ion Battery Remaining Useful Life Prediction Model Based on CEEMDAN Data Preprocessing and HSSA-LSTM-TCN

by Shaoming Qiu, Bo Zhang, Yana Lv, Jie Zhang and Chao Zhang

World Electr. Veh. J. 2024, 15(5), 177; https://doi.org/10.3390/wevj15050177 - 24 Apr 2024

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a [...] Read more.

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) data preprocessing and IHSSA-LSTM-TCN. Firstly, CEEMDAN is used to decompose lithium-ion battery capacity data into high-frequency and low-frequency components. Subsequently, for the high-frequency component, a Temporal Convolutional Network (TCN) prediction model is employed. For the low-frequency component, an Improved Sparrow Search Algorithm (IHSSA) is utilized, which incorporates iterative chaotic mapping and a variable spiral coefficient to optimize the hyperparameters of Long Short-Term Memory (LSTM). The IHSSA-LSTM prediction model is obtained and used for prediction. Finally, the predicted values of the sub-models are combined to obtain the final RUL result. The proposed model is validated using the publicly available NASA dataset and CALCE dataset. The results demonstrate that this model outperforms other models, indicating good predictive performance and robustness. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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20 pages, 9302 KiB

Open AccessArticle

Research on Thermal Runaway Characteristics of High-Capacity Lithium Iron Phosphate Batteries for Electric Vehicles

by Qing Zhu, Kunfeng Liang and Xun Zhou

World Electr. Veh. J. 2024, 15(4), 147; https://doi.org/10.3390/wevj15040147 - 03 Apr 2024

Viewed by 638

Abstract

With the rapid development of the electric vehicle industry, the widespread utilization of lithium-ion batteries has made it imperative to address their safety issues. This paper focuses on the thermal safety concerns associated with lithium-ion batteries during usage by specifically investigating high-capacity lithium [...] Read more.

With the rapid development of the electric vehicle industry, the widespread utilization of lithium-ion batteries has made it imperative to address their safety issues. This paper focuses on the thermal safety concerns associated with lithium-ion batteries during usage by specifically investigating high-capacity lithium iron phosphate batteries. To this end, thermal runaway (TR) experiments were conducted to investigate the temperature characteristics on the battery surface during TR, as well as the changes in battery mass and expansion rate before and after TR. Meanwhile, by constructing a TR simulation model tailored to lithium iron phosphate batteries, an analysis was performed to explore the variations in internal material content, the proportion of heat generation from each exothermic reaction, and the influence of the heat transfer coefficient during the TR process. The results indicate that as the heating power increases, the response time of lithium-ion batteries to TR advances. Furthermore, the heat released from the negative electrode–electrolyte reaction emerges as the primary heat source throughout the entire TR process, contributing to 63.1% of the total heat generation. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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14 pages, 2804 KiB

Open AccessArticle

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

by Wenming Dai, Yong Xiang, Wenyi Zhou and Qiao Peng

World Electr. Veh. J. 2024, 15(2), 72; https://doi.org/10.3390/wevj15020072 - 18 Feb 2024

Viewed by 974

Abstract

Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by [...] Read more.

Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R² while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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18 pages, 5587 KiB

Open AccessArticle

Thermal Performance Enhancement of Lithium-Ion Batteries Using Phase Change Material and Fin Geometry Modification

by Sarmad Ali, Muhammad Mahabat Khan and Muhammad Irfan

World Electr. Veh. J. 2024, 15(2), 42; https://doi.org/10.3390/wevj15020042 - 30 Jan 2024

Viewed by 1299

Abstract

The rapid increase in emissions and the depletion of fossil fuels have led to a rapid rise in the electric vehicle (EV) industry. Electric vehicles predominantly rely on lithium-ion batteries (LIBs) to power their electric motors. However, the charging and discharging processes of [...] Read more.

The rapid increase in emissions and the depletion of fossil fuels have led to a rapid rise in the electric vehicle (EV) industry. Electric vehicles predominantly rely on lithium-ion batteries (LIBs) to power their electric motors. However, the charging and discharging processes of LIB packs generate heat, resulting in a significant decline in the battery performance of EVs. Consequently, there is a pressing need for effective battery thermal management systems (BTMSs) for lithium-ion batteries in EVs. In the current study, a novel experimental BTMS was developed for the thermal performance enhancement of an LIB pack comprising 2 × 2 cells. Three distinct fin configurations (circular, rectangular, and tapered) were integrated for the outer wall of the lithium-ion cells. Additionally, the cells were fully submerged in phase change material (PCM). The study considered 1C, 2C, and 3C cell discharge rates, affiliated with their corresponding volumetric heat generation rates. The combination of rectangular fins and PCM manifested superior performance, reducing the mean cell temperature by 29.71% and 28.36% compared to unfinned lithium-ion cells under ambient conditions at the 1C and 2C discharge rates. Furthermore, at the 3C discharge rate, lithium-ion cells equipped with rectangular fins demonstrated a delay of 40 min in reaching the maximum surface temperature of 40

°

C compared to the unfinned ambient case. After 60 min of battery discharge at the 3C rate, the cell surface temperature of the rectangular fin case only reached 42.7

°

C. Furthermore, numerical simulations showed that the Nusselt numbers for lithium-ion cells with rectangular fins improved by 9.72% compared to unfinned configurations at the 3C discharge rate. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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19 pages, 6757 KiB

Open AccessArticle

Numerical Investigation of Heat Production in the Two-Wheeler Electric Vehicle Battery via Torque Load Variation Test

by Hariyotejo Pujowidodo, Bambang Teguh Prasetyo, Respatya Teguh Soewono, Himawan Sutriyanto, Achmad Maswan, Muhammad Penta Helios, Kanon Prabandaru Sumarah, Bhakti Nuryadin, Andhy Muhammad Fathoni, Dwi Handoko Arthanto, Riki Jaka Komara, Agus Prasetyo Nuryadi, Fitrianto, Chairunnisa and I.G.A. Uttariyani

World Electr. Veh. J. 2024, 15(1), 13; https://doi.org/10.3390/wevj15010013 - 02 Jan 2024

Viewed by 1290

Abstract

Experimental studies were conducted to investigate the effect of varying torque loads on the temperature distribution on the surface of lithium-ion batteries (72 volts–20 Ah) in real commercial two-wheeler electric vehicles as part of our previous research. An electric vehicle engine was installed [...] Read more.

Experimental studies were conducted to investigate the effect of varying torque loads on the temperature distribution on the surface of lithium-ion batteries (72 volts–20 Ah) in real commercial two-wheeler electric vehicles as part of our previous research. An electric vehicle engine was installed in a dyno testing laboratory and used as the main load for the battery. Ambient temperature and relative humidity were controlled using an air conditioning system. The test results are presented as surface temperature distributions on each side of the battery at various torque loads. The highest temperature on the battery’s surface was found to be approximately 40 °C at a torque load of 100%. Unfortunately, the heat generated by the battery during testing could not be measured for further research. This paper presents a numerical study of battery heat generation at 100% torque load using Ansys Fluent 2020 R1©. This tool is employed to calculate the heat flux from the battery surface to the ambient air. The CFD tool was initially validated against available experimental data and commonly used correlations for natural convection along a vertically heated wall. Good agreements between the current predictions and experimental data were observed for laminar flow regimes. Convective heat transfer between the battery surface and ambient air was simulated. The results indicate that the commonly used heat transfer correlation for vertical plates accurately predicts the heat transfer rate on the battery surface, and it was found that the heat generation rate is 1199 W/m³. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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10 pages, 1494 KiB

Open AccessEditor’s ChoiceArticle

High-Speed Laser Drying of Lithium-Ion Battery Anodes: Challenges and Opportunities

by Samuel Fink, Delil Demir, Markus Börner, Vinzenz Göken and Christian Vedder

World Electr. Veh. J. 2023, 14(9), 255; https://doi.org/10.3390/wevj14090255 - 09 Sep 2023

Cited by 2 | Viewed by 2135

Abstract

In modern electrode manufacturing for lithium-ion batteries, the drying of the electrode pastes consumes a considerable amount of space and energy. To increase the efficiency of the drying process and reduce the footprint of the drying equipment, a laser-based drying process is investigated. [...] Read more.

In modern electrode manufacturing for lithium-ion batteries, the drying of the electrode pastes consumes a considerable amount of space and energy. To increase the efficiency of the drying process and reduce the footprint of the drying equipment, a laser-based drying process is investigated. Evaporation rates of up to 318 g m⁻² s⁻¹ can be measured, which is orders of magnitude higher than the evaporation rates in conventional furnace drying processes. Optical measurements of the slurry components in the visible and near-infrared spectrum are conducted. Thermal analyses the of laser-dried samples reveal that the commonly used binders carboxymethyl-cellulose (CMC) and styrene–butadiene rubber (SBR) are not affected by the laser drying process within the investigated process window. The results indicated that with the combination of a fast laser drying step and a subsequent convection drying step, high evaporation rates can be achieved while maintaining the integrity and adhesion of the anode. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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13 pages, 2544 KiB

Open AccessArticle

Research on Calendar Aging for Lithium-Ion Batteries Used in Uninterruptible Power Supply System Based on Particle Filtering

by Wei Xu and Hongzhi Tan

World Electr. Veh. J. 2023, 14(8), 209; https://doi.org/10.3390/wevj14080209 - 08 Aug 2023

Cited by 1 | Viewed by 1356

Abstract

The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible [...] Read more.

The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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15 pages, 4722 KiB

Open AccessArticle

State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory

by Yanmei Li, Laijin Luo, Chaolong Zhang and Huihan Liu

World Electr. Veh. J. 2023, 14(7), 188; https://doi.org/10.3390/wevj14070188 - 14 Jul 2023

Cited by 4 | Viewed by 2079

Abstract

The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this [...] Read more.

The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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13 pages, 2318 KiB

Open AccessArticle

An Improved Gaussian Process Regression Based Aging Prediction Method for Lithium-Ion Battery

by Weiwei Qu, Hu Deng, Yi Pang and Zhanfeng Li

World Electr. Veh. J. 2023, 14(6), 153; https://doi.org/10.3390/wevj14060153 - 09 Jun 2023

Viewed by 1421

Abstract

A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to [...] Read more.

A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to simulate working conditions. The complicated degradation processes at different ranges of the state of charge (SOC) under different discharge rates were analyzed. A composed kernel function was conducted to optimize the hyperparameter. The inputs for the kernel function of GPR were improved by coupling the constant and variant characteristics. Moreover, previous aging information was employed as a characteristic to improve the reliability of the prediction. Experiments were conducted on a lithium–cobalt battery at three different SOC ranges under three discharge rates to verify the performance of the proposed method. Some tips to slow the aging process based on the coupled stress were discovered. Results show that the proposed method accurately estimated the degradation rate with a maximum estimation root-mean-square error of 0.14% and regression coefficient of 0.9851. Because of the proposed method’s superiority to the exponential equation and GPR by fitting all cells under a different operating mode, it is better for reflecting the true degradation in actual EV. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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15 pages, 5049 KiB

Open AccessArticle

An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning

by Yujie Wang, Wenhuan Li, Zeyan Liu and Ling Li

World Electr. Veh. J. 2023, 14(3), 57; https://doi.org/10.3390/wevj14030057 - 24 Feb 2023

Cited by 3 | Viewed by 2298

Abstract

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and [...] Read more.

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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Review

Jump to: Research

25 pages, 1882 KiB

Open AccessReview

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

by Feng Zhao, Yun Guo and Baoming Chen

World Electr. Veh. J. 2024, 15(4), 131; https://doi.org/10.3390/wevj15040131 - 25 Mar 2024

Cited by 1 | Viewed by 768

Abstract

With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the [...] Read more.

With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

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