Pub Date : 2024-02-27DOI: 10.3390/batteries10030078
Nikko Cano Talplacido, Denis Cumming
Thermal monitoring of lithium-ion batteries ensures their safe and optimal operation. To collect the most accurate temperature data of LIBs, previous studies used thermocouples in the cell and proved them to be technically viable. However, the cost and scale-up limitations of this method restricted its use in many applications, hindering its mass adoption. This work developed a low-cost and scalable screen-printed carbon black thermocouple to study its applicability for the thermal monitoring of LIB. Given the appropriate manufacturing parameters, it was found that thermal sensors may be printed on the electrodes, installed on a pouch cell, and once calibrated, operate with excellent sensitivity. However, to reliably use a printed carbon black thermocouple in operando of a pouch cell, its chemical resistance against electrolytes was found to require further development.
{"title":"Printed Carbon Black Thermocouple as an In Situ Thermal Sensor for Lithium-Ion Cell","authors":"Nikko Cano Talplacido, Denis Cumming","doi":"10.3390/batteries10030078","DOIUrl":"https://doi.org/10.3390/batteries10030078","url":null,"abstract":"Thermal monitoring of lithium-ion batteries ensures their safe and optimal operation. To collect the most accurate temperature data of LIBs, previous studies used thermocouples in the cell and proved them to be technically viable. However, the cost and scale-up limitations of this method restricted its use in many applications, hindering its mass adoption. This work developed a low-cost and scalable screen-printed carbon black thermocouple to study its applicability for the thermal monitoring of LIB. Given the appropriate manufacturing parameters, it was found that thermal sensors may be printed on the electrodes, installed on a pouch cell, and once calibrated, operate with excellent sensitivity. However, to reliably use a printed carbon black thermocouple in operando of a pouch cell, its chemical resistance against electrolytes was found to require further development.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.3390/batteries10030080
Thang Phan Nguyen, I. Kim
The long-term stability of energy-storage devices for green energy has received significant attention. Lithium-ion batteries (LIBs) based on materials such as metal oxides, Si, Sb, and Sn have shown superior energy density and stability owing to their intrinsic properties and the support of conductive carbon, graphene, or graphene oxides. Abnormal capacities have been recorded for some transition metal oxides, such as NiO, Fe2O3, and MnO/Mn3O4. Recently, the restructuring of NiO into LiNiO2 anode materials has yielded an ultrastable anode for LIBs. Herein, the effect of the thin film thickness on the restructuring of the NiO anode was investigated. Different electrode thicknesses required different numbers of cycles for restructuring, resulting in significant changes in the reconstituted cells. NiO thicknesses greater than 39 μm reduced the capacity to 570 mAh g−1. The results revealed the limitation of the layered thickness owing to the low diffusion efficiency of Li ions in the thick layers, resulting in non-uniformity of the restructured LiNiO2. The NiO anode with a thickness of approximately 20 μm required only 220 cycles to be restructured at 0.5 A g−1, while maintaining a high-rate performance for over 500 cycles at 1.0 A g−1, and a high capacity of 1000 mAh g−1.
绿色能源储能设备的长期稳定性受到了极大关注。基于金属氧化物、硅、锑和锡等材料的锂离子电池（LIBs）因其内在特性以及导电碳、石墨烯或石墨烯氧化物的支持而显示出卓越的能量密度和稳定性。一些过渡金属氧化物，如氧化镍、氧化铁和氧化锰/氧化锰 4，也出现了异常容量。最近，将 NiO 重组为 LiNiO2 阳极材料的方法为 LIB 提供了一种超稳定的阳极。本文研究了薄膜厚度对氧化镍阳极重组的影响。不同的电极厚度需要不同次数的重组循环，从而导致重组电池发生显著变化。氧化镍厚度大于 39 μm 时，电池容量降至 570 mAh g-1。结果表明，分层厚度的限制是由于锂离子在厚层中的扩散效率较低，导致重组后的 LiNiO2 不均匀。厚度约为 20 μm 的氧化镍阳极在 0.5 A g-1 的条件下只需进行 220 个周期的重组，而在 1.0 A g-1 的条件下则可保持 500 个周期以上的高速率性能，并具有 1000 mAh g-1 的高容量。
{"title":"Film Thickness Effect in Restructuring NiO into LiNiO2 Anode for Highly Stable Lithium-Ion Batteries","authors":"Thang Phan Nguyen, I. Kim","doi":"10.3390/batteries10030080","DOIUrl":"https://doi.org/10.3390/batteries10030080","url":null,"abstract":"The long-term stability of energy-storage devices for green energy has received significant attention. Lithium-ion batteries (LIBs) based on materials such as metal oxides, Si, Sb, and Sn have shown superior energy density and stability owing to their intrinsic properties and the support of conductive carbon, graphene, or graphene oxides. Abnormal capacities have been recorded for some transition metal oxides, such as NiO, Fe2O3, and MnO/Mn3O4. Recently, the restructuring of NiO into LiNiO2 anode materials has yielded an ultrastable anode for LIBs. Herein, the effect of the thin film thickness on the restructuring of the NiO anode was investigated. Different electrode thicknesses required different numbers of cycles for restructuring, resulting in significant changes in the reconstituted cells. NiO thicknesses greater than 39 μm reduced the capacity to 570 mAh g−1. The results revealed the limitation of the layered thickness owing to the low diffusion efficiency of Li ions in the thick layers, resulting in non-uniformity of the restructured LiNiO2. The NiO anode with a thickness of approximately 20 μm required only 220 cycles to be restructured at 0.5 A g−1, while maintaining a high-rate performance for over 500 cycles at 1.0 A g−1, and a high capacity of 1000 mAh g−1.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.3390/batteries10030077
Mike Kopp, A. Fill, Marco Ströbel, K. Birke
Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.
{"title":"A Novel Long Short-Term Memory Approach for Online State-of-Health Identification in Lithium-Ion Battery Cells","authors":"Mike Kopp, A. Fill, Marco Ströbel, K. Birke","doi":"10.3390/batteries10030077","DOIUrl":"https://doi.org/10.3390/batteries10030077","url":null,"abstract":"Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.3390/batteries10030075
Richard Žilka, Ondrej Lipták, Martin Klaučo
This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.
{"title":"Stochastic Control of Battery Energy Storage System with Hybrid Dynamics","authors":"Richard Žilka, Ondrej Lipták, Martin Klaučo","doi":"10.3390/batteries10030075","DOIUrl":"https://doi.org/10.3390/batteries10030075","url":null,"abstract":"This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.3390/batteries10030076
Sahithi Maddipatla, Lingxi Kong, M. Pecht
Cylindrical lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage applications. However, safety risks due to thermal runaway-induced fire and explosions have prompted the need for safety analysis methodologies. Though cylindrical batteries often incorporate safety devices, the safety of the battery also depends on its design and manufacturing processes. This study conducts a design and process failure mode and effect analysis (DFMEA and PFMEA) for the design and manufacturing of cylindrical lithium-ion batteries, with a focus on battery safety.
{"title":"Safety Analysis of Lithium-Ion Cylindrical Batteries Using Design and Process Failure Mode and Effect Analysis","authors":"Sahithi Maddipatla, Lingxi Kong, M. Pecht","doi":"10.3390/batteries10030076","DOIUrl":"https://doi.org/10.3390/batteries10030076","url":null,"abstract":"Cylindrical lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage applications. However, safety risks due to thermal runaway-induced fire and explosions have prompted the need for safety analysis methodologies. Though cylindrical batteries often incorporate safety devices, the safety of the battery also depends on its design and manufacturing processes. This study conducts a design and process failure mode and effect analysis (DFMEA and PFMEA) for the design and manufacturing of cylindrical lithium-ion batteries, with a focus on battery safety.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.3390/batteries10030074
Xiaoming Han, Zhentao Dai, Mifeng Ren, Jing Cui, Yunfeng Shi
Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge voltage-capacity curve. Secondly, a sequence-to-sequence model based on CNN, SE-net, and GRU is designed. Finally, a trend matching loss function is designed based on the common characteristics of capacity fade curves to constrain the encoding features of the sequence-to-sequence model, facilitating the learning of the underlying relationship between inputs and outputs. TM-Seq2Seq model is verified on a public dataset with 132 battery cells and multiple fast charging strategies. The experimental results indicate that, compared to other popular models, the TM-Seq2Seq model has lower prediction errors.
{"title":"One-Time Prediction of Battery Capacity Fade Curve under Multiple Fast Charging Strategies","authors":"Xiaoming Han, Zhentao Dai, Mifeng Ren, Jing Cui, Yunfeng Shi","doi":"10.3390/batteries10030074","DOIUrl":"https://doi.org/10.3390/batteries10030074","url":null,"abstract":"Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge voltage-capacity curve. Secondly, a sequence-to-sequence model based on CNN, SE-net, and GRU is designed. Finally, a trend matching loss function is designed based on the common characteristics of capacity fade curves to constrain the encoding features of the sequence-to-sequence model, facilitating the learning of the underlying relationship between inputs and outputs. TM-Seq2Seq model is verified on a public dataset with 132 battery cells and multiple fast charging strategies. The experimental results indicate that, compared to other popular models, the TM-Seq2Seq model has lower prediction errors.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.3390/batteries10030071
Chunsong Lin, Xianguo Tuo, Longxing Wu, Guiyu Zhang, Xiangling Zeng
Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.
{"title":"Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries","authors":"Chunsong Lin, Xianguo Tuo, Longxing Wu, Guiyu Zhang, Xiangling Zeng","doi":"10.3390/batteries10030071","DOIUrl":"https://doi.org/10.3390/batteries10030071","url":null,"abstract":"Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.3390/batteries10030072
Luigi Aiello, Peter Ruchti, Simon Vitzthum, Federico Coren
In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 MPa, 1.2 MPa) and three electrical discharge rates (0.5 C, 1.5 C, 3.0 C). Electrochemical processes and overall efficiency are significantly affected by temperature and pressure, influencing capacity and charge–discharge rates. In previous studies, temperature and pressure were not controlled simultaneously due to technological limitations. A novel test bench was developed to investigate these influences by controlling the surface temperature and mechanical pressure on a pouch LIB during electrical charging and discharging. This test rig permits an accurate assessment of mechanical, thermal and electrical parameters, while decoupling thermal and mechanical influences during electrical operation. The results of the study confirm what has been found in the literature: an increase in pressure leads to a decrease in performance, while an increase in temperature leads to an increase in performance. However, the extent to which the pressure impacts performance is determined by the temperature and the applied electrical discharge rate. At 5 °C and 0.5 C, an increase in pressure from 0.2 MPa to 1.2 MPa results in a 5.84% decrease in discharged capacity. At 45 °C the discharge capacity decreases by 2.17%. Regarding the impact of the temperature, at discharge rate of 0.5 C, with an applied pressure of 0.2 MPa, an increase in temperature from 25 °C to 45 °C results in an increase of 4.27% in discharged capacity. The impact on performance varies significantly at different C-rates. Under the same pressure (0.2 MPa) and temperature variation (from 25 °C to 45 °C), increasing the electrical discharge rate to 1.5 C results in a 43.04% increase in discharged capacity. The interplay between temperature, pressure and C-rate has a significant, non-linear impact on performance. This suggests that the characterisation of an LIB would require the active control of both temperature and pressure during electrical operation.
本研究测量了袋式锂离子电池（LIB）在温度、压力和放电率变化方面的性能。在三种温度（5 °C、25 °C、45 °C）、四种压力（0.2 兆帕、0.5 兆帕、0.8 兆帕、1.2 兆帕）和三种放电速率（0.5 摄氏度、1.5 摄氏度、3.0 摄氏度）下进行了敏感性研究。电化学过程和整体效率受到温度和压力的显著影响，并对容量和充放电速率产生影响。在以往的研究中，由于技术限制，温度和压力无法同时控制。为了研究这些影响因素，我们开发了一种新型试验台，在充放电过程中控制袋状 LIB 的表面温度和机械压力。该试验台允许对机械、热和电参数进行精确评估，同时将电操作过程中的热影响和机械影响分离开来。研究结果证实了文献中的结论：压力增加会导致性能下降，而温度升高会导致性能上升。不过，压力对性能的影响程度取决于温度和应用的放电速率。在 5 °C 和 0.5 C 条件下，压力从 0.2 MPa 增加到 1.2 MPa 会导致放电容量下降 5.84%。在 45 °C 时，放电容量减少 2.17%。关于温度的影响，在 0.5 C 的放电率和 0.2 MPa 的应用压力下，温度从 25 °C 升至 45 °C 会导致放电容量增加 4.27%。在不同的 C 速率下，对性能的影响也大不相同。在相同的压力（0.2 兆帕）和温度变化（从 25 °C 到 45 °C）条件下，将放电速率提高到 1.5 C 会使放电容量增加 43.04%。温度、压力和 C 率之间的相互作用对性能有显著的非线性影响。这表明，要确定 LIB 的特性，就必须在电气运行期间对温度和压力进行积极控制。
{"title":"Influence of Pressure, Temperature and Discharge Rate on the Electrical Performances of a Commercial Pouch Li-Ion Battery","authors":"Luigi Aiello, Peter Ruchti, Simon Vitzthum, Federico Coren","doi":"10.3390/batteries10030072","DOIUrl":"https://doi.org/10.3390/batteries10030072","url":null,"abstract":"In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 MPa, 1.2 MPa) and three electrical discharge rates (0.5 C, 1.5 C, 3.0 C). Electrochemical processes and overall efficiency are significantly affected by temperature and pressure, influencing capacity and charge–discharge rates. In previous studies, temperature and pressure were not controlled simultaneously due to technological limitations. A novel test bench was developed to investigate these influences by controlling the surface temperature and mechanical pressure on a pouch LIB during electrical charging and discharging. This test rig permits an accurate assessment of mechanical, thermal and electrical parameters, while decoupling thermal and mechanical influences during electrical operation. The results of the study confirm what has been found in the literature: an increase in pressure leads to a decrease in performance, while an increase in temperature leads to an increase in performance. However, the extent to which the pressure impacts performance is determined by the temperature and the applied electrical discharge rate. At 5 °C and 0.5 C, an increase in pressure from 0.2 MPa to 1.2 MPa results in a 5.84% decrease in discharged capacity. At 45 °C the discharge capacity decreases by 2.17%. Regarding the impact of the temperature, at discharge rate of 0.5 C, with an applied pressure of 0.2 MPa, an increase in temperature from 25 °C to 45 °C results in an increase of 4.27% in discharged capacity. The impact on performance varies significantly at different C-rates. Under the same pressure (0.2 MPa) and temperature variation (from 25 °C to 45 °C), increasing the electrical discharge rate to 1.5 C results in a 43.04% increase in discharged capacity. The interplay between temperature, pressure and C-rate has a significant, non-linear impact on performance. This suggests that the characterisation of an LIB would require the active control of both temperature and pressure during electrical operation.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lithium-based electrolytes are, at least from a thermodynamic standpoint, the most suitable ion-transport materials for energy storage systems. However, lithium-based ionic conductors suffer from safety concerns, and the limited availability of lithium in the Earth’s crust is at the root of the need to consider alternative metal ions. Notably, sodium stands out as the sixth most-prevalent element; therefore, when considering mineral reserves, it as a very attractive candidate as an alternative to the status quo. Even if the specific energy and energy density of sodium are indeed inferior with respect to those of lithium, there is substantial economic appeal in promoting the use of the former metal in stationary energy storage applications. For these reasons, the promise of sodium is likely to extend to other commercial applications, including portable electronics, as well as hybrid and electric vehicles. Widely used organic liquid electrolytes, regardless of their chosen metal cation, are disadvantageous due to leakage, evaporation, and high flammability. Polymer electrolytes are acknowledged as the most effective candidates to overcome these obstacles and facilitate the advancement of next-generation energy storage applications. In this contribution, an in-depth and comprehensive review of sodium polymer electrolytes for primary and secondary batteries is proposed. The overarching goal was to gain insight into successful synthetic strategies and their implications for conduction parameters and conductivity mechanisms. The focus lies on solid, gel, and composite polymer electrolytes. Our hope is that the proposed discussion will be helpful to all operators in the field, whether in tackling fundamental research problems or resolving issues of practical significance.
{"title":"Sodium Polymer Electrolytes: A Review","authors":"Sumit Kumar, Rajesh Raghupathy, Michele Vittadello","doi":"10.3390/batteries10030073","DOIUrl":"https://doi.org/10.3390/batteries10030073","url":null,"abstract":"Lithium-based electrolytes are, at least from a thermodynamic standpoint, the most suitable ion-transport materials for energy storage systems. However, lithium-based ionic conductors suffer from safety concerns, and the limited availability of lithium in the Earth’s crust is at the root of the need to consider alternative metal ions. Notably, sodium stands out as the sixth most-prevalent element; therefore, when considering mineral reserves, it as a very attractive candidate as an alternative to the status quo. Even if the specific energy and energy density of sodium are indeed inferior with respect to those of lithium, there is substantial economic appeal in promoting the use of the former metal in stationary energy storage applications. For these reasons, the promise of sodium is likely to extend to other commercial applications, including portable electronics, as well as hybrid and electric vehicles. Widely used organic liquid electrolytes, regardless of their chosen metal cation, are disadvantageous due to leakage, evaporation, and high flammability. Polymer electrolytes are acknowledged as the most effective candidates to overcome these obstacles and facilitate the advancement of next-generation energy storage applications. In this contribution, an in-depth and comprehensive review of sodium polymer electrolytes for primary and secondary batteries is proposed. The overarching goal was to gain insight into successful synthetic strategies and their implications for conduction parameters and conductivity mechanisms. The focus lies on solid, gel, and composite polymer electrolytes. Our hope is that the proposed discussion will be helpful to all operators in the field, whether in tackling fundamental research problems or resolving issues of practical significance.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.3390/batteries10030070
Hong Yin, Yuliang Liu, Yifeng Zhu, Fengxiang Ye, Guangliang Xu, Mengfang Lin, Wenbin Kang
Aqueous zinc ion batteries are highly sought after for the next generation of sustainable energy storage systems. However, their development is significantly impeded by the presence of undesired zinc dendrites, which greatly reduce their cycle life. It is well-received that surface passivation by introducing foreign metals represents a compelling measure to enhance the stability of Zn anodes. Nevertheless, the vast potential of effecting concerted interplay between multiple metal elements for enhanced overall performance in Zn ion batteries remains elusive, due to the overwhelming challenge in creating uniform textures from hetero-units and understanding the mechanism underlying the synergistic performance gain. In this work, an innovative bimetallic overlaying strategy is proposed that renders possible the synergy between AgZn3 and CuZn5 in effecting uniform Zn deposition in a laterally confined and compact manner. The seeded growth of Zn on the bimetal-modulated interface effectively reduces the nucleation potential barrier, yielding a low nucleation overpotential (25 mV). In full cell testing with a commercial MnO2 applied as the cathode, superb cycling stability, surpassing the results reported in previous works, is achieved. The cell delivers an outstanding remaining capacity of 215 mA h g−1 after 300 cycles with almost no capacity degradation observed. The simple and highly efficient bimetal design, which synergizes the strengths of distinct metals, has the potential to drive innovations in the development of multicomponent aqueous Zn batteries with exceptional performance.
锌离子水电池是下一代可持续储能系统的首选。然而，它们的发展却因不受欢迎的锌枝晶的存在而严重受阻，这大大降低了它们的循环寿命。人们普遍认为，通过引入外来金属进行表面钝化是提高锌阳极稳定性的有效措施。然而，由于从异种单元中产生均匀的质地以及了解协同增效的内在机制是一项巨大的挑战，因此在锌离子电池中实现多种金属元素之间的协同作用以提高整体性能的巨大潜力仍然难以实现。在这项工作中，提出了一种创新的双金属叠层策略，使 AgZn3 和 CuZn5 能够协同作用，以横向限制和紧凑的方式实现均匀的锌沉积。锌在双金属调制界面上的种子生长有效降低了成核电位障碍，从而产生了较低的成核过电位（25 mV）。在使用商用二氧化锰作为阴极的完整电池测试中，电池实现了极佳的循环稳定性，超过了以前工作中报告的结果。经过 300 次循环后，电池的剩余容量高达 215 mA h g-1，几乎没有观察到容量衰减。这种简单而高效的双金属设计能协同不同金属的优势，有望推动具有卓越性能的多组分锌水电池的创新发展。
{"title":"Bimetal-Initiated Concerted Zn Regulation Enabling Highly Stable Aqueous Zn-Ion Batteries","authors":"Hong Yin, Yuliang Liu, Yifeng Zhu, Fengxiang Ye, Guangliang Xu, Mengfang Lin, Wenbin Kang","doi":"10.3390/batteries10030070","DOIUrl":"https://doi.org/10.3390/batteries10030070","url":null,"abstract":"Aqueous zinc ion batteries are highly sought after for the next generation of sustainable energy storage systems. However, their development is significantly impeded by the presence of undesired zinc dendrites, which greatly reduce their cycle life. It is well-received that surface passivation by introducing foreign metals represents a compelling measure to enhance the stability of Zn anodes. Nevertheless, the vast potential of effecting concerted interplay between multiple metal elements for enhanced overall performance in Zn ion batteries remains elusive, due to the overwhelming challenge in creating uniform textures from hetero-units and understanding the mechanism underlying the synergistic performance gain. In this work, an innovative bimetallic overlaying strategy is proposed that renders possible the synergy between AgZn3 and CuZn5 in effecting uniform Zn deposition in a laterally confined and compact manner. The seeded growth of Zn on the bimetal-modulated interface effectively reduces the nucleation potential barrier, yielding a low nucleation overpotential (25 mV). In full cell testing with a commercial MnO2 applied as the cathode, superb cycling stability, surpassing the results reported in previous works, is achieved. The cell delivers an outstanding remaining capacity of 215 mA h g−1 after 300 cycles with almost no capacity degradation observed. The simple and highly efficient bimetal design, which synergizes the strengths of distinct metals, has the potential to drive innovations in the development of multicomponent aqueous Zn batteries with exceptional performance.","PeriodicalId":8755,"journal":{"name":"Batteries","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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