Pub Date : 2024-07-01DOI: 10.3390/batteries10070237
Rodica Negroiu, C. Marghescu, I. Bacis, M. Burcea, Andrei Drumea, L. Dinca, Ion Razvan Radulescu
Environmental pollution is currently one of the most worrying factors that endangers human health. Therefore, attempts are being made to reduce it by various means. One of the most important sources of pollution in terms of the current RoHS and REACH directives is the pollution caused by the use of chemical products for the production of sources for the storage and generation of electricity. The aim of this article is therefore to develop supercapacitors made of biodegradable materials and to investigate their electrical performance. Among the materials used to make these electrodes, activated carbon was identified as the main material and different combinations of gelatin, calligraphy ink and glycerol were used as the binders. The electrolyte consists of a hydrogel based on gelatin, NaCl 20 wt% solution and glycerol. In the context of this research, the electrolyte, which has the consistency of a gel, fulfills the dual function of the separator in the structure of the manufactured cells. Due to its structure, the electrolyte has good mechanical properties and can easily block the contact between the two electrodes. Most of the materials used for the production of supercapacitor cells are interchangeable materials, which are mainly used in other application fields such as the food or cosmetics industries, but were also successfully used for the investigations carried out in this research. Thus, remarkable results were recorded regarding a specific capacitance between 101.46 F/g and 233.26 F/g and an energy density between 3.52 Wh/kg and 8.09 Wh/kg, with a slightly lower power density between 66.66 W/kg and 85.76 W/kg for the manufactured supercapacitors.
{"title":"Edible Gelatin and Cosmetic Activated Carbon Powder as Biodegradable and Replaceable Materials in the Production of Supercapacitors","authors":"Rodica Negroiu, C. Marghescu, I. Bacis, M. Burcea, Andrei Drumea, L. Dinca, Ion Razvan Radulescu","doi":"10.3390/batteries10070237","DOIUrl":"https://doi.org/10.3390/batteries10070237","url":null,"abstract":"Environmental pollution is currently one of the most worrying factors that endangers human health. Therefore, attempts are being made to reduce it by various means. One of the most important sources of pollution in terms of the current RoHS and REACH directives is the pollution caused by the use of chemical products for the production of sources for the storage and generation of electricity. The aim of this article is therefore to develop supercapacitors made of biodegradable materials and to investigate their electrical performance. Among the materials used to make these electrodes, activated carbon was identified as the main material and different combinations of gelatin, calligraphy ink and glycerol were used as the binders. The electrolyte consists of a hydrogel based on gelatin, NaCl 20 wt% solution and glycerol. In the context of this research, the electrolyte, which has the consistency of a gel, fulfills the dual function of the separator in the structure of the manufactured cells. Due to its structure, the electrolyte has good mechanical properties and can easily block the contact between the two electrodes. Most of the materials used for the production of supercapacitor cells are interchangeable materials, which are mainly used in other application fields such as the food or cosmetics industries, but were also successfully used for the investigations carried out in this research. Thus, remarkable results were recorded regarding a specific capacitance between 101.46 F/g and 233.26 F/g and an energy density between 3.52 Wh/kg and 8.09 Wh/kg, with a slightly lower power density between 66.66 W/kg and 85.76 W/kg for the manufactured supercapacitors.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.3390/batteries10070236
Andrew Gausden, B. Cerik
This study investigates the potential link between the increasing prevalence of single-use vapes (SUVs) and the rising frequency of waste and recycling fires in the UK. Incorrectly discarded Li-ion cells from SUVs can suffer mechanical damage, potentially leading to thermal runaway (TR) depending on the cells’ state of charge (SOC). Industry-standard abuse tests (short-circuit and nail test) and novel impact and crush tests, simulating damage during waste management processes, were conducted on Li-ion cells from two market-leading SUVs. The novel tests created internal short circuits, generating higher temperatures than the short-circuit test required for product safety. The cells in used SUVs had an average SOC ≤ 50% and reached a maximum temperature of 131 °C, below the minimum ignition temperature of common waste materials. The high temperatures were short-lived and had limited heat transfer to adjacent materials. The study concludes that Li-ion cells in used SUVs at ≤50% SOC cannot generate sufficient heat and temperature to ignite common waste and recycling materials. These findings have implications for understanding the fire risk associated with discarded SUVs in waste management facilities.
本研究调查了英国一次性吸管（SUV）的日益普及与废物和回收火灾频率上升之间的潜在联系。从 SUV 中不正确丢弃的锂离子电池可能会受到机械损伤，根据电池的充电状态 (SOC)，有可能导致热失控 (TR)。我们对两款市场领先的 SUV 的锂离子电池进行了行业标准的滥用测试（短路和钉子测试）以及新颖的冲击和挤压测试，模拟废物管理过程中的损坏情况。新型测试会造成内部短路，产生比产品安全所需的短路测试更高的温度。使用过的 SUV 中的电池平均 SOC ≤ 50%，最高温度达到 131 °C，低于常见废料的最低点火温度。高温持续时间很短，传导到邻近材料的热量有限。研究得出结论，废旧 SUV 中的锂离子电池在 SOC ≤50% 的情况下无法产生足够的热量和温度来点燃普通废料和回收材料。这些研究结果对了解废物管理设施中废弃 SUV 的火灾风险具有重要意义。
{"title":"Single-Use Vape Batteries: Investigating Their Potential as Ignition Sources in Waste and Recycling Streams","authors":"Andrew Gausden, B. Cerik","doi":"10.3390/batteries10070236","DOIUrl":"https://doi.org/10.3390/batteries10070236","url":null,"abstract":"This study investigates the potential link between the increasing prevalence of single-use vapes (SUVs) and the rising frequency of waste and recycling fires in the UK. Incorrectly discarded Li-ion cells from SUVs can suffer mechanical damage, potentially leading to thermal runaway (TR) depending on the cells’ state of charge (SOC). Industry-standard abuse tests (short-circuit and nail test) and novel impact and crush tests, simulating damage during waste management processes, were conducted on Li-ion cells from two market-leading SUVs. The novel tests created internal short circuits, generating higher temperatures than the short-circuit test required for product safety. The cells in used SUVs had an average SOC ≤ 50% and reached a maximum temperature of 131 °C, below the minimum ignition temperature of common waste materials. The high temperatures were short-lived and had limited heat transfer to adjacent materials. The study concludes that Li-ion cells in used SUVs at ≤50% SOC cannot generate sufficient heat and temperature to ignite common waste and recycling materials. These findings have implications for understanding the fire risk associated with discarded SUVs in waste management facilities.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature.
{"title":"State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter","authors":"Hongli Ma, Xinyuan Bao, António Lopes, Liping Chen, Guoquan Liu, Min Zhu","doi":"10.3390/batteries10060198","DOIUrl":"https://doi.org/10.3390/batteries10060198","url":null,"abstract":"Estimation of the state-of-charge (SOC) of lithium-ion batteries (LIBs) is fundamental to assure the normal operation of both the battery and battery-powered equipment. This paper derives a new SOC estimation method (CNN-UKF) that combines a convolutional neural network (CNN) and an unscented Kalman filter (UKF). The measured voltage, current and temperature of the LIB are the input of the CNN. The output of the hidden layer feeds the linear layer, whose output corresponds to an initial network-based SOC estimation. The output of the CNN is then used as the input of a UKF, which, using self-correction, yields high-precision SOC estimation results. This method does not require tuning of network hyperparameters, reducing the dependence of the network on hyperparameter adjustment and improving the efficiency of the network. The experimental results show that this method has higher accuracy and robustness compared to SOC estimation methods based on CNN and other advanced methods found in the literature.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/batteries10060196
Jie Jiao, Guangsheng Feng, Gang Yuan
The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly.
{"title":"Research on the Human–Robot Collaborative Disassembly Line Balancing of Spent Lithium Batteries with a Human Factor Load","authors":"Jie Jiao, Guangsheng Feng, Gang Yuan","doi":"10.3390/batteries10060196","DOIUrl":"https://doi.org/10.3390/batteries10060196","url":null,"abstract":"The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/batteries10060197
Jadranka Milikić, Filipe Gusmão, S. Knežević, N. Gavrilov, Anup Paul, Diogo M. F. Santos, Biljana Šljukić
Polyoxometalates (POMs) with transition metals (Co, Cu, Fe, Mn, Ni) of Keggin structure and lamellar-stacked multi-layer morphology were synthesized. They were subsequently explored as bifunctional electrocatalysts for oxygen electrodes, i.e., oxygen reduction (ORR) and evolution (OER) reaction, for aqueous rechargeable metal-air batteries in alkaline media. The lowest Tafel slope (85 mV dec−1) value and the highest OER current density of 93.8 mA cm−2 were obtained for the Fe-POM electrocatalyst. Similar OER electrochemical catalytic activity was noticed for the Co-POM electrocatalyst. This behavior was confirmed by electrochemical impedance spectroscopy, where Fe-POM gave the lowest charge transfer resistance of 3.35 Ω, followed by Co-POM with Rct of 15.04 Ω, during the OER. Additionally, Tafel slope values of 85 and 109 mV dec−1 were calculated for Fe-POM and Co-POM, respectively, during the ORR. The ORR at Fe-POM proceeded by mixed two- and four-electron pathways, while ORR at Co-POM proceeded exclusively by the four-electron pathway. Finally, capacitance studies were conducted on the synthesized POMs.
{"title":"Transition Metal-Based Polyoxometalates for Oxygen Electrode Bifunctional Electrocatalysis","authors":"Jadranka Milikić, Filipe Gusmão, S. Knežević, N. Gavrilov, Anup Paul, Diogo M. F. Santos, Biljana Šljukić","doi":"10.3390/batteries10060197","DOIUrl":"https://doi.org/10.3390/batteries10060197","url":null,"abstract":"Polyoxometalates (POMs) with transition metals (Co, Cu, Fe, Mn, Ni) of Keggin structure and lamellar-stacked multi-layer morphology were synthesized. They were subsequently explored as bifunctional electrocatalysts for oxygen electrodes, i.e., oxygen reduction (ORR) and evolution (OER) reaction, for aqueous rechargeable metal-air batteries in alkaline media. The lowest Tafel slope (85 mV dec−1) value and the highest OER current density of 93.8 mA cm−2 were obtained for the Fe-POM electrocatalyst. Similar OER electrochemical catalytic activity was noticed for the Co-POM electrocatalyst. This behavior was confirmed by electrochemical impedance spectroscopy, where Fe-POM gave the lowest charge transfer resistance of 3.35 Ω, followed by Co-POM with Rct of 15.04 Ω, during the OER. Additionally, Tafel slope values of 85 and 109 mV dec−1 were calculated for Fe-POM and Co-POM, respectively, during the ORR. The ORR at Fe-POM proceeded by mixed two- and four-electron pathways, while ORR at Co-POM proceeded exclusively by the four-electron pathway. Finally, capacitance studies were conducted on the synthesized POMs.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"29 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/batteries10060195
Shengjun Chen, Wenrui Wang, Xinyue Zhang, Xiaofeng Wang
Graphene has a high specific surface area and high electrical conductivity, and its addition to activated carbon electrodes should theoretically significantly improve the energy storage performance of supercapacitors. Unfortunately, such an ideal outcome is seldom verified in practical commercial supercapacitor design and production. In this paper, the oxygen-containing functional groups in graphene/activated carbon hybrids, which are prone to induce side reactions, are removed in the material synthesis stage by a special process design, and electrodes with high densities and low internal resistances are prepared by a dry process. On this basis, a carbon-coated aluminum foil collector with a full tab structure is designed and assembled with graphene/activated carbon hybrid electrodes to form a commercial supercapacitor in cylindrical configuration. The experimental tests confirmed that such supercapacitors have high capacity density, power density, low internal resistance (about 0.06 mΩ), good high-current charging/discharging characteristics, and a long lifetime, with more than 80% capacity retention after 10 W cycles.
石墨烯具有高比表面积和高导电性，理论上将其添加到活性炭电极中应能显著提高超级电容器的储能性能。遗憾的是，这种理想结果在实际商业超级电容器的设计和生产中很少得到验证。本文通过特殊的工艺设计，在材料合成阶段去除石墨烯/活性碳杂化物中容易诱发副反应的含氧官能团，并通过干法工艺制备出高密度、低内阻的电极。在此基础上，设计了具有全片结构的碳涂层铝箔集电体，并将其与石墨烯/活性炭混合电极组装在一起，形成了圆柱形结构的商用超级电容器。实验测试证实，这种超级电容器具有高容量密度、功率密度、低内阻（约 0.06 mΩ）、良好的大电流充放电特性和较长的使用寿命，10 W 循环后容量保持率超过 80%。
{"title":"High-Performance Supercapacitors Based on Graphene/Activated Carbon Hybrid Electrodes Prepared via Dry Processing","authors":"Shengjun Chen, Wenrui Wang, Xinyue Zhang, Xiaofeng Wang","doi":"10.3390/batteries10060195","DOIUrl":"https://doi.org/10.3390/batteries10060195","url":null,"abstract":"Graphene has a high specific surface area and high electrical conductivity, and its addition to activated carbon electrodes should theoretically significantly improve the energy storage performance of supercapacitors. Unfortunately, such an ideal outcome is seldom verified in practical commercial supercapacitor design and production. In this paper, the oxygen-containing functional groups in graphene/activated carbon hybrids, which are prone to induce side reactions, are removed in the material synthesis stage by a special process design, and electrodes with high densities and low internal resistances are prepared by a dry process. On this basis, a carbon-coated aluminum foil collector with a full tab structure is designed and assembled with graphene/activated carbon hybrid electrodes to form a commercial supercapacitor in cylindrical configuration. The experimental tests confirmed that such supercapacitors have high capacity density, power density, low internal resistance (about 0.06 mΩ), good high-current charging/discharging characteristics, and a long lifetime, with more than 80% capacity retention after 10 W cycles.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the feasibility of integrating battery technology into electric buses, addressing the imperative to reduce carbon emissions within the transport sector. A comprehensive review and analysis of diverse literature sources establish the present and prospective landscape of battery electric buses within the public transportation domain. Existing battery technology and infrastructure constraints hinder the comprehensive deployment of electric buses across all routes currently served by internal combustion engine counterparts. However, forward-looking insights indicate a promising trajectory with the potential for substantial advancements in battery technology coupled with significant investments in charging infrastructure. Such developments hold promise for electric buses to fulfill a considerable portion of a nation’s public transit requirements. Significant findings emphasize that electric buses showcase considerably lower emissions than fossil-fuel-driven counterparts, especially when operated with zero-carbon electricity sources, thereby significantly mitigating the perils of climate change.
{"title":"An Investigation into the Viability of Battery Technologies for Electric Buses in the UK","authors":"Tahmid Muhith, Santosh Behara, Munnangi Anji Reddy","doi":"10.3390/batteries10030091","DOIUrl":"https://doi.org/10.3390/batteries10030091","url":null,"abstract":"This study explores the feasibility of integrating battery technology into electric buses, addressing the imperative to reduce carbon emissions within the transport sector. A comprehensive review and analysis of diverse literature sources establish the present and prospective landscape of battery electric buses within the public transportation domain. Existing battery technology and infrastructure constraints hinder the comprehensive deployment of electric buses across all routes currently served by internal combustion engine counterparts. However, forward-looking insights indicate a promising trajectory with the potential for substantial advancements in battery technology coupled with significant investments in charging infrastructure. Such developments hold promise for electric buses to fulfill a considerable portion of a nation’s public transit requirements. Significant findings emphasize that electric buses showcase considerably lower emissions than fossil-fuel-driven counterparts, especially when operated with zero-carbon electricity sources, thereby significantly mitigating the perils of climate change.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"74 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-02DOI: 10.3390/batteries10030087
Chongbin Sun, Wenhu Qin, Zhonghua Yun
A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based on fennec fox optimization algorithm–mixed extreme learning machine (FFA-MELM). Firstly, health indicators are extracted from lithium-battery-charging data, and grey relational analysis (GRA) is employed to identify highly correlated features with the state-of-health of the battery. Subsequently, a state-of-health estimation model based on mixed extreme learning machine is constructed, and the hyperparameters of the model are optimized using the fennec fox optimization algorithm to improve estimation accuracy and convergence speed. The experimental results demonstrate that the proposed method has significantly improved the accuracy of the state-of-health estimation for lithium batteries compared to the extreme learning machine. Furthermore, it can achieve precise state-of-health estimation results for multiple batteries, even under complex operating conditions and with limited charge/discharge cycle data.
{"title":"A State-of-Health Estimation Method for Lithium Batteries Based on Fennec Fox Optimization Algorithm–Mixed Extreme Learning Machine","authors":"Chongbin Sun, Wenhu Qin, Zhonghua Yun","doi":"10.3390/batteries10030087","DOIUrl":"https://doi.org/10.3390/batteries10030087","url":null,"abstract":"A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based on fennec fox optimization algorithm–mixed extreme learning machine (FFA-MELM). Firstly, health indicators are extracted from lithium-battery-charging data, and grey relational analysis (GRA) is employed to identify highly correlated features with the state-of-health of the battery. Subsequently, a state-of-health estimation model based on mixed extreme learning machine is constructed, and the hyperparameters of the model are optimized using the fennec fox optimization algorithm to improve estimation accuracy and convergence speed. The experimental results demonstrate that the proposed method has significantly improved the accuracy of the state-of-health estimation for lithium batteries compared to the extreme learning machine. Furthermore, it can achieve precise state-of-health estimation results for multiple batteries, even under complex operating conditions and with limited charge/discharge cycle data.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"12 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.3390/batteries10030085
Hanwen Zhang, A. Fotouhi, D. Auger, Matt Lowe
Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.
{"title":"Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System","authors":"Hanwen Zhang, A. Fotouhi, D. Auger, Matt Lowe","doi":"10.3390/batteries10030085","DOIUrl":"https://doi.org/10.3390/batteries10030085","url":null,"abstract":"Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"114 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.3390/batteries10030083
Yadyra Ortiz, Paul Arévalo, Diego Peña, F. Jurado
Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources.
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