Pub Date : 2024-11-15DOI: 10.1016/j.etran.2024.100381
Sagar Vashisht , Rajat , Dibakar Rakshit
Electric vehicles (EVs) play a crucial role in reducing fuel consumption and emissions, underscoring the importance of lithium-ion batteries (Li-ion) in powering these vehicles. However, Li-ion batteries are susceptible to degradation, capacity loss, and catastrophic failure due to temperature fluctuations, necessitating efficient thermal management. This review explores advancements and challenges in PCM-based battery thermal management systems (BTMS), focusing on internal and external factors influencing performance. It discusses internal factors such as material-level improvements in PCM-based BTMS, including solutions like SiC and EG-based PCM, flexible composite PCM, and serpentine-shaped PCM. External factors, such as fluid flow dynamics, cell spacing, and shape, significantly influence BTMS performance. Critical considerations include evaluating air- and liquid-based approaches and integrating heat pipes with PCM for passive BTMS. Furthermore, understanding the influence of these factors on temperature uniformity and heat dissipation is essential. The paper concludes by outlining future trends in PCM-based battery thermal management, emphasizing the utilization of flexible PCM and copper foam-enhanced PCM alongside hybrid BTMS configurations to optimize performance. By comprehensively addressing internal and external factors, BTMS can enhance Li-ion battery efficiency and lifespan in EVs.
{"title":"Recent advances and perspectives in enhancing thermal state of lithium-ion batteries with phase change materials: Internal and external heat transfer enhancement factors","authors":"Sagar Vashisht , Rajat , Dibakar Rakshit","doi":"10.1016/j.etran.2024.100381","DOIUrl":"10.1016/j.etran.2024.100381","url":null,"abstract":"<div><div>Electric vehicles (EVs) play a crucial role in reducing fuel consumption and emissions, underscoring the importance of lithium-ion batteries (Li-ion) in powering these vehicles. However, Li-ion batteries are susceptible to degradation, capacity loss, and catastrophic failure due to temperature fluctuations, necessitating efficient thermal management. This review explores advancements and challenges in PCM-based battery thermal management systems (BTMS), focusing on internal and external factors influencing performance. It discusses internal factors such as material-level improvements in PCM-based BTMS, including solutions like SiC and EG-based PCM, flexible composite PCM, and serpentine-shaped PCM. External factors, such as fluid flow dynamics, cell spacing, and shape, significantly influence BTMS performance. Critical considerations include evaluating air- and liquid-based approaches and integrating heat pipes with PCM for passive BTMS. Furthermore, understanding the influence of these factors on temperature uniformity and heat dissipation is essential. The paper concludes by outlining future trends in PCM-based battery thermal management, emphasizing the utilization of flexible PCM and copper foam-enhanced PCM alongside hybrid BTMS configurations to optimize performance. By comprehensively addressing internal and external factors, BTMS can enhance Li-ion battery efficiency and lifespan in EVs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100381"},"PeriodicalIF":15.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.etran.2024.100379
Joon Moon , Athar Hanif , Qadeer Ahmed
This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel, depending on the specific price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.
{"title":"Comprehensive energy footprint of electrified fleets: School bus fleet case study","authors":"Joon Moon , Athar Hanif , Qadeer Ahmed","doi":"10.1016/j.etran.2024.100379","DOIUrl":"10.1016/j.etran.2024.100379","url":null,"abstract":"<div><div>This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel, depending on the specific price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100379"},"PeriodicalIF":15.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.etran.2024.100380
Nitesh Gupta , Shanhai Ge , Tatsuro Sasaki , Kaiqiang Qin , Ryan S. Longchamps , Koichiro Aotani , Chao-Yang Wang
The lithium metal battery technologies that can fulfil the high energy density goal have grave safety concerns and lead to fire/smoke, leading to battery failure. Out of all the causes of fire, internal short circuits (ISC) are the most common. The ISC safety test is considered a crucial checkpoint for battery design, but the present tests, like nail penetration and ball indentation, lack certainty and reproducibility in declaring battery safety. In light of these experimental limitations, we present an experimentally validated ISC simulation method that can elucidate fundamental mechanisms underlying ISC. The experimental/simulation method isolates the shorted single-layer from the unshorted layers, which helps in scrutinizing ISC and thermal runaway (TR) phenomenon. The present ISC model is flexible and computationally inexpensive compared to other 3D electrochemical thermal coupled (ECT) ISC simulations for a whole battery pack. We show the experimental validation of terminal voltage, short-circuit current, shorting resistance, internal temperature and other derived parameters of an ISC simulation of anode-free cell. Finally, the simulation model was used to do a parametric study for an anode-free battery (AFB) and the effect of cell design, and shorting parameters on ISC was scrutinized.
{"title":"Simulation of single-layer internal short circuit in anode-free batteries","authors":"Nitesh Gupta , Shanhai Ge , Tatsuro Sasaki , Kaiqiang Qin , Ryan S. Longchamps , Koichiro Aotani , Chao-Yang Wang","doi":"10.1016/j.etran.2024.100380","DOIUrl":"10.1016/j.etran.2024.100380","url":null,"abstract":"<div><div>The lithium metal battery technologies that can fulfil the high energy density goal have grave safety concerns and lead to fire/smoke, leading to battery failure. Out of all the causes of fire, internal short circuits (ISC) are the most common. The ISC safety test is considered a crucial checkpoint for battery design, but the present tests, like nail penetration and ball indentation, lack certainty and reproducibility in declaring battery safety. In light of these experimental limitations, we present an experimentally validated ISC simulation method that can elucidate fundamental mechanisms underlying ISC. The experimental/simulation method isolates the shorted single-layer from the unshorted layers, which helps in scrutinizing ISC and thermal runaway (TR) phenomenon. The present ISC model is flexible and computationally inexpensive compared to other 3D electrochemical thermal coupled (ECT) ISC simulations for a whole battery pack. We show the experimental validation of terminal voltage, short-circuit current, shorting resistance, internal temperature and other derived parameters of an ISC simulation of anode-free cell. Finally, the simulation model was used to do a parametric study for an anode-free battery (AFB) and the effect of cell design, and shorting parameters on ISC was scrutinized.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100380"},"PeriodicalIF":15.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1016/j.etran.2024.100375
Akram Elomiya , Jiří Křupka , Vladimir Simic , Libor Švadlenka , Petr Průša , Stefan Jovčić
The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.
{"title":"An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas","authors":"Akram Elomiya , Jiří Křupka , Vladimir Simic , Libor Švadlenka , Petr Průša , Stefan Jovčić","doi":"10.1016/j.etran.2024.100375","DOIUrl":"10.1016/j.etran.2024.100375","url":null,"abstract":"<div><div>The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100375"},"PeriodicalIF":15.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.etran.2024.100378
Aihua Tang , Yuchen Xu , Pan Liu , Jinpeng Tian , Zikang Wu , Yuanzhi Hu , Quanqing Yu
Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.
{"title":"Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage","authors":"Aihua Tang , Yuchen Xu , Pan Liu , Jinpeng Tian , Zikang Wu , Yuanzhi Hu , Quanqing Yu","doi":"10.1016/j.etran.2024.100378","DOIUrl":"10.1016/j.etran.2024.100378","url":null,"abstract":"<div><div>Accurate monitoring of the capacity degradation of batteries is critical to their stable operation. However, evaluating the maximum capacity with limited cycle information alone is insufficient to fully indicate the extent of battery degradation. Here, this study propose a battery degradation monitoring method using relaxation voltage combined with encoder-decoder to extend traditional maximum capacity estimation to the entire voltage-capacity (V-Q) curve estimation. The encoder-decoder is constructed using a two-stage training strategy of unsupervised pre-training and transfer learning. Firstly, the short-time relaxation voltage sequence are input the autoencoder for unsupervised pre-training. Through this auto-encoding process, the encoder acquires feature learning capability on the unlabeled relaxation voltages under the same test conditions. Subsequently, the two-stage training process is completed by freezing the encoder weights and performing transfer learning on the decoder to map the relaxation voltage sequence to its corresponding V-Q curve. The proposed method achieves more advanced prediction performance than direct training at the same epochs. This means higher accuracy in using V-Q curves and the derived incremental capacity curves for comprehensive battery degradation monitoring. Validated on 130 battery samples from different laboratories, the proposed method predicts high-fidelity V-Q curves with a root-mean-square error of less than 0.03 Ah. This study highlights the ability to adopt relaxation voltages for battery degradation monitoring, which is expected to enable fast and comprehensive aging diagnostics in non-constant current charging situations due to the short relaxation time required and without additional cycling information.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100378"},"PeriodicalIF":15.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.etran.2024.100377
Shilin Wang , Chenyu Zhang , Dapeng Chen , Yiming Qin , Lejun Xu , Yitong Li , Qinzheng Wang , Xuning Feng , Huaibin Wang
When a thermal runaway accident occurs in a lithium-ion battery energy storage station, the battery emits a large amount of flammable electrolyte vapor and thermal runaway gas, which may cause serious combustion and explosion accidents when they are ignited in a confined space. With the gradual development of large-scale energy storage batteries, the composition and explosive characteristics of thermal runaway products in large-scale lithium iron phosphate batteries for energy storage remain unclear. In this paper, the content and components of the two-phase eruption substances of 340Ah lithium iron phosphate battery were determined through experiments, and the explosion parameters of the two-phase battery eruptions were studied by using the improved and optimized 20L spherical explosion parameter test system, which reveals the explosion law and hazards of the two-phase battery eruptions. Studies have shown that in a two-phase system explosion, EMC can make the two-phase system more explosive and more powerful, and the thermal runaway gas expands its explosion concentration range. The coupling explosion of the two enhanced the sensitivity and explosive power of the two-phase ejecta. Increasing the concentration of any combustible in a two-phase system will cause the explosion intensity parameters of the system to increase. However, when the combustible concentration exceeds the optimal explosion concentration, the explosion intensity parameters will decrease or even no explosion will occur. Both explosion intensity parameters and sensitivity parameters are more sensitive to EMC concentration, and the higher the EMC concentration, the stronger its dominant role in the explosion of the two-phase system. This work can lay the foundation for revealing the disaster-causing mechanism of explosion accidents in lithium-ion battery energy storage power stations, guide the safe design of energy storage systems and the prevention and control of explosion accidents, and provide theoretical and data support for the investigation of explosion accidents in energy storage power stations.
{"title":"Explosion characteristics of two-phase ejecta from large-capacity lithium iron phosphate batteries","authors":"Shilin Wang , Chenyu Zhang , Dapeng Chen , Yiming Qin , Lejun Xu , Yitong Li , Qinzheng Wang , Xuning Feng , Huaibin Wang","doi":"10.1016/j.etran.2024.100377","DOIUrl":"10.1016/j.etran.2024.100377","url":null,"abstract":"<div><div>When a thermal runaway accident occurs in a lithium-ion battery energy storage station, the battery emits a large amount of flammable electrolyte vapor and thermal runaway gas, which may cause serious combustion and explosion accidents when they are ignited in a confined space. With the gradual development of large-scale energy storage batteries, the composition and explosive characteristics of thermal runaway products in large-scale lithium iron phosphate batteries for energy storage remain unclear. In this paper, the content and components of the two-phase eruption substances of 340Ah lithium iron phosphate battery were determined through experiments, and the explosion parameters of the two-phase battery eruptions were studied by using the improved and optimized 20L spherical explosion parameter test system, which reveals the explosion law and hazards of the two-phase battery eruptions. Studies have shown that in a two-phase system explosion, EMC can make the two-phase system more explosive and more powerful, and the thermal runaway gas expands its explosion concentration range. The coupling explosion of the two enhanced the sensitivity and explosive power of the two-phase ejecta. Increasing the concentration of any combustible in a two-phase system will cause the explosion intensity parameters of the system to increase. However, when the combustible concentration exceeds the optimal explosion concentration, the explosion intensity parameters will decrease or even no explosion will occur. Both explosion intensity parameters and sensitivity parameters are more sensitive to EMC concentration, and the higher the EMC concentration, the stronger its dominant role in the explosion of the two-phase system. This work can lay the foundation for revealing the disaster-causing mechanism of explosion accidents in lithium-ion battery energy storage power stations, guide the safe design of energy storage systems and the prevention and control of explosion accidents, and provide theoretical and data support for the investigation of explosion accidents in energy storage power stations.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100377"},"PeriodicalIF":15.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1016/j.etran.2024.100373
Pei Peng, Zhendong Sun, Yujie Wang, Zonghai Chen
Proton exchange membrane fuel cells (PEMFCs) vehicles are regarded as the most promising green transportation option, but their widespread adoption is hindered by cost and longevity, and temperature of PEMFCs stack is a crucial factor affecting both efficiency and longevity. Current researches on temperature control mainly focus on the iterative updates of control methods and performance optimization, while there is relatively little research on the detailed analysis of control objectives. Therefore this paper developed an active optimal control strategy for stack temperature with adaptive control objective to enhance the output performance of PEMFCs in hybrid systems. To this end, firstly, a quantitative mapping relationship between operating temperature and current was established through experimental calibration, identifying the optimal temperature path for maximizing output voltage at different current levels. Secondly, a control-oriented voltage model was developed to describe the phenomenon observed experimentally, where the output voltage initially increased and then decreased with the monotonically increasing stack temperature, provided that other parameters remain constant. Finally, an active optimal control strategy is proposed, which actively adjusts the temperature control objective in real-time according to the prevailing operating current and the predetermined optimal temperature path. The comparative validations under both static and dynamic conditions, utilizing three different control methods, demonstrated that the proposed active optimal control strategy clearly outperforms normal control strategy. The maximum performance enhancements achieved were 1.15%, 1.21%, and 1.30%, respectively.
{"title":"Experimental analysis and optimal control of temperature with adaptive control objective for fuel cells","authors":"Pei Peng, Zhendong Sun, Yujie Wang, Zonghai Chen","doi":"10.1016/j.etran.2024.100373","DOIUrl":"10.1016/j.etran.2024.100373","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) vehicles are regarded as the most promising green transportation option, but their widespread adoption is hindered by cost and longevity, and temperature of PEMFCs stack is a crucial factor affecting both efficiency and longevity. Current researches on temperature control mainly focus on the iterative updates of control methods and performance optimization, while there is relatively little research on the detailed analysis of control objectives. Therefore this paper developed an active optimal control strategy for stack temperature with adaptive control objective to enhance the output performance of PEMFCs in hybrid systems. To this end, firstly, a quantitative mapping relationship between operating temperature and current was established through experimental calibration, identifying the optimal temperature path for maximizing output voltage at different current levels. Secondly, a control-oriented voltage model was developed to describe the phenomenon observed experimentally, where the output voltage initially increased and then decreased with the monotonically increasing stack temperature, provided that other parameters remain constant. Finally, an active optimal control strategy is proposed, which actively adjusts the temperature control objective in real-time according to the prevailing operating current and the predetermined optimal temperature path. The comparative validations under both static and dynamic conditions, utilizing three different control methods, demonstrated that the proposed active optimal control strategy clearly outperforms normal control strategy. The maximum performance enhancements achieved were 1.15%, 1.21%, and 1.30%, respectively.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100373"},"PeriodicalIF":15.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.etran.2024.100374
Maher G.M. Abdolrasol , Afida Ayob , M.S. Hossain Lipu , Shaheer Ansari , Tiong Sieh Kiong , Mohamad Hanif Md Saad , Taha Selim Ustun , Akhtar Kalam
Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation.
{"title":"Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives","authors":"Maher G.M. Abdolrasol , Afida Ayob , M.S. Hossain Lipu , Shaheer Ansari , Tiong Sieh Kiong , Mohamad Hanif Md Saad , Taha Selim Ustun , Akhtar Kalam","doi":"10.1016/j.etran.2024.100374","DOIUrl":"10.1016/j.etran.2024.100374","url":null,"abstract":"<div><div>Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100374"},"PeriodicalIF":15.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G's potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework's potential to support auxiliary grid services.
{"title":"Trustworthy V2G scheduling and energy trading: A blockchain-based framework","authors":"Yunwang Chen , Xiang Lei , Songyan Niu , Linni Jian","doi":"10.1016/j.etran.2024.100376","DOIUrl":"10.1016/j.etran.2024.100376","url":null,"abstract":"<div><div>The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G's potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework's potential to support auxiliary grid services.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100376"},"PeriodicalIF":15.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.etran.2024.100372
Wenbo Lu , Zheng Yuan , Ting Wang , Peikun Li , Yong Zhang
To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.
{"title":"Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles","authors":"Wenbo Lu , Zheng Yuan , Ting Wang , Peikun Li , Yong Zhang","doi":"10.1016/j.etran.2024.100372","DOIUrl":"10.1016/j.etran.2024.100372","url":null,"abstract":"<div><div>To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100372"},"PeriodicalIF":15.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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