Pub Date : 2025-11-13DOI: 10.1016/j.etran.2025.100512
Kwang Hoon Baek , Xinyi Wu , Yan Zhou , Ram Vijayagopal , Namdoo Kim , Amgad Elgowainy
Projecting the transition from combustion engines to battery-based powertrains is complex becuase it involves numerous interdependent decisions. This study estimates total cost of ownership (TCO) to assess the economic viability of powertrain electrification, focusing exclusively on advances in vehicle and fuel technologies. Under two bounding technology-progress scenarios, we develop vehicle designs and fuel cost trajectories, which serve as inputs to TCO projections for selected classes from 2021 to 2050.
We analyzed a small sport utility vehicle (SUV) to represent the light-duty vehicle (LDV) sector, and four medium- and heavy-duty vehicle (MHDV) classes: Class 6 box delivery, Class 8 drayage, Class 8 long-haul, and Class 8 transit bus. For each class, we compared the TCO of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) against conventional internal combustion engine vehicles (ICEVs).
The results show that modern ICEVs generally have lower TCO; however, BEVs and FCHEVs could match or have lower TCOs than ICEVs over time, depending on technological progress. In LDVs, BEV300 is projected to deliver the lowest TCO by 2050, particularly under the high-progress scenario. In MHDVs, both BEVs and FCHEVs could become more cost-competitive than ICEVs by 2050 in the high-progress case.
Beyond these results, the findings suggest further investigation is warranted for BEV charging infrastructure, FCHEV hydrogen refueling infrastructure, and MHDV charging strategies. These factors could reduce the fuel-cost share of TCO and enhance the competitiveness of BEVs and FCHEVs relative to ICEVs.
{"title":"Total cost of ownership of vehicle electrification and fuel switching options for light-duty and heavy-duty vehicles","authors":"Kwang Hoon Baek , Xinyi Wu , Yan Zhou , Ram Vijayagopal , Namdoo Kim , Amgad Elgowainy","doi":"10.1016/j.etran.2025.100512","DOIUrl":"10.1016/j.etran.2025.100512","url":null,"abstract":"<div><div>Projecting the transition from combustion engines to battery-based powertrains is complex becuase it involves numerous interdependent decisions. This study estimates total cost of ownership (TCO) to assess the economic viability of powertrain electrification, focusing exclusively on advances in vehicle and fuel technologies. Under two bounding technology-progress scenarios, we develop vehicle designs and fuel cost trajectories, which serve as inputs to TCO projections for selected classes from 2021 to 2050.</div><div>We analyzed a small sport utility vehicle (SUV) to represent the light-duty vehicle (LDV) sector, and four medium- and heavy-duty vehicle (MHDV) classes: Class 6 box delivery, Class 8 drayage, Class 8 long-haul, and Class 8 transit bus. For each class, we compared the TCO of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) against conventional internal combustion engine vehicles (ICEVs).</div><div>The results show that modern ICEVs generally have lower TCO; however, BEVs and FCHEVs could match or have lower TCOs than ICEVs over time, depending on technological progress. In LDVs, BEV300 is projected to deliver the lowest TCO by 2050, particularly under the high-progress scenario. In MHDVs, both BEVs and FCHEVs could become more cost-competitive than ICEVs by 2050 in the high-progress case.</div><div>Beyond these results, the findings suggest further investigation is warranted for BEV charging infrastructure, FCHEV hydrogen refueling infrastructure, and MHDV charging strategies. These factors could reduce the fuel-cost share of TCO and enhance the competitiveness of BEVs and FCHEVs relative to ICEVs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100512"},"PeriodicalIF":17.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681618","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 : 2025-11-10DOI: 10.1016/j.etran.2025.100511
Christoph Wellmann , Pekka Rahkola , Sai Santhosh Tota , Mikko Pihlatie , Abdul Rahman Khaleel , Christopher Marx , Akshay Sharma , Markus Eisenbarth , Jakob Andert
Driven by global regulations and the urgent need for a sustainable transition to zero-emission fleets in the transport sector, revolutionizing powertrain systems and their respective development processes have become more and more prevalent. Ambitious goals have been established for the latest public-funded research projects, such as ESCALATE (Powering European Union Net Zero Future by Escalating Zero Emission Heavy Duty Vehicles (HDV) and Logistic Intelligence), to increase the efficiency of the powertrain by up to 10% and thus maximize the operational range above 750 km. All of this will be achieved by introducing cost-effective, modular, and scalable electric powertrain components combined with advanced system control algorithms, targeting a broad market coverage with flexible vehicle architectures. In this context, the paper presents a completely virtual frontloading strategy to create a modular and highly integrated e-Axle system, leveraging a dual permanent magnet synchronous machine configuration to improve multiple performance indicators. These are the performance output, in terms of power and torque, system efficiency, and noise-vibration-harshness (NVH) criteria. To allow for an holistic system parametrization, a combined electric machine and transmission synthesis, using an active learning-based, multi-layer nested optimization approach together with a model predictive control strategy for motion and thermal domain has been employed. This development framework is integrating electric machine dimensions and transmission gear ratios as design parameters, as well as thermal actuation and torque as control parameters, to ensure a system right-sizing in a given use-case environment. By including monetary considerations with genetic algorithms, an extension for a powertrain family identification to a complete HDV fleet is facilitated. To demonstrate the feasibility of this framework, a concept assessment and validation has been carried out. The key achievements include a close matching of the defined KPIs, namely the peak wheel torque of 56150 Nm and continuous power of 381 kW – about 2%, respectively 0.2% above the target – and an enhanced peak power capability of 536 kW. In terms of energy efficiency, the multi-stage gear boxes support a well optimized operation in the VECTO long haul cycle, indicating a 40-ton vehicle energy consumption of around 109.7 kWh per 100 km, while the 76-ton variant consumes approximately 204.6 kWh per 100 km. Further the predictive cruise control strategy led to a consumption reduction of about 2.6%–3.4%.
{"title":"Machine-learning integrated multi-domain co-optimization for electrified heavy duty fleets","authors":"Christoph Wellmann , Pekka Rahkola , Sai Santhosh Tota , Mikko Pihlatie , Abdul Rahman Khaleel , Christopher Marx , Akshay Sharma , Markus Eisenbarth , Jakob Andert","doi":"10.1016/j.etran.2025.100511","DOIUrl":"10.1016/j.etran.2025.100511","url":null,"abstract":"<div><div>Driven by global regulations and the urgent need for a sustainable transition to zero-emission fleets in the transport sector, revolutionizing powertrain systems and their respective development processes have become more and more prevalent. Ambitious goals have been established for the latest public-funded research projects, such as ESCALATE (Powering European Union Net Zero Future by Escalating Zero Emission Heavy Duty Vehicles (HDV) and Logistic Intelligence), to increase the efficiency of the powertrain by up to 10% and thus maximize the operational range above 750 km. All of this will be achieved by introducing cost-effective, modular, and scalable electric powertrain components combined with advanced system control algorithms, targeting a broad market coverage with flexible vehicle architectures. In this context, the paper presents a completely virtual frontloading strategy to create a modular and highly integrated e-Axle system, leveraging a dual permanent magnet synchronous machine configuration to improve multiple performance indicators. These are the performance output, in terms of power and torque, system efficiency, and noise-vibration-harshness (NVH) criteria. To allow for an holistic system parametrization, a combined electric machine and transmission synthesis, using an active learning-based, multi-layer nested optimization approach together with a model predictive control strategy for motion and thermal domain has been employed. This development framework is integrating electric machine dimensions and transmission gear ratios as design parameters, as well as thermal actuation and torque as control parameters, to ensure a system right-sizing in a given use-case environment. By including monetary considerations with genetic algorithms, an extension for a powertrain family identification to a complete HDV fleet is facilitated. To demonstrate the feasibility of this framework, a concept assessment and validation has been carried out. The key achievements include a close matching of the defined KPIs, namely the peak wheel torque of 56150 Nm and continuous power of 381 kW – about 2%, respectively 0.2% above the target – and an enhanced peak power capability of 536 kW. In terms of energy efficiency, the multi-stage gear boxes support a well optimized operation in the VECTO long haul cycle, indicating a 40-ton vehicle energy consumption of around 109.7 kWh per 100 km, while the 76-ton variant consumes approximately 204.6 kWh per 100 km. Further the predictive cruise control strategy led to a consumption reduction of about 2.6%–3.4%.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"27 ","pages":"Article 100511"},"PeriodicalIF":17.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537511","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 : 2025-11-05DOI: 10.1016/j.etran.2025.100510
Jiexin Zou , Yuanbin Sun , Xiuyue Wang , Juntao Chen , Jingke Mo , Siguang Wu , Qiren Chen , Cenkai Zhao , Haijiang Wang , Min Wang
The performance of proton exchange membrane electrolyzer cells (PEMECs) at high current density is constrained by mass transport limitation in conventional porous transport layer (PTL), which is the critical barrier to their large-scale adoption for green hydrogen production. In this paper, a laser-ablated non-penetrating-hole PTL (NP-PTL) with architected pores demonstrates an over 50 % reduction in mass transport overpotential compared to commercial Ti-felt PTL. Through a synergistic combination of in-situ optical diagnostics and two-phase flow modeling, we elucidate the mechanism by which the laser-engineered NP-PTL structure reduces mass transport resistance under high current density operation. Unlike fully perforated designs, the non-penetrating hole architecture maintains optimal contact between the PTL and catalyst layer (CL), minimizing the increase in high-frequency resistance (HFR) and further improving overall electrolyzer efficiency. The NP-PTL not only enhances performance but also exhibits promising initial operational stability, maintaining steady performance during 100-h testing. The laser ablation strategy for fabricating PTL with non-perforated structures offer a novel approach to enhance the performance of PEMECs, thereby accelerating the commercialization of PEMECs.
{"title":"Hierarchical porous transport layers for enhancing mass transport in proton exchange membrane electrolyzer cells","authors":"Jiexin Zou , Yuanbin Sun , Xiuyue Wang , Juntao Chen , Jingke Mo , Siguang Wu , Qiren Chen , Cenkai Zhao , Haijiang Wang , Min Wang","doi":"10.1016/j.etran.2025.100510","DOIUrl":"10.1016/j.etran.2025.100510","url":null,"abstract":"<div><div>The performance of proton exchange membrane electrolyzer cells (PEMECs) at high current density is constrained by mass transport limitation in conventional porous transport layer (PTL), which is the critical barrier to their large-scale adoption for green hydrogen production. In this paper, a laser-ablated non-penetrating-hole PTL (NP-PTL) with architected pores demonstrates an over 50 % reduction in mass transport overpotential compared to commercial Ti-felt PTL. Through a synergistic combination of in-situ optical diagnostics and two-phase flow modeling, we elucidate the mechanism by which the laser-engineered NP-PTL structure reduces mass transport resistance under high current density operation. Unlike fully perforated designs, the non-penetrating hole architecture maintains optimal contact between the PTL and catalyst layer (CL), minimizing the increase in high-frequency resistance (HFR) and further improving overall electrolyzer efficiency. The NP-PTL not only enhances performance but also exhibits promising initial operational stability, maintaining steady performance during 100-h testing. The laser ablation strategy for fabricating PTL with non-perforated structures offer a novel approach to enhance the performance of PEMECs, thereby accelerating the commercialization of PEMECs.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100510"},"PeriodicalIF":17.0,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519754","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 : 2025-11-04DOI: 10.1016/j.etran.2025.100509
Xing Shu , Jiangwei Shen , Fengxiang Guo , Yonggang Liu , Yuanjian Zhang , Zheng Chen , Hongqian Zhao
Lithium-ion batteries have become the dominant power source in electric transportation due to their high energy density and long cycle life. Nevertheless, prolonged operation leads to irreversible degradation, and results in capacity fade and safety risks. Accurate estimation of state of health (SOH) is therefore critical for ensuring reliable electric vehicle operation and for guiding maintenance strategies. In recent years, extensive research has been devoted to SOH estimation, supported by both laboratory investigations and field applications. Unlike previous reviews that mainly focus on either laboratory data or algorithmic modeling, this review uniquely bridges the gap between lab-based methods and practical applications using real vehicle operational data by providing a comparative analysis of datasets, estimation methods, and application challenges. A systematic survey of recent advances is provided in lithium-ion battery SOH estimation. First, accelerated aging experiments under laboratory conditions and operational data acquisition in real-world scenarios are reviewed and compared. The extraction of health features, feature optimization, and dimensionality reduction techniques are elaborated. Second, the progress in modeling methods is summarized, including shallow neural networks, convolutional neural networks, recurrent neural networks, attention-based networks, and physics-informed networks. Third, SOH label acquisition methods and estimation approaches for real-world datasets are analyzed. Finally, three major challenges are discussed for improving SOH estimation accuracy in practice, including bridging the gap between laboratory and real-world conditions, achieving more reliable SOH labeling, and reducing the dependence on large-scale training data.
{"title":"Towards real-world battery health intelligence: A review of machine learning advances and challenges in SOH estimation","authors":"Xing Shu , Jiangwei Shen , Fengxiang Guo , Yonggang Liu , Yuanjian Zhang , Zheng Chen , Hongqian Zhao","doi":"10.1016/j.etran.2025.100509","DOIUrl":"10.1016/j.etran.2025.100509","url":null,"abstract":"<div><div>Lithium-ion batteries have become the dominant power source in electric transportation due to their high energy density and long cycle life. Nevertheless, prolonged operation leads to irreversible degradation, and results in capacity fade and safety risks. Accurate estimation of state of health (SOH) is therefore critical for ensuring reliable electric vehicle operation and for guiding maintenance strategies. In recent years, extensive research has been devoted to SOH estimation, supported by both laboratory investigations and field applications. Unlike previous reviews that mainly focus on either laboratory data or algorithmic modeling, this review uniquely bridges the gap between lab-based methods and practical applications using real vehicle operational data by providing a comparative analysis of datasets, estimation methods, and application challenges. A systematic survey of recent advances is provided in lithium-ion battery SOH estimation. First, accelerated aging experiments under laboratory conditions and operational data acquisition in real-world scenarios are reviewed and compared. The extraction of health features, feature optimization, and dimensionality reduction techniques are elaborated. Second, the progress in modeling methods is summarized, including shallow neural networks, convolutional neural networks, recurrent neural networks, attention-based networks, and physics-informed networks. Third, SOH label acquisition methods and estimation approaches for real-world datasets are analyzed. Finally, three major challenges are discussed for improving SOH estimation accuracy in practice, including bridging the gap between laboratory and real-world conditions, achieving more reliable SOH labeling, and reducing the dependence on large-scale training data.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100509"},"PeriodicalIF":17.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465208","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 : 2025-11-04DOI: 10.1016/j.etran.2025.100508
Yan Wang , Yu Wang , Chengshan Xu , Feng Dai , Xilong Zhang , Hewu Wang , Xuning Feng
With the global transformation of energy structure and rapid development of energy storage technologies, battery energy storage stations (BESS) have been widely applied. However, vertically stacked lithium iron phosphate (LFP) battery modules in BESS are highly susceptible to thermal runaway (TR) jet combustion, leading to collective violent battery combustion under the influence of vertical flames. Extensive experimental investigations have been conducted on the failure process involving heat generation - jet combustion - (thermal runaway propagation)TRP in double - layer LFP battery modules. However, experimental methods struggle to clarify the influence mechanisms of vertical flames on battery TRP and energy flow pathways. This paper establishes a chain-type failure model for heat generation - jet combustion - TRP in double-layer battery modules based on non-premixed combustion theory. After calibrating critical parameters such as battery temperature and heat release rate (HRR), the model predicts battery TRP patterns and energy flow pathways in double-layer quadruple-battery modules under vertical flame influence. Results revealed that 71.2 % of flame-released heat radiates to the lateral surface of the upper battery module, while only 28.8 % radiates to the battery bottom. At 2133 s, the battery module has a group deflagration with a maximum HRR of 336.5 kW. The outermost upper-layer battery absorbs heat most rapidly, leading to earliest valve venting and TR. This creates an “inverse sequence” TRP pattern in the upper battery layer. The findings provide theoretical references for fire propagation protection and safety design in BESS.
{"title":"Modeling study on fire propagation behavior and analysis of energy flow paths in double-layer LFP battery module","authors":"Yan Wang , Yu Wang , Chengshan Xu , Feng Dai , Xilong Zhang , Hewu Wang , Xuning Feng","doi":"10.1016/j.etran.2025.100508","DOIUrl":"10.1016/j.etran.2025.100508","url":null,"abstract":"<div><div>With the global transformation of energy structure and rapid development of energy storage technologies, battery energy storage stations (BESS) have been widely applied. However, vertically stacked lithium iron phosphate (LFP) battery modules in BESS are highly susceptible to thermal runaway (TR) jet combustion, leading to collective violent battery combustion under the influence of vertical flames. Extensive experimental investigations have been conducted on the failure process involving heat generation - jet combustion - (thermal runaway propagation)TRP in double - layer LFP battery modules. However, experimental methods struggle to clarify the influence mechanisms of vertical flames on battery TRP and energy flow pathways. This paper establishes a chain-type failure model for heat generation - jet combustion - TRP in double-layer battery modules based on non-premixed combustion theory. After calibrating critical parameters such as battery temperature and heat release rate (HRR), the model predicts battery TRP patterns and energy flow pathways in double-layer quadruple-battery modules under vertical flame influence. Results revealed that 71.2 % of flame-released heat radiates to the lateral surface of the upper battery module, while only 28.8 % radiates to the battery bottom. At 2133 s, the battery module has a group deflagration with a maximum HRR of 336.5 kW. The outermost upper-layer battery absorbs heat most rapidly, leading to earliest valve venting and TR. This creates an “inverse sequence” TRP pattern in the upper battery layer. The findings provide theoretical references for fire propagation protection and safety design in BESS.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100508"},"PeriodicalIF":17.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465207","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 : 2025-10-31DOI: 10.1016/j.etran.2025.100507
Jiyoung Kim , Charles Mish , Alexandre T.R. Guibert , Filippo Agnelli , Marta Vicencio , So-Yeon Ham , Min-Sang Song , Ying Shirley Meng , Jeong Beom Lee , H. Alicia Kim
Sulfide-based all-solid-state batteries (ASSBs) are promising candidates for applications requiring high energy density and enhanced safety, with the potential to replace conventional Li-ion batteries. Despite significant advances in material design and engineering, the impact of material properties and process variables on cell energy density remains poorly understood. In this study, we employed a validated pseudo-two-dimensional (P2D) model to investigate how volumetric and gravimetric energy densities of ASSBs change as function of various cell design parameters and to perform mathematical optimization to maximize energy densities. Model parameters were derived from pellet cell experiments, incorporating a cathode composite with high-capacity NCM811 and densely packed fine argyrodite, alongside a bulk solid electrolyte separator with high ionic conductivity. The model's accuracy was confirmed by comparing simulation results with experimental voltage profiles, resulting in a root mean square error of 0.028 mV and an energy discrepancy of 0.7 %. Using the validated P2D model, we set energy densities as objective functions and scaled the pellet cell structure to automotive pouch cell dimensions to assess practical energy densities. A comprehensive sensitivity study was conducted on design parameters within the solid electrolyte separator and cathode composite. The weight percentage of the cathode active material was identified as a highly sensitive parameter, with other cathode composite parameters showing strong dependence on it. Employing a gradient-free direct search optimization method, we identified optimal design parameters that improved the volumetric and gravimetric energy densities by 62.5 % and 66.3 %, respectively, relative to reference values based on experimental parameters for a single cell.
{"title":"Design parameter optimization for sulfide-based all-solid-state batteries with high energy density","authors":"Jiyoung Kim , Charles Mish , Alexandre T.R. Guibert , Filippo Agnelli , Marta Vicencio , So-Yeon Ham , Min-Sang Song , Ying Shirley Meng , Jeong Beom Lee , H. Alicia Kim","doi":"10.1016/j.etran.2025.100507","DOIUrl":"10.1016/j.etran.2025.100507","url":null,"abstract":"<div><div>Sulfide-based all-solid-state batteries (ASSBs) are promising candidates for applications requiring high energy density and enhanced safety, with the potential to replace conventional Li-ion batteries. Despite significant advances in material design and engineering, the impact of material properties and process variables on cell energy density remains poorly understood. In this study, we employed a validated pseudo-two-dimensional (P2D) model to investigate how volumetric and gravimetric energy densities of ASSBs change as function of various cell design parameters and to perform mathematical optimization to maximize energy densities. Model parameters were derived from pellet cell experiments, incorporating a cathode composite with high-capacity NCM811 and densely packed fine argyrodite, alongside a bulk solid electrolyte separator with high ionic conductivity. The model's accuracy was confirmed by comparing simulation results with experimental voltage profiles, resulting in a root mean square error of 0.028 mV and an energy discrepancy of 0.7 %. Using the validated P2D model, we set energy densities as objective functions and scaled the pellet cell structure to automotive pouch cell dimensions to assess practical energy densities. A comprehensive sensitivity study was conducted on design parameters within the solid electrolyte separator and cathode composite. The weight percentage of the cathode active material was identified as a highly sensitive parameter, with other cathode composite parameters showing strong dependence on it. Employing a gradient-free direct search optimization method, we identified optimal design parameters that improved the volumetric and gravimetric energy densities by 62.5 % and 66.3 %, respectively, relative to reference values based on experimental parameters for a single cell.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100507"},"PeriodicalIF":17.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465943","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 : 2025-10-29DOI: 10.1016/j.etran.2025.100506
Xikai Tu , Jin Wu , Zhiming Tao , Chang Wen , Zhengkai Tu
Proton exchange membrane fuel cell (PEMFC)-powered unmanned aerial vehicles (UAVs) exhibit nonlinear and coupled behavior under dynamic flight conditions. To enable intelligent management, this study proposes an adaptive air stoichiometric ratio (ASR) strategy that dynamically responds to real-time variations in payload and acceleration. Although ASR optimization has been studied under steady-state conditions, its regulation under realistic UAV dynamics remains underexplored and experimentally unverified. We develop a coupled framework integrating UAV flight dynamics, an air-cooled PEMFC model, and mass–heat transfer multiphysics, and validate it through dynamic flight tests. Results show that optimal ASR varies significantly with operating conditions: with a 40 kg payload, ASR increases from 52 to 81 as acceleration changes from −0.6 to 0.6 m/s2; at 0.6 m/s2, raising the payload from 25 to 40 kg increases ASR from 24 to 81. Optimal ASR regulation improves stack voltage by 0.170 V, reduces hydrogen consumption by 2.05 mg per 100 m of flight, lowers temperature by 18.32 %, and enhances efficiency, voltage uniformity, and temperature uniformity. Notably, ASR exhibits a nonlinear influence on performance: it improves markedly from 25 to 28 kg, remains stable between 28 and 31 kg, and rises again at 40 kg. Experimental validation (error <1.1 %) confirms model accuracy and demonstrates the effectiveness of ASR optimization in PEMFC-powered UAVs. Beyond UAV applications, the proposed adaptive ASR strategy offers a pathway toward intelligent air management in fuel-cell propulsion systems, with direct relevance to emerging electric transportation modes such as urban air mobility vehicles, cargo drones, and hybrid-electric aircraft.
{"title":"Dynamic flight challenges in PEMFC-powered UAVs: Towards intelligent management and sustainable propulsion","authors":"Xikai Tu , Jin Wu , Zhiming Tao , Chang Wen , Zhengkai Tu","doi":"10.1016/j.etran.2025.100506","DOIUrl":"10.1016/j.etran.2025.100506","url":null,"abstract":"<div><div>Proton exchange membrane fuel cell (PEMFC)-powered unmanned aerial vehicles (UAVs) exhibit nonlinear and coupled behavior under dynamic flight conditions. To enable intelligent management, this study proposes an adaptive air stoichiometric ratio (ASR) strategy that dynamically responds to real-time variations in payload and acceleration. Although ASR optimization has been studied under steady-state conditions, its regulation under realistic UAV dynamics remains underexplored and experimentally unverified. We develop a coupled framework integrating UAV flight dynamics, an air-cooled PEMFC model, and mass–heat transfer multiphysics, and validate it through dynamic flight tests. Results show that optimal ASR varies significantly with operating conditions: with a 40 kg payload, ASR increases from 52 to 81 as acceleration changes from −0.6 to 0.6 m/s<sup>2</sup>; at 0.6 m/s<sup>2</sup>, raising the payload from 25 to 40 kg increases ASR from 24 to 81. Optimal ASR regulation improves stack voltage by 0.170 V, reduces hydrogen consumption by 2.05 mg per 100 m of flight, lowers temperature by 18.32 %, and enhances efficiency, voltage uniformity, and temperature uniformity. Notably, ASR exhibits a nonlinear influence on performance: it improves markedly from 25 to 28 kg, remains stable between 28 and 31 kg, and rises again at 40 kg. Experimental validation (error <1.1 %) confirms model accuracy and demonstrates the effectiveness of ASR optimization in PEMFC-powered UAVs. Beyond UAV applications, the proposed adaptive ASR strategy offers a pathway toward intelligent air management in fuel-cell propulsion systems, with direct relevance to emerging electric transportation modes such as urban air mobility vehicles, cargo drones, and hybrid-electric aircraft.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100506"},"PeriodicalIF":17.0,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416082","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 : 2025-10-24DOI: 10.1016/j.etran.2025.100505
Lei Shi , Ruitao Li , Chang Du , Julong Zhou , Yahui Yi , Ze Liu , Jianbin Su , Liqin Qian , Tiancai Ma , Shijia Yang , Chengyu Xia , Xingwang Tang
The adaptability of fuel cell vehicles in extreme low-temperature environments remains a critical challenge urgently requiring breakthroughs in the transportation sector. As a core factor influencing low-temperature operational reliability, freeze/thaw cycles directly impact the performance stability and service life of fuel cells, and hold decisive significance for advancing the popularization and application of fuel cell vehicles in cold regions. This study investigates the degradation mechanisms of fuel cells subjected to freeze/thaw cycles under varying initial membrane dissolved water content and assembly force conditions. Building on three preliminary experiments-initial water content calibration, freezing time retardation analysis, and initial microstructure characterization of the catalytic layer and gas diffusion layer-a 100-h freeze/thaw test was conducted. Electrochemical impedance spectroscopy, cyclic voltammetry, and other characterization techniques were employed to assess the fuel cell degradation process. The degradation rate during freeze/thaw cycles was quantified using the distribution of relaxation times method and electrochemical active surface area. High-magnification optical microscopy and scanning electron microscopy were utilized to examine the microstructure of disassembled gas diffusion layers post-freeze/thaw cycles, offering insights into structural damage to both the catalytic layer and gas diffusion layer. Results reveal that higher assembly forces exacerbate gas diffusion layer degradation, leading to slower mass transport and increased mass transport resistance-with the distribution of relaxation times low-frequency peak rising by 202 % under a 13 N m assembly force after 5 cycles. Additionally, higher initial membrane dissolved water content slightly accelerates GDL degradation and significantly contributes to catalytic layer degradation, as evidenced by a 29 % reduction in electrochemical active surface area for 100 % initial water content after 5 cycles. The degradation mechanisms of fuel cells under freeze-thaw cycles revealed in this study provide crucial support for improving the low-temperature reliability of fuel cell vehicles in the transportation sector and promoting their commercialization and application in cold regions.
燃料电池汽车在极端低温环境下的适应性仍然是交通运输领域急需突破的关键挑战。冻融循环作为影响燃料电池低温运行可靠性的核心因素，直接影响燃料电池的性能稳定性和使用寿命，对推进燃料电池汽车在寒冷地区的推广应用具有决定性意义。本研究探讨了不同初始膜溶解水含量和组装力条件下燃料电池在冻融循环下的降解机理。在初始含水量标定、冻结时间延迟分析、催化层和气体扩散层初始微观结构表征三个初步实验的基础上，进行了100 h冻融试验。利用电化学阻抗谱、循环伏安法和其他表征技术对燃料电池的降解过程进行了评价。利用弛豫时间分布法和电化学活性表面积对冻融循环过程中的降解速率进行了量化。利用高倍光学显微镜和扫描电子显微镜对冻融循环后分解的气体扩散层的微观结构进行了观察，从而深入了解了催化层和气体扩散层的结构破坏情况。结果表明，较高的装配力加剧了气体扩散层的降解，导致质量输运变慢和质量输运阻力增加，在13 N m装配力下，5次循环后，低频峰的弛豫次数分布增加了202%。此外，较高的初始膜溶解水含量会略微加速GDL的降解，并显著促进催化层的降解。5个循环后，当初始水含量达到100%时，电化学活性表面积减少29%。本研究揭示的燃料电池在冻融循环下的降解机理，为提高燃料电池汽车在交通运输领域的低温可靠性，促进其在寒冷地区的商业化应用提供了重要支持。
{"title":"Multi-parameter degradation of PEMFCs in freeze/thaw cycles: Impacts of assembly force and initial membrane water content on cold start durability for transportation applications","authors":"Lei Shi , Ruitao Li , Chang Du , Julong Zhou , Yahui Yi , Ze Liu , Jianbin Su , Liqin Qian , Tiancai Ma , Shijia Yang , Chengyu Xia , Xingwang Tang","doi":"10.1016/j.etran.2025.100505","DOIUrl":"10.1016/j.etran.2025.100505","url":null,"abstract":"<div><div>The adaptability of fuel cell vehicles in extreme low-temperature environments remains a critical challenge urgently requiring breakthroughs in the transportation sector. As a core factor influencing low-temperature operational reliability, freeze/thaw cycles directly impact the performance stability and service life of fuel cells, and hold decisive significance for advancing the popularization and application of fuel cell vehicles in cold regions. This study investigates the degradation mechanisms of fuel cells subjected to freeze/thaw cycles under varying initial membrane dissolved water content and assembly force conditions. Building on three preliminary experiments-initial water content calibration, freezing time retardation analysis, and initial microstructure characterization of the catalytic layer and gas diffusion layer-a 100-h freeze/thaw test was conducted. Electrochemical impedance spectroscopy, cyclic voltammetry, and other characterization techniques were employed to assess the fuel cell degradation process. The degradation rate during freeze/thaw cycles was quantified using the distribution of relaxation times method and electrochemical active surface area. High-magnification optical microscopy and scanning electron microscopy were utilized to examine the microstructure of disassembled gas diffusion layers post-freeze/thaw cycles, offering insights into structural damage to both the catalytic layer and gas diffusion layer. Results reveal that higher assembly forces exacerbate gas diffusion layer degradation, leading to slower mass transport and increased mass transport resistance-with the distribution of relaxation times low-frequency peak rising by 202 % under a 13 N m assembly force after 5 cycles. Additionally, higher initial membrane dissolved water content slightly accelerates GDL degradation and significantly contributes to catalytic layer degradation, as evidenced by a 29 % reduction in electrochemical active surface area for 100 % initial water content after 5 cycles. The degradation mechanisms of fuel cells under freeze-thaw cycles revealed in this study provide crucial support for improving the low-temperature reliability of fuel cell vehicles in the transportation sector and promoting their commercialization and application in cold regions.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100505"},"PeriodicalIF":17.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416083","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 : 2025-10-21DOI: 10.1016/j.etran.2025.100496
Kai Shen , Jiaqi Yuan , Peng Ding , Bin He , Gan Song , Xing Pang , Yufang Lu , Xin Lai , Xiangqi Meng , Xuning Feng , Yuejiu Zheng
With the increasingly trend toward the large-scale batteries, a multitude of new and diverse-shaped large batteries, such as blade battery and 4680 cell, have been applied. Temperature inhomogeneity is a new critical issue that arises during the operation of large-size blade batteries, it can have adverse effects on the performance, life, and safety of both individual cell and battery packs. But the temperature distribution estimation is difficult to be estimated because of uneven electro-thermal coupling effects and heat transfer induced during the Li-ion transport and deintercalation process between the planar electrodes. A new equivalent heat generation internal resistance method was used to construct the thermal model for the blade battery, which includes the pole and body regions. And the heat balance method is employed to establish the differential equation for the temperature distribution in the battery body region. To validate the accuracy and efficiency of the model, tests were conducted under different environmental conditions spanning steady-state and dynamic operating regimes. The results show that blade battery temperature inhomogeneity cannot be ignored. The proposed model can estimate the inhomogeneous temperature distribution of a large-size blade battery in less than 1 s. Under normal temperature steady-state and dynamic operating conditions, the maximum real-time error is controlled within 0.8 °C. Under low-temperature or high-current conditions, the maximum real-time errors are kept within 1.88 °C and 1.35 °C, respectively. This model can quickly and accurately predict the real-time evolution of the temperature distribution for blade batteries. And this approach provides innovative insights into real-time temperature monitoring and management for large-scale battery applications.
{"title":"Fast reconstruction of non-uniform temperature fields in large-scale blade battery enabled by partitioned equivalent heat generation resistance modeling","authors":"Kai Shen , Jiaqi Yuan , Peng Ding , Bin He , Gan Song , Xing Pang , Yufang Lu , Xin Lai , Xiangqi Meng , Xuning Feng , Yuejiu Zheng","doi":"10.1016/j.etran.2025.100496","DOIUrl":"10.1016/j.etran.2025.100496","url":null,"abstract":"<div><div>With the increasingly trend toward the large-scale batteries, a multitude of new and diverse-shaped large batteries, such as blade battery and 4680 cell, have been applied. Temperature inhomogeneity is a new critical issue that arises during the operation of large-size blade batteries, it can have adverse effects on the performance, life, and safety of both individual cell and battery packs. But the temperature distribution estimation is difficult to be estimated because of uneven electro-thermal coupling effects and heat transfer induced during the Li-ion transport and deintercalation process between the planar electrodes. A new equivalent heat generation internal resistance method was used to construct the thermal model for the blade battery, which includes the pole and body regions. And the heat balance method is employed to establish the differential equation for the temperature distribution in the battery body region. To validate the accuracy and efficiency of the model, tests were conducted under different environmental conditions spanning steady-state and dynamic operating regimes. The results show that blade battery temperature inhomogeneity cannot be ignored. The proposed model can estimate the inhomogeneous temperature distribution of a large-size blade battery in less than 1 s. Under normal temperature steady-state and dynamic operating conditions, the maximum real-time error is controlled within 0.8 °C. Under low-temperature or high-current conditions, the maximum real-time errors are kept within 1.88 °C and 1.35 °C, respectively. This model can quickly and accurately predict the real-time evolution of the temperature distribution for blade batteries. And this approach provides innovative insights into real-time temperature monitoring and management for large-scale battery applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100496"},"PeriodicalIF":17.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362272","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 : 2025-10-20DOI: 10.1016/j.etran.2025.100503
Qian Huo , Zhikai Ma , Tao Zhang , Zepeng Gao
Accurate fault diagnosis of power batteries is crucial for ensuring the safe and reliable operation of electric vehicles (EVs). Existing fault diagnosis methods have increasingly adopted deep neural networks due to their powerful learning and feature extraction capabilities. However, two significant limitations remain. Firstly, these methods fail to exploit the time–frequency coupling characteristics from multiple battery operational signals, leading to suboptimal feature representation. Secondly, the employed deep network models, such as Transformers, often require substantial computational resources, making them unsuitable for real-time deployment. To address these challenges, this paper proposes a novel fault diagnosis framework that integrates frequency slice wavelet transform (FSWT) with a lightweight SpikingFormer architecture. FSWT is employed to decompose and analyze multiple raw battery signals, capturing comprehensive time–frequency domain features that enhance fault representation. SpikingFormer, inspired by spiking neural networks, serves as an efficient alternative to the Transformer model, reducing computational complexity through event-driven processing while maintaining its capability to capture long-term dependencies. The proposed method, validated using real-world EV battery datasets collected from 100 EVs over a period of 6 to 12 months, demonstrates superior performance compared to state-of-the-art (SOTA) techniques. Specifically, it achieves a 4%–6.8% increase in mean fault-diagnosis accuracy and reduces the time-to-fault error by 1.2–3.2 min. Moreover, its inference time accounts for only 2.8%–28.4% of that required by SOTA methods, while its energy consumption is limited to 13.3%–14.4% of their levels.
{"title":"Lightweight fault diagnosis for EV battery packs via SpikingFormer and frequency slice wavelet transform","authors":"Qian Huo , Zhikai Ma , Tao Zhang , Zepeng Gao","doi":"10.1016/j.etran.2025.100503","DOIUrl":"10.1016/j.etran.2025.100503","url":null,"abstract":"<div><div>Accurate fault diagnosis of power batteries is crucial for ensuring the safe and reliable operation of electric vehicles (EVs). Existing fault diagnosis methods have increasingly adopted deep neural networks due to their powerful learning and feature extraction capabilities. However, two significant limitations remain. Firstly, these methods fail to exploit the time–frequency coupling characteristics from multiple battery operational signals, leading to suboptimal feature representation. Secondly, the employed deep network models, such as Transformers, often require substantial computational resources, making them unsuitable for real-time deployment. To address these challenges, this paper proposes a novel fault diagnosis framework that integrates frequency slice wavelet transform (FSWT) with a lightweight SpikingFormer architecture. FSWT is employed to decompose and analyze multiple raw battery signals, capturing comprehensive time–frequency domain features that enhance fault representation. SpikingFormer, inspired by spiking neural networks, serves as an efficient alternative to the Transformer model, reducing computational complexity through event-driven processing while maintaining its capability to capture long-term dependencies. The proposed method, validated using real-world EV battery datasets collected from 100 EVs over a period of 6 to 12 months, demonstrates superior performance compared to state-of-the-art (SOTA) techniques. Specifically, it achieves a 4%–6.8% increase in mean fault-diagnosis accuracy and reduces the time-to-fault error by 1.2–3.2 min. Moreover, its inference time accounts for only 2.8%–28.4% of that required by SOTA methods, while its energy consumption is limited to 13.3%–14.4% of their levels.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100503"},"PeriodicalIF":17.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362275","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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