Green Energy and Intelligent Transportation最新文献

{"title":"Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China","authors":"Hui Zhang ,&nbsp;Yiyue Luo ,&nbsp;Naikan Ding ,&nbsp;Toshiyuki Yamamoto ,&nbsp;Chenming Fan ,&nbsp;Chunhui Yang ,&nbsp;Wei Xu ,&nbsp;Chaozhong Wu","doi":"10.1016/j.geits.2024.100246","DOIUrl":"10.1016/j.geits.2024.100246","url":null,"abstract":"<div><div>Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision, especially in developing countries. Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. However, current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior, and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption. One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is “graph spectrums”, which allows for an effective and illustrative representation of complex driving behavior characteristics. This study presented an assessment method of ecological driving for electric vehicles based on the graph. Firstly, a multi-source refined data set was constructed through naturalistic driving experiments (NDE). Four typical traffic state (CCCF: congested close car-following; CSSF: constrained slow free-flow; CSCF: constrained slow car-following; UFFF: unconstrained fast free-flow) were classified through longitudinal acceleration data, and driving behavior graph was constructed to realize the visual representation of driving behavior. Then, the energy consumption graph was constructed using the energy loss of 100 ​km (EL) index. After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph, proposing the quantitative analysis of fifteen drivers' ecology driving behavior. The results show that: 1) The graphical method can describe the individual features of a driver’s ecological driving behavior; 2) Rapid acceleration of driving behavior leads to high energy consumption; 3) In the comparison among the six eco-drivers and energy-intensive drivers, founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state; 4) The driving behavior was more complex and unecological in CCCF traffic state; 5) Fifteen drivers had lower ecological scores in start-up driving. This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors, but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling mechanisms of electrolyte wetting process in three-dimensional electrode structures: Insights from realistic architectures","authors":"Fei Chen ,&nbsp;Tianxin Chen ,&nbsp;Zhenxuan Wu ,&nbsp;Zihan Zhou ,&nbsp;Kunjie Lu ,&nbsp;Jinyao Su ,&nbsp;Yihua Wang ,&nbsp;Jianfeng Hua ,&nbsp;Xin Lai ,&nbsp;Xuebin Han ,&nbsp;Minggao Ouyang ,&nbsp;Yuejiu Zheng","doi":"10.1016/j.geits.2024.100248","DOIUrl":"10.1016/j.geits.2024.100248","url":null,"abstract":"<div><div>The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short-term memory","authors":"Chaoran Li ,&nbsp;Sichen Zhu ,&nbsp;Liuli Zhang ,&nbsp;Xinjian Liu ,&nbsp;Menghan Li ,&nbsp;Haiqin Zhou ,&nbsp;Qiang Zhang ,&nbsp;Zhonghao Rao","doi":"10.1016/j.geits.2024.100226","DOIUrl":"10.1016/j.geits.2024.100226","url":null,"abstract":"<div><div>State of charge (SOC) plays a vital role in the safe, efficient, and stable operation of lithium-ion batteries. Since the difference between the surface temperature and core temperature of batteries under severe conditions can reach 5–10 ​°C, using the surface temperature as input feature of SOC estimation is unreasonable. Due to the high requirement for storage space, SOC estimation methods based on deep learning methods are limited to implement in embedded devices. In this paper, to achieve reasonable and high accuracy SOC estimation and provide support for battery thermal management, SOC estimation based on state of temperature (SOT) is implemented. And weight clustered-convolutional neural network-long short-term memory (WC-CNN-LSTM) is proposed to achieve high accuracy SOT and SOC estimation with small model sizes. A self-established dataset is used to verify the effectiveness of the proposed method and model. The WC-CNN-LSTM model with the number of clusters of 400 could achieve comparative accuracy with the baseline model with a 52.98% smaller model size and 25.08% more time consumption for model training on SOT estimation. And it could also achieve consistent and even better accuracy on SOC estimation with the baseline model with a small model size.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radial distribution systems performance enhancement through RE (Renewable Energy) integration and comprehensive contingency ranking analysis","authors":"Muthukumaran Thulasingam,&nbsp;Ajay D Vimal Raj Periyanayagam","doi":"10.1016/j.geits.2024.100245","DOIUrl":"10.1016/j.geits.2024.100245","url":null,"abstract":"<div><div>This research focuses on restructuring medium-level voltage (MLV) distribution systems by integrating distributed renewable energy resources (DER) at multiple feed points. It examines the impact of incorporating renewable energy and evaluates system performance metrics such as robustness, static voltage stability, line carrying capacity, utility grid effectiveness, and losses within the conventional radial distribution framework commonly used in educational institutions. The contingency ranking of the real-time radial distribution system (RTRDS) for a typical educational institution consisting of <em>N</em> buses was conducted. Parameters such as the Voltage Performance Index (PIV) and Flow Performance Index (PIF) were evaluated. The results support the integration of distributed renewable energy sources within the existing radial distribution grid infrastructure. This research proposes enhanced contingency analyses through a straightforward reconfiguration process involving an additional tie line (<em>N</em> + 1) for the existing <em>N</em> bus radial distribution system (RDS). Load flow analysis of the RDS with distributed renewable energy resources (DER) for both <em>N</em> bus and <em>N</em> + 1 bus systems was conducted using the Gauss-Seidel and Newton–Raphson methods. Simulation results indicate that baseline loading is consistently maintained by grid sources and DER sources connected at multiple feed points. The proposed configuration of the <em>N</em> + 1 bus system for the existing RTRDS was evaluated for voltage performance and compared with the Grey Wolf Optimization (GWO) algorithm. The results indicate that the <em>N</em> + 1 bus configuration modeled using the MiPower tool performed comparably to the GWO results. Additionally, the contingency ranking for the proposed <em>N</em> + 1 configuration was validated using the IEEE 10 and 30 bus system.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study of the magnetic field emissions from a vehicle-mounted wireless power transfer system for safe operation when charging EV batteries","authors":"Craig McIntyre,&nbsp;Silvia Konaklieva,&nbsp;Artur Benedito Nunes,&nbsp;Richard A. McMahon","doi":"10.1016/j.geits.2024.100247","DOIUrl":"10.1016/j.geits.2024.100247","url":null,"abstract":"<div><div>Wireless Power Transfer (WPT) is an alternative method of Electric Vehicle (EV) battery charging, particularly for fleet vehicles and people with mobility issues. The safe operation of WPT systems should therefore be of interest and importance to system designers, installers, and end-users. One aspect of safe operation is the potential exposure to high-power electromagnetic fields. There are international guidelines with recommended exposure limits that system designers can design and test to. Simulations can be used to predict magnetic field levels, but these should be developed in conjunction with physical measurements to improve the accuracy of such simulations.</div><div>1 Several factors can influence the WPT generated electromagnetic field, in regions where end users could be located during charging operation. These factors were studied for an in-house designed WPT system retrofitted to an electric vehicle. The magnetic field was physically measured around the vehicle for different operating conditions (alignment, power transfer level and probe position) to assess performance against recommended exposure levels, observe any trends in measurements and study the impact of the probe position.</div><div>Coil currents were measured and used within an initial simulation to predict magnetic field for comparison to physical values. The initial simulation predicted the trend of the magnetic field with reasonable accuracy. Where there was a difference in magnitude, the physical measurements highlighted that a High Frequency (HF cable) used within the vehicle assembly (not included in initial simulation) contributed to the magnetic field intensity. Overall, magnetic fields were within permitted exposure limits at 10 ​kW power and good alignment, and with misaligned coils, the system showed only minor exceedance of the most stringent limits, and DC–DC system efficiency was only slightly reduced.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model","authors":"Guangying Zhu ,&nbsp;Jianguo Chen ,&nbsp;Xuyang Liu ,&nbsp;Tao Sun ,&nbsp;Xin Lai ,&nbsp;Yuejiu Zheng ,&nbsp;Yue Guo ,&nbsp;Rohit Bhagat","doi":"10.1016/j.geits.2024.100167","DOIUrl":"10.1016/j.geits.2024.100167","url":null,"abstract":"<div><div>Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed ion-electron conducting LixAg alloy anode enabling stable Li plating/stripping in solid-state batteries via enhanced Li diffusion kinetic","authors":"Anran Cheng ,&nbsp;Pei Gao ,&nbsp;Ruxing Wang ,&nbsp;Kangli Wang ,&nbsp;Kai Jiang","doi":"10.1016/j.geits.2024.100179","DOIUrl":"10.1016/j.geits.2024.100179","url":null,"abstract":"<div><div>Although showing huge potential in prospering the marketplace of all-solid-state lithium metal batteries (ASSLMBs), garnet-type solid electrolytes (Li_6.5La₃Zr_1.5Ta_0.6O₁₂, LLZTO) are critically plagued by interface instability with Li anode and the vulnerability to Li dendrite, which are attributed to poor Li diffusion kinetic in bulk Li metal. Herein, a Li_<em>x</em>Ag solid solution alloy with high Li diffusion kinetic is reported as a mixed ion-electron conductor (MIEC) alloy anode. The high Li diffusion kinetic stemming from a low eutectic point and a high mutual solubility of Li_<em>x</em>Ag could reduce the Li concentration gradient in the anode, regulate Li electrochemical potential, and change the relative local overpotential for Li stripping/plating in the anode. Notably, Li stripping/plating prefers energetically at the Li_<em>x</em>Ag/current collector interface rather than the LLZTO/Li_<em>x</em>Ag interface. Therefore, the contact loss is avoided at the LLZTO/Li_<em>x</em>Ag interface. As a result, excellent cycling stability (∼1,200 ​h at 0.2 ​mA/cm²), and dendrites tolerance (critical current density of 1.2 ​mA/cm²) are demonstrated by using Li_<em>x</em>Ag as anode. Further research has elucidated that those alloys with low eutectic temperature and high mutual solubility with lithium should be focused on, as they would provide and maintain a soft lattice and a high lithium diffusion rate during composition change. This provides a basis for the selection of alloy phases in negative electrode materials, as well as their application in garnet-based ASSLMBs.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale analysis and hierarchical optimization design of a 2D twill woven composite front firewall for electric vehicles","authors":"Junfei Yan ,&nbsp;Jian Song ,&nbsp;Bengang Yi ,&nbsp;Yi Quan ,&nbsp;Cheng Xu ,&nbsp;Wenyuan Gong ,&nbsp;Zhaojun Du ,&nbsp;Tengyong Liu ,&nbsp;Changchun Xie ,&nbsp;Darong Liang ,&nbsp;Zihao Pu ,&nbsp;Zhexuan Dong","doi":"10.1016/j.geits.2025.100251","DOIUrl":"10.1016/j.geits.2025.100251","url":null,"abstract":"<div><div>In high-performance electric sports vehicles, the application of woven composite materials with the purpose of lightweight has become an inevitable choice. It is considerably difference between traditional metal materials and composites for the lightweight design strategy of electric vehicle structures, due to the multi-scale and anisotropic characteristics of fiber reinforced composites. Nevertheless, most of scholars are focus on the meso-scale mechanical responses of woven composites, and few studies are involved in their multi-scale mechanical behaviors and hierarchical design strategy of composite structures in electric vehicles. In this work, a multi-scale analysis strategy was proposed to investigate mechanical behaviors of composite front firewall. Subsequently, a hierarchical optimization strategy with the objective of lightweight design of composite front firewall was carried out. Finally, a reasonable layout scheme of composite front firewall was quantitatively obtained. The maximum errors between the predicted and theoretical/experimental results in terms of equivalent engineering constants of fiber yarns and 2D twill woven composites (2DTWCs) were 8.8 ​GPa and 7%, respectively. It indicates that the multi-scale models can be used to evaluate the mechanical properties of 2DTWCs. Additionally, the total weight of optimized composite front firewall was reduced by 36% in comparison with the reference, and simultaneously the total stiffness was improved by 26%. Hence, it is an effective strategy to design lightweight composite structures of electric vehicles. We hope the proposed multi-scale and hierarchical design strategy could promote the further development of composite structures in high-performance electric sports vehicles.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an analytical model to evaluate the effect of the ported shroud on centrifugal compressors","authors":"Carlo Cravero ,&nbsp;Philippe Joe Leutcha ,&nbsp;Davide Marsano","doi":"10.1016/j.geits.2024.100249","DOIUrl":"10.1016/j.geits.2024.100249","url":null,"abstract":"<div><div>Extending the operational range of centrifugal compressors is strategically vital for turbocharging internal combustion engines, particularly in enhancing efficiency and expanding operational capabilities. This extension is crucial for reducing environmental impact by enabling engines to perform more efficiently under a wider range of conditions. In the transition from conventional thermal reciprocating engines, fuel cells, especially proton exchange membrane fuel cells (PEMFCs), are emerging as strong alternatives. In automotive applications, PEMFCs often require turbocharging to supply compressed air to the cathode system of the fuel cell stack. This integration is essential for utilizing the heat from the fuel cell's waste products, thereby improving overall system efficiency. Ongoing research and development in radial turbomachinery are critical for optimizing the performance of these propulsion systems. Specifically, adapting turbocharger designs to meet the unique requirements of fuel cell systems and extending their operational range are essential tasks. Using a simplified CFD model, the impact of a ported shroud on compressor performance and range extension has been investigated. Flow structure analysis identified that the primary role of the ported shroud is to modify the relative flow angle on the rotor at the highest span channel. Additionally, a simplified analytical model was developed to quantify the effectiveness of different ported shroud geometries on the compressor by examining changes in tangential velocity after mixing with the flow from the cavity.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning models for predicting Ebus battery consumption rates in cold climates with and without diesel auxiliary heating","authors":"Kareem Othman ,&nbsp;Diego Da Silva ,&nbsp;Amer Shalaby ,&nbsp;Baher Abdulhai","doi":"10.1016/j.geits.2024.100250","DOIUrl":"10.1016/j.geits.2024.100250","url":null,"abstract":"<div><div>The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses (Ebuses). To optimize the deployment and operational strategies of Ebuses, it is imperative to accurately predict their energy consumption under varying conditions, particularly in cold climates where battery life is typically degraded. The exploration of this aspect within the Canadian context has been limited. In addition, we have found that existing models in the literature perform poorly in the Canadian environment, giving rise to the need for new models using Canadian data. This paper focuses on the development, comparison, and evaluation of various data-driven models designed to predict the energy consumption of different Ebuses with different heating technologies under a wide range of climate conditions. We specifically use Canadian data as a good representative of cold climates in general. The results show that the performance of the different bus types varies substantially under the exact same conditions. In addition, tree-based family of models proves to be the most suitable approach for predicting the Ebus consumption rate. The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate, with a resulting mean absolute error of 0.09–0.1 ​kWh/km observed across the different models. Furthermore, SHAP analysis shows that the main variables influencing the energy consumption rate depend on the type of heating system (using the battery for heating or using an auxiliary system that utilizes diesel for heating) adopted.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}