Etransportation最新文献

{"title":"In-situ CNT-loaded organic cathodes for sulfide all-solid-state Li metal batteries","authors":"Fengmei Song ,&nbsp;Zhixuan Wang ,&nbsp;Guochen Sun ,&nbsp;Tenghuan Ma ,&nbsp;Dengxu Wu ,&nbsp;Liquan Chen ,&nbsp;Hong Li ,&nbsp;Fan Wu","doi":"10.1016/j.etran.2023.100261","DOIUrl":"https://doi.org/10.1016/j.etran.2023.100261","url":null,"abstract":"<div><p><span>Organic cathodes show promising advantages of extensive resources, high theoretical specific capacity, and mild synthesis conditions, etc., but suffer from low density, poor electronic conductivity, and high solubility in liquid electrolytes. Herein, an in-situ coating method is developed to overcome the above issues by realizing high-performance sulfide all-solid-state batteries with organic Li</span>₄C₈H₂O₆ cathode. Li₄C₈H₂O₆ composite cathodes with carbon nanotubes (CNTs) and vapor grown carbon fiber (VGCF) were systematically studied to reveal that CNTs accelerate the electrochemical decomposition of sulfide electrolyte, despite the effectively improved electronic conductivity, rate capability and active material utilization. Therefore, in-situ coating of Li₄C₈H₂O₆ onto CNTs (Li₄C₈H₂O₆@CNT) is developed to further improve the contact between Li₄C₈H₂O₆<span> and CNTs, but to reduce the contact of CNTs with sulfide solid electrolyte and its decomposition. As a result, the Li</span>₄C₈H₂O₆@CNT electrode demonstrates a high capacity of 200.3 mAh/g, and a high active material utilization rate (83.4% at 0.1C). It also exhibits a specific capacity of 85.9 mAh/g at a high cathode loading of 40 wt% and a high rate of 1C.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49901144","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}

{"title":"Automation program for optimum design of electric vehicle powertrain systems based on artificial neural network","authors":"Kihan Kwon ,&nbsp;Sang-Kil Lim ,&nbsp;Dongwoo Kim ,&nbsp;Kijong Park","doi":"10.1016/j.etran.2023.100267","DOIUrl":"10.1016/j.etran.2023.100267","url":null,"abstract":"<div><p><span>Many studies have been conducted on various powertrain systems, such as multi-motor, multi-speed, or both, to enhance the energy efficiency and dynamic performance of electric vehicles (EVs). This study developed an automated design program to obtain the optimal design of EVs for various powertrain systems. The program consists of an EV simulation and </span>artificial neural network<span> (ANN) modeling and optimization tools. The EV simulation tool employs an integrated EV model that can analyze the efficiency and performance of various powertrain systems in a single environment. The ANN modeling and optimization tool first constructs an ANN model, and then performs optimization using the ANN model to address excessive computational efforts arising from the multi-objective genetic algorithm. This study verified the developed program by conducting analysis and optimization of five powertrain systems with the same EV specifications. A multi-objective optimization problem was formulated by considering the design variables as the torque distribution between the motors and gear shifting patterns and ratios of the transmission, and the objectives as the energy consumption and acceleration time. A comparison of the optimization results among the five powertrain systems quantitatively showed the positive effects of the multi-motor and multi-speed powertrain systems. Furthermore, the ANN-based multi-objective optimization process allowed for the efficient determination of the optimum design solutions for the proposed EV powertrain systems. Consequently, these results demonstrated the effectiveness of the automation program in rapid decision-making on EV powertrain system configurations, satisfying each designer’s requirements.</span></p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48836038","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}

{"title":"Durability of advanced low temperature lithium compound electrode ceramic fuel cell for transportation","authors":"Kai Wei ,&nbsp;Zhuo Chen ,&nbsp;Gang Chen ,&nbsp;Siwen Xu ,&nbsp;Shujiang Geng","doi":"10.1016/j.etran.2023.100276","DOIUrl":"10.1016/j.etran.2023.100276","url":null,"abstract":"<div><p><span>In recent years, a ceramic fuel cell with lithium compound such as Ni</span>₀_·₈Co₀_·₁₅Al₀_·₀₅LiO₂ (NCAL) as its electrode is reduced in H₂<span> to produce lithium compounds containing molten salt and diffused into oxide electrolyte to form an “oxide-lithium compounds molten salt” composite electrolyte with ultra-high ionic conductivity<span>, which made the cell have remarkable low-temperature power generation performance. It is found that the dynamic migration of lithium compounds produced by NCAL anode in the cell with Ce</span></span>_0.9Gd_0.1O_2-δ<span> (GDC) as electrolyte during the constant current durability test is the main reason for the cell performance degradation. In this paper, we found that adding different mass ratios of NaFeO</span>₂ to the GDC electrolyte to construct GDC/NaFeO₂ composite electrolyte can significantly affect the durability of the cell. Under the constant current density test conditions of 550 °C, 0.2 A cm⁻², the performance of the cell with GDC/NaFeO₂ composite with a mass ratios of 8/2 as electrolyte maintained relatively good durability in the constant current test of 50 h. The characterization results show that the NaFeO₂ reacts with lithium compounds such as LiOH to generate LiFeO₂ and NaOH, and NaFeO₂ is reduced to Fe and NaOH by H₂. A proper amount of NaFeO₂ in the GDC/NaFeO₂<span> composite electrolyte can produce sodium compound molten salt during the performance test to replace the role of lithium compound molten salt in improving the electrolyte ionic conductivity and the cell sealing, while slowing down the dynamic migration of the molten salt in the cell, thus improving the durability of the cell. The findings in this paper provide evidence and relevant theories for the performance degradation and durability improvement mechanism of this new type of lithium compound electrode ceramic fuel cell (LCCFCs), and propose new strategies for obtaining LCCFCs with better durability.</span></p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48619078","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}

{"title":"A novel pre-diagnosis method for health status of proton exchange membrane fuel cell stack based on entropy algorithms","authors":"Lei Zhao ,&nbsp;Jichao Hong ,&nbsp;Hao Yuan ,&nbsp;Pingwen Ming ,&nbsp;Xuezhe Wei ,&nbsp;Haifeng Dai","doi":"10.1016/j.etran.2023.100278","DOIUrl":"10.1016/j.etran.2023.100278","url":null,"abstract":"<div><p><span>Effective and accurate cell health status diagnosis is key to ensuring the stable operation of the fuel cell stack<span>. The reliability of the current voltage value-based method is challenging due to the solid time-varying nature of fuel cells. This paper utilizes modified Shannon entropy to propose a novel method for fuel cell health status evaluation and pre-diagnosis. It is revealed that fuel cell health status can be effectively characterized by quantifying the voltage fluctuation degree using modified Shannon entropy. Furthermore, its sensitivity, universality, and reliability are verified by different types of experimental data, including extreme operating conditions, </span></span>membrane electrode assembly's severe inconsistent aging, and unreasonable structures. Then, an abnormal coefficient considering the stack inconsistency is proposed utilizing the entropy combined with the Z-score method and can diagnose in-stack abnormal cells in advance based only on timing voltage. Further, the fuel cell's abnormality level can be determined in real time according to the established three-level health status management strategy. Corresponding treatments are recommended. Finally, the method's application prospect in practical systems such as vehicles and big data platforms is explored due to the small computation and easy implementation, which builds a foundation for the future fuel cell health management system.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49390121","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}

{"title":"Role of the temperature and aging in mechanical modeling of the active coating in Li-ion battery","authors":"Pengfei Ying,&nbsp;Chen Wang,&nbsp;Yong Xia","doi":"10.1016/j.etran.2023.100273","DOIUrl":"10.1016/j.etran.2023.100273","url":null,"abstract":"<div><p>Safety of lithium-ion batteries under mechanical loading poses a significant and urgent challenge in the Electric Vehicle (EV) industry. To assess the safety tolerance of the entire battery system, it is crucial to model the batteries subjected to mechanical abuse. The mechanical behavior of batteries is affected by temperature and aging, leading to substantial changes in the properties of active layers. Ignoring these factors may lead to an incomplete estimation of the batteries’ mechanical response.</p><p>This study examines the results of component tests conducted on batteries at various temperatures and states of health (SOH). The analysis reveals that the particle and adhesion aspects contribute independently to the temperature effect and the aging effect. By incorporating the mechanical interpretation of parameters in the Drucker-Prager Cap (DPC) model, a methodology for characterizing the mechanical properties of in-situ active coatings under different aging conditions and temperatures is introduced. Additionally, the formulation of temperature effects on batteries at different SOH levels is presented. The comparison between finite element (FE) simulations and component tests further confirms the validity of the engineering relationship.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48322902","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}

{"title":"Deep learning powered rapid lifetime classification of lithium-ion batteries","authors":"Zicheng Fei ,&nbsp;Zijun Zhang ,&nbsp;Fangfang Yang ,&nbsp;Kwok-Leung Tsui","doi":"10.1016/j.etran.2023.100286","DOIUrl":"https://doi.org/10.1016/j.etran.2023.100286","url":null,"abstract":"<div><p><span><span><span>Lithium-ion batteries (LIBs) are currently the primary energy storage devices for modern electric vehicles (EVs). Early-cycle lifetime/quality classification of LIBs is a promising technology for many EV-related applications, such as fast-charging optimization design, production evaluation, battery pack design, second-life recycling, etc. The key challenge of the research problem is to develop an accurate classification method based on very limited early-cycle data, which contain very little information regarding battery degradation. To respond to such emerging need and tackle such technical challenge, this study develops a novel deep learning powered method for enabling the rapid LIB lifetime classification via very limited early-cycle data. First, the proposed method considers an innovative high-dimensional tensor input integrating early-cycle </span>battery voltage, current, and temperature data to organically fuse the spatial, temporal, and physical battery information. Next, a convolutional sparse autoencoder-based feature engineering framework is developed to process such tensor input, automatically extract high-level latent features, and embed high-dimensional input information into a more </span>compact representation<span>. Finally, a regularized logistic regression model is developed to classify batteries into different lifetime groups based on a joint consideration of latent features as well as battery nominal and operational parameters. The effectiveness and robustness of the proposed method is verified on experimental data of battery degradation with three different chemistries and under multiple charge/discharge conditions. The performance of the proposed method is competitive by comparing with a set of well-known and recent benchmarking methods. In scenarios with only first-20-cycle degradation data available, the </span></span>classification accuracy of the proposed method can reach 96.6%. In scenarios with only first-5-cycle data available, our classification accuracy can still reach 92.1%.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49866370","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}

{"title":"System identification and state estimation of a reduced-order electrochemical model for lithium-ion batteries","authors":"Yujie Wang ,&nbsp;Xingchen Zhang ,&nbsp;Kailong Liu ,&nbsp;Zhongbao Wei ,&nbsp;Xiaosong Hu ,&nbsp;Xiaolin Tang ,&nbsp;Zonghai Chen","doi":"10.1016/j.etran.2023.100295","DOIUrl":"https://doi.org/10.1016/j.etran.2023.100295","url":null,"abstract":"<div><p><span><span>Lithium-ion batteries commonly used in electric vehicles are an indispensable part of the development process of decarbonization, electrification, and intelligence in transportation. From intelligent designing, manufacturing to controlling, an intelligent battery management system<span> plays a crucial role in the long life, high efficiency, and safe operation of lithium-ion batteries. As a first-principle model, the electrochemical parameters of the electrochemical model have physical meanings and reflect the internal state of the lithium-ion batteries. The application of electrochemical models in an advanced intelligent battery management system is a future trend that promises to mitigate battery life degradation and prevent safety incidents. The reduced-order electrochemical model is expected to alleviate the requirements of advanced battery management systems for high accuracy and fast computing of lithium-ion battery models. However, the existing model order reduction methods have the drawbacks of high computational complexity and small application scope, so that inconvenient to apply onboard. In order to solve the existing obstacles, this paper applies the pseudo-spectral method to solve the solid-phase diffusion equation, while the liquid-phase concentration equation is simplified by the Galerkin method. Subsequently, a </span></span>particle swarm optimization algorithm is used to identify 11 parameters of the electrochemical model. To further improve the accuracy of the electrochemical model, the above system identification method is applied to segment identification, especially for high or low state-of-charge (SoC) conditions in this study. Finally, based upon the derived model, estimation of SoC is performed using a particle filter. The results show that the proposed reduced-order electrochemical model achieves a low </span>Mean Absolute Error (MAE) of 8.4 mV and a MAE of 0.54 % on estimation of SoC based on the envisaged particle filter. This work is expected to provide the basis for the subsequent development of lithium-ion battery electrochemical models with smaller identification parameters and faster identification processes.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49866375","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}

{"title":"Fast multilayer temperature distribution estimation for lithium-ion battery pack","authors":"Zhechen Guo ,&nbsp;Jun Xu ,&nbsp;Xingzao Wang ,&nbsp;Xuesong Mei","doi":"10.1016/j.etran.2023.100266","DOIUrl":"10.1016/j.etran.2023.100266","url":null,"abstract":"<div><p><span>Fast and accurate temperature estimation is crucial for ensuring battery packs' safety and operation performance. However, the full-scale online temperature estimation is still challenging. In this work, a novel reduced-order multi-physics model for a lithium-ion battery pack is proposed for real-time temperature distribution estimation, containing the distributed </span>equivalent circuit model, the three-heat-source thermal model, and the flow resistance network model. The proposed model is conducted on a direct contact liquid-cooled battery pack composed of three modules connected in series. An online parameterization methodology with a closed loop observer is designed, and the key parameters can be automatically identified and corrected. The validation results suggest that the multilayer temperature distribution of cell, module, and pack levels can be commendably described under both steady and transient conditions, where the maximum error can be controlled within 2.8 °C. Besides, the temperature variation of the coolant can be estimated during the operation. The proposed model shows excellent potential in onboard temperature estimation with tens of milliseconds for each temperature update.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41963404","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}

{"title":"Charging and discharging optimization strategy for electric vehicles considering elasticity demand response","authors":"Liang Zhang ,&nbsp;Chenglong Sun ,&nbsp;Guowei Cai ,&nbsp;Leong Hai Koh","doi":"10.1016/j.etran.2023.100262","DOIUrl":"10.1016/j.etran.2023.100262","url":null,"abstract":"<div><p><span><span><span>The electrification of urban transportation systems is a critical step toward achieving low-carbon transportation and meeting climate commitments. With the support of the Chinese government for the electric vehicle industry, the </span>penetration rate of electric vehicles has continued to increase. In the context of large-scale electric vehicles connected to the grid, a coordinated charging-discharging system is particularly vital studied to avoid grid overload caused by customers' random charging. In this paper, a two-stage </span>optimization strategy<span> for electric vehicle charging and discharging that considers elasticity demand response based on particle swarm optimization was proposed, allowing the user to respond autonomously according to the reference value of the charge and discharge demand response and select the optimization weight independently to meet their travel and charging needs. To facilitate the user to balance the charging cost and the charging energy, we have introduced the virtual </span></span>SOC<span> to calculate the optimization result in advance. The results show that the optimized scheme can reduce the charging cost by 40%∼110%, and the load variance of the distribution network can be reduced by 19%∼100%, realizing the \"win-win\" benefit of the grid side and the user side. In addition, our research found that under the proposed strategy, the cost of battery loss caused by cyclic charging and discharging is negligible compared to the discharge benefit.</span></p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43062923","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}

{"title":"Brake-by-wire system for passenger cars: A review of structure, control, key technologies, and application in X-by-wire chassis","authors":"Lei Zhang ,&nbsp;Qi Wang ,&nbsp;Jun Chen ,&nbsp;Zhen-Po Wang ,&nbsp;Shao-Hua Li","doi":"10.1016/j.etran.2023.100292","DOIUrl":"https://doi.org/10.1016/j.etran.2023.100292","url":null,"abstract":"<div><p>Electrification, networking and intelligence are the major development trends of the modern automobile industry for improving efficiency and safety and reducing emissions of the transport system. As an integral component of a wire-controlled chassis in passenger cars, brake-by-wire (BBW) system plays a key role in improving braking energy efficiency, safety, and ride comfort. This paper presents a systematic and complete review on BBW and its related technologies in passenger car applications. First, the architectures and working principles of major BBW systems are covered in details. Then, state-of-art control strategies for BBW systems are expounded. In particular, BBW-involved active safety control are presented. Finally, the remaining challenges and future research directions are discussed. The integrated design of BBW is a major development direction while other BBW systems except the Electro-Hydraulic System still need to solve some key technological challenges. Besides, efficient coordinated control of the X-by-wire chassis remains an open topic. In particular, the application of BBW in the X-by-wire chassis is a research hotspot.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":11.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92100510","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}