Etransportation最新文献

{"title":"Battery pack diagnostics for electric vehicles: Transfer of differential voltage and incremental capacity analysis from cell to vehicle level","authors":"Philip Bilfinger,&nbsp;Philipp Rosner,&nbsp;Markus Schreiber,&nbsp;Thomas Kröger,&nbsp;Kareem Abo Gamra,&nbsp;Manuel Ank,&nbsp;Nikolaos Wassiliadis,&nbsp;Brian Dietermann,&nbsp;Markus Lienkamp","doi":"10.1016/j.etran.2024.100356","DOIUrl":"10.1016/j.etran.2024.100356","url":null,"abstract":"<div><p>Aging of lithium-ion battery cells reduces a battery electric vehicle’s achievable range, power capabilities and resale value. Therefore, suitable characterization methods for monitoring the battery pack’s state of health are of high interest to academia and industry and are subject to current research. On cell level under laboratory conditions, differential voltage and incremental capacity analysis are established characterization methods for analyzing battery aging. In this article, experiments are conducted on the battery electric vehicles Volkswagen ID.3 and Tesla Model 3, examining the transferability of differential voltage and incremental capacity analysis from cell to vehicle level. Hereby, the vehicles are monitored during AC charging, ensuring applicability in real-life scenarios. Overall, transferability from cell to vehicle level is given as aging-related characteristics can be detected in vehicle measurements. Hereby, loss of lithium inventory is identified as the primary cause for capacity loss in the usage time of these vehicles. Both methods have limitations, such as data quality restrictions or vehicle specific behavior, but are suitable as diagnostics tools that can enable a vehicle level state of health estimation.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100356"},"PeriodicalIF":15.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590116824000468/pdfft?md5=693ee9b139b9f57b229a7abfa0a53d34&pid=1-s2.0-S2590116824000468-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical review of radial field in-wheel motors: technical progress and future trends","authors":"Kehan Yan,&nbsp;Zunyan Hu,&nbsp;Jiayi Hu,&nbsp;Jianqiu Li,&nbsp;Ben Zhang,&nbsp;Jinpeng Song,&nbsp;Jingkang Li,&nbsp;Le Chen,&nbsp;Hang Li,&nbsp;Liangfei Xu","doi":"10.1016/j.etran.2024.100353","DOIUrl":"10.1016/j.etran.2024.100353","url":null,"abstract":"<div><p>In-wheel motors (IWMs) are considered ideal drivetrains for electric vehicles (EVs), but their applications remain preliminary. In particular, the torque density of IWMs cannot meet the performance requirements of all vehicle types. This review reports the evolutionary progress of IWMs toward torque density improvement and discusses four critical technologies together for the first time: deceleration mode, electromagnetic topology, heat dissipation, and in-wheel structure. The direct drive, outer rotor, and water cooling IWMs are well-suited to most passenger vehicles. Furthermore, the adaptability of IWMs to vehicle types is analyzed. Medium and large passenger and sport utility vehicles have limited installation space for the reducer and largely depend on IWMs’ torque. When the torque weight density of an IWM with structural components improves, IWMs will be adopted widely. Further evolution of IWMs will involve employing novel materials, refined design optimization, and seamless structural integration. Novel materials will enhance the torque output capability and transcend existing limitations. The intelligent design optimization balances torque and efficiency, achieving the required energy conversion quality. The degree of structural integration determines the weight and reliability of the entire IWM and its auxiliary parts.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100353"},"PeriodicalIF":15.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985003","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":"Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning","authors":"Ziqi Zhang ,&nbsp;Zhong Chen ,&nbsp;Erdem Gümrükcü ,&nbsp;Zhenya Ji ,&nbsp;Ferdinanda Ponci ,&nbsp;Antonello Monti","doi":"10.1016/j.etran.2024.100352","DOIUrl":"10.1016/j.etran.2024.100352","url":null,"abstract":"<div><p>Public charging stations (CSs) serve for electric vehicles (EVs) to charge during urban travel. Optimizing the charging time, location distribution, and power of EVs can increase the revenue of charging system operators (CSOs) and provide flexible regulation resources for the power grid. However, the optimization scheduling of CSs involves the charging choices of various users, which are influenced by their autonomy and bounded rationality. To guide users and encourage their participation in the charging schedule, we introduce the Nudge method from behavioral economics. To achieve collaborative optimization of non-economic Nudges and economic incentive strategies applying to multiple charging stations in a complex nonlinear environment involving users, CSO, and the transportation network, we leverage multi-agent deep reinforcement learning (MADRL). We construct a simulation environment using historical and survey data tailored to real users. This environment facilitates the training of agent groups to enhance decision-making processes. Case studies in a metropolis demonstrate that the agent group aimed at revenue improvement yields significant improvements in the CSO's revenue compared to fixed service fees and pricing strategies without Nudges. Moreover, the agent group aimed at power curve tracking achieves a lower average relative error in aligning the total charging power with the desired curve of the power system. This paper integrates sociological methods into the optimization of physical systems by MADRL, providing a new approach for the scheduling of EV charging considering user behavior.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100352"},"PeriodicalIF":15.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885754","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":"Electrified fleet and infrastructure aware energy efficient routing","authors":"Joon Moon ,&nbsp;Hamza Anwar ,&nbsp;Manfredi Villani ,&nbsp;Muhammad Qaisar Fahim ,&nbsp;Priyank Jain ,&nbsp;Kesavan Ramakrishnan ,&nbsp;Qadeer Ahmed","doi":"10.1016/j.etran.2024.100351","DOIUrl":"10.1016/j.etran.2024.100351","url":null,"abstract":"<div><p>This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100351"},"PeriodicalIF":15.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590116824000419/pdfft?md5=bc59cd94115e70cb95862b51c8fb55e4&pid=1-s2.0-S2590116824000419-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Venting particle-induced arc of lithium-ion batteries during the thermal runaway","authors":"Cheng Li,&nbsp;Hewu Wang,&nbsp;Yalun Li,&nbsp;Languang Lu","doi":"10.1016/j.etran.2024.100350","DOIUrl":"10.1016/j.etran.2024.100350","url":null,"abstract":"<div><p>Thermal runaway of lithium-ion batteries will release a large amount of particles with elevated temperature and high velocity, probably resulting in arc failures. In this study, we adopted an 117Ah fully-charged prismatic battery with Li(Ni₀_.₈Co₀_.₁Mn_0.1)O₂ cathode to collect the vented particles in an inert atmosphere after thermal runaway. All settled particles were classified into six groups to investigate the influence of electrode spacing, particle size and load resistance on the critical breakdown voltage as well as arc characteristics. As a result, a novel breakdown arc failure called venting particle-induced arc was firstly revealed and verified in the battery system. These settled particles significantly decrease the critical breakdown voltage for arc failure. The critical breakdown voltage exhibits a positively quadratic correlation with electrode spacing within 1–8 mm, while it is negatively correlated with particle sizes. Furthermore, a critical voltage map for breakdown arc under various particle sizes and electrode spacing was proposed. The results provide guidance to electrical hazards prevention in the battery system.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100350"},"PeriodicalIF":15.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844084","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":"Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics","authors":"Junwei Zhao ,&nbsp;Xiangyang Xu ,&nbsp;Wei Guo ,&nbsp;Peng Dong ,&nbsp;Kun Yao ,&nbsp;Xuewu Liu","doi":"10.1016/j.etran.2024.100348","DOIUrl":"10.1016/j.etran.2024.100348","url":null,"abstract":"<div><p>The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100348"},"PeriodicalIF":15.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702200","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":"Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves","authors":"Yue Sun ,&nbsp;Rui Xiong ,&nbsp;Xiangfeng Meng ,&nbsp;Xuanrou Deng ,&nbsp;Hailong Li ,&nbsp;Fengchun Sun","doi":"10.1016/j.etran.2024.100347","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100347","url":null,"abstract":"<div><p>Degradation prediction is crucial for ensuring safe and reliable operation of batteries. However, relying solely on capacity to characterize aging cannot comprehensively represent the health status of the battery. This work explores the potential of using a limited number of partial voltage-capacity curves to evaluate battery degradation with the aid of deep learning approaches, which can be used for onboard applications. A sequence-to-sequence model is proposed to predict the electrochemical impedance spectra during battery degradation. It only uses capacity sequences within a specific voltage range at fixed voltage increments from a limited number of cycles, which can be flexibly adapted to different life stages in an end-to-end manner. The proposed method has been validated based on the developed degradation dataset. The root mean square errors for the prediction of impedance spectra are less than 1.48 mΩ. Capacities and resistances associated with electrochemical processes can be further extracted from the obtained impedance spectra, facilitating a comprehensive evaluation of battery degradation. As a limited number of measured data are needed, the proposed method can reduce data storage requirements and computational demands, which enables fast and comprehensive aging diagnosis.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100347"},"PeriodicalIF":15.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582933","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":"State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations","authors":"Qinan Zhou ,&nbsp;Dyche Anderson ,&nbsp;Jing Sun","doi":"10.1016/j.etran.2024.100346","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100346","url":null,"abstract":"<div><p>State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100346"},"PeriodicalIF":15.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605865","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":"Significance of homogeneous conductive network in layered oxide-based cathode for high-rate capability of electric vehicle batteries","authors":"Qiao Hu ,&nbsp;Li Wang ,&nbsp;Jinli Liu ,&nbsp;Guangming Han ,&nbsp;Jiaying Liao ,&nbsp;Dongsheng Ren ,&nbsp;Jianfeng Yao ,&nbsp;Zonghai Chen ,&nbsp;Xiangming He","doi":"10.1016/j.etran.2024.100345","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100345","url":null,"abstract":"<div><p>The trade-off between battery energy density and power performance is the core problem that puzzles the development of electric vehicles (EVs). Although intensive researches are performed to explore active materials with good dynamics, the heterogeneous reactivity has been identified as an important cause for inferior capability and early death, especially for electrodes characterized with high areal loading and high compacted density. Herein, the heterogeneity and its origination of layered oxide-based (LiNi_xCo_yMn_1-x-yO₂, NCM) electrodes at high C-rate are investigated through operando X-ray diffraction and <em>ex-situ</em> time-of-flight secondary ion mass spectrometry probe. By introducing Li₃V₂(PO₄)₃@G composite as a mixed conductor additive, the heterogeneous reactivity intro-particles are successfully mitigated, enabling NCM electrodes with both high rate capability, high energy density and high cyclability. In detail, the capacity retention at 20C is increased by 2.3 times, and the capacity retention at 0.5C after 160 full cycles is increased by 1.6 times, without electrolyte additive or material modification. This study demonstrates the significance of the homogeneous electronic/ionic transportation network to the rate capability and lifetime of an electrode, and discloses the design strategy of multifunctional additives to enhance the power density of a battery by maximizing the utility of the active particles.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100345"},"PeriodicalIF":15.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485957","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":"Cost-effective sizing method of Vehicle-to-Building chargers and energy storage systems during the planning stage of smart micro-grid","authors":"Ziliang Wei,&nbsp;Yang Geng,&nbsp;Hao Tang,&nbsp;Yang Zhao,&nbsp;Borong Lin","doi":"10.1016/j.etran.2024.100343","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100343","url":null,"abstract":"<div><p>Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100343"},"PeriodicalIF":11.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323041","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}