Etransportation最新文献

Lithium-ion batteries (LIBs) play a pivotal role in promoting transportation electrification and clean energy storage. The safe and efficient operation is the biggest challenge for LIBs. Smart batteries and intelligent management systems are one of the effective solutions to address this issue. Multiparameter monitoring is regarded as a promising tool to achieve the goal. This paper provides an overview of the state of the art in multiparameter monitoring approaches for LIBs. Further, the sensing principle, experimental configuration, and sensor performance are elaborated and discussed. The results show that internal parameter monitoring of cells is more attractive and challenging than external parameter monitoring. Temperature, deformation, and gas are the most concerned parameters inside batteries. Finally, the outlooks and challenges for the implementation and application of LIB multiparameter monitoring are investigated from two aspects: internal parameters monitoring and application of the monitored multivariate data. Compact, precise, and stable sensors compatible with the internal environment of batteries as well as efficient and intelligent algorithms for battery management are still awaiting breakthroughs.

Accurate battery health estimation is pivotal for ensuring the safety and performance of electric vehicles (EVs). While predominant research has centered on laboratory-level single cells, the accurate estimation of battery system capacity using real-world data remains a challenge, due to the vast diversity in battery types, operating conditions, data recordings, etc. To this end, we release three large-scale field datasets of 464 EVs from three manufacturers, comprising over 1.2 million charging snippets. The EVs’ capacity and State of Health (SOH) are effectively labeled using K-means to cluster and concatenate charging snippets. A robust data-driven framework integrating a Gated Convolutional Neural Network (GCNN) for estimating battery capacity is proposed, and the results outperform other machine learning models. In addition, a fine-tuning technique is employed to further enhance model efficacy on new datasets and with limited training data. This research not only advances battery health estimations but also paves the way for broader applications in battery management systems (BMSs), offering a scalable solution to real-world challenges in battery technology.

Understanding the failure behaviors and failure mechanisms of lithium-ion batteries under mechanical abuse is essential for numerical reconstruction of abuse scenarios for different types of cells. This study investigates the mechanical-electrical-thermal characteristics, components tensile properties and failure mechanisms of LiFePO₄ (LFP), Li(Ni_0.5Mn_0.3Co_0.2)O₂ (NMC), and Li₂TiO₃ (LTO) cells through indentation experiments, including ball intrusion, cylindrical intrusion, and out-of-plane compression modes at quasi-static loading rates. Additional ball intrusion experiments were conducted at varying loading rates. This study compares the effects of different material systems on battery performance under standardized mechanical abuse conditions. Post-test examinations analyze surface damage and internal component fracture morphology. Two distinct fracture modes were observed: ductile fracture and brittle fracture. The findings suggest that, under the same loading mode, LTO cells exhibit distinct failure behavior compared to NMC and LFP cells, attributed to differing material properties and resulting fracture modes during intrusion. Based on the analysis of the tensile results of the battery components, the cell fracture mode may be related to the tensile strength of the separator. The loading rate significantly impacts the mechanical-electrical-thermal performance of pouch cells, resulting in increased cell stiffness and shorter internal short circuit duration at higher loading speeds. However, the effect of loading rate is consistent across cells with different material systems.

State estimators are crucial for the effective use of batteries in real-world applications. Insufficient algorithms can lead to user dissatisfaction, safety risks, and accelerated battery degradation, posing significant risks to manufacturers. Developing algorithms for battery management systems (BMS) involves defining requirements, implementing algorithms, and validating them, which is a complex process. The performance of BMS algorithms is influenced by constraints related to hardware, data storage, calibration processes during development and use, and costs. Additionally, state estimation methods vary widely, requiring specific data that impact algorithm performance.

This study investigates these complexities in the development of state estimators and underscores the importance of their performance. We established an approach for selecting test scenarios, based on expert interviews, which considers computational capabilities and specific application scenarios. A model-based simulation environment is introduced to handle the complexities of validation. This environment enables thorough validation of the algorithms under real-application conditions, different test scenarios, and parameter variations.

We exemplarily perform a validation for three State of Charge (SoC) estimators under diverse conditions and cell variations. The results show the performance dependencies on temperatures, cell chemistries, initial SoCs and measurement inaccuracies. Additionally, the cell-to-cell variations highlight the complexity and effort of algorithm validation. Introducing an additional scenario parameter expands the range of test scenarios, emphasizing the necessity to select scenarios that accurately reflect field conditions and worst-case situations.

Electromobility is a key technology to decarbonize transportation and thereby avoid the worst impacts of anthropogenic climate change. To power such vehicles when away from their home or depot, public charging infrastructure is required which can be split into enroute and destination charging. We define the latter as charging events that occur while users are busy with other activities. To fulfill this purpose, chargers need to be placed in locations where people spend time. This paper introduces a novel approach to do so based on a neural network trained on several thousand public charging stations in Germany. Within the training sample, the approach is able to predict how much energy was charged per station and day with an ${\text{R}}^2$ of 0.61 for the training set and a RMSE of 13 kWh/day. Using the network, we predict utilization across urban, suburban and industrial areas in Europe and make those predictions available through an easy-to-use web interface. It is further possible to perform predictions and, thereby, extrapolate the learnings from Germany to any country with sufficient OpenStreetMap data. The introduced holistic methodology with its prediction and visualization phase is a first-of-its-kind by applying large-scale measured charging data to the placement problem while being usable in areas which have not yet rolled out electromobility.

Due to the high flammability and combustion enthalpy, electrolyte solvents such as dimethyl carbonate (DMC) are regarded as the main fuel in combustion reactions for lithium-ion batteries (LIBs). Herein, to understand the combustion reaction kinetics of LIB fires and explore the efficient extinguishing agent, the chemical oxidation kinetics of DMC at 740–1160 K are studied through a jet-stirred reactor system coupled to the synchrotron vacuum ultraviolet photoionization mass spectrometry and GC. The major consumption path of DMC is the H-abstraction reaction of OH∙ and H∙ radicals. CH₃∙ radicals produce to CH₄, C₂H₄ and other common alkane gases in LIB fires through H-abstraction reactions and compound reaction. On this basis, the extinguishing mechanism of F-500 extinguishing agent for LIB fires is studied. The hydrophilic (-[CH₂-CH₂-O]₅) and oleophilic ([C₁₆H₃₃]-) groups give F-500 molecules the amphiphilic characteristics of adsorbing on the solution surface and associating inside the solution to form micelles. Based on the results of dynamic light scattering and cryo-electron microscopy, the size and number of micelles continue to increase and the structure of micelles gradually changes from spherical to rod-shaped, which enhance the solubilization effect. F-500 can strengthen the extinguishing effectiveness of water mist by capturing and encapsulating the DMC inside the water to form “DMC-F-500-Water” microcapsule. DMC is dispersed in the water, which leads to the heat loss and the reduction of concentration and flammability. Moreover, the adsorption of F-500 molecules along the solid-liquid-gas three-phase contact line can reduce the interfacial tension of water and promote wetting process, which leads to the larger spreading area and speed of evaporation. During the application of the extinguishing agent, F-500 agent can improve the cooling efficiency of water. This work provides a reference for the design and development of novel extinguishing agent for LIB fires.

Particle emissions released by lithium-ion traction batteries (LIBs) during thermal runaway (TR) are considered to be one of the fire hazard sources for new energy vehicles. Moreover, the particle emissions may persist in the environment and cause damage even after a fire is extinguished. Therefore, the formation mechanisms of the particle emissions from LIBs during TR are summarized firstly in this review. Effects of influencing factors on particle emission characteristics and biotoxicity are also explored. Furthermore, simulation models of LIB particle emissions are summarized. Particle emissions calculated for 2021 to 2023 are presented, and particle emissions from 2024 to 2030 are predicted. Finally, the existing research results and the problems with LIB particle emissions are summarized, and future research challenges and directions are prospected. This review aims to evoke interest in particle emissions from lithium-ion traction batteries during TR and provide a reference for suppressing and managing particle emissions to improve the safety of LIBs and mitigate environmental hazards.

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.

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.

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.