Etransportation最新文献

Accurate and reliable prediction of the future capacity degradation of lithium-ion batteries is crucial for their application in electric vehicles. Recent publications have highlighted the effectiveness of deep learning, in particular, in generating precise forecasts regarding the aging patterns. However, large quantities of training data covering various aging behaviors are required to train such models effectively. Collecting such a large database centrally is not feasible due to privacy and data communication restrictions of data owners, such as testing facilities or fleet operators. Federated learning provides a solution to this open issue. A framework, which incorporates federated learning into the training of a data-based battery aging model, is presented in this paper. The benefit of federated learning is that even data owners with sensible information can participate in a collaborative model training, since the model training is only conducted locally and all the data remains local and does not have to be disclosed. Thus, more data owners are likely to participate in this collaborative training. This will improve the prediction performance due to the enlarged dataset that can be utilized for the model training. This work shows that the prediction accuracy of the model trained with federated learning is only slightly worse than the prediction results obtained by the ideal case in which all aging data is stored in a central database. A sensitivity analysis is presented to prove the robustness of federated learning even if the datasets between participating data owners are highly imbalanced or exhibit different aging behaviors. Within exemplary scenarios, it is shown that individual data holders can reduce their prediction errors from $MAP{E}_{mean}=7.07\text{\%}$ to $MAP{E}_{mean}=0.91\text{\%}$ by participating in the proposed federated learning-based framework.

As the global popularity of electric vehicles (EVs) continues to grow, the demand for EV batteries has significantly increased. Unfortunately, when the battery state of health (SoH) reaches a certain value, the batteries may no longer meet the driving requirements of EVs, resulting in many retired batteries. Properly disposing of these batteries is a major challenge. In this paper, we will take fast charging stations (FCSs) as an example to evaluate the economic feasibility of reusing retired batteries, which is one of the most environmentally friendly methods of disposal. To make a fair comparison between fresh and retired batteries, a bi-level sizing framework is designed to determine the optimal sizes of fresh and retired batteries, as well as photovoltaic (PV) panels in FCSs, to minimize the total cost. To ensure the high fidelity of results, diverse EV travel patterns are considered to generate the charging power demand. Additionally, a dynamic battery degradation model focused on batteries that have not yet reached their turning point is developed. This model is designed to precisely quantify the gradual reduction in battery capacity. We conducted several representative case studies using real-world data, and the simulation results indicate that FCSs with fresh batteries can achieve 42.2 % cost savings compared to those without energy storage systems, while retired batteries can achieve an additional 5.41 %–11.79 % cost savings under different scenarios. These findings can be generalized that retired batteries are promising in small-scale applications due to the relatively low refurbishment cost, and a new market stream can be potentially generated in this direction.

The construction and development of the new power system with new energy sources as the main component will face significant challenges in terms of scarcity of flexible resources. User-side adjustable loads and energy storage, particularly electric vehicles (EVs), will serve as substantial reservoirs of flexibility, providing stability to the new power system. The rapid deployment of renewable energy and the surpassing of expectations in the penetration rate of EVs in China present opportunities for the significant growth of virtual power plants (VPPs) and vehicle-to-grid (V2G) interactions. The enormous potential and advantages of V2G as a primary user-side resource are further revealed. Under China's current electricity market policies, the pilot projects of user-side interactions are being analyzed. Furthermore, the prospects for the future development of VPPs and V2G, along with relevant recommendations and perspectives regarding business models, technologies, and policies, are highlighted. The development of VPPs and V2G interactions in China holds immense potential for leveraging adjustable resources, especially EVs and energy storages with lithium-ion batteries, to enhance grid flexibility and stability.

The electrification of the transportation sector leads to an increased deployment of lithium-ion batteries in vehicles. Today, traction batteries are installed, for example, in electric cars, electric buses, and electric boats. These use-cases place different demands on the battery. In this work, simulated data from 60 electric cars and field data from 82 electric buses and six electric boats from Germany are used to quantify a set of stress factors relevant to battery operation and life expectancy depending on the mode of transportation. For this purpose, the open-source tool SimSES designed initially to simulate battery operation in stationary applications is extended toward analyzing mobile applications. It now allows users to simulate electric vehicles while driving and charging. The analyses of the three means of transportation show that electric buses, for example, consume between 1 and 1.5 kWh/km and that consumption is lowest at ambient temperatures around 20 °C. Electric buses are confronted with 0.4–1 equivalent full cycle per day, whereas the analyzed set of car batteries experience less than 0.18 and electric boats between 0.026 and 0.3 equivalent full cycles per day. Other parameters analyzed include mean state-of-charges, mean charging rates, and mean trip cycle depths. Beyond these evaluations, the battery parameters of the transportation means are compared with those of three stationary applications. We reveal that stationary storage systems in home storage and balancing power applications generate similar numbers of equivalent full cycles as electric buses, which indicates that similar batteries could be used in these applications. Furthermore, we simulate the influence of different charging strategies and show their severe impact on battery degradation stress factors in e-transportation. To facilitate widespread and diverse usage, all profile and analysis data relevant to this work is provided as open data as part of this work.

In this study, we carry out a fundamental and modeling study to investigate, for the first time, the gas coverage at the catalyst surface and its impacts on performance loss in polymer electrolyte membrane electrolysis cells (PEMECs). Oxygen, produced in the anode catalyst layer (CL) through the oxygen evolution reaction (OER), is removed via the pore network of the anode CL and porous transport layer (PTL) to the flow field. Oxygen gas bubbles can cover the catalyst surface and reduce the area for catalyst OER activity and hence cell performance. To investigate the oxygen bubbles’ impact, we consider various degrees of gas coverage and temperatures (25 °C, 80 °C, and 95 °C) in the range of current density from 0 to 7 A/cm². We also, for the first time, elucidate the impacts of CL’s material properties on gas coverage morphology in the nano/micropores of CLs. Analytical solutions are derived for the gas fraction and gas composition at different temperatures and pressures. It was found that the gas fraction can be as high as 85% with water vapor contributing to 71% of the total gas coverage when operating at 95 °C and 1 atm. The modeling results indicate the gas coverage can contribute 57% of the total overpotential at 95 °C, 7 A/cm², and a coverage coefficient of 7. The work contributes to a fundamental understanding of the impacts of two-phase phenomena on PEMEC performance and is valuable for catalyst layer design and optimization.

The traction substations of urban electric transport grids are oversized and underutilized in terms of their capacity. While their over-sizing is an unfortunate waste, their under-utilization creates the major hurdle for the integration of renewables into these grids due to the lack of a base load. Therefore, integrating smart grid loads such as EV chargers is not only an opportunity but a necessity for the sustainable transport grid of the future.

This paper examines six methods for increasing the potential of EV chargers in three case studies of a trolleygrid, namely a higher substation no-load voltage, a higher substation power capacity, a smart charging method, adding a third overheard parallel line, adding a bilateral connection, and installing a multi-port converter between two substations. From the case studies, the most promising and cost-effective method seems to be introducing a bilateral connection, bringing a charging capacity for up to 175 electric cars per day. Meanwhile, other costly and complex methods, such as smart charging with grid state sensors and communication, can offer charging room for over 200 electric cars per day. Furthermore, using solar PV systems to power the grid showed a more than doubling of the directly utilized energy by installing a 150kW charger, from 19% to 41%. This reduces the power mismatch between the trolleygrid and the PV system from 81% to 59% and thereby reduces the severe economic need for storage, AC grid power exchange, or PV power curtailment while allowing a high penetration of renewables.

The safety issue of the lithium-ion batteries is the key to their application and development. The management of lithium-ion batteries has been a hot topic of research for many years, which involves a number of scientific and engineering issues. This paper summarized the current research advances in lithium-ion battery management systems, covering battery modeling, state estimation, health prognosis, charging strategy, fault diagnosis, and thermal management methods, and provides the future trends of each aspect, in hopes to give inspiration and suggestion for future lithium-ion battery control and management.

Data selection is a practical technique for improving parameter estimation accuracy through the strategic selection of information-rich data for use in the estimation algorithm. Traditional selection criteria have been either heuristic or sensitivity-based, without consideration of uncertainties in measurement, model, or parameter. In this paper, we propose an uncertainty-aware data selection framework that selects data segments based on the potential of the ingrained data structures to mitigate the influence of system uncertainties on the estimation result. The framework comprises two components: the data quality rating and data selection algorithm. The data quality rating is a metric for evaluating the uncertainty-propagating data structures of a data segment, and the data selection algorithm automatically integrates the data selection into the estimation procedure. Furthermore, a novel adaptive approximation of model/measurement uncertainty is derived and implemented in the data quality rating formula to enhance performance in the presence of time-varying sensor bias/noise and unmodeled system dynamics. The framework is validated through an advanced battery management system application, where two lithium-ion battery health-related electrochemical parameters are separately estimated under random drive-cycle input data to emulate battery state of health monitoring for an electric vehicle. We show that the drive-cycle data, which are frequently used for battery state of health estimation as the only available data during battery operation, may not provide accurate estimation results due to the existence of large portions of low-quality data (low sensitivity and high uncertainty). By extracting the high-quality data segments, the data selection framework reduced experimental estimation errors by one order of magnitude when compared with the conventional approach of estimating without data selection.

Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

Solid polymer electrolytes (SPEs) have been widely adopted in solid-state lithium metal batteries (SSLMBs) recently due to their excellent flexibility, superior processability and outstanding safety. However, balancing excellent ionic conductivity and mechanical robustness of SPEs is still a bottleneck. Currently, most researches focus on the incorporation of various fillers into polymer matrix to overcome the mentioned obstacle. Nevertheless, the large specific surface area of fillers makes them easy to agglomerate, resulting in uneven dispersion in SPEs, and this would interrupt the continuity of lithium ion migration and cause unstable filler/polymer interfaces. Therefore, direct regulation of polymer electrolytes at molecular level instead of incorporating fillers will effectively avoid the above obstacle while obtaining excellent performance. In this review, three kinds of mechanisms of electrolyte regulation at the molecular level are presented, including designing polymer matrix, adding soluble additives, and building molecular interactions. Through molecularly regulating the polymer matrix, constructing three-dimensional (3D) networks, grafting of special functional groups, and coordination interactions in SPEs, the creation of second phases and unstable filler/polymer interfaces are all avoided. It is hoped that this review can inspire an in-depth understanding on direct regulation of SPEs at the molecular level, further improving ionic conductivity and mechanical robustness of SPEs.