Etransportation最新文献

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.

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.

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(Ni_0.8Co_0.1Mn_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.

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.