Etransportation最新文献

Thermal runaway propagation mitigation is a prerequisite in battery development for electric vehicles to meet legal requirements and ensure vehicle occupants’ safety. Thermal runaway propagation depends on many factors, e.g., cell spacing, intermediate materials, and the entire cell stack setup. Furthermore, the choice of cell chemistry plays a decisive role in the safety design of a battery system. However, many studies considering cell chemistry only focus on the cell level or neglect the energetic impacts of safety measures on system integration. This leads to a neglect of the conflict of objectives between battery safety and energy density. In this article, a comprehensive analysis of the thermal runaway propagation in lithium-ion batteries with NMC-811 and LFP cathodes from a Mini Cooper SE and Tesla Model 3 SR+ is presented. The focus is set on the identification of differences in battery safety, the derivation of safety requirements, and the evaluation of their impact on system integration. A comparative analysis identified significantly higher safety requirements for Graphite $|$ NMC-811 than for Graphite $|$ LFP cell chemistries. Regarding cell energy, thermal runaway reaction speed is nine times faster in NMC-811 cells and five times faster considering the whole propagation interval than LFP cells. However, since LFP cell chemistries have significantly lower energy densities than ternary cell chemistries, it must be verified whether the disadvantages in energy density can be compensated by advanced system integration. An analysis of cell-to-pack ratios for both cell chemistries has revealed that, based on average values, the gravimetric disadvantages are reduced to 16%, and the volumetric disadvantages can be completely compensated for at the pack level. However, future research should further focus on this issue as an accurate safety-related design depending on cell chemistry could enable a cost–benefit evaluation under the constraints of safety standards in the development of batteries for electric vehicles.

The estimation of lithium battery pack is always an essential but troubling issue which has difficulty on considering the inconsistency during state estimation. Herein, an innovative statistical distribution-based pack-integrated model for lithium-ion batteries is proposed and applied for state estimation including state of charge and state of energy. The proposed method highlights the modelling concepts that the terminal voltage of the pack-integrated virtual cell is determined by all cells inside the pack, which takes the advantages of a designed dynamic-weighted terminal voltage according to the voltage distribution inside battery pack. Then, the issue of battery pack modelling and state estimation can be transferred into a virtual single cell and no longer have to consider the inconsistency within battery pack, with the advantages for further extending application from conventional battery modelling method based on single cell. Two kinds of mainstream batteries are experimented for validating, including lithium iron phosphate battery and LiNi_0·5Co_0·2Mn_0·3O₂, battery, and both have satisfactory precision, where the maximum error is about 1%–2%, and root mean squared error (RMSE) is eliminated to about 1%. The proposed method is validated with better precision performances on estimating states of battery pack with less calculation and storage, and can be applied both on embedded systems and cloud management platforms.

All-solid-state batteries (ASSBs) are attracting significant attention as alternatives to conventional lithium-ion batteries due to their safety and higher energy density. However, electrochemical reactions between the solid electrolytes and active materials result in the degradation of electrochemical cell performances. A conventional approach is to employ protective layers onto the active materials, but this approach could have the drawback of being costly and time-consuming. The artificial cathode electrolyte interphase (CEI) layer generated by reactions between components within the electrode could provide a solution to these challenges. However, this approach can cause component degradation due to its intrinsically degradative nature of the forming process. In this study, we demonstrate the ASSBs with enhanced electrochemical performances by introducing lithium oxy-thiophosphate species (P-O_x-S_y-···Li⁺, LPOS) and LiCl artificial CEI layer, which could be spontaneously formed during heat treatment by chemical reactions between the solid electrolytes and residual Li compounds on the LiNi_0.9Co_0.05Mn_0.05O₂ (NCM) without the degradation. The LPOS-LiCl layer effectively suppresses the side reactions between solid electrolytes and NCM during the repeated electrochemical cyclings. As a result, the NCM full-cell (3.7 mAh cm⁻²) with the LPOS-LiCl artificial CEI layer exhibits 80.0 % cycle retention after 300 cycles at 0.2 C rate and room temperature. Moreover, it demonstrates 58 % higher Li-ion mobility and 36 % lower internal resistance after cycling compared to the NCM full-cell without the LPOS-LiCl artificial CEI layer.

Energy consumption and driving safety of a vehicle are greatly influenced by the driving behaviors of the vehicle in front (also termed the preceding vehicle). Inappropriate responses to unanticipated changes in the preceding vehicle can lead to decreased energy efficiency and an increased risk of rear-end collisions. To address this issue, this study proposes an innovative Adaptive Eco-cruising Control Strategy (AECS) for connected electric vehicles (CEVs) considering the dynamic behavior prediction of the preceding vehicle. The AECS, which is designed with a two-stage receding horizon control framework, can adapt to scenarios where the preceding vehicle cuts in or moves out in a safer and energy-efficient manner compared to traditional eco-cruising strategies, which merely focus on a constant preceding vehicle. In the first stage, a prediction model for characterizing the dynamic behavior of preceding vehicles is developed using the Bayesian network. This model is trained using real-world vehicle driving data, allowing it to anticipate the driving trajectories of vehicles changing lanes in front. In the second stage, an energy-saving, safety, and driving comfort-oriented optimization problem is formulated as a quadratic programming form. The eco-cruising speed is then optimized to adapt to the dynamic traffic environment, especially when the preceding vehicle changes over time. Finally, several simulations are conducted to validate the AECS. The results demonstrate that the AECS can improve the energy efficiency of CEVs by up to 11.80% and 19.53% on average compared to the existing cruise control strategies and ensure vehicle driving safety and comfort, without compromising travel time. Additionally, the vehicle cut-in position, the cut-in vehicle speed, and the ego vehicle speed affect the energy efficiency improvement performance of the AECS.

The rapid expansion of electric vehicle market brings a huge stock of batteries, which can potentially serve as distributed energy storage systems to provide grid services through Vehicle-to-Grid (V2G) technology. Existing research on V2G's economic viability often simplifies intricate technical details and neglects the influence of key parameters on the results. To address these gaps, a technology-rich model was developed to evaluate the vehicle-side costs and profits of V2G. Given the current state of V2G-related technologies and costs, V2G's levelized cost of storage ranges from $0.085/kWh to $0.243/kWh, and its net present value ranges from $-1,317 to $3,013, depending on the operational strategies implemented. The variations in assessment results due to changes in key parameters were further evaluated to analyze the impacts of technological advancements and user behavior. With advancements in battery technologies, the net present value of V2G is expected to reach approximately $7,000. These findings underscore V2G's potential cost competitiveness against mainstream stationary energy storage technologies and suggest that, with appropriate technological development and usage scenarios, V2G could play a pivotal role in the new electricity system with renewable energy sources as the main component, offering substantial profitability.

All-solid-state lithium-sulfur batteries (ASSLSBs) based on sulfide solid electrolyte (SSE) hold great promise as the next-generation energy storage technology with great potential for high energy density and improved safety. However, the development of practical ASSLSBs is restricted by the scalable fabrication of sulfur cathode sheets with outstanding electrochemical performance, which remains a complex and challenging endeavor. Herein, we employ dry electrode technology to fabricate free-standing sulfur cathode sheets with both high sulfur content and loading. By utilizing polytetrafluoroethylene (PTFE) binders with unique fibrous morphologies in the dry electrodes, we achieved sulfur cathode sheets with high flexibility without compromising ionic and electronic conductivity. Remarkably, even with thickened dry cathode sheets featuring high sulfur loading of 4.5 mg cm^-2, the sulfur cathodes exhibit high initial discharge capacity of 1114.8 mAh g^-1 with good cycle stability and rate capability. Additionally, we successfully demonstrate the construction of sheet-type all-solid-state Li_3.75Si/SSE/S cells, showcasing favorable electrochemical performance with a high reversible capacity of 1067.4 mAh g^-1 after 30 cycles even at a high sulfur loading of 4.5 mg cm^-2 and high current density of 1 mA cm^-2 (0.2C). Our findings represent a demonstration of batteries coupled with high-capacity sulfur cathode and lithiated silicon anode exhibiting exceptional electrochemical performance. It also underscores the significant potential of dry-process technology in addressing the critical challenges associated with the practical production of ASSLSBs. This contribution propels ongoing endeavors in the development of next-generation energy storage systems.

Lithium-ion batteries (LIBs) play an important role for the global net-zero emission trend. They are suitable for the power interaction with the power grid with high penetration renewable energy. However, the detail evolution of the LIBs participating in frequency regulation (FR) service at low temperature is critical for the all-climate application, especially the capacity decay and the related economic loss. This study reveals that the primary degradation mechanisms for FR operation at low temperature include lithium plating of anode and lattice distortion of cathode. Surprisingly, FR with appropriate parameters for batteries at low temperature does not introduce additional capacity decay due to the great temperature rise brought about and the optimized interfacial mass transfer. This study then analyses the economy of electric vehicles (EVs) participating in FR service, which is called vehicle-to-grid (V2G). A better temperature control can improve the profit of 35.88 $/kW. An appropriate capability is also vital to improve the profit of FR service. Moreover, suitable FR conditions for LIBs can even bring a certain degree of capacity improvement at low temperature. This work guides the design criteria of non-destructive LIB interaction for future grid.

As a nonlinear and dynamic system, the polymer electrolyte membrane fuel cell (PEMFC) system requires a comprehensive failure prediction and health management system to ensure its safety and reliability. In this study, a data-driven PEMFC health diagnosis framework is proposed, coupling the fault embedding model, sensor pre-selection method and deep learning diagnosis model. Firstly, a physical-based mechanism fault embedding model of PEMFC is developed to collect the data on various health states. This model can be utilized to determine the effects of different faults on cell performance and assist in the pre-selection of sensors. Then, considering the effect of fault pattern on decline, a sensor pre-selection method based on the analytical model is proposed to filter the insensitive variable from the sensor set. The diagnosis accuracy and computational time could be improved 3.7% and 40% with the help of pre-selection approach, respectively. Finally, the data collected by the optimal sensor set is utilized to develop the fault diagnosis model based on 1D-convolutional neural network (CNN). The results show that the proposed health diagnosis framework has better diagnosis performance compared with other popular diagnosis models and is conducive to online diagnosis, with 99.2% accuracy, higher computational efficiency, faster convergence speed and smaller training error. It is demonstrated that faster convergence speed and smaller training error are reflected in the proposed health diagnosis framework, which can significantly reduce computational costs.