Etransportation最新文献

Diagnostics of battery health, which encompass evaluation metrics such as state of health, remaining useful lifetime, and end of life, are critical across various applications, from electric vehicles to emergency backup systems and grid-scale energy storage. Diagnostic evaluations not only inform about the state of the battery system but also help minimize downtime, leading to reduced maintenance costs and fewer safety hazards. Researchers have made significant advancements using lab data and sophisticated algorithms. Nonetheless, bridging the gap between academic findings and their industrial application remains a significant hurdle. Herein, we initially highlight the importance of diverse data sources for achieving the prediction task. We then discuss academic breakthroughs, separating them into categories like mechanistic models, data-driven machine learning, and multi-model fusion techniques. Inspired by these progressions, several studies focus on the real-world battery diagnostics using field data, which are subsequently analyzed and discussed. We emphasize the challenges associated with translating these lab-focused models into dependable, field-applicable predictions. Finally, we investigate the frontier of battery health diagnostics, shining a light on innovative methodologies designed for the ever-changing energy sector. It's crucial to harmonize tangible, real-world data with emerging technology, such as cloud-based big data, physics-integrated deep learning, immediate model verification, and continuous lifelong machine learning. Bridging the gap between laboratory research and field application is essential for genuine technological progress, ensuring that battery systems are effortlessly integrated into all-encompassing energy solutions.

Alkaline water electrolysis (AWE) has the highest technological maturity among all the water electrolysis technologies for hydrogen production, however, reducing the minimum load boundary and improving the electrolysis efficiency are the technical challenges of the AWE system that still exist and urgently require optimization. The minimum load is primarily limited by the hydrogen to oxygen (HTO) from cross-diaphragm transfer and lye mixing, with HTO above 2.0% being a significant safety risk. Reducing the lye flow rate and pressure are effective while two of the few ways by regulating the operating parameters to improve the HTO thus extend the minimum load boundary, but will worsen electrolysis efficiency. Therefore, this study proposes a synergistic regulation strategy of pressure and lye flow rate: maximizing pressure and lye flow rate during high load period to ensure high electrolysis efficiency; adjusting lye flow rate and pressure during medium load period to ensure HTO≤2.0% and maximize the electrolysis efficiency; and reducing lye flow rate and pressure to a low level during the low load period to broaden the minimum load so as to improve overall efficiency of AWE system when loading with fluctuant green electric. This work elaborates the HTO routes, influencing factors and parameter optimization mechanism by building a system-level steady-state and dynamic gas purity model. The optimal combination curve of pressure and lye flow rate is obtained and its control effect on performance parameters, in terms of minimum load, system energy consumption, energy utilization, electrolysis efficiency and so on, is compared and verified in high dynamic wind and photovoltaic (PV) power scenarios. Finally, the optimal wind & PV power ratios are explored based on the optimal operation curve, which will provide a reference for the future large-scale development of hydrogen production scenarios direct-coupled with wind and PV power. The minimum load is extended from 42.0% in the lye flow rate alone control to 21.2% in the pressure alone control and finally to 15.6% in the lye flow rate and pressure synergistic control method. In the absence of electrical replenishment, wind and PV energy utilization efficiency can reach up to 98.3% and 95.6%, respectively.

Effective detecting thermal runaway risk in batteries are crucial for the rapid development and widespread adoption of electric vehicles. In this study, a strategy based on signal analysis is developed to realize the early warning of battery thermal runaway risk at the weekly level, without being limited by battery material systems. Firstly, a longitudinal outlier average method is developed to quantify the potential risk of thermal runaway in batteries and compared with a preset threshold to identify cells with performance anomalies. Secondly, an alarm assessment mechanism is developed, which integrates ongoing and historical operating data of suspicious cells across multiple decision layers. By employing an improved information entropy weighting method, this mechanism provides a comprehensive assessment of battery pack consistency, addressing issues related to false alarms and sporadic alerts. Finally, the effectiveness of this strategy is validated through actual vehicles involved in thermal runaway.

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.