Etransportation最新文献

The trade-off between battery energy density and power performance is the core problem that puzzles the development of electric vehicles (EVs). Although intensive researches are performed to explore active materials with good dynamics, the heterogeneous reactivity has been identified as an important cause for inferior capability and early death, especially for electrodes characterized with high areal loading and high compacted density. Herein, the heterogeneity and its origination of layered oxide-based (LiNi_xCo_yMn_1-x-yO₂, NCM) electrodes at high C-rate are investigated through operando X-ray diffraction and ex-situ time-of-flight secondary ion mass spectrometry probe. By introducing Li₃V₂(PO₄)₃@G composite as a mixed conductor additive, the heterogeneous reactivity intro-particles are successfully mitigated, enabling NCM electrodes with both high rate capability, high energy density and high cyclability. In detail, the capacity retention at 20C is increased by 2.3 times, and the capacity retention at 0.5C after 160 full cycles is increased by 1.6 times, without electrolyte additive or material modification. This study demonstrates the significance of the homogeneous electronic/ionic transportation network to the rate capability and lifetime of an electrode, and discloses the design strategy of multifunctional additives to enhance the power density of a battery by maximizing the utility of the active particles.

Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.

The proton exchange membrane fuel cell (PEMFC) power source is a promising solution for the unmanned aerial vehicles (UAVs) to extend the flight endurance. However, the light weighted PEMFC stack design with improved performance remains a critical challenge for the UAVs applications. In this study, two air-cooled PEMFC stacks based on metal and graphite bipolar plates are designed respectively to optimize the fuel cell power density with comparative tests and simulations under varied operating conditions. The designed metal and graphite stacks could reach the power densities of 1189 W/kg and 792 W/kg, of which the graphite one is integrated in a hybrid power system for the UAVs and operated for a flight test with ∼45 min. Validated by the experiment, a three-dimensional coupled model is developed to comparatively study the internal performance and thermal behaviors of the two stacks. Compared with the graphite stack, the metal one outputs higher voltage by 4 %, weighs lighter by 31 % and improves air forced thermal dissipation with enhanced water retention ability. The proposed model and comparative analysis reveal the mechanisms of stack performance variation under different designs and operations, which are beneficial for the optimization of UAVs fuel cell power system.

Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.

The energy flow distribution characteristics of electric vehicles operating in various propulsion modes and all climatic scenarios have not been thoroughly explored. To achieve effective electric-thermal collaborative energy management, intelligent control methods must be applied considering various climatic conditions to alleviate mileage anxiety. In this study, we developed a novel electric–thermal collaborative energy management strategy based on an improved deep neural network and energy quantification model to increase the global energy conversion efficiency. The complete energy consumption distribution characteristics are summarized under various strategies and propulsion modes based on an experiment data collected by the vehicle control unit that involves battery self-heating, cabin heating, acceleration consumption, and fuel consumption in the temperature range of −10°C-35 °C. Our findings indicate that, for a fuel cell hybrid bus in the cycle including the initial cabin heating process, the heating consumption in the pure electric mode was 9.9 kWh/cycle and 13 kWh/cycle when the ambient temperature is −2 °C and −10 °C, respectively, accounting for 33 % and 42 % of the total consumption, respectively. After using the waste heat from the fuel cell, the consumption of electric heating under the same conditions is only 3.7 kWh/cycle. In the high-temperature scenario, the cabin cooling consumption is 3.26 kWh/cycle, accounting for only 18 % of the total energy consumption. Finally, in low-temperature scenarios, the electric–thermal collaborative strategy reduced the cost by 14.7 % and 9.2 % in the pure electric and hybrid modes, respectively. Thus, our approach significantly improves energy utilization and conversion efficiency, especially at low temperatures.

Lithium-titanate-oxide (LTO) based lithium-ion batteries show promise for longer lifespan, higher power capability, and lower life cycle cost for energy storage and electric transportation applications than graphite-based counterparts. However, the degradation mechanisms of LTO-based cells in the high and low state-of-charge (SOC) intervals and different discharge cut-off voltages are not clearly investigated. In this study, the application-related lifetime performance of high-power Li₄Ti₅O₁₂/LiCoO₂ batteries is investigated at five independent SOC intervals with 20 % depth-of-discharge (DOD) and three discharge cut-off voltages. Our results show that degradation increases significantly when the batteries are cycled within lower SOC intervals or with lower cut-off voltages. Additionally, thermodynamic degradation is more significant when cycled at 20 % DOD, while kinetic degradation dominates at 100 % DOD. For thermodynamic degradation, the determining degradation mode is shown to be the loss of active material in the negative electrode, while the active material loss at the cathode has a greater impact on the equilibrium voltage curve. The kinetic degradation is mainly due to the slower charge transfer process and diffusion process at the cathode, which increases polarization impedance.

Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.

The current battery management system is limited to testing external characteristics, leaving the battery's internal status as a “black box”. Advanced characterization techniques and battery sensing technologies are needed to assess the battery's internal state. However, due to their short lifespan, low sensitivity, invasive nature, and high cost, these technologies face challenges in practical applications and commercialization. Here, we propose a smart battery implanted with a potential sensor for in-situ measurement of anode potential, enabling the recognition of severe side reactions and abnormal Li plating behavior. Specifically, the potential sensing material is directly integrated into the battery separator, which provides a reliable potential reference and serves as a sensing terminal. The porous structure of the separator facilitates lithium-ion transport while simultaneously enabling high-accuracy monitoring with non-destructive implantation. Additionally, the potential sensing separator can detect pre-existing or latent defects in the battery at an early stage, which are difficult to discern from the battery's external characteristics in a timely manner. Furthermore, we have developed a multi-point potential sensor monitoring system that can not only monitor the distribution of anode potential but also pinpoint the location of battery defects.

In recent years, the development of electric vehicles (EVs) thermal management systems has underscored the crucial role in ensuring driving safety and optimizing driving range has become increasingly prominent. However, the inherent dynamic complexity of EV operation coupled with automatic control systems, can sometimes lead to unstable behavior, resulting in performance degradation and safety risks for compressors and batteries. To effectively address this issue, an evaluation was conducted on the dynamic control characteristics of an EV thermal management system utilizing CO₂ as the refrigerant in this study. Through mathematical modeling and experimental analysis, the erratic nature of the dynamic thermal process was first identified. The underlying reasons were elucidated, focusing on system control characteristics and intrinsic mechanisms. It was found that control disorder could induce abnormal actions in thermal management system components like compressors and expansion valve, leading to significant performance decline and issues such as liquid carryover in compressor suction. Furthermore, specific control disorder regions of CO₂ heat pumps for EVs were delineated, providing a framework for assessing the likelihood of system control disorder. Notably, control disorder was more likely to occur under conditions of low indoor air flow rate, high ambient temperature, and low supply air temperature. Given the widespread nature of this issue and the lack of suitable solutions, two control disorder suppression schemes were developed using V2X technology and validated through simulation. Results showed that adoption of V2X communication technology prevented an average of 70.1 % COP degradation, ensuring stability and safety of compressors and batteries under various operating conditions. The research provides useful information for exploring the dynamic characteristics of CO₂ thermal management systems, offering a novel approach to enhance the system stability and efficiency.

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.