Green Energy and Intelligent Transportation最新文献

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.902,8% compared to 6.312,7% for ELM, and a lower Mean Absolute Error (MAE) of 4.432,1% versus 5.111,2% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.

This paper evaluates rail transit within the context of the transit policies implemented in Lima, Peru. First it reviews the implementation of rapid transit, and bus reform. Secondly, it evaluates the outcomes of such policies by using Total Factor Productivity for policy effectiveness, Data Envelopment Analysis for rapid transit performance, and Generalized Cost of Travel for improvements. This paper finds that implementation failed in enforcing key requirements for rail transit regarding penetration of CBD and short transfers to bus transit; and that the basic assumptions of bus reform did not hold regarding bus oversupply, bus congestion or bus pollution. This paper also finds that outcomes of policies failed dramatically in achieving the planning goals; however, rail transit (Metro) shows high level of resilience in serving large ridership at high speed. On the other hand, bus reform was associated with a disproportionate increase of motorization, well over the effect of income growth or car attractiveness, and more related to the excessive reduction of bus transit capacity ill-advised from unproved bus reform assumptions. This paper recommends expanding rail rapid transit due to its intensive use of green renewable energy and its potential of demand growth if combined with modern Intelligent Transportation services, but this opportunity can be wasted without the proposed policy constraint to achieve lower Generalized Cost of Travel at any governmental intervention for bus reform, instead of just reducing bus transit capacity as implemented. Finally, this paper recommends government to government contracts to build rail transit and to enforce proper planning.

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

The successful application of new technologies such as remotely piloted aircraft systems, distributed electric propulsion systems, and automatic control systems on electric vertical take-off and landing(eVTOL) aircraft has prompted Urban Air Mobility (UAM) to be mentioned frequently. UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas, which is thought to share some of the traffic on the ground. One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs, so forecasting UAM demand is necessary. We conduct UAM demand forecasting based on the four-step method, focusing on improving the third-step modal split, and propose a demand forecasting model based on the logit model. The model combines a nested logit (NL) model with a multinomial logit (MNL) model to solve the problem of non-existent UAM sharing rates. We use Chengdu, China as an example, and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method. The results show that UAM is suitable for shared operation during the early stages. With a fully shared operation, the UAM share rate increases by 0.73% for every kilometer increase in distance. Moreover, UAM is more competitive than other modes for delivery distances exceeding 15 ​km. Finally, using the distributions of the share rate and traffic flow pattern from the simulation, we propose the routes that can be prioritized for UAM operations in Chengdu.

The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.

Iron-chromium redox flow battery (ICRFB) is an electrochemical energy storage technology that plays a vital role in dealing with the problems of discontinuity and instability of massive new energy generation and improving the acceptance capacity of the power grid. Carbon cloth electrode (CC) is the main site where the electrochemical reaction occurs, which always suffers from the disadvantages of poor electrochemical reactivity. A new N-B co-doped co-regulation Ti composite CC electrode (T-B-CC) is firstly generated and applied to ICRFB, where the REDOX reaction can be promoted significantly owing to the plentiful active sites generated on the modified electrode. As contrasted with ICRFB with normal CC electrode, after 50 battery charge/discharge cycles, the discharge capacity (1,990.3 ​mAh vs 1,155.8 ​mAh) and electrolyte utilization (61.88% vs 35.94%) of ICRFB with CC electrode (T-B-CC) are significantly improved. Furthermore, the energy efficiency (EE) is maintained at about 82.7% under 50 cycles, which is 9.3% higher than that of the pristine electrically assembled cells. The co-modulation of heteroatom doping and the introduction of Ti catalysts is a simple and easy method to improve the dynamics of the Cr³⁺/Cr²⁺ and Fe³⁺/Fe²⁺ reactions, enhancing the performance of ICRFBs.

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.

Battery life prediction is of great significance to the safe operation, and reduces the maintenance costs. This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery. By feature extraction, eight features are obtained to fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward, uneven distribution of high-dimensional feature space and the local escape ability, respectively. Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The algorithm can improve the local escape ability and convergence performance and find the global optimum. The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance. Compared with other algorithms, the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%. With the advance of the start point, the RUL prediction accuracy of the proposed algorithm does not change much.