Green Energy and Intelligent Transportation最新文献

{"title":"Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares","authors":"","doi":"10.1016/j.geits.2024.100207","DOIUrl":"10.1016/j.geits.2024.100207","url":null,"abstract":"<div><p>Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery, thereby influencing safety of entire electric vehicles. Precise estimation of battery model parameters using key measured signals is essential. However, measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors, potentially diminishing model estimation accuracy. Addressing the challenge of accuracy reduction caused by noise, this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares (BCFFRLS) method. Initially, a variational error model is crafted to estimate the average weighted variance of random noise. Subsequently, an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors, compensating for bias in the parameter estimates. To assess the proposed method's effectiveness in improving parameter identification accuracy, lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule (UDDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC). The proposed method, alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares (FFRLS)—was employed for battery model parameter identification. Comparative analysis reveals substantial improvements, with the mean absolute error reduced by 25%, 28%, and 15%, and the root mean square error reduced by 25.1%, 42.7%, and 15.9% in UDDS, HPPC, and DST operating conditions, respectively, when compared to the FFRLS method.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000598/pdfft?md5=999324213bcb12e2532de767f001b72e&pid=1-s2.0-S2773153724000598-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model","authors":"","doi":"10.1016/j.geits.2024.100152","DOIUrl":"10.1016/j.geits.2024.100152","url":null,"abstract":"<div><p>The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 ​°C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 ​°C.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000045/pdfft?md5=9681b8ae8614f1d24c6c280a906197f0&pid=1-s2.0-S2773153724000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries","authors":"","doi":"10.1016/j.geits.2024.100163","DOIUrl":"10.1016/j.geits.2024.100163","url":null,"abstract":"<div><p>Accurate state of charge (SOC) estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles, and it is also a key technology component in battery management systems. In recent years, lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity. However, these methods commonly face the issue of poor model generalization and limited robustness. To address such issues, this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression (SA-SVR) combined with minimum error entropy based extended Kalman filter (MEE-EKF) algorithm. Firstly, a probability-based SA algorithm is employed to optimize the internal parameters of the SVR, thereby enhancing the precision of original SOC estimation. Secondly, utilizing the framework of the Kalman filter, the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF, while the ampere-hour integral physical model serves as the state equation, effectively attenuating the measurement noise, enhancing the estimation accuracy, and improving generalization ability. The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training. The results demonstrate that the proposed method achieves a mean absolute error below 0.60% and a root mean square error below 0.73% across all operating conditions, showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms. The high precision and generalization capability of the proposed method are evident, ensuring accurate SOC estimation for electric vehicles.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277315372400015X/pdfft?md5=b835d40a859f17ab622d7eebb38969bc&pid=1-s2.0-S277315372400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A joint model of infrastructure planning and smart charging strategies for shared electric vehicles","authors":"","doi":"10.1016/j.geits.2024.100168","DOIUrl":"10.1016/j.geits.2024.100168","url":null,"abstract":"<div><p>This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000203/pdfft?md5=e126d36b4e1e633af38616e6f348520e&pid=1-s2.0-S2773153724000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on reinforcement learning-based highway autonomous vehicle control","authors":"","doi":"10.1016/j.geits.2024.100156","DOIUrl":"10.1016/j.geits.2024.100156","url":null,"abstract":"<div><p>Autonomous driving is an active area of research in artificial intelligence and robotics. Recent advances in deep reinforcement learning (DRL) show promise for training autonomous vehicles to handle complex real-world driving tasks. This paper reviews recent advancement on the application of DRL to highway lane change, ramp merge, and platoon coordination. In particular, similarities, differences, limitations, and best practices regarding the DRL formulations, DRL training algorithms, simulations, and metrics are reviewed and discussed. The paper starts by reviewing different traffic scenarios that are discussed by the literature, followed by a thorough review on the DRL technology such as the state representation methods that capture interactive dynamics critical for safe and efficient merging and the reward formulations that manage key metrics like safety, efficiency, comfort, and adaptability. Insights from this review can guide future research toward realizing the potential of DRL for automated driving in complex traffic under uncertainty.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000082/pdfft?md5=c58a7142127b320882e70443e8d65385&pid=1-s2.0-S2773153724000082-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized ANN for LiFePO4 battery charge estimation using principal components based feature generation","authors":"","doi":"10.1016/j.geits.2024.100175","DOIUrl":"10.1016/j.geits.2024.100175","url":null,"abstract":"<div><p>Electric vehicles (EVs) have gained prominence in the present energy transition scenario. Widespread adoption of EVs necessitates an accurate State of Charge estimation (SoC) algorithm. Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions, marking a crucial milestone in sustainable electric vehicle technology. In this research study, machine learning methods, particularly Artificial Neural Networks (ANN), are employed for SoC estimation of LiFePO₄ batteries, resulting in efficient and accurate estimation algorithms. The investigation first focuses on developing a custom-designed battery pack with 12 ​V, 4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup. The voltage, current and open-circuit voltage of the battery are monitored with computerized battery analyzer. The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi. Principal components are derived for the collected battery data set and analyzed for feature engineering. Three principal components were generated as input parameters for the developed ANN. Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN. While considering eleven combinations for ten different optimizers loss function is minimized. Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an <em>RMSE</em> value of 0.4083 and an R2 Score of 0.9998. The proposed algorithm was also implemented for two different types of datasets, a UDDS drive cycle and a standard cell-level dataset. The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000276/pdfft?md5=51341f0c7a05dd6f990d5a27600b45f9&pid=1-s2.0-S2773153724000276-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards vehicle electrification: A mathematical prediction of battery electric vehicle ownership growth, the case of Turkey","authors":"","doi":"10.1016/j.geits.2024.100166","DOIUrl":"10.1016/j.geits.2024.100166","url":null,"abstract":"<div><p>Many countries are relying on electric vehicles to achieve their future greenhouse gas reduction targets; thus, they are setting regulations to force car manufacturers to a complete shift into producing fully electric vehicles, which will significantly influence the adoption rates of electric vehicles. This research investigates the temporal evolution of battery electric vehicle (BEV) ownership growth in Turkey, drawing insights from both historical and current trends. By employing and optimizing the Gompertz model, we provide a year-by-year projection of BEV ownership rates, aiding in exploring the anticipated timeline for BEV market saturation. Our findings indicate that the introduction of BEVs into the Turkish motorization market is poised to push further market saturation by approximately 15 years, to occur in around 2095 as opposed to 2080s. Furthermore, our analysis underscores the rapid growth pace in BEV ownership compared to the ownership of internal combustion engine vehicles (ICEVs). The main aim of this research is to provide Turkish policymakers and transport planners with solid insights into how the vehicle market will perform in the short and long run, allowing them to prepare a smooth transition from traditional vehicles to BEVs.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000185/pdfft?md5=2f8f155cc7de671a23dff9a41739793a&pid=1-s2.0-S2773153724000185-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An active equalization strategy for series-connected lithium-ion battery packs based on a dual threshold trigger mechanism","authors":"Hui Pang ,&nbsp;Wenzhi Nan ,&nbsp;Xiaofei Liu ,&nbsp;Fengbin Wang ,&nbsp;Kaiqiang Chen ,&nbsp;Yupeng Chen","doi":"10.1016/j.geits.2024.100206","DOIUrl":"https://doi.org/10.1016/j.geits.2024.100206","url":null,"abstract":"<div><p>It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge (SOC) in series-connected lithium-ion battery (LIB) pack in the electric vehicle application. In this regard, a novel dual threshold trigger mechanism based active equalization strategy (DTTM-based AES) is proposed to overcome the inherent inconsistency of cells and to improve the equalization efficiency for a series-connected LIB pack. First, a modified dual-layer inductor equalization circuit is constructed to make it possible for the energy transfer path optimization. Next, based on the designed dual threshold trigger mechanism provoked by battery voltage and SOC, an active equalization strategy is proposed, each single cell's SOC in the battery packs is estimated using the extended Kalman particle filter algorithm. Besides, on the basis of the modified equalization circuit, the improved particle swarm optimization is adopted to optimize the energy transfer path with aiming to reduce the equalization time. Lastly, the simulation and experimental results are provided to validate the proposed DTTM-based AES.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000586/pdfft?md5=360afc9caa18ec86fe0ad43368f39d0b&pid=1-s2.0-S2773153724000586-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal heat flux distribution prediction in an electrical vehicle battery cell using finite element analysis and neural network","authors":"Luttfi A. Al-Haddad ,&nbsp;Latif Ibraheem ,&nbsp;Ahmed I. EL-Seesy ,&nbsp;Alaa Abdulhady Jaber ,&nbsp;Sinan A. Al-Haddad ,&nbsp;Reza Khosrozadeh","doi":"10.1016/j.geits.2024.100155","DOIUrl":"10.1016/j.geits.2024.100155","url":null,"abstract":"<div><p>In terms of battery design and evaluation, Electric Vehicles (EVs) are receiving a great deal of attention as a modern, eco-friendly, sustainable transportation method. In this paper, a novel battery pack is designed to maintain a uniform temperature distribution, allowing the battery to operate within its optimal temperature range. The proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is maintained. First, a 3-D model of a battery cell was created, followed by thermal simulation for 15C, 25C, and 35C ambient temperatures. The simulation results reveal that the temperature distribution is nearly uniform, with slightly higher values in the middle portion of the cell height. Second, using finite element analysis (FEA), it was determined that the heat flux per unit area is nearly uniform with a slight increase at the edges. Third, a machine learning model is proposed by utilizing a neural network (NN). Lastly, the heat flux values were predicted using the NN model that was proposed. The model was assessed based on statistical measures where a root mean square error (RMSE) value of 0.87% was achieved. The NN outperformed FEA in terms of time consumption with a high prediction accuracy, leveraging the potential of adopting machine learning over FEA in related operational assessments.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000070/pdfft?md5=264e5abe03090da19df5d2c136e08ee4&pid=1-s2.0-S2773153724000070-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system","authors":"Malak Abid Ali Khan ,&nbsp;Hongbin Ma ,&nbsp;Arshad Farhad ,&nbsp;Asad Mujeeb ,&nbsp;Imran Khan Mirani ,&nbsp;Muhammad Hamza","doi":"10.1016/j.geits.2024.100204","DOIUrl":"https://doi.org/10.1016/j.geits.2024.100204","url":null,"abstract":"<div><p>LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.</p></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2773153724000562/pdfft?md5=6f1cedcafc15a78f311218be820d0515&pid=1-s2.0-S2773153724000562-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}