http://yetl.yabesh.ir/yetl1/handle/yetl/19035 2026-04-26T18:11:53Z 2026-04-26T18:11:53Z Lopez-Salazar, Camilo Alberto Ekwaro-Osire, Stephen Rasty, Jahan Parker, Olin http://yetl.yabesh.ir/yetl1/handle/yetl/4310483 2026-02-17T21:41:12Z 2025-01-01T00:00:00Z

A Probabilistic Approach for Electric Vehicle Battery Risk Estimation Based on a Safe Operating Area Lopez-Salazar, Camilo Alberto; Ekwaro-Osire, Stephen; Rasty, Jahan; Parker, Olin

2025-01-01T00:00:00Z Mukhopadhyay, Arunava Bose, Bibaswan Garg, Akhil Ahuja, Hemant Moulik, Bedatri Gao, Liang http://yetl.yabesh.ir/yetl1/handle/yetl/4310478 2026-02-17T21:41:03Z 2025-01-01T00:00:00Z

Energy Management of Hybrid Electric Vehicle Considering Battery and Fuel Cell Parameters Using Multi-Objective Optimization for Dynamic Driving Cycles Mukhopadhyay, Arunava; Bose, Bibaswan; Garg, Akhil; Ahuja, Hemant; Moulik, Bedatri; Gao, Liang A good strategy for energy management is essential to control the power distribution between fuel cells and batteries in hybrid electric cars. Various energy management systems have been explored in the literature, focusing on optimizing the fuel cell characteristics. The literature review reveals researchers have not adequately addressed the effect of key battery parameters for developing energy management strategies for realistic driving conditions. This research proposes a novel energy management strategy with a multi-objective optimization for fuel cell battery hybrids, focusing on fuel efficiency, energy utilization, and drivability. Energetic macroscopic representation is a framework for powertrain modeling, aiding in creating the energy management system (EMS). The main goal is to provide a systematic control framework that integrates local bus voltage and traction control controllers with a global controller for energy management systems. The unique EMS regulates power flows by dynamically modifying battery and fuel cell operation's rate limitations and saturation levels. The thresholds for rate restriction and saturation are optimized offline using the multi-objective optimization. The impact of optimization parameters on the optimization goals is examined using three standard driving cycles. The simulation findings demonstrate that the efficacy of local controllers is contingent upon the driving cycle. Battery management excels in low dynamic power cycles, whereas fuel cell management is superior in high constant power cycles. The EMS may allocate power between the battery and the fuel cell, allowing the battery to manage transients. Altering the operational restrictions modifies the power distribution ratio while meeting the power requirements. Restricting battery power improves battery longevity by 50%. The modification of weights among the optimization targets is also taken into account. Conversely, a greater emphasis on reducing gasoline usage undermines battery energy. Minimization of power errors or drivability is prioritized above everything else. The results demonstrate that the suggested method can function effectively with an accuracy of 91% relative to optimal circumstances. The energy distribution between the battery and fuel cell enhances the longevity of both power sources.

2025-01-01T00:00:00Z Shen, Hengjie Guan, Ying Li, Minghai http://yetl.yabesh.ir/yetl1/handle/yetl/4310473 2026-02-17T21:40:53Z 2025-01-01T00:00:00Z

Analysis of Thermal Runaway Temperature Characteristics, Gas Composition, and Thermal Runaway Products of Semi-Solid Li(Ni0.6Co0.2Mn0.2)O2 Battery Shen, Hengjie; Guan, Ying; Li, Minghai In order to enhance the energy density of lithium-ion batteries (LIBs), semi-solid batteries, as a transitional product in the development of all-solid-state batteries, have garnered attention from numerous enterprises and research institutions. This study, through a designed experiment, triggers the thermal runaway (TR) of semi-solid Li(Ni0.6Co0.2Mn0.2)O2 (NCM622) batteries by lateral heating. The experiment utilizes a self-developed sealing device and a combination of temperature sensors and high-speed photography to record and analyze the temperature characteristics, gas emission characteristics, and emission morphology during the TR of the semi-solid NCM622 battery. Further research and analysis were conducted on the composition of the gas and products produced during TR. The results indicate that there is no apparent correlation between the severity of gas generation during TR and the rate of temperature rise. The highest temperature during battery TR can reach 950.33 °C, and the peak gas production rate can reach 300 L/s. The gas produced during TR mainly consists of CO, CO2, and H2, with CO accounting for up to 57.14%. The particulate matter produced during TR contains a large amount of organic elements such as C and O, as well as metal elements such as Ni, Al, Cu, and Co. This study fills a gap in the research content in the field of thermal safety of semi-solid LIBs, and the research data provide a reference for passive battery safety.

2025-01-01T00:00:00Z Wu, Minghu Xia, Jinchi http://yetl.yabesh.ir/yetl1/handle/yetl/4310467 2026-02-17T21:40:37Z 2025-01-01T00:00:00Z

Electric Vehicles Charging Time Prediction Based on Multimodel Fusion Wu, Minghu; Xia, Jinchi With the rapid development of the global electric vehicle (EV) market, accurately predicting charging times is of significant importance for promoting the widespread adoption of EVs and enhancing the efficiency of charging infrastructure. Existing prediction methods often disregard battery aging and predominantly use single-model approaches, resulting in limited predictive accuracy. This article proposes a multimodel fusion-based method for predicting EV charging times. The approach utilizes data from ten EVs across various regions and operational conditions. Driving segment data are used to identify the ohmic internal resistance of the equivalent circuit model as a battery health indicator, employing the forgetting factor recursive least squares method. Key features such as state of charge, current, and ambient temperature are also extracted. Initial charging time predictions are generated using XGBoost, LightGBM, and CatBoost models and are subsequently fused using a random forest model to improve accuracy and robustness. Experimental results demonstrate that the proposed method achieves superior prediction performance under both fast and slow charging strategies, with a root mean square error of 0.130 h and a mean absolute percentage error of 5.676%. This research introduces a robust approach for enhancing the accuracy of EV charging time predictions.

2025-01-01T00:00:00Z