Fast self-heating battery with anti-aging awareness for freezing climates application

Xiong, Rui; Li, Zhengyang; Yang, Ruixin; Shen, Weixiang; Ma, Suxiao; Sun, Fuchun

doi:10.1016/j.apenergy.2022.119762

Cited by 30 publications

(5 citation statements)

References 25 publications

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“…[95][96][97][98] Wang et al [99] proposed to use multi-stage alternating current to heat the low-temperature battery and make the battery rise from −20°C to 0°C in 5 min without depleting the battery SOH. Xiong et al [100] used genetic algorithms to optimize the battery's self-heating strategy, reducing the above heating time to 70 s, and only attenuating 7.72% of capacity per 200 heats. The battery self-heating technology can quickly raise the battery temperature to the appropriate range, while the EV power exchange technology can extend the battery life by allowing the battery to be charged under optimal working conditions, and it can avoid peak electricity consumption or intermittent renewable energy to indirectly reduce carbon emissions, making the battery swapping technology a very promising development route for EV charging.…”

Section: Refined Onboard Battery Managementmentioning

confidence: 99%

Electric vehicle lifecycle carbon emission reduction: A review

Gao,

Xie,

Yang

et al. 2023

Carbon Neutralization

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Under the global carbon neutrality initiative, carbon emissions from the transportation sector are becoming increasingly prominent due to the growth in vehicle ownership. And electric mobility may be a potentially effective measure to reduce road traffic carbon emissions and achieve a green transformation of transportation. This paper systematically collates the relevant carbon accounting standards for the automotive industry and elaborates the current status of road transport greenhouse gas emissions by combining the data from the International Energy Agency. And by comparing the lifecycle carbon footprint of various energy types of vehicles, the necessity and feasibility of electric mobility to reduce carbon emissions are discussed. However, the comparison of vehicle lifecycle carbon footprints shows that electric vehicles (EVs) are not as environmentally friendly as expected, although they can significantly reduce road traffic carbon emissions. The high carbon emissions from the manufacturing process of the core components of EVs, especially the power battery, reduce the low‐carbon potential of electric mobility. Therefore, the carbon emission reduction strategies and outcomes of automakers in the automotive industry chain have been further reviewed. Finally, focusing on vehicle power batteries, this article reviews the technologies such as refined management and echelon utilization that can make EVs more environmentally friendly and promote carbon neutrality in the transportation sector.

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Section: Refined Onboard Battery Managementmentioning

confidence: 99%

Electric vehicle lifecycle carbon emission reduction: A review

Gao,

Xie,

Yang

et al. 2023

Carbon Neutralization

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“…Furthermore, it is noteworthy that GAs can potentially be perfectly integrated with the parameter coordination of the LPM, as indicated by a few studies in fields such as mechanical and electrical engineering. Examples include the effective structural vulnerability assessment of tubular structures [23] , research on fast self-heating batteries with age-aware consciousness [24] , optimization of the optimal solution for the model of a vertical falling film absorption heat pump [25] , and multi-objective design optimization of current sensor Rogowski coils [26] . Drawing inspiration from the successful application of GAs in these critical domains, the integration of this strategy with the persistent bottleneck of parameter coordination in LPMs can be achieved seamlessly.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Driven Rapid and Precise Mimicry of Cardiovascular Fluctuations

Liao,

Li,

Jiang

et al. 2024

Preprint

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BackgroundPhysics-based reduced-order models have gained significant attention for capturing cardiovascular fluctuations. However, achieving quick and precise mimicry of these fluctuations has been a persistent issue for decades.MethodsInspired by the principle of natural selection, we used a complex whole-body circulation model as an example and utilized genetic algorithms to automate the coordination of model parameters. Additionally, we introduced a “pseudo-distance” metric to evaluate the similarity between the simulated fluctuation curves and the target curves.ResultsThrough rapid iterations (40 times), this strategy achieved a complete match with the target in both blood pressure and flow fluctuation amplitude and time domains, resulting in highly realistic fluctuation mimicry.ConclusionThis study addresses the major challenge of reduced-order models in the mimicry of blood circulation, ending the history of manual parameter coordination that took months or even years.Clinical PerspectiveWhat Is New?Physics-based reduced-order models are essential for analyzing whole-body hemodynamic status, but they have struggled with complex parameter coordination for decades. This study completely addressed this challenge by employing “genetic algorithms” and an updated “pseudo-distance” criterion, achieving precise mimicry of waveforms both spatially and temporally. Additionally, this work eliminates the dependency on large datasets, making personalized modeling more accessible and practical.What Are the Clinical Implications?This study lowers the barrier for researchers utilizing these models, significantly advancing the modeling of blood circulation and potentially benefitting physiological analysis, clinical diagnostics, and treatment planning in various cardiovascular events.

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“…Due to high specific energy, high operating voltage, and environmental friendliness, lithium-ion batteries (LiBs) are currently the most widely used batteries in EVs [2]. To ensure the efficient and safe operation of the LiBs, it is an essential requirement for EVs to equip with battery management systems (BMSs) and the development of effective management algorithms for BMSs is highly dependent on battery operation data [3]. However, the operation data for important scenarios such as thermal runaways and faults are difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

Xiong

Zhu

Zhang

et al. 2023

Chin. J. Mech. Eng.

Self Cite

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The development of a battery management algorithm is highly dependent on high-quality battery operation data, especially the data in extreme conditions such as low temperatures. The data in faults are also essential for failure and safety management research. This study developed a battery big data platform to realize vehicle operation, energy interaction and data management. First, we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously. Second, charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle. Third, the data transmission network was designed, a real-time monitoring interface was developed, and the self-developed battery management system was used to measure, collect, upload, and store battery operation data in real time. Finally, experimental validation was performed on the platform. Results demonstrate the efficiency and reliability of the platform. Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

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Fast self-heating battery with anti-aging awareness for freezing climates application

Cited by 30 publications

References 25 publications

Electric vehicle lifecycle carbon emission reduction: A review

Electric vehicle lifecycle carbon emission reduction: A review

Genetic-Driven Rapid and Precise Mimicry of Cardiovascular Fluctuations

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

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