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Electric-thermal collaborative control and multimode energy flow analysis of fuel cell hybrid electric vehicles in low-temperature regions 低温区域燃料电池混合动力电动汽车的电热协同控制和多模式能量流分析
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-28 DOI: 10.1016/j.etran.2024.100341
Xiao Yu , Cheng Lin , Peng Xie , Yu Tian , Haopeng Chen , Kai Liu , Huimin Liu

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.

电动汽车在各种推进模式和各种气候条件下运行时的能量流分布特征尚未得到深入探讨。要实现有效的电热协同能源管理,必须考虑各种气候条件,采用智能控制方法来缓解里程焦虑。在本研究中,我们基于改进的深度神经网络和能量量化模型,开发了一种新型电热协同能源管理策略,以提高全局能量转换效率。根据车辆控制单元收集的实验数据,总结了在-10°C-35°C温度范围内,各种策略和推进模式下的完整能耗分布特征,包括电池自加热、座舱加热、加速消耗和燃油消耗。我们的研究结果表明,对于燃料电池混合动力客车,在包括初始车厢加热过程的循环中,当环境温度为-2 ℃和-10 ℃时,纯电动模式下的加热消耗分别为 9.9 kWh/循环和 13 kWh/循环,分别占总消耗的 33% 和 42%。在使用燃料电池的余热后,相同条件下的电加热消耗仅为 3.7 千瓦时/周期。在高温情况下,车厢制冷消耗为 3.26 千瓦时/周期,仅占总能耗的 18%。最后,在低温情况下,电热协同战略在纯电动和混合动力模式下分别降低了 14.7% 和 9.2% 的成本。因此,我们的方法大大提高了能源利用率和转换效率,尤其是在低温条件下。
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引用次数: 0
Thermodynamic and kinetic degradation of LTO batteries: Impact of different SOC intervals and discharge voltages in electric train applications LTO 电池的热力学和动力学降解:电动列车应用中不同 SOC 间隔和放电电压的影响
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-21 DOI: 10.1016/j.etran.2024.100340
Haoze Chen , Ahmed Chahbaz , Sijia Yang , Weige Zhang , Dirk Uwe Sauer , Weihan Li

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.

与基于石墨的锂离子电池相比,基于钛酸锂(LTO)的锂离子电池具有更长的使用寿命、更高的功率能力和更低的生命周期成本,可用于储能和电动交通应用。然而,LTO 电池在高低充电状态(SOC)区间和不同放电截止电压下的降解机制尚未得到明确研究。本研究调查了高功率锂 4Ti5O12/LiCoO2 电池在五个独立的 SOC 间隔、20% 的放电深度 (DOD) 和三种放电截止电压下与应用相关的寿命性能。结果表明,当电池在较低的 SOC 间隔内循环或使用较低的截止电压时,降解率会显著增加。此外,在 20% DOD 循环时,热力学降解更为显著,而在 100% DOD 循环时,动力学降解占主导地位。就热力学降解而言,决定性的降解模式是负极活性材料的损耗,而阴极活性材料的损耗对平衡电压曲线的影响更大。动力学降解主要是由于阴极的电荷转移过程和扩散过程较慢,从而增加了极化阻抗。
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引用次数: 0
Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data 实现真实世界的健康状况评估:第 1 部分:使用锂离子电池实验室数据的电池级方法
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-17 DOI: 10.1016/j.etran.2024.100338
Yufang Lu , Jiazhen Lin , Dongxu Guo , Jingzhao Zhang , Chen Wang , Guannan He , Minggao Ouyang

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.

对于锂离子电池(LIB)的电池管理系统(BMS)来说,准确而快速的健康状态(SOH)估算至关重要。鉴于电池类型和工作条件的多变性以及有限的数据样本,传统的数据驱动方法无法满足要求,尤其是在电动汽车和储能系统等实际应用中。为此,我们开发了一种基于元学习的方法,使用门控卷积神经网络-模型诊断元学习(GCNNs-MAML)模型来寻找合适的初始参数,通过少量训练就能快速适应新的给定乳头样本。它使用多个现有历史数据集进行元训练,然后使用训练模型的初始参数在新的小规模数据上进行元测试。GCNNs-MAML 模型仅从 5% 的骑行数据中随机抽取 800 秒的充电片段进行训练,在剩余 95% 的测试样本中,其 SOH 估计的平均有效误差率为 1.8%,最小有效误差率为 1.3%。结果表明,它明显优于基于特征和基于学习的方法。基于元学习的方法表现出高精度、鲁棒性和强大的泛化能力,这意味着它在现实世界的应用和少量样本条件下具有巨大的潜力。
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引用次数: 0
Implanted potential sensing separator enables smart battery internal state monitor and safety alert 植入式电位感应隔板可实现智能电池内部状态监控和安全警报
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-13 DOI: 10.1016/j.etran.2024.100339
Anyu Su , Shuoyuan Mao , Languang Lu , Xuebing Han , Minggao Ouyang

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.

目前的电池管理系统仅限于测试外部特性,而电池的内部状态则是一个 "黑盒子"。评估电池内部状态需要先进的表征技术和电池传感技术。然而,由于其寿命短、灵敏度低、侵入性强和成本高昂,这些技术在实际应用和商业化方面面临着挑战。在这里,我们提出了一种植入电位传感器的智能电池,用于原位测量阳极电位,从而识别严重的副反应和异常的锂电镀行为。具体来说,电位传感材料直接集成到电池隔膜中,隔膜可提供可靠的电位参考,并充当传感终端。隔膜的多孔结构有利于锂离子传输,同时通过无损植入实现高精度监测。此外,电位传感隔膜还能在早期检测出电池中存在的或潜在的缺陷,而这些缺陷很难从电池的外部特征中及时发现。此外,我们还开发了一种多点电位传感器监测系统,不仅能监测阳极电位的分布,还能精确定位电池缺陷的位置。
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引用次数: 0
Enhancing control disorder and implementing V2X-Based suppression methods for electric vehicle CO2 thermal management systems 增强电动汽车二氧化碳热管理系统的控制紊乱和实施基于 V2X 的抑制方法
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-11 DOI: 10.1016/j.etran.2024.100336
Fan Jia , Xiang Yin , Feng Cao , Ce Cui , Jianmin Fang , Xiaolin Wang

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.

近年来,电动汽车(EV)热管理系统的发展凸显了其在确保驾驶安全和优化行驶里程方面的重要作用。然而,电动汽车运行固有的动态复杂性加上自动控制系统,有时会导致行为不稳定,从而造成压缩机和电池性能下降并带来安全风险。为了有效解决这一问题,本研究对使用二氧化碳作为制冷剂的电动汽车热管理系统的动态控制特性进行了评估。通过数学建模和实验分析,首先确定了动态热过程的不稳定性。研究重点从系统控制特性和内在机制入手,阐明了其根本原因。研究发现,控制失调会诱发压缩机和膨胀阀等热管理系统组件的异常动作,从而导致性能显著下降,并引发压缩机吸入液体携带等问题。此外,还划定了电动汽车二氧化碳热泵的特定控制失调区域,为评估系统控制失调的可能性提供了一个框架。值得注意的是,在室内空气流速低、环境温度高和供气温度低的条件下,更容易出现控制失调。鉴于这一问题的普遍性和缺乏合适的解决方案,我们利用 V2X 技术开发了两种控制失调抑制方案,并通过模拟进行了验证。结果表明,采用 V2X 通信技术平均防止了 70.1% 的 COP 下降,确保了压缩机和电池在各种运行条件下的稳定性和安全性。这项研究为探索二氧化碳热管理系统的动态特性提供了有用信息,为提高系统稳定性和效率提供了一种新方法。
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引用次数: 0
Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data 通过虚拟数据的多物理信息 DeepONet 预测锂离子电池的热失控现象
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-05-09 DOI: 10.1016/j.etran.2024.100337
Jinho Jeong , Eunji Kwak , Jun-hyeong Kim , Ki-Yong Oh

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.

快速准确地预测锂离子电池(LIB)热失控(TR)的替代模型至关重要,然而复杂的热失控现象给实现这两方面的充分性能带来了巨大挑战,尤其是传统的有限元模型(FEM)需要耗费大量的时间和成本。本研究提出了一种带有编码器的多物理信息深度算子网络(MPI-DeepONet)来解决这些问题。该建议的神经网络旨在预测各种热滥用条件下的 TR,提供快速准确的 TR 预测替代模型。在本研究中,带有编码器的 MPI-DeepONet 使用多物理场有限元的虚拟数据进行训练,以克服实际 TR 数据稀缺的问题。DeepONet 的结构可以解决内插法和外推法问题,一旦训练完成,就可以对多种热滥用条件进行预测。能量平衡和化学反应方程的监督进一步增强了神经网络,通过有效捕捉 TR 的复杂现象,确保在数据有限的情况下仍能进行准确、稳健的预测。根据实际实验和烧蚀研究进行的定量分析证实了所建议的神经网络的有效性。值得注意的是,MPI-DeepONet 对测试集中温度预测的平均 RMSE 为 13.2 °C,明显优于纯数据驱动 DeepONet 的 25.4 °C。这一改进凸显了将多物理约束整合到神经网络中的重要性。对 LFP 和 NMC 电池的 TR 预测准确,进一步证明了所提出的神经网络的通用性。所提出的神经网络所具有的特征能够实时量化锂电池中关键成分的内部温度分布和无量纲浓度,而直接测量这些成分是具有挑战性的,其速度比有限元分析至少快 10,000 倍。拟议的神经网络为先进的电池管理系统提供了全面的信息,以确保锂电池组的安全性和可靠性,通过人工智能转型加速电动交通系统的数字孪生。
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引用次数: 0
Dynamic mechanical behaviors of load-bearing battery structure upon low-velocity impact loading in electric vehicles 电动汽车低速冲击加载时承重电池结构的动态力学行为
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-04-29 DOI: 10.1016/j.etran.2024.100334
Ruiqi Hu , Dian Zhou , Yikai Jia , Yang Chen , Chao Zhang

As the electrification trend of vehicles continues, new energy vehicles such as electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are being equipped with new functional energy storage devices demanding a trade-off between electrical and mechanical property. Accordingly, composite-battery integrated structures which simultaneously carry mechanical resistance and energy-storage capacity, are being explored to offer great potential for the next generation of EVs or PHEVs. Herein, the dynamic responses and failure mechanisms of the integrated structure under the commonly occurring low-velocity impact events are studied both experimentally and numerically. A macro-scale finite element (FE) model was developed by implementing constitutive models of component materials, including lithium‐ion polymer (LiPo) battery cells, polymer foams, and carbon fiber-reinforced polymers (CFRP). The numerical method demonstrates good feasibility and accurately predicts impact behaviors, with the maximum error of the peak impact load not exceeding 8 %. The integrated structures are proven to reduce mechanical damage while maintaining mechanical and electrochemical performance within a range of impacts. The electrical and mechanical behaviors and their correlations were revealed. Sensitivity of the mechanical behaviors and electrical failure to battery arrangement were discussed as well as the structure design on energy absorption capacity. These results hold significant potential for the safety and lightweight design of energy storage composite structures incorporating lithium-ion batteries.

随着汽车电气化趋势的不断发展,电动汽车(EV)和插电式混合动力汽车(PHEV)等新能源汽车正在配备新的功能性储能装置,这些装置需要在电气性能和机械性能之间进行权衡。因此,同时具有机械阻力和储能能力的复合材料电池集成结构正在被探索之中,为下一代电动汽车或混合动力电动汽车(PHEV)提供了巨大潜力。本文通过实验和数值方法研究了集成结构在常见的低速冲击事件下的动态响应和失效机制。通过实施包括锂离子聚合物(LiPo)电池芯、聚合物泡沫和碳纤维增强聚合物(CFRP)在内的组件材料构成模型,建立了宏观尺度的有限元(FE)模型。该数值方法具有良好的可行性,能准确预测冲击行为,冲击载荷峰值的最大误差不超过 8%。经证明,集成结构可减少机械损伤,同时在一定冲击范围内保持机械和电化学性能。研究揭示了电气和机械行为及其相关性。还讨论了机械行为和电气故障对电池布置的敏感性,以及结构设计对能量吸收能力的影响。这些结果为结合锂离子电池的储能复合结构的安全性和轻量化设计提供了巨大的潜力。
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引用次数: 0
Revealing the mechanism of pack ceiling failure induced by thermal runaway in NCM batteries: A coupled multiphase fluid-structure interaction model for electric vehicles 揭示 NCM 电池热失控导致电池组顶盖失效的机理:电动汽车多相流体-结构-相互作用耦合模型
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-04-24 DOI: 10.1016/j.etran.2024.100335
Junyuan Li , Peng Gao , Bang Tong , Zhixiang Cheng , Mingwei Cao , Wenxin Mei , Qingsong Wang , Jinhua Sun , Peng Qin

Structure failure of lithium-ion battery (LIB) pack ceiling leads to the unintended release of combustible and poisonous substances during thermal runaway (TR), resulting in personnel injuries and financial losses. However, limited research has been conducted on the mechanism behind pack ceiling failures. In this study, we developed a coupled multiphase fluid-structure interaction (FSI) model to simulate the evolution of up-cover baffle under the TR impact of a 52 Ah NCM battery. Our findings reveal several important insights:1) the maximum force and temperature on the baffle are 13.01 N and 598.5 °C in experiment; 2) the simulation shows that particles exert higher temperature and greater force on the baffle compared to the gas phase; 3) the overall equivalent stress in the stainless-steel baffle surpasses the tensile strength that incurs crack on the baffles. According to the validated model, we find that the baffle structure failure is caused by the thermal stress from particle-structure heat conduction. Furthermore, this observation is applicable to the structure failure problems associated to the thermal runaway of high-density battery that involves enormous particles. In addition, the insulation layer is found to be more effective than the gap distance in protecting the pack ceiling. These findings offer a valuable insight into the structure design of LIB pack, and provide the guidance toward future battery integration technologies.

锂离子电池组(LIB)顶盖结构失效会导致热失控(TR)过程中可燃和有毒物质的意外释放,造成人员伤亡和经济损失。然而,对电池组天花板失效背后机理的研究还很有限。在本研究中,我们开发了一种多相流固耦合(FSI)模型,用于模拟 52 Ah NCM 电池在 TR 冲击下上盖挡板的演变过程。我们的研究结果揭示了几个重要的观点:1)实验中,挡板上的最大力和温度分别为 13.01 N 和 598.5 °C;2)模拟结果表明,与气相相比,颗粒对挡板施加了更高的温度和更大的力;3)不锈钢挡板上的整体等效应力超过了在挡板上产生裂纹的拉伸强度。根据验证模型,我们发现障板结构失效是由粒子-结构热传导产生的热应力引起的。此外,这一观点也适用于涉及巨大颗粒的高密度电池热失控相关的结构破坏问题。此外,在保护电池组顶盖方面,绝缘层比间隙距离更有效。这些发现为锂电池组的结构设计提供了宝贵的见解,并为未来的电池集成技术提供了指导。
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引用次数: 0
Lithium-ion battery sudden death: Safety degradation and failure mechanism 锂离子电池猝死:安全退化和失效机制
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-04-16 DOI: 10.1016/j.etran.2024.100333
Guangxu Zhang , Xuezhe Wei , Xueyuan Wang , Jiangong Zhu , Siqi Chen , Gang Wei , Xiaopeng Tang , Xin Lai , Haifeng Dai

Environmental pollution and energy scarcity have acted as catalysts for the energy revolution, particularly driving the rapid progression of vehicle electrification. Lithium-ion batteries play a fundamental role as the pivotal components in electric vehicles. Nevertheless, battery sudden death poses substantial challenges to battery design and management. This work comprehensively investigates the failure mechanism of cell sudden death under different degradation paths and its impact on cell performances. Multi-angle characterization analysis shows that lithium plating is the primary failure mechanism of battery sudden death under different degradation paths. However, the formation mechanisms of lithium plating differ in various degradation paths. In the path-L and path-F, the limited lithium intercalation rate in graphite leads to lithium plating, while localized anode drying and uneven potential distribution are the causes in the path-H and path-R. Furthermore, sudden death significantly reduces the cell electrochemical performances and thermal safety, but the cell performance evolution varies under different degradation paths. Sudden death primarily affects the anode interface polarization process in the path-L and path-F, with a more severe impact on cell thermal safety. However, sudden death mainly affects the charge transfer process, with a relatively milder impact on cell thermal safety. These findings can provide valuable insights for optimizing battery design and management.

环境污染和能源短缺是能源革命的催化剂,尤其推动了汽车电气化的快速发展。锂离子电池作为电动汽车的关键部件发挥着重要作用。然而,电池猝死给电池设计和管理带来了巨大挑战。这项研究全面探讨了不同降解路径下电池猝死的失效机理及其对电池性能的影响。多角度表征分析表明,镀锂是不同降解路径下电池猝死的主要失效机制。然而,在不同的降解路径下,锂镀层的形成机制也不尽相同。在路径-L 和路径-F 中,石墨中有限的锂插层速率导致镀锂,而在路径-H 和路径-R 中,局部负极干燥和电位分布不均是镀锂的原因。此外,猝死会大大降低电池的电化学性能和热安全性,但在不同的降解路径下,电池的性能演变也各不相同。在路径-L 和路径-F 中,猝死主要影响阳极界面极化过程,对电池热安全性的影响更为严重。然而,猝死主要影响电荷转移过程,对电池热安全性的影响相对较小。这些发现可为优化电池设计和管理提供有价值的见解。
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引用次数: 0
How does room temperature cycling ageing affect lithium-ion battery behaviors under extreme indentation? 室温循环老化如何影响锂离子电池在极端压痕下的行为?
IF 11.9 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2024-04-16 DOI: 10.1016/j.etran.2024.100331
Yunlong Qu, Bobin Xing, Yong Xia, Qing Zhou

Safety of lithium-ion battery (LIB) cells throughout the whole lifecycle has drawn enormous research interest. Understanding how cycling ageing affects the mechanical-electrical-thermal responses of LIB cells under mechanical abuse is meaningful for more considerate safety design. In the present study, impact of room temperature ageing on morphology of lithium-ion pouch cell was experimentally explored at first, which clearly identified the deposition phenomenon on electrodes induced by electrolyte consumption. Spherical indentation along out-of-plane direction was carried out on both pristine and aged cells, in which the mechanical-electrical-thermal responses were all monitored. Test results indicate that the mechanical response of the aged cells is quite distinct from the pristine ones, characterized by a rightward shift of the force-displacement curve. Electrical and thermal responses of the aged cells were comparatively less severe. It is inferred that those deposits generated during the ageing process postpone the failure of cells. The short circuit of aged cells behaves relatively tenderly as short contact is alleviated by deposits on the surface of electrodes. By combining the present results with previous researches, correlation between the ageing mechanism and the mechanical abuse failure was sorted for different cells subjected to different ageing processes. It is recognized that changes in mechanical, electrical, and thermal responses of aged cells are highly dependent on both ageing condition and battery configuration.

锂离子电池(LIB)在整个生命周期中的安全性引起了人们极大的研究兴趣。了解循环老化如何影响锂离子电池在机械滥用情况下的机械、电气和热响应,对于更周全的安全设计非常有意义。本研究首先通过实验探讨了室温老化对锂离子袋式电池形貌的影响,明确了电解液消耗引起的电极沉积现象。在原始电池和老化电池上沿平面外方向进行球形压痕试验,监测其机械、电气和热响应。测试结果表明,老化电池的机械响应与原始电池截然不同,其特征是力-位移曲线右移。老化电池的电反应和热反应则相对较轻。由此推断,老化过程中产生的沉积物会推迟电池的失效时间。由于电极表面的沉积物缓解了短路接触,因此老化电池的短路表现相对较轻。通过将目前的研究结果与之前的研究结果相结合,对不同老化过程中的不同电池的老化机制和机械滥用失效之间的相关性进行了分类。我们认识到,老化电池的机械、电气和热反应变化与老化条件和电池配置密切相关。
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引用次数: 0
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Etransportation
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