Pub Date : 2024-07-05DOI: 10.1016/j.etran.2024.100347
Yue Sun , Rui Xiong , Xiangfeng Meng , Xuanrou Deng , Hailong Li , Fengchun Sun
Degradation prediction is crucial for ensuring safe and reliable operation of batteries. However, relying solely on capacity to characterize aging cannot comprehensively represent the health status of the battery. This work explores the potential of using a limited number of partial voltage-capacity curves to evaluate battery degradation with the aid of deep learning approaches, which can be used for onboard applications. A sequence-to-sequence model is proposed to predict the electrochemical impedance spectra during battery degradation. It only uses capacity sequences within a specific voltage range at fixed voltage increments from a limited number of cycles, which can be flexibly adapted to different life stages in an end-to-end manner. The proposed method has been validated based on the developed degradation dataset. The root mean square errors for the prediction of impedance spectra are less than 1.48 mΩ. Capacities and resistances associated with electrochemical processes can be further extracted from the obtained impedance spectra, facilitating a comprehensive evaluation of battery degradation. As a limited number of measured data are needed, the proposed method can reduce data storage requirements and computational demands, which enables fast and comprehensive aging diagnosis.
{"title":"Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves","authors":"Yue Sun , Rui Xiong , Xiangfeng Meng , Xuanrou Deng , Hailong Li , Fengchun Sun","doi":"10.1016/j.etran.2024.100347","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100347","url":null,"abstract":"<div><p>Degradation prediction is crucial for ensuring safe and reliable operation of batteries. However, relying solely on capacity to characterize aging cannot comprehensively represent the health status of the battery. This work explores the potential of using a limited number of partial voltage-capacity curves to evaluate battery degradation with the aid of deep learning approaches, which can be used for onboard applications. A sequence-to-sequence model is proposed to predict the electrochemical impedance spectra during battery degradation. It only uses capacity sequences within a specific voltage range at fixed voltage increments from a limited number of cycles, which can be flexibly adapted to different life stages in an end-to-end manner. The proposed method has been validated based on the developed degradation dataset. The root mean square errors for the prediction of impedance spectra are less than 1.48 mΩ. Capacities and resistances associated with electrochemical processes can be further extracted from the obtained impedance spectra, facilitating a comprehensive evaluation of battery degradation. As a limited number of measured data are needed, the proposed method can reduce data storage requirements and computational demands, which enables fast and comprehensive aging diagnosis.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100347"},"PeriodicalIF":15.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1016/j.etran.2024.100346
Qinan Zhou , Dyche Anderson , Jing Sun
State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.
{"title":"State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations","authors":"Qinan Zhou , Dyche Anderson , Jing Sun","doi":"10.1016/j.etran.2024.100346","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100346","url":null,"abstract":"<div><p>State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100346"},"PeriodicalIF":15.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1016/j.etran.2024.100345
Qiao Hu , Li Wang , Jinli Liu , Guangming Han , Jiaying Liao , Dongsheng Ren , Jianfeng Yao , Zonghai Chen , Xiangming He
The trade-off between battery energy density and power performance is the core problem that puzzles the development of electric vehicles (EVs). Although intensive researches are performed to explore active materials with good dynamics, the heterogeneous reactivity has been identified as an important cause for inferior capability and early death, especially for electrodes characterized with high areal loading and high compacted density. Herein, the heterogeneity and its origination of layered oxide-based (LiNixCoyMn1-x-yO2, NCM) electrodes at high C-rate are investigated through operando X-ray diffraction and ex-situ time-of-flight secondary ion mass spectrometry probe. By introducing Li3V2(PO4)3@G composite as a mixed conductor additive, the heterogeneous reactivity intro-particles are successfully mitigated, enabling NCM electrodes with both high rate capability, high energy density and high cyclability. In detail, the capacity retention at 20C is increased by 2.3 times, and the capacity retention at 0.5C after 160 full cycles is increased by 1.6 times, without electrolyte additive or material modification. This study demonstrates the significance of the homogeneous electronic/ionic transportation network to the rate capability and lifetime of an electrode, and discloses the design strategy of multifunctional additives to enhance the power density of a battery by maximizing the utility of the active particles.
{"title":"Significance of homogeneous conductive network in layered oxide-based cathode for high-rate capability of electric vehicle batteries","authors":"Qiao Hu , Li Wang , Jinli Liu , Guangming Han , Jiaying Liao , Dongsheng Ren , Jianfeng Yao , Zonghai Chen , Xiangming He","doi":"10.1016/j.etran.2024.100345","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100345","url":null,"abstract":"<div><p>The trade-off between battery energy density and power performance is the core problem that puzzles the development of electric vehicles (EVs). Although intensive researches are performed to explore active materials with good dynamics, the heterogeneous reactivity has been identified as an important cause for inferior capability and early death, especially for electrodes characterized with high areal loading and high compacted density. Herein, the heterogeneity and its origination of layered oxide-based (LiNi<sub>x</sub>Co<sub>y</sub>Mn<sub>1-x-y</sub>O<sub>2</sub>, NCM) electrodes at high C-rate are investigated through operando X-ray diffraction and <em>ex-situ</em> time-of-flight secondary ion mass spectrometry probe. By introducing Li<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub>@G composite as a mixed conductor additive, the heterogeneous reactivity intro-particles are successfully mitigated, enabling NCM electrodes with both high rate capability, high energy density and high cyclability. In detail, the capacity retention at 20C is increased by 2.3 times, and the capacity retention at 0.5C after 160 full cycles is increased by 1.6 times, without electrolyte additive or material modification. This study demonstrates the significance of the homogeneous electronic/ionic transportation network to the rate capability and lifetime of an electrode, and discloses the design strategy of multifunctional additives to enhance the power density of a battery by maximizing the utility of the active particles.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"22 ","pages":"Article 100345"},"PeriodicalIF":15.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1016/j.etran.2024.100343
Ziliang Wei, Yang Geng, Hao Tang, Yang Zhao, Borong Lin
Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.
{"title":"Cost-effective sizing method of Vehicle-to-Building chargers and energy storage systems during the planning stage of smart micro-grid","authors":"Ziliang Wei, Yang Geng, Hao Tang, Yang Zhao, Borong Lin","doi":"10.1016/j.etran.2024.100343","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100343","url":null,"abstract":"<div><p>Demand side management (DSM) is a great challenge for new power systems based on renewable energy. Vehicle-to-Building (V2B) and Energy Storage Systems (ESS) are two important and effective tools. However, existing studies lack the sizing method of bidirectional chargers and ESSs. This study has proposed a cost-effective sizing method of V2B chargers and ESSs during the planning stage. By developing a linear model that clusters electric vehicle users based on mobility patterns and employing mixed integer linear programming for day-ahead control strategies, the method minimizes the dynamic payback period of initial investments. Tested in an office park featuring photovoltaic generation, the optimal configuration of 50% V2B chargers and 1 ESS significantly reduces cumulative peak-hour load and peak power by 51.3% and 42.4%, respectively. The price and rated power of EV chargers on the optimal sizing result are also investigated, providing guidance for the design and operation of micro-grid systems. Furthermore, the study suggests further exploration into actual data acquisition, real-time control strategy enhancement, and comprehensive user behavior for broader application.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100343"},"PeriodicalIF":11.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.1016/j.etran.2024.100344
Cong Yin , Shiyang Hua , Wei Nie , Haiyu Yang , Hao Tang
The proton exchange membrane fuel cell (PEMFC) power source is a promising solution for the unmanned aerial vehicles (UAVs) to extend the flight endurance. However, the light weighted PEMFC stack design with improved performance remains a critical challenge for the UAVs applications. In this study, two air-cooled PEMFC stacks based on metal and graphite bipolar plates are designed respectively to optimize the fuel cell power density with comparative tests and simulations under varied operating conditions. The designed metal and graphite stacks could reach the power densities of 1189 W/kg and 792 W/kg, of which the graphite one is integrated in a hybrid power system for the UAVs and operated for a flight test with ∼45 min. Validated by the experiment, a three-dimensional coupled model is developed to comparatively study the internal performance and thermal behaviors of the two stacks. Compared with the graphite stack, the metal one outputs higher voltage by 4 %, weighs lighter by 31 % and improves air forced thermal dissipation with enhanced water retention ability. The proposed model and comparative analysis reveal the mechanisms of stack performance variation under different designs and operations, which are beneficial for the optimization of UAVs fuel cell power system.
{"title":"Comparative study on air-cooled fuel cell stacks with metal and graphite bipolar plate designs for unmanned aerial vehicles","authors":"Cong Yin , Shiyang Hua , Wei Nie , Haiyu Yang , Hao Tang","doi":"10.1016/j.etran.2024.100344","DOIUrl":"10.1016/j.etran.2024.100344","url":null,"abstract":"<div><p>The proton exchange membrane fuel cell (PEMFC) power source is a promising solution for the unmanned aerial vehicles (UAVs) to extend the flight endurance. However, the light weighted PEMFC stack design with improved performance remains a critical challenge for the UAVs applications. In this study, two air-cooled PEMFC stacks based on metal and graphite bipolar plates are designed respectively to optimize the fuel cell power density with comparative tests and simulations under varied operating conditions. The designed metal and graphite stacks could reach the power densities of 1189 W/kg and 792 W/kg, of which the graphite one is integrated in a hybrid power system for the UAVs and operated for a flight test with ∼45 min. Validated by the experiment, a three-dimensional coupled model is developed to comparatively study the internal performance and thermal behaviors of the two stacks. Compared with the graphite stack, the metal one outputs higher voltage by 4 %, weighs lighter by 31 % and improves air forced thermal dissipation with enhanced water retention ability. The proposed model and comparative analysis reveal the mechanisms of stack performance variation under different designs and operations, which are beneficial for the optimization of UAVs fuel cell power system.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100344"},"PeriodicalIF":11.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.
{"title":"Optimal performance and preliminary parameter matching for hydrogen fuel cell powertrain system of electric aircraft","authors":"Yuanyuan Li, Zunyan Hu, Yifu Zhang, Jianqiu Li, Liangfei Xu, Minggao Ouyang","doi":"10.1016/j.etran.2024.100342","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100342","url":null,"abstract":"<div><p>Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100342"},"PeriodicalIF":11.9,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-28DOI: 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.
{"title":"Electric-thermal collaborative control and multimode energy flow analysis of fuel cell hybrid electric vehicles in low-temperature regions","authors":"Xiao Yu , Cheng Lin , Peng Xie , Yu Tian , Haopeng Chen , Kai Liu , Huimin Liu","doi":"10.1016/j.etran.2024.100341","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100341","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100341"},"PeriodicalIF":11.9,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141239559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 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。
{"title":"Thermodynamic and kinetic degradation of LTO batteries: Impact of different SOC intervals and discharge voltages in electric train applications","authors":"Haoze Chen , Ahmed Chahbaz , Sijia Yang , Weige Zhang , Dirk Uwe Sauer , Weihan Li","doi":"10.1016/j.etran.2024.100340","DOIUrl":"https://doi.org/10.1016/j.etran.2024.100340","url":null,"abstract":"<div><p>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 Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub>/LiCoO<sub>2</sub> 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.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100340"},"PeriodicalIF":11.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590116824000304/pdfft?md5=e664e31ada5fcd6df4deb3569d73b77a&pid=1-s2.0-S2590116824000304-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 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.
{"title":"Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data","authors":"Yufang Lu , Jiazhen Lin , Dongxu Guo , Jingzhao Zhang , Chen Wang , Guannan He , Minggao Ouyang","doi":"10.1016/j.etran.2024.100338","DOIUrl":"10.1016/j.etran.2024.100338","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100338"},"PeriodicalIF":11.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141039668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 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.
{"title":"Implanted potential sensing separator enables smart battery internal state monitor and safety alert","authors":"Anyu Su , Shuoyuan Mao , Languang Lu , Xuebing Han , Minggao Ouyang","doi":"10.1016/j.etran.2024.100339","DOIUrl":"10.1016/j.etran.2024.100339","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"21 ","pages":"Article 100339"},"PeriodicalIF":11.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}