Al is considered as a promising lithium-ion battery(LIBs)anode materials owing to its high theoretical capacity and appropri-ate lithation/de-lithation potential.Unfortunately,its inevitable volume expansion causes th...Al is considered as a promising lithium-ion battery(LIBs)anode materials owing to its high theoretical capacity and appropri-ate lithation/de-lithation potential.Unfortunately,its inevitable volume expansion causes the electrode structure instability,leading to poor cyclic stability.What’s worse,the natural Al2O3 layer on commercial Al pellets is always existed as a robust insulating barrier for elec-trons,which brings the voltage dip and results in low reversible capacity.Herein,this work synthesized core-shell Al@C-Sn pellets for LIBs by a plus-minus strategy.In this proposal,the natural Al2O3 passivation layer is eliminated when annealing the pre-introduced SnCl2,meanwhile,polydopamine-derived carbon is introduced as dual functional shell to liberate the fresh Al core from re-oxidization and alle-viate the volume swellings.Benefiting from the addition of C-Sn shell and the elimination of the Al2O3 passivation layer,the as-prepared Al@C-Sn pellet electrode exhibits little voltage dip and delivers a reversible capacity of 1018.7 mAh·g^(-1) at 0.1 A·g^(-1) and 295.0 mAh·g^(-1) at 2.0 A·g^(-1)(after 1000 cycles),respectively.Moreover,its diffusion-controlled capacity is muchly improved compared to those of its counterparts,confirming the well-designed nanostructure contributes to the rapid Li-ion diffusion and further enhances the lithium storage activity.展开更多
Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charg...Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.展开更多
The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above...The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.展开更多
To effectively separate and recover Co(Ⅱ) from the leachate of spent lithium-ion battery cathodes,we investigated solvent extraction with quaternary ammonium salt N263 in the sodium nitrite system.NO_(2)^(-)combines ...To effectively separate and recover Co(Ⅱ) from the leachate of spent lithium-ion battery cathodes,we investigated solvent extraction with quaternary ammonium salt N263 in the sodium nitrite system.NO_(2)^(-)combines with Co(Ⅱ) to form an anion [Co(NO_(2))_(3)]^(-),and it is then extracted by N263.The extraction of Co(Ⅱ) is related to the concentration of NO_(2)^(-).The extraction efficiency of Co(Ⅱ) reaches the maximum of99.16%,while the extraction efficiencies of Ni(Ⅱ),Mn(Ⅱ),and Li(Ⅰ) are 9.27%-9.80% under the following conditions:30vol% of N263 and15vol% of iso-propyl alcohol in sulfonated kerosene,the volume ratio of the aqueous-to-organic phase is 2:1,the extraction time is 30 min,and1 M sodium nitrite in 0.1 MHNO_(3).The theoretical stages require for the Co(Ⅱ) extraction are performed in the McCabe–Thiele diagram,and the extraction efficiency of Co(Ⅱ) reaches more than 99.00% after three-stage counter-current extraction with Co(Ⅱ) concentration of 2544mg/L.When the HCl concentration is 1.5 M,the volume ratio of the aqueous-to-organic phase is 1:1,the back-extraction efficiency of Co(Ⅱ)achieves 91.41%.After five extraction and back-extraction cycles,the Co(Ⅱ) extraction efficiency can still reach 93.89%.The Co(Ⅱ) extraction efficiency in the actual leaching solution reaches 100%.展开更多
Green energy storage devices play vital roles in reducing fossil fuel emissions and achieving carbon neutrality by 2050.Growing markets for portable electronics and electric vehicles create tremendous demand for advan...Green energy storage devices play vital roles in reducing fossil fuel emissions and achieving carbon neutrality by 2050.Growing markets for portable electronics and electric vehicles create tremendous demand for advanced lithium-ion batteries(LIBs)with high power and energy density,and novel electrode material with high capacity and energy density is one of the keys to next-generation LIBs.Silicon-based materials,with high specific capacity,abundant natural resources,high-level safety and environmental friendliness,are quite promising alternative anode materials.However,significant volume expansion and redundant side reactions with electrolytes lead to active lithium loss and decreased coulombic efficiency(CE)of silicon-based material,which hinders the commercial application of silicon-based anode.Prelithiation,preembedding extra lithium ions in the electrodes,is a promising approach to replenish the lithium loss during cycling.Recent progress on prelithiation strategies for silicon-based anode,including electrochemical method,chemical method,direct contact method,and active material method,and their practical potentials are reviewed and prospected here.The development of advanced Si-based material and prelithiation technologies is expected to provide promising approaches for the large-scale application of silicon-based materials.展开更多
The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,whi...The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.展开更多
As the energy density of battery increases rapidly,lithium-ion batteries(LIBs)are facing serious safety issue with thermal runaway,which largely limits the large-scale applications of high-energy-density LIBs.It is ge...As the energy density of battery increases rapidly,lithium-ion batteries(LIBs)are facing serious safety issue with thermal runaway,which largely limits the large-scale applications of high-energy-density LIBs.It is generally agreed that the chemical crosstalk between the cathode and anode leads to thermal runaway of LIBs.Herein,a multifunctional high safety electrolyte is designed with synergistic construction of cathode electrolyte interphase and capture of reactive free radicals to limit the intrinsic pathway of thermal runaway.The cathode electrolyte interphase not only resists the gas attack from the anode but suppresses the parasitic side reactions induced by electrolyte.And the function of free radical capture has the ability of reducing heat release from thermal runaway of battery.The dual strategy improves the intrinsic safety of battery prominently that the triggering temperature of thermal runaway is increased by 24.4℃and the maximum temperature is reduced by 177.7℃.Simultaneously,the thermal runaway propagation in module can be self-quenched.Moreover,the electrolyte design balances the trade-off of electrochemical and safety performance of high-energy batteries.The capacity retention of LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)|graphite pouch cell has been significantly increased from 53.85%to 97.05%with higher coulombic efficiency of 99.94%at operating voltage extended up to 4.5 V for 200 cycles.Therefore,this work suggests a feasible strategy to mitigate the safety risk of high-energy-density LIBs without sacrificing electrochemical performances.展开更多
Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagn...Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.展开更多
The electrode material is regarded as one of the key factors that determine the performance of lithium-ion batteries(LIBs).However,it is still a challenge to search for an anode material with large capacity,low diffus...The electrode material is regarded as one of the key factors that determine the performance of lithium-ion batteries(LIBs).However,it is still a challenge to search for an anode material with large capacity,low diffusion barrier,and good stability.In the present work,two new CrP_(2) monolayers(Pmmn-CrP_(2) and Pmma-CrP_(2)) are predicted by means of first principles swarm structure search.Our study shows that both the two CrP_(2) monolayers have high dynamical and thermal stability,as well as excellent electron conductivity.Additionally,Pmmn-CrP_(2) exhibits a remarkably high storage capacity of 705 mA·h·g^(-1) for Li,meanwhile the diffusion energy barrier of Li on the surface of this monolayer is 0.21 eV,ensuring it as a high-performance anode material for LIBs.We hope that our study will inspire researchers to search for new-type two-dimensional(2D) transition metal phosphides for the electrode materials of LIB s.展开更多
Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this pap...Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.展开更多
Dear Editor,State of health(SOH)estimation is critical for the management of lithium-ion batteries(LIBs).Data-driven estimation methods are appealing with the availability of real-world battery data.However,time-and d...Dear Editor,State of health(SOH)estimation is critical for the management of lithium-ion batteries(LIBs).Data-driven estimation methods are appealing with the availability of real-world battery data.However,time-and data-costly training for batteries with different chemistries and models barriers their efficient deployment.展开更多
Dear Editor,Any fault of a battery system that is not handled timely can cause catastrophic consequences.Therefore,it is significant to diagnose battery faults early and accurately.Due to the complex nonlinear feature...Dear Editor,Any fault of a battery system that is not handled timely can cause catastrophic consequences.Therefore,it is significant to diagnose battery faults early and accurately.Due to the complex nonlinear features and inconsistency of lithium batteries,traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages.展开更多
With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction e...With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.展开更多
A composite separator of SiC/PVDF-HFP was synthesized for lithium-ion batteries with high thermal and mechanical stabilities.Benefiting from the nanoscale,high hardness,and melting point of SiC,SiC/PVDFHFP with highly...A composite separator of SiC/PVDF-HFP was synthesized for lithium-ion batteries with high thermal and mechanical stabilities.Benefiting from the nanoscale,high hardness,and melting point of SiC,SiC/PVDFHFP with highly uniform microstructure was obtained.This polarization caused by barrier penetration was significantly restrained.Due to the Si-F bond between SiC and PVDF-HFP,the structural stability has been obviously enhanced,which could suppress the growth of lithium(Li) dendrite.Furthermore,some 3D reticulated Si nanowires are found on the surface of Li anode,which also greatly inhibit Li dendrites and result in irregular flakes of Li metal.Especially,the shrinkage of 6% SiC/PVDF-HFP at 150℃ is only 5%,which is notably lower than those of PVDF-HFP and Celgard2500.The commercial LiFePO_(4) cell assembled with 6% SiC/PVDF-HFP possesses a specific capacity of 157.8 mA h g^(-1) and coulomb efficiency of 98% at 80℃.In addition,the tensile strength and modulus of 6% SiC/PVDF-HFP could reach 14.6 and 562 MPa,respectively.And a small deformation(1000 nm) and strong deformation recovery are obtained under a high additional load(2.3 mN).Compared with PVDF-HFP and Celgard2500,the symmetric Li cell assembled with 6% SiC/PVDF-HFP has not polarized after 900 cycles due to its excellent mechanical stabilities.This strategy provides a feasible solution for the composite separator of high-safety batteries with a high temperature and impact resistance.展开更多
Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,w...Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.展开更多
High-voltage lithium-ion batteries(HVLIBs) are considered as promising devices of energy storage for electric vehicle, hybrid electric vehicle, and other high-power equipment. HVLIBs require their own platform voltage...High-voltage lithium-ion batteries(HVLIBs) are considered as promising devices of energy storage for electric vehicle, hybrid electric vehicle, and other high-power equipment. HVLIBs require their own platform voltages to be higher than 4.5 V on charge. Lithium nickel manganese spinel LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode is the most promising candidate among the 5 V cathode materials for HVLIBs due to its flat plateau at 4.7 V. However, the degradation of cyclic performance is very serious when LNMO cathode operates over 4.2 V. In this review, we summarize some methods for enhancing the cycling stability of LNMO cathodes in lithium-ion batteries, including doping, cathode surface coating,electrolyte modifying, and other methods. We also discuss the advantages and disadvantages of different methods.展开更多
In this paper, a state of charge(SOC) estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability(ISS) theory has been proposed. According to the electrochemical perf...In this paper, a state of charge(SOC) estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability(ISS) theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process. The nonlinear relation between open circuit voltage(OCV) and SOC is obtained from a rapid test. Exponential fitting method is used to identify the parameters of the model. A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation. The designed observer is tested on AMESim and Simulink co-simulation. The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent.展开更多
Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in th...Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.展开更多
Thermal runaway caused by overcharging results in catastrophic disasters. The influences of charging rate, ambient temperature and aging on thermal runaway caused by overcharging are studied qualitatively and quantita...Thermal runaway caused by overcharging results in catastrophic disasters. The influences of charging rate, ambient temperature and aging on thermal runaway caused by overcharging are studied qualitatively and quantitatively in this manuscript. The results of overcharging tests indicate that high charging rate and ambient temperature increase thermal runaway risk. Aging in 40 ℃ decreases thermal runaway risk. The risk increase of battery with high overcharging rate and in high ambient temperature is due to fast lithium plating reaction and accelerated SEI decomposition, respectively. The risk decrease of aged battery is due to the occurrence of SEI before overcharging tests. SEI suppresses the side reactions between lithium plating and electrolyte. The results of orthogonal tests indicate that the rank of effect is: discharging rate > ambient temperature > aging. The heat generation is calculated based on the results of overcharging tests. The calculation results indicate that heat generated by side reactions contributes more to the total heat generation. Although thermal runaway does not occur during overcharging with low current, the heat dissipation of the lithium-ion battery is the most and deserves focus. The results are important to the design of battery management system and thermal management system to prevent thermal runaway induced by overcharging in total lifespan of battery.展开更多
The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging...The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.展开更多
基金supported by the National Natural Science Foundation of China(No.62105277)the Natural Science Foundation of Henan Province(No.232300420139)the Internationalization Training of High-Level Talents of Henan Province,and Nanhu Scholars Program for Young Scholars of XYNU.
文摘Al is considered as a promising lithium-ion battery(LIBs)anode materials owing to its high theoretical capacity and appropri-ate lithation/de-lithation potential.Unfortunately,its inevitable volume expansion causes the electrode structure instability,leading to poor cyclic stability.What’s worse,the natural Al2O3 layer on commercial Al pellets is always existed as a robust insulating barrier for elec-trons,which brings the voltage dip and results in low reversible capacity.Herein,this work synthesized core-shell Al@C-Sn pellets for LIBs by a plus-minus strategy.In this proposal,the natural Al2O3 passivation layer is eliminated when annealing the pre-introduced SnCl2,meanwhile,polydopamine-derived carbon is introduced as dual functional shell to liberate the fresh Al core from re-oxidization and alle-viate the volume swellings.Benefiting from the addition of C-Sn shell and the elimination of the Al2O3 passivation layer,the as-prepared Al@C-Sn pellet electrode exhibits little voltage dip and delivers a reversible capacity of 1018.7 mAh·g^(-1) at 0.1 A·g^(-1) and 295.0 mAh·g^(-1) at 2.0 A·g^(-1)(after 1000 cycles),respectively.Moreover,its diffusion-controlled capacity is muchly improved compared to those of its counterparts,confirming the well-designed nanostructure contributes to the rapid Li-ion diffusion and further enhances the lithium storage activity.
基金supported by the National Natural Science Foundation of China(No.U20A20310 and No.52176199)sponsored by the Program of Shanghai Academic/Technology Research Leader(No.22XD1423800)。
文摘Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.
基金sponsored by the Science and Technology Program of State Grid Corporation of China(4000-202355090A-1-1ZN)。
文摘The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.
基金financially supported by the National Natural Science Foundation of China(No.51804084)the Natural Science Foundation of Guangxi Province,China(No.2021GXNSFAA220096)the Science and Technology Major Project of Guangxi Province,China(No.AA17204100)。
文摘To effectively separate and recover Co(Ⅱ) from the leachate of spent lithium-ion battery cathodes,we investigated solvent extraction with quaternary ammonium salt N263 in the sodium nitrite system.NO_(2)^(-)combines with Co(Ⅱ) to form an anion [Co(NO_(2))_(3)]^(-),and it is then extracted by N263.The extraction of Co(Ⅱ) is related to the concentration of NO_(2)^(-).The extraction efficiency of Co(Ⅱ) reaches the maximum of99.16%,while the extraction efficiencies of Ni(Ⅱ),Mn(Ⅱ),and Li(Ⅰ) are 9.27%-9.80% under the following conditions:30vol% of N263 and15vol% of iso-propyl alcohol in sulfonated kerosene,the volume ratio of the aqueous-to-organic phase is 2:1,the extraction time is 30 min,and1 M sodium nitrite in 0.1 MHNO_(3).The theoretical stages require for the Co(Ⅱ) extraction are performed in the McCabe–Thiele diagram,and the extraction efficiency of Co(Ⅱ) reaches more than 99.00% after three-stage counter-current extraction with Co(Ⅱ) concentration of 2544mg/L.When the HCl concentration is 1.5 M,the volume ratio of the aqueous-to-organic phase is 1:1,the back-extraction efficiency of Co(Ⅱ)achieves 91.41%.After five extraction and back-extraction cycles,the Co(Ⅱ) extraction efficiency can still reach 93.89%.The Co(Ⅱ) extraction efficiency in the actual leaching solution reaches 100%.
基金This work was supported by Guangdong Basic and Applied Basic Research Foundation(2019A1515110530,2022A1515010486)Shenzhen Science and Technology Program(JCYJ20210324140804013)Tsinghua Shenzhen International Graduate School(QD2021005N,JC2021007).
文摘Green energy storage devices play vital roles in reducing fossil fuel emissions and achieving carbon neutrality by 2050.Growing markets for portable electronics and electric vehicles create tremendous demand for advanced lithium-ion batteries(LIBs)with high power and energy density,and novel electrode material with high capacity and energy density is one of the keys to next-generation LIBs.Silicon-based materials,with high specific capacity,abundant natural resources,high-level safety and environmental friendliness,are quite promising alternative anode materials.However,significant volume expansion and redundant side reactions with electrolytes lead to active lithium loss and decreased coulombic efficiency(CE)of silicon-based material,which hinders the commercial application of silicon-based anode.Prelithiation,preembedding extra lithium ions in the electrodes,is a promising approach to replenish the lithium loss during cycling.Recent progress on prelithiation strategies for silicon-based anode,including electrochemical method,chemical method,direct contact method,and active material method,and their practical potentials are reviewed and prospected here.The development of advanced Si-based material and prelithiation technologies is expected to provide promising approaches for the large-scale application of silicon-based materials.
基金supported by the National Key R.D Program of China(2021YFB2401904)the Joint Fund project of the National Natural Science Foundation of China(U21A20485)+1 种基金the National Natural Science Foundation of China(61976175)the Key Laboratory Project of Shaanxi Provincial Education Department Scientific Research Projects(20JS109)。
文摘The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.
基金supported by the National Key R&D ProgramStrategic Scientific and Technological Innovation Cooperation(2022YFB3803500)the National Key Research and Development Program of China(2019YFA0705700)+1 种基金the National Natural Science Foundation of China(52076121,51904016,and 52004138)the Fundamental Research Funds for the Central Universities。
文摘As the energy density of battery increases rapidly,lithium-ion batteries(LIBs)are facing serious safety issue with thermal runaway,which largely limits the large-scale applications of high-energy-density LIBs.It is generally agreed that the chemical crosstalk between the cathode and anode leads to thermal runaway of LIBs.Herein,a multifunctional high safety electrolyte is designed with synergistic construction of cathode electrolyte interphase and capture of reactive free radicals to limit the intrinsic pathway of thermal runaway.The cathode electrolyte interphase not only resists the gas attack from the anode but suppresses the parasitic side reactions induced by electrolyte.And the function of free radical capture has the ability of reducing heat release from thermal runaway of battery.The dual strategy improves the intrinsic safety of battery prominently that the triggering temperature of thermal runaway is increased by 24.4℃and the maximum temperature is reduced by 177.7℃.Simultaneously,the thermal runaway propagation in module can be self-quenched.Moreover,the electrolyte design balances the trade-off of electrochemical and safety performance of high-energy batteries.The capacity retention of LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)|graphite pouch cell has been significantly increased from 53.85%to 97.05%with higher coulombic efficiency of 99.94%at operating voltage extended up to 4.5 V for 200 cycles.Therefore,this work suggests a feasible strategy to mitigate the safety risk of high-energy-density LIBs without sacrificing electrochemical performances.
基金supported in part by the National Natural Science Foundation of China (52107229, 62203423, and 61903114)in part by the Fujian Provincial Natural Science Foundation (2022J01504)。
文摘Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.
基金Project supported by the National Natural Science Foundation of China(Grant No.11964006)the Science and Technology Foundation of Kaili University(Grant No.2022ZD06)the Specialized Research Fund for the Doctoral Program of Kaili University(Grant Nos.BS201601 and BS201702)。
文摘The electrode material is regarded as one of the key factors that determine the performance of lithium-ion batteries(LIBs).However,it is still a challenge to search for an anode material with large capacity,low diffusion barrier,and good stability.In the present work,two new CrP_(2) monolayers(Pmmn-CrP_(2) and Pmma-CrP_(2)) are predicted by means of first principles swarm structure search.Our study shows that both the two CrP_(2) monolayers have high dynamical and thermal stability,as well as excellent electron conductivity.Additionally,Pmmn-CrP_(2) exhibits a remarkably high storage capacity of 705 mA·h·g^(-1) for Li,meanwhile the diffusion energy barrier of Li on the surface of this monolayer is 0.21 eV,ensuring it as a high-performance anode material for LIBs.We hope that our study will inspire researchers to search for new-type two-dimensional(2D) transition metal phosphides for the electrode materials of LIB s.
基金funded by the Artificial Intelligence Technology Project of Xi’an Science and Technology Bureau in China(No.21RGZN0014)。
文摘Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.
基金partially supported by the Shenzhen Municipal Science and Technology Innovation Committee(RCBS20210609104423057)Fujian Key Laboratory of New Energy Generation and Power Conversion(KLIF-202104)the National Natural Science Foundation of China(52072038)。
文摘Dear Editor,State of health(SOH)estimation is critical for the management of lithium-ion batteries(LIBs).Data-driven estimation methods are appealing with the availability of real-world battery data.However,time-and data-costly training for batteries with different chemistries and models barriers their efficient deployment.
基金supported by the National Natural Science Foundation of China(62173211,61821004,62122041)Natural Science Foundation of Shandong Province,China(ZR2021JQ25,ZR2019ZD09)。
文摘Dear Editor,Any fault of a battery system that is not handled timely can cause catastrophic consequences.Therefore,it is significant to diagnose battery faults early and accurately.Due to the complex nonlinear features and inconsistency of lithium batteries,traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages.
基金supported by the financial support from the National Key Research and Development Program of China(2022YFB3807200)the Fundamental Research Funds for the Central Universities(2242022K330047)+3 种基金the dual creative talents from Jiangsu Province(JSSCBS20210152,JSSCBS20210100)the National Natural Science Foundation of Jiangsu Province(BK20221456,BK20200375)the Natural Science Foundation of China with(22109021)the Research Fund Program of Guangdong Provincial Key Lab of Green Chemical Product Technology(6802008024)。
文摘With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.
基金supported by the Natural Science Foundation of Science and Technology Department of Sichuan Province, China (23NSFSC6224)the Higher Education Talent Training Quality and Teaching Reform Project of Sichuan Province, China (JG2021-1098)+3 种基金the Industry-university cooperation collaborative education project of the Ministry of Education, China (221001359095358 and 220604738021813)the Development Research Center of Sichuan Cuisine (CC21Z02)the “Sichuang Fusion” Youth Red Dream Building Project of Chengdu University,China (cxcysc2022001)the Solid-state Fermentation Resource Utilization Key Laboratory of Sichuan Province (2020GTJ002)。
文摘A composite separator of SiC/PVDF-HFP was synthesized for lithium-ion batteries with high thermal and mechanical stabilities.Benefiting from the nanoscale,high hardness,and melting point of SiC,SiC/PVDFHFP with highly uniform microstructure was obtained.This polarization caused by barrier penetration was significantly restrained.Due to the Si-F bond between SiC and PVDF-HFP,the structural stability has been obviously enhanced,which could suppress the growth of lithium(Li) dendrite.Furthermore,some 3D reticulated Si nanowires are found on the surface of Li anode,which also greatly inhibit Li dendrites and result in irregular flakes of Li metal.Especially,the shrinkage of 6% SiC/PVDF-HFP at 150℃ is only 5%,which is notably lower than those of PVDF-HFP and Celgard2500.The commercial LiFePO_(4) cell assembled with 6% SiC/PVDF-HFP possesses a specific capacity of 157.8 mA h g^(-1) and coulomb efficiency of 98% at 80℃.In addition,the tensile strength and modulus of 6% SiC/PVDF-HFP could reach 14.6 and 562 MPa,respectively.And a small deformation(1000 nm) and strong deformation recovery are obtained under a high additional load(2.3 mN).Compared with PVDF-HFP and Celgard2500,the symmetric Li cell assembled with 6% SiC/PVDF-HFP has not polarized after 900 cycles due to its excellent mechanical stabilities.This strategy provides a feasible solution for the composite separator of high-safety batteries with a high temperature and impact resistance.
基金National Natural Science Foundation of China(Grant No.52107229)the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(Grant No.20KFKT02)。
文摘Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.
基金supported by the foundation on the Creative Research Team Construction Promotion Project of Beijing Municipal Institutions and Science and Technology Foundation(ykj-2016-00161)partly supported by International Research Promotion Program(IRPR)of Osaka University
文摘High-voltage lithium-ion batteries(HVLIBs) are considered as promising devices of energy storage for electric vehicle, hybrid electric vehicle, and other high-power equipment. HVLIBs require their own platform voltages to be higher than 4.5 V on charge. Lithium nickel manganese spinel LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode is the most promising candidate among the 5 V cathode materials for HVLIBs due to its flat plateau at 4.7 V. However, the degradation of cyclic performance is very serious when LNMO cathode operates over 4.2 V. In this review, we summarize some methods for enhancing the cycling stability of LNMO cathodes in lithium-ion batteries, including doping, cathode surface coating,electrolyte modifying, and other methods. We also discuss the advantages and disadvantages of different methods.
基金supported by the High Technology Research and Development Program of Jilin(20130204021GX)the Specialized Research Fund for Graduate Course Identification System Program(Jilin University)of China(450060523183)+2 种基金the National Natural Science Foundation of China(61520106008,U1564207,61503149)the Education Department of Jilin Province of China(2016430)the Graduate Innovation Fund of Jilin University(2016030)
文摘In this paper, a state of charge(SOC) estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability(ISS) theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process. The nonlinear relation between open circuit voltage(OCV) and SOC is obtained from a rapid test. Exponential fitting method is used to identify the parameters of the model. A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation. The designed observer is tested on AMESim and Simulink co-simulation. The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent.
基金financially supported by the National Natural Science Foundation of China(NSFC,U20A20310,52107230,52176199,52102470)the support of the research project Model2Life(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF)。
文摘Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.
基金the support given by National Natural Science Foundation of China(51874184)the Key Natural Science Foundation in Jiangsu Province(18KJA620003)Jiangsu Project Plan for Outstanding Talents Team in Six Research Fields(TD-XNYQC-002)。
文摘Thermal runaway caused by overcharging results in catastrophic disasters. The influences of charging rate, ambient temperature and aging on thermal runaway caused by overcharging are studied qualitatively and quantitatively in this manuscript. The results of overcharging tests indicate that high charging rate and ambient temperature increase thermal runaway risk. Aging in 40 ℃ decreases thermal runaway risk. The risk increase of battery with high overcharging rate and in high ambient temperature is due to fast lithium plating reaction and accelerated SEI decomposition, respectively. The risk decrease of aged battery is due to the occurrence of SEI before overcharging tests. SEI suppresses the side reactions between lithium plating and electrolyte. The results of orthogonal tests indicate that the rank of effect is: discharging rate > ambient temperature > aging. The heat generation is calculated based on the results of overcharging tests. The calculation results indicate that heat generated by side reactions contributes more to the total heat generation. Although thermal runaway does not occur during overcharging with low current, the heat dissipation of the lithium-ion battery is the most and deserves focus. The results are important to the design of battery management system and thermal management system to prevent thermal runaway induced by overcharging in total lifespan of battery.
基金supported by research on value model and technology application of patent operation of science and technology project(52094020000U)National Natural Science Foundation of China(52177193).
文摘The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.
