Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrat...Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.展开更多
In this paper,overcharge behaviors and thermal runaway(TR)features of large format lithium-ion(Liion)cells with different cathode materials(LiFePO4(LFP),Li[Ni_(1/3)Co_(1/3)Mn_(1/3)]O_(2)(NCM111),Li[Ni_(0.6)Co_(0.2)Mn_...In this paper,overcharge behaviors and thermal runaway(TR)features of large format lithium-ion(Liion)cells with different cathode materials(LiFePO4(LFP),Li[Ni_(1/3)Co_(1/3)Mn_(1/3)]O_(2)(NCM111),Li[Ni_(0.6)Co_(0.2)Mn_(0.2)]O_(2)(NCM622)and Li[Ni_(0.8)Co_(0.1)Mn_(0.1)]O_(2)(NCM811))were investigated.The results showed that,under the same overcharge condition,the TR of LFP Li-ion cell occurred earlier compared with the NCM Li-ion cells,indicating its poor overcharge tolerance and high TR risk.However,when TR occurred,LFP Li-ion cell exhibited lower maximum temperature and mild TR response.All NCM Liion cells caught fire or exploded during TR,while the LFP Li-ion cell only released a large amount of smoke without fire.According to the overcharge behaviors and TR features,a safety assessment score system was proposed to evaluate the safety of the cells.In short,NCM Li-ion cells have better performance in energy density and overcharge tolerance(or low TR risk),while LFP Li-ion cell showed less severe response to overcharging(or less TR hazards).For NCM Li-ion cells,as the ratio of nickel in cathode material increases,the thermal stability of the cathode materials becomes poorer,and the TR hazards increase.Such a comparison study on large format Li-ion cells with different cathode materials can provide deeper insights into the overcharge behaviors and TR features,and provide guidance for engineers to reasonably choose battery materials in automotive applications.展开更多
基金supported by the National Natural Science Foundation of China(52375144 and 52205153)the Shanghai Pujiang Programme(23PJD019)the Shanghai Gaofeng Project for University Academic Program Development。
文摘Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.
基金supported by the National Natural Science Foundation of China(Nos.U1564206,U1764258)the National Key R&D Program of China(No.2018YFB0105700)+1 种基金the support from China Scholarship Council(No.201806030115)supported by the Department of Energy(DOE),Office of Electricity(OE)at Oak Ridge National Laboratory managed by UL-Battelle LLC under contract DE-AC05-00OR22725。
文摘In this paper,overcharge behaviors and thermal runaway(TR)features of large format lithium-ion(Liion)cells with different cathode materials(LiFePO4(LFP),Li[Ni_(1/3)Co_(1/3)Mn_(1/3)]O_(2)(NCM111),Li[Ni_(0.6)Co_(0.2)Mn_(0.2)]O_(2)(NCM622)and Li[Ni_(0.8)Co_(0.1)Mn_(0.1)]O_(2)(NCM811))were investigated.The results showed that,under the same overcharge condition,the TR of LFP Li-ion cell occurred earlier compared with the NCM Li-ion cells,indicating its poor overcharge tolerance and high TR risk.However,when TR occurred,LFP Li-ion cell exhibited lower maximum temperature and mild TR response.All NCM Liion cells caught fire or exploded during TR,while the LFP Li-ion cell only released a large amount of smoke without fire.According to the overcharge behaviors and TR features,a safety assessment score system was proposed to evaluate the safety of the cells.In short,NCM Li-ion cells have better performance in energy density and overcharge tolerance(or low TR risk),while LFP Li-ion cell showed less severe response to overcharging(or less TR hazards).For NCM Li-ion cells,as the ratio of nickel in cathode material increases,the thermal stability of the cathode materials becomes poorer,and the TR hazards increase.Such a comparison study on large format Li-ion cells with different cathode materials can provide deeper insights into the overcharge behaviors and TR features,and provide guidance for engineers to reasonably choose battery materials in automotive applications.
