Pub Date : 2023-08-01DOI: 10.1016/j.est.2023.107176
Qiang Zheng, Xiaoguang Yin, Dongxiao Zhang
Li-ion battery is a complex physicochemical system that generally takes observable current and terminal voltage as input and output, while leaving some unobservable quantities, e.g., Li-ion concentration, for serving as internal variables (states) of the system. On-line estimation for the unobservable states plays a key role in battery management system since they reflect battery safety and degradation conditions. Several kinds of models that map from current to voltage have been established for state estimation, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure.
{"title":"Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks","authors":"Qiang Zheng, Xiaoguang Yin, Dongxiao Zhang","doi":"10.1016/j.est.2023.107176","DOIUrl":"https://doi.org/10.1016/j.est.2023.107176","url":null,"abstract":"Li-ion battery is a complex physicochemical system that generally takes observable current and terminal voltage as input and output, while leaving some unobservable quantities, e.g., Li-ion concentration, for serving as internal variables (states) of the system. On-line estimation for the unobservable states plays a key role in battery management system since they reflect battery safety and degradation conditions. Several kinds of models that map from current to voltage have been established for state estimation, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure.","PeriodicalId":94331,"journal":{"name":"Journal of energy storage","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135276942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.1016/j.est.2023.107414
Lingping Zeng, Stephanie Vialle, Jonathan Ennis-King, Lionel Esteban, Mohammad Sarmadivaleh, Joel Sarout, Jeremie Dautriat, Ausama Giwelli, Quan Xie
Underground hydrogen storage in depleted gas reservoirs is a promising and economical option for large-scale renewable energy storage to achieve net-zero carbon emission. While caprock plays an important role in sealing capacity, current knowledge is still limited on the effect of H2-brine-rock geochemical interactions on caprock integrity, raising concerns about the viability of long-term UHS. To address this problem, we developed kinetic batch models to characterize the time-dependent redox-reactions which are unique for underground hydrogen storage. This is combined with analytical estimates for the extent of hydrogen penetration into caprock. Our results show that the dissolution degrees of all tested minerals in three types of shales are <1 % in 30 years, indicating a strong caprock integrity and containment ability during underground hydrogen storage from a geochemical perspective. Reactive transport calculations indicate that hydrogen only affects a few metres of the caprock above the reservoir, so that storage integrity of thick caprocks will be unaffected. Similarly, the overall amount of hydrogen penetrating into caprock is likely to be a tiny fraction of the amount stored, typically much <1 %. Overall, our results suggest that H2-brine-shale geochemical interactions may not compromise caprock integrity during underground hydrogen storage.
{"title":"Role of geochemical reactions on caprock integrity during underground hydrogen storage","authors":"Lingping Zeng, Stephanie Vialle, Jonathan Ennis-King, Lionel Esteban, Mohammad Sarmadivaleh, Joel Sarout, Jeremie Dautriat, Ausama Giwelli, Quan Xie","doi":"10.1016/j.est.2023.107414","DOIUrl":"https://doi.org/10.1016/j.est.2023.107414","url":null,"abstract":"Underground hydrogen storage in depleted gas reservoirs is a promising and economical option for large-scale renewable energy storage to achieve net-zero carbon emission. While caprock plays an important role in sealing capacity, current knowledge is still limited on the effect of H2-brine-rock geochemical interactions on caprock integrity, raising concerns about the viability of long-term UHS. To address this problem, we developed kinetic batch models to characterize the time-dependent redox-reactions which are unique for underground hydrogen storage. This is combined with analytical estimates for the extent of hydrogen penetration into caprock. Our results show that the dissolution degrees of all tested minerals in three types of shales are <1 % in 30 years, indicating a strong caprock integrity and containment ability during underground hydrogen storage from a geochemical perspective. Reactive transport calculations indicate that hydrogen only affects a few metres of the caprock above the reservoir, so that storage integrity of thick caprocks will be unaffected. Similarly, the overall amount of hydrogen penetrating into caprock is likely to be a tiny fraction of the amount stored, typically much <1 %. Overall, our results suggest that H2-brine-shale geochemical interactions may not compromise caprock integrity during underground hydrogen storage.","PeriodicalId":94331,"journal":{"name":"Journal of energy storage","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135165195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1016/j.est.2023.107052
Xinsheng Wu, Jay F. Whitacre
This work shows how PEO-based solid state-electrolyte materials can be more stable than commonly expected when used with some types of high voltage cathode materials. Potentiodynamic and galvanostatic tests were performed in test cells using PEO electrolyte layers with either LiNixMnyCozO2 or LiCoO2 cathode materials. We found that the high voltage instability of PEO-based solid-state cells is profoundly affected by the interfacial instability of the cathode material used Specifically, the in the presence of PEO electrolyte, LiCoO2 electrodes were observed to undergo an irreversible oxidation process where they eventually shattered into small pieces, thus leading to a rapid irreversible loss in capacity. In contrast, we found that the PEO-based solid-state electrolytes could be stably cycled with high-nickel content cathode materials (NCM 811, 532, and 111) stably at a cell potential up to 4.5 V vs. Li/Li+ over many cycles with minimal capacity deterioration; this unexpected degree of stability in light of possible PEO/cathode interfacial stability concepts.
{"title":"Reevaluating the stability of the PEO-based solid-state electrolytes for high voltage solid-state batteries","authors":"Xinsheng Wu, Jay F. Whitacre","doi":"10.1016/j.est.2023.107052","DOIUrl":"https://doi.org/10.1016/j.est.2023.107052","url":null,"abstract":"This work shows how PEO-based solid state-electrolyte materials can be more stable than commonly expected when used with some types of high voltage cathode materials. Potentiodynamic and galvanostatic tests were performed in test cells using PEO electrolyte layers with either LiNixMnyCozO2 or LiCoO2 cathode materials. We found that the high voltage instability of PEO-based solid-state cells is profoundly affected by the interfacial instability of the cathode material used Specifically, the in the presence of PEO electrolyte, LiCoO2 electrodes were observed to undergo an irreversible oxidation process where they eventually shattered into small pieces, thus leading to a rapid irreversible loss in capacity. In contrast, we found that the PEO-based solid-state electrolytes could be stably cycled with high-nickel content cathode materials (NCM 811, 532, and 111) stably at a cell potential up to 4.5 V vs. Li/Li+ over many cycles with minimal capacity deterioration; this unexpected degree of stability in light of possible PEO/cathode interfacial stability concepts.","PeriodicalId":94331,"journal":{"name":"Journal of energy storage","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1016/j.est.2023.107096
Zhendong Zhang, Zehua Zhu, Ziqiang Yang, Lei Sheng
The liquid-cooling system (LCS) of lithium-ion battery (LIB) pack is crucial in prolonging battery lifespan and improving electric vehicle (EV) reliability. This study purposes to control the battery pack's thermal distribution within a desirable level per a new-designed LCS. Both the special experimental platform and LCS model coupled with EV dynamic model are established to pinpoint the optimal matching parameters of components and the system's operational control-strategies. The results show that the deviation between experiment and simulation is within 3.0 % under conventional conditions. Higher flowrate and lower inlet temperature lead to lower battery temperature, while delaying the cooling intervention could reduce power consumption of 20 % around. The multi-objective optimization is conducted to further slash power consumption at 2750 W, and battery temperature at 30.83 °C during normal 1C discharge, by using response surface method combined with genetic algorithm II. Moreover, the present optimization also demonstrates a well-balanced solution between the battery temperature and power consumption under drive cycle. Combined with experiment and simulation, this work is valuable for one to design an excellent LCS for LIB packs of EV.
{"title":"Numerical-experimental method to devise a liquid-cooling test system for lithium-ion battery packs","authors":"Zhendong Zhang, Zehua Zhu, Ziqiang Yang, Lei Sheng","doi":"10.1016/j.est.2023.107096","DOIUrl":"https://doi.org/10.1016/j.est.2023.107096","url":null,"abstract":"The liquid-cooling system (LCS) of lithium-ion battery (LIB) pack is crucial in prolonging battery lifespan and improving electric vehicle (EV) reliability. This study purposes to control the battery pack's thermal distribution within a desirable level per a new-designed LCS. Both the special experimental platform and LCS model coupled with EV dynamic model are established to pinpoint the optimal matching parameters of components and the system's operational control-strategies. The results show that the deviation between experiment and simulation is within 3.0 % under conventional conditions. Higher flowrate and lower inlet temperature lead to lower battery temperature, while delaying the cooling intervention could reduce power consumption of 20 % around. The multi-objective optimization is conducted to further slash power consumption at 2750 W, and battery temperature at 30.83 °C during normal 1C discharge, by using response surface method combined with genetic algorithm II. Moreover, the present optimization also demonstrates a well-balanced solution between the battery temperature and power consumption under drive cycle. Combined with experiment and simulation, this work is valuable for one to design an excellent LCS for LIB packs of EV.","PeriodicalId":94331,"journal":{"name":"Journal of energy storage","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1016/j.est.2022.105978
Friedrich von Bülow, Tobias Meisen
The ageing of Lithium-ion batteries can be described as change of state of health (∆SOH). It depends on the battery's operation during charging, discharging, and rest phases. Mapping the operation conditions during these phases for long time windows to a ∆SOH enables forecasting the battery's SOH. With SOH forecasting fleet managers of battery electric vehicle (BEV) fleets can plan vehicle replacement and optimize the fleet's operational strategy. Inspired by the applicability from a user's perspective of fleet managers and battery designers, this work motivates and defines key criteria for SOH forecasting models. The key criteria concern the encoding of information in the model inputs, model transferability to other batteries, and the applicability to 2nd life battery applications. Based on these key criteria we review SOH forecasting models. Currently, only few models satisfy the majority of the defined key criteria, while three others only fail at two key criteria. The majority (71 %) of the methods use machine learning models which can be seen as current research trend due to the complex dependence of battery operational data and battery ageing. We show limitations of the applicability and comparability of existing models due to different data sets, different metrics, different output values, and different forecast horizons. Furthermore, code and data are only rarely shared and publicly available.
{"title":"A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions","authors":"Friedrich von Bülow, Tobias Meisen","doi":"10.1016/j.est.2022.105978","DOIUrl":"https://doi.org/10.1016/j.est.2022.105978","url":null,"abstract":"The ageing of Lithium-ion batteries can be described as change of state of health (∆SOH). It depends on the battery's operation during charging, discharging, and rest phases. Mapping the operation conditions during these phases for long time windows to a ∆SOH enables forecasting the battery's SOH. With SOH forecasting fleet managers of battery electric vehicle (BEV) fleets can plan vehicle replacement and optimize the fleet's operational strategy. Inspired by the applicability from a user's perspective of fleet managers and battery designers, this work motivates and defines key criteria for SOH forecasting models. The key criteria concern the encoding of information in the model inputs, model transferability to other batteries, and the applicability to 2nd life battery applications. Based on these key criteria we review SOH forecasting models. Currently, only few models satisfy the majority of the defined key criteria, while three others only fail at two key criteria. The majority (71 %) of the methods use machine learning models which can be seen as current research trend due to the complex dependence of battery operational data and battery ageing. We show limitations of the applicability and comparability of existing models due to different data sets, different metrics, different output values, and different forecast horizons. Furthermore, code and data are only rarely shared and publicly available.","PeriodicalId":94331,"journal":{"name":"Journal of energy storage","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135843526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：