This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of fre...This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of freedom control structure with a reference model.The underlying powertrain model is derived and combined into the corresponding interconnected system required forμ-and H∞-synthesis.The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects.It is observed that theμ-controller offers performance advantages in particular for the acceleration control problem,but at the price of a high-order controller.展开更多
This contribution presents an outline of a new mathematical formulation for Classical Non-Equilibrium Thermodynamics (CNET) based on a contact structure in differential geometry. First a non-equilibrium state space is...This contribution presents an outline of a new mathematical formulation for Classical Non-Equilibrium Thermodynamics (CNET) based on a contact structure in differential geometry. First a non-equilibrium state space is introduced as the third key element besides the first and second law of thermodynamics. This state space provides the mathematical structure to generalize the Gibbs fundamental relation to non-equilibrium thermodynamics. A unique formulation for the second law of thermodynamics is postulated and it showed how the complying concept for non-equilibrium entropy is retrieved. The foundation of this formulation is a physical quantity, which is in non-equilibrium thermodynamics nowhere equal to zero. This is another perspective compared to the inequality, which is used in most other formulations in the literature. Based on this mathematical framework, it is proven that the thermodynamic potential is defined by the Gibbs free energy. The set of conjugated coordinates in the mathematical structure for the Gibbs fundamental relation will be identified for single component, closed systems. Only in the final section of this contribution will the equilibrium constraint be introduced and applied to obtain some familiar formulations for classical (equilibrium) thermodynamics.展开更多
Electrochemical impedance spectroscopy,a method for battery diagnostics,is used to estimate the internal temperature of a lithium-ion battery cell during highly dynamic load profiles.For the first time,a recurrent neu...Electrochemical impedance spectroscopy,a method for battery diagnostics,is used to estimate the internal temperature of a lithium-ion battery cell during highly dynamic load profiles.For the first time,a recurrent neural network is trained and evaluated with operando impedance data for temperature estimation.Furthermore,an approach is considered that guides the training process of the neural network by incorporating physical constraints.The model’s development based on an extensive series of measurements with different load profiles,tested under realistic conditions on large-format lithium-ion cells.The estimation accuracy of the data-driven approach is evaluated and compared against model-based methods,including the extended Kalman filter.An impedance correction model is proposed,which leads to a significant enhancement of the model-based estimation.The recurrent neural network under consideration achieves a mean square error of 1.07℃ for the investigated testing profiles in the temperature range up to 60℃.展开更多
Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s st...Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s state of health(SOH)and yield optimized usage strategy,and with that,a prolonged lifetime.In this study two data-driven models are coupled with physics-based models and compared in regards of their OCV and DM estimation accuracy:Two temporal convolutional—long short-term memory neural networks(TCN-LSTM)are trained from synthetic NCA-graphite battery data for OCV curve estimation(model 1)and alignment parameter estimation(model 2).Both models are fine-tuned with varying amounts of experimental NMC-graphite battery data during the transfer learning(TL)step.In the subsequent physics-constraining part the DMs are derived via optimization(model 1),i.e.,fitting the OCV with half cell open-circuit potentials,or directly via mathematical equations(model 2).Both models prove that fine-tuning data from one aging path suffices,if it includes the maximum appearing DMs of the target domain.For these use cases both models maintain OCV mean absolute errors(MAEs),DM MAEs and SOH mean absolute percentage errors(MAPEs)under 10 mV,3.10%and 1.98%,respectively.The model 2 has less computational complexity and reaches slightly better results but requires labeled target data including alignment parameters for its application.This study shows that synthetic data is eligible for TL,even for varying cell chemistries,and that the mechanistic model helps to physically constrain the output.展开更多
Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes.However,its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction re...Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes.However,its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction(ORR)within fuel cells.The complex nature of the ORR and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers.This scenario opens new avenues for the implementation of novel quantum computing workflows.Here,we present a state-of-the-art study that combines classical and quantum computational approaches to investigate ORR on platinum-based surfaces.Our research demonstrates,for the first time,the feasibility of implementing this workflow on the H1-series trappedion quantumcomputer and identify the challenges of the quantum chemistry modelling of this reaction.The results highlight the great potentiality of quantum computers in solving notoriously difficult systems with strongly correlated electronic structures and suggest platinum/cobalt as ideal candidate for showcasing quantum advantage in future applications.展开更多
FeNC catalysts are promising substitutes of platinum-type catalysts for the oxygen reduction reaction(ORR).While previous research disclosed that high pyrolysis temperatures are required to achieve good stability,it w...FeNC catalysts are promising substitutes of platinum-type catalysts for the oxygen reduction reaction(ORR).While previous research disclosed that high pyrolysis temperatures are required to achieve good stability,it was identified that a trade-off needs to be made regarding the active site density.The central question is,if a good stability can also be reached at milder pyrolysis conditions but longer duration retaining more active sites,while enabling the defect-rich carbon to heal during a long residence time?To address this,a variation of pyrolysis temperatures and durations is used in FeNC fabrication.Carbon morphology and iron species are characterized by Raman spectroscopy and Mössbauer spectroscopy,respectively.Fuel cell(FC)activity and stability data are acquired.The results are compared to ORR activity and selectivity data from rotating ring disc electrode experiments and resulting durability in accelerated stress tests mimicking the load cycle and start-up and shut-down cycle conditions.It is discussed how pyrolysis temperature and duration affect FC activity and stability.But,more important,the results connect the pyrolysis conditions to the required accelerated stress test protocol combination to enable a prediction of the catalyst stability in fuel cells.展开更多
Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state ...Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)estimation.This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model approach.The data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use cases.The TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage windows.The results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all C-rates.The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these cases.The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.展开更多
文摘This paper considers the application of robust control methods(μ-and H∞-synthesis)to the speed and acceleration control problem encountered in electric vehicle powertrains.To this end,we consider a two degree of freedom control structure with a reference model.The underlying powertrain model is derived and combined into the corresponding interconnected system required forμ-and H∞-synthesis.The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects.It is observed that theμ-controller offers performance advantages in particular for the acceleration control problem,but at the price of a high-order controller.
文摘This contribution presents an outline of a new mathematical formulation for Classical Non-Equilibrium Thermodynamics (CNET) based on a contact structure in differential geometry. First a non-equilibrium state space is introduced as the third key element besides the first and second law of thermodynamics. This state space provides the mathematical structure to generalize the Gibbs fundamental relation to non-equilibrium thermodynamics. A unique formulation for the second law of thermodynamics is postulated and it showed how the complying concept for non-equilibrium entropy is retrieved. The foundation of this formulation is a physical quantity, which is in non-equilibrium thermodynamics nowhere equal to zero. This is another perspective compared to the inequality, which is used in most other formulations in the literature. Based on this mathematical framework, it is proven that the thermodynamic potential is defined by the Gibbs free energy. The set of conjugated coordinates in the mathematical structure for the Gibbs fundamental relation will be identified for single component, closed systems. Only in the final section of this contribution will the equilibrium constraint be introduced and applied to obtain some familiar formulations for classical (equilibrium) thermodynamics.
文摘Electrochemical impedance spectroscopy,a method for battery diagnostics,is used to estimate the internal temperature of a lithium-ion battery cell during highly dynamic load profiles.For the first time,a recurrent neural network is trained and evaluated with operando impedance data for temperature estimation.Furthermore,an approach is considered that guides the training process of the neural network by incorporating physical constraints.The model’s development based on an extensive series of measurements with different load profiles,tested under realistic conditions on large-format lithium-ion cells.The estimation accuracy of the data-driven approach is evaluated and compared against model-based methods,including the extended Kalman filter.An impedance correction model is proposed,which leads to a significant enhancement of the model-based estimation.The recurrent neural network under consideration achieves a mean square error of 1.07℃ for the investigated testing profiles in the temperature range up to 60℃.
文摘Open-circuit voltage(OCV)updates are the key to accurate state of charge(SOC)estimates over lifetime.Degradation modes(DM)are directly coupled to OCV estimation.They offer a more detailed analysis of the battery’s state of health(SOH)and yield optimized usage strategy,and with that,a prolonged lifetime.In this study two data-driven models are coupled with physics-based models and compared in regards of their OCV and DM estimation accuracy:Two temporal convolutional—long short-term memory neural networks(TCN-LSTM)are trained from synthetic NCA-graphite battery data for OCV curve estimation(model 1)and alignment parameter estimation(model 2).Both models are fine-tuned with varying amounts of experimental NMC-graphite battery data during the transfer learning(TL)step.In the subsequent physics-constraining part the DMs are derived via optimization(model 1),i.e.,fitting the OCV with half cell open-circuit potentials,or directly via mathematical equations(model 2).Both models prove that fine-tuning data from one aging path suffices,if it includes the maximum appearing DMs of the target domain.For these use cases both models maintain OCV mean absolute errors(MAEs),DM MAEs and SOH mean absolute percentage errors(MAPEs)under 10 mV,3.10%and 1.98%,respectively.The model 2 has less computational complexity and reaches slightly better results but requires labeled target data including alignment parameters for its application.This study shows that synthetic data is eligible for TL,even for varying cell chemistries,and that the mechanistic model helps to physically constrain the output.
文摘Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes.However,its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction(ORR)within fuel cells.The complex nature of the ORR and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers.This scenario opens new avenues for the implementation of novel quantum computing workflows.Here,we present a state-of-the-art study that combines classical and quantum computational approaches to investigate ORR on platinum-based surfaces.Our research demonstrates,for the first time,the feasibility of implementing this workflow on the H1-series trappedion quantumcomputer and identify the challenges of the quantum chemistry modelling of this reaction.The results highlight the great potentiality of quantum computers in solving notoriously difficult systems with strongly correlated electronic structures and suggest platinum/cobalt as ideal candidate for showcasing quantum advantage in future applications.
基金Bundesministerium für Bildung und Forschung,Grant/Award Number:03XP0092。
文摘FeNC catalysts are promising substitutes of platinum-type catalysts for the oxygen reduction reaction(ORR).While previous research disclosed that high pyrolysis temperatures are required to achieve good stability,it was identified that a trade-off needs to be made regarding the active site density.The central question is,if a good stability can also be reached at milder pyrolysis conditions but longer duration retaining more active sites,while enabling the defect-rich carbon to heal during a long residence time?To address this,a variation of pyrolysis temperatures and durations is used in FeNC fabrication.Carbon morphology and iron species are characterized by Raman spectroscopy and Mössbauer spectroscopy,respectively.Fuel cell(FC)activity and stability data are acquired.The results are compared to ORR activity and selectivity data from rotating ring disc electrode experiments and resulting durability in accelerated stress tests mimicking the load cycle and start-up and shut-down cycle conditions.It is discussed how pyrolysis temperature and duration affect FC activity and stability.But,more important,the results connect the pyrolysis conditions to the required accelerated stress test protocol combination to enable a prediction of the catalyst stability in fuel cells.
文摘Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)estimation.This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model approach.The data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use cases.The TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage windows.The results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all C-rates.The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these cases.The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.
