Pub Date : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813912
Claudio Burgos-Mellado, F. Donoso, T. Dragičević
This paper proposes a three-phase AC battery based on the modular multilevel converter (MMC) and investigates the effects of cyber attacks on it. The AC battery concept allows plug and play combinatorial integration of diverse battery cells with different characteristics such as nominal voltage, state of charge (SoC), state of health (SoH), and capacity into modular and reconfigurable battery packs that can cost-effectively cover a broad range of applications from electrified vehicles to stationary storage. To this end, in each sub-module (SM) of the MMC, battery cells (or modules) are connected to its capacitor, enabling a cell-to-cell control. In this scenario, the traditional battery management system (BMS) can be replaced by control schemes for the converter aiming to equalise critical parameters associated with battery cells. Unlike previous works, the proposed battery concept considers a local controllers (LC) in each SM of the MMC, achieving a modularisation in computing capacity for the MMC control system. Under this framework, a distributed control scheme based on the consensus theory is proposed for SoC regulation among the battery cells. Also, it is shown that cyber attacks are real threats to this electrical system. In particular, this work studies the effects of the specific cyber attack named false data injection attack (FDIA) on the proposed distributed control scheme for SoC regulation.
{"title":"AC Battery: Modular Layout and Cyber-secure Cell-level Control for Cost-Effective Transportation Electrification","authors":"Claudio Burgos-Mellado, F. Donoso, T. Dragičević","doi":"10.1109/ITEC53557.2022.9813912","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813912","url":null,"abstract":"This paper proposes a three-phase AC battery based on the modular multilevel converter (MMC) and investigates the effects of cyber attacks on it. The AC battery concept allows plug and play combinatorial integration of diverse battery cells with different characteristics such as nominal voltage, state of charge (SoC), state of health (SoH), and capacity into modular and reconfigurable battery packs that can cost-effectively cover a broad range of applications from electrified vehicles to stationary storage. To this end, in each sub-module (SM) of the MMC, battery cells (or modules) are connected to its capacitor, enabling a cell-to-cell control. In this scenario, the traditional battery management system (BMS) can be replaced by control schemes for the converter aiming to equalise critical parameters associated with battery cells. Unlike previous works, the proposed battery concept considers a local controllers (LC) in each SM of the MMC, achieving a modularisation in computing capacity for the MMC control system. Under this framework, a distributed control scheme based on the consensus theory is proposed for SoC regulation among the battery cells. Also, it is shown that cyber attacks are real threats to this electrical system. In particular, this work studies the effects of the specific cyber attack named false data injection attack (FDIA) on the proposed distributed control scheme for SoC regulation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131131811","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814035
Ryan Greenough, Graham McClone, M. Alvarez, Adil Khurram, J. Kleissl
A decentralized algorithm called proximal message passing (PMP) is applied to solve the AC-OPF problem for distribution networks with distributed energy resources (DERs). The second order cone relaxation of the AC-OPF is considered in the PMP algorithm which had previously been implemented only using the linearized DC power flow. In the PMP algorithm, each node shares local information regarding power and voltage (primal variables) and nodal price (dual variables) with its neighbors to minimize the local objective function at each time step. The local objective function consists of generation costs and a penalty associated with violating power flow constraints. The solution of the optimization problem provides day-ahead schedules for the economic dispatch of DERs and generators. Simulation results are presented for a modified IEEE 13 bus system and convergence of the PMP algorithm is discussed in simulations.
{"title":"Decentralized Economic Dispatch via Proximal Message Passing","authors":"Ryan Greenough, Graham McClone, M. Alvarez, Adil Khurram, J. Kleissl","doi":"10.1109/ITEC53557.2022.9814035","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814035","url":null,"abstract":"A decentralized algorithm called proximal message passing (PMP) is applied to solve the AC-OPF problem for distribution networks with distributed energy resources (DERs). The second order cone relaxation of the AC-OPF is considered in the PMP algorithm which had previously been implemented only using the linearized DC power flow. In the PMP algorithm, each node shares local information regarding power and voltage (primal variables) and nodal price (dual variables) with its neighbors to minimize the local objective function at each time step. The local objective function consists of generation costs and a penalty associated with violating power flow constraints. The solution of the optimization problem provides day-ahead schedules for the economic dispatch of DERs and generators. Simulation results are presented for a modified IEEE 13 bus system and convergence of the PMP algorithm is discussed in simulations.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"5 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120918311","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814063
M. Bradley
Aviation has been investigating and developing alternate electrified propulsion and power system architectures in earnest for more than 15 years. Until more recently, most architectures have utilized batteries or generators, often in a hybrid system with jet fuel burning turbines or internal combustion engines. Interest has increased significantly in architectures using fuel cell systems alone or as hybrid systems, especially using Hydrogen as a fuel. This paper reviews previous work on non-fuel cell architectures and then identifies and classifies various options for fuel cell powertrain architectures that are most suitable to the unique requirements of aviation applications. These include high altitude operation, high sensitivities to system weight and volume, high differences in power during different mission phases, and compatibility with the current aviation infrastructure and certification processes. Seven different pure fuel cell and fuel cell hybrid architectures are identified and illustrated schematically. Features and benefits are discussed, but there is no clear best choice. Recommendations are made for future activities and development.
{"title":"Identification and Descriptions of Fuel Cell Architectures for Aircraft Applications","authors":"M. Bradley","doi":"10.1109/ITEC53557.2022.9814063","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814063","url":null,"abstract":"Aviation has been investigating and developing alternate electrified propulsion and power system architectures in earnest for more than 15 years. Until more recently, most architectures have utilized batteries or generators, often in a hybrid system with jet fuel burning turbines or internal combustion engines. Interest has increased significantly in architectures using fuel cell systems alone or as hybrid systems, especially using Hydrogen as a fuel. This paper reviews previous work on non-fuel cell architectures and then identifies and classifies various options for fuel cell powertrain architectures that are most suitable to the unique requirements of aviation applications. These include high altitude operation, high sensitivities to system weight and volume, high differences in power during different mission phases, and compatibility with the current aviation infrastructure and certification processes. Seven different pure fuel cell and fuel cell hybrid architectures are identified and illustrated schematically. Features and benefits are discussed, but there is no clear best choice. Recommendations are made for future activities and development.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121289568","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814050
Atriya Biswas, Yue Wang, A. Emadi
The performance of reinforcement learning-based energy management system for a pure hybrid electric vehicle critically depends on the articulation of immediate reward function. The current brief systematically unveils the fundamental reliance of reinforcement learning-based agent’s performance on the articulation of immediate reward function. Third generation Toyota hybrid system is chosen as the electrified powertrain for formulating the energy management problem. An asynchronous advantage actor-critic-based reinforcement learning framework is chosen as the control strategy for the energy management system of the aforementioned powertrain. The chosen powertrain architecture offers two degrees-of-freedom, i.e., engine speed and engine torque. Since reinforcement learning agent is solely responsible for controlling these two variables over a given drive cycle without any tactical controllers, reinforcement learning-based agent not only has to find the near-optimal trajectory for the control variables, but should also consider the feasibility criteria for practical operation. Since reinforcement learning agent chooses the control variables randomly without any feasibility check, immediate reward function should be articulated in such a way so that the agent is discouraged to choose any control variable resulting in infeasible powertrain operation.
{"title":"Effect of immediate reward function on the performance of reinforcement learning-based energy management system","authors":"Atriya Biswas, Yue Wang, A. Emadi","doi":"10.1109/ITEC53557.2022.9814050","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814050","url":null,"abstract":"The performance of reinforcement learning-based energy management system for a pure hybrid electric vehicle critically depends on the articulation of immediate reward function. The current brief systematically unveils the fundamental reliance of reinforcement learning-based agent’s performance on the articulation of immediate reward function. Third generation Toyota hybrid system is chosen as the electrified powertrain for formulating the energy management problem. An asynchronous advantage actor-critic-based reinforcement learning framework is chosen as the control strategy for the energy management system of the aforementioned powertrain. The chosen powertrain architecture offers two degrees-of-freedom, i.e., engine speed and engine torque. Since reinforcement learning agent is solely responsible for controlling these two variables over a given drive cycle without any tactical controllers, reinforcement learning-based agent not only has to find the near-optimal trajectory for the control variables, but should also consider the feasibility criteria for practical operation. Since reinforcement learning agent chooses the control variables randomly without any feasibility check, immediate reward function should be articulated in such a way so that the agent is discouraged to choose any control variable resulting in infeasible powertrain operation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121354961","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 : 2022-06-15DOI: 10.1109/itec53557.2022.9813905
Maryam Alizadeh, Sumedh Dhale, A. Emadi
In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.
{"title":"Real-Time Ambient Temperature Estimation Using Kalman Filter and Traction Power-Aware Cabin Climate Control in Battery Electric Vehicles","authors":"Maryam Alizadeh, Sumedh Dhale, A. Emadi","doi":"10.1109/itec53557.2022.9813905","DOIUrl":"https://doi.org/10.1109/itec53557.2022.9813905","url":null,"abstract":"In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127146106","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813925
Eduardo Louback, Jigar N. Mistry, Peter Azer, B. Bilgin
One key aspect to be considered when designing an electric vehicle (EV) inverter is its dynamic response to vibrational loads. The source of these vibrational loads can be as simple as driving the vehicle, where the displacement of the suspension generates vibration that is transferred through the powertrain components, exciting the inverter. Additionally, with the increased adoption of integrated drives for EVs, the inverter is placed in close proximity to the motor or the gearbox, which can induce even more vibrations. Therefore, modal analysis is performed to extract the modal shapes and natural frequencies of the inverter. Ideally, an equipment should not be subjected to vibrations at its natural frequencies because that can lead to resonance, potentially causing a mechanical or operational failure. However, it is usually not possible to completely avoid the natural frequencies. In such cases, harmonic analysis is performed to understand the peak dynamic response of the inverter and ensure that it is within the operational limits. Nevertheless, only a few papers have discussed how to perform vibration analysis of traction inverters. Thus, this paper presents a brief overview of the fundamentals of mechanical vibrations, focusing on modal and harmonic analyses of a high-power traction inverter. Along with the vibration theory, simulation results carried out with ANSYS Mechanical are presented and used to assess the dynamic performance of the inverter under a wide range of vibration loads and excitation frequencies. The results indicate that the inverter is appropriate for in-vehicle operation and, although each inverter design presents different responses to vibrational loads, the results and assumptions adopted in this paper could serve as a reference for future work.
{"title":"Dynamic Vibrational Analysis of a Traction Inverter Housing","authors":"Eduardo Louback, Jigar N. Mistry, Peter Azer, B. Bilgin","doi":"10.1109/ITEC53557.2022.9813925","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813925","url":null,"abstract":"One key aspect to be considered when designing an electric vehicle (EV) inverter is its dynamic response to vibrational loads. The source of these vibrational loads can be as simple as driving the vehicle, where the displacement of the suspension generates vibration that is transferred through the powertrain components, exciting the inverter. Additionally, with the increased adoption of integrated drives for EVs, the inverter is placed in close proximity to the motor or the gearbox, which can induce even more vibrations. Therefore, modal analysis is performed to extract the modal shapes and natural frequencies of the inverter. Ideally, an equipment should not be subjected to vibrations at its natural frequencies because that can lead to resonance, potentially causing a mechanical or operational failure. However, it is usually not possible to completely avoid the natural frequencies. In such cases, harmonic analysis is performed to understand the peak dynamic response of the inverter and ensure that it is within the operational limits. Nevertheless, only a few papers have discussed how to perform vibration analysis of traction inverters. Thus, this paper presents a brief overview of the fundamentals of mechanical vibrations, focusing on modal and harmonic analyses of a high-power traction inverter. Along with the vibration theory, simulation results carried out with ANSYS Mechanical are presented and used to assess the dynamic performance of the inverter under a wide range of vibration loads and excitation frequencies. The results indicate that the inverter is appropriate for in-vehicle operation and, although each inverter design presents different responses to vibrational loads, the results and assumptions adopted in this paper could serve as a reference for future work.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357061","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813980
Yixin Huangfu, Linnea Campbell, S. Habibi
Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.
{"title":"Temperature Effect on Thermal Imaging and Deep Learning Detection Models","authors":"Yixin Huangfu, Linnea Campbell, S. Habibi","doi":"10.1109/ITEC53557.2022.9813980","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813980","url":null,"abstract":"Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127322789","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814024
Sebastian Menner, M. Buchholz
Knowledge of local temperature-dependent current distributions helps battery management systems (BMS) to ensure an optimal operation. However, current measurements for all cells within a battery pack are technically not feasible and common model-based methods are too complex for a real-time application on simple BMS computing hardware. We already published a model to determine local cell currents based on the linearization of temperature-current dependencies. During evaluation with different cells, however, this model exhibited weaknesses for longer cycles with high discharge current. Therefore, we propose an extended version of this model that ensures reliable results also for such load profiles. For this purpose, subspace identification methods are used, which allow a purely data-based, user-friendly and robust model identification. We compare two different algorithms, which both will be shown to provide good results. The parameterization of this extended model is still based on only few measurement data, which can be easily determined. The memory requirement remains very low and the calculation of the model is simple enough to meet real-time requirements even on simple control units.
{"title":"Extended Gradient-Based Model for Real-Time Determination of Local Temperature-Dependent Currents Within Lithium-Ion Batteries","authors":"Sebastian Menner, M. Buchholz","doi":"10.1109/ITEC53557.2022.9814024","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814024","url":null,"abstract":"Knowledge of local temperature-dependent current distributions helps battery management systems (BMS) to ensure an optimal operation. However, current measurements for all cells within a battery pack are technically not feasible and common model-based methods are too complex for a real-time application on simple BMS computing hardware. We already published a model to determine local cell currents based on the linearization of temperature-current dependencies. During evaluation with different cells, however, this model exhibited weaknesses for longer cycles with high discharge current. Therefore, we propose an extended version of this model that ensures reliable results also for such load profiles. For this purpose, subspace identification methods are used, which allow a purely data-based, user-friendly and robust model identification. We compare two different algorithms, which both will be shown to provide good results. The parameterization of this extended model is still based on only few measurement data, which can be easily determined. The memory requirement remains very low and the calculation of the model is simple enough to meet real-time requirements even on simple control units.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767512","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9813934
Xiwen Xu, Tiefu Zhao, Shen-En Chen, N. Braxtan, D. Ward
Inductive power transfer (IPT) technology has gradually matured in electric vehicle (EV) charging. However, most of the existing designs have not considered the system inductance variation caused by factory manufacturing tolerance and ambient environment change, which can weaken the power transfer capability of the IPT systems significantly. In this paper, the effects of the inductance variation on the power transfer capability of IPT systems were investigated. A 10% coil tolerance can lead to a power reduction of up to 61.3%. To fill this gap, this paper proposed a frequency modulated maximum power point tracking (MPPT) method to adjust the inverter frequency to achieve its maximum power point. The simulation results under different circumstances were analyzed. The experimental results show the feasibility of this method to improve the power transfer capability.
{"title":"A Frequency Modulated Maximum Power Point Tracking Method for Wireless Charging Systems","authors":"Xiwen Xu, Tiefu Zhao, Shen-En Chen, N. Braxtan, D. Ward","doi":"10.1109/ITEC53557.2022.9813934","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9813934","url":null,"abstract":"Inductive power transfer (IPT) technology has gradually matured in electric vehicle (EV) charging. However, most of the existing designs have not considered the system inductance variation caused by factory manufacturing tolerance and ambient environment change, which can weaken the power transfer capability of the IPT systems significantly. In this paper, the effects of the inductance variation on the power transfer capability of IPT systems were investigated. A 10% coil tolerance can lead to a power reduction of up to 61.3%. To fill this gap, this paper proposed a frequency modulated maximum power point tracking (MPPT) method to adjust the inverter frequency to achieve its maximum power point. The simulation results under different circumstances were analyzed. The experimental results show the feasibility of this method to improve the power transfer capability.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134008350","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 : 2022-06-15DOI: 10.1109/ITEC53557.2022.9814022
Alberto Barragán-Moreno, Pere Izquierdo Gomez, T. Dragičević
The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of the traditional histogram-based analysis. The method is tested by comparing the accumulated damage estimations of two support-vector regression algorithms when trained with each type of cycle-counting procedure. NASA’s randomized battery usage data set is used as source of stress data. A 50% error reduction was observed when using the fuzzy logic-based approach compared to the traditional one. Thus, the proposed method can effectively improve the accuracy of diagnosis algorithms without penalizing their performance and memory-saving features.
{"title":"Enhancement of Stress Cycle-counting Algorithms for Li-ion Batteries by means of Fuzzy Logic","authors":"Alberto Barragán-Moreno, Pere Izquierdo Gomez, T. Dragičević","doi":"10.1109/ITEC53557.2022.9814022","DOIUrl":"https://doi.org/10.1109/ITEC53557.2022.9814022","url":null,"abstract":"The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of the traditional histogram-based analysis. The method is tested by comparing the accumulated damage estimations of two support-vector regression algorithms when trained with each type of cycle-counting procedure. NASA’s randomized battery usage data set is used as source of stress data. A 50% error reduction was observed when using the fuzzy logic-based approach compared to the traditional one. Thus, the proposed method can effectively improve the accuracy of diagnosis algorithms without penalizing their performance and memory-saving features.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128130459","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}
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