GC
G.R. Chandra Mouli
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5 records found
1
Since 2007, the impact of aviation industry emissions on climate change has garnered significant scientific attention. Electric aircraft (EA) are emerging as a promising solution to mitigate these effects. However, the design of battery packs capable of withstanding particular obstacles encountered by EA remains a challenge. During flight, critical challenges include meeting flight energy demands with batteries that have a lower energy density compared to conventional fuel aircraft, managing thermal conditions to ensure optimal battery performance, and reducing the weight of the battery system without compromising functionality. The integration of battery thermal management systems (BTMS) to maintain battery efficiency further increases the energy requirements and weight of the battery system. This paper aims to optimize mass energy density and Battery Thermal Management System ( BTMS ) energy consumption under the condition of meeting the energy, power and temperature requirements of large-scale battery packs for regional All-Electric Aircraft (AEA).
This thesis presents the design of an efficient and lightweight battery pack and its BTMS for pure electric aircraft. Based on a predefined flight cycle, the size of the battery required to meet the energy demands of the electric aircraft is calculated. Using experimental data on battery charging and discharging under varying temperatures and C-rates, an equivalent circuit model (ECM) of the battery cell is established. This ECM predicts the electrical and thermal behavior of the battery during flight. Subsequently, the material selection, specifications, dimensions, and structure of the battery module and BTMS are designed. With a focus on maintaining cell temperature and minimizing BTMS energy consumption, simulations of the packaged battery module are conducted. These simulations are used to explore and optimize various parameters. Finally, the modeling and simulation of the complete battery pack are performed. The simulation results demonstrate that the design effectively meets the operational requirements of pure electric aircraft in the targeted use scenario. ...
This thesis presents the design of an efficient and lightweight battery pack and its BTMS for pure electric aircraft. Based on a predefined flight cycle, the size of the battery required to meet the energy demands of the electric aircraft is calculated. Using experimental data on battery charging and discharging under varying temperatures and C-rates, an equivalent circuit model (ECM) of the battery cell is established. This ECM predicts the electrical and thermal behavior of the battery during flight. Subsequently, the material selection, specifications, dimensions, and structure of the battery module and BTMS are designed. With a focus on maintaining cell temperature and minimizing BTMS energy consumption, simulations of the packaged battery module are conducted. These simulations are used to explore and optimize various parameters. Finally, the modeling and simulation of the complete battery pack are performed. The simulation results demonstrate that the design effectively meets the operational requirements of pure electric aircraft in the targeted use scenario. ...
Since 2007, the impact of aviation industry emissions on climate change has garnered significant scientific attention. Electric aircraft (EA) are emerging as a promising solution to mitigate these effects. However, the design of battery packs capable of withstanding particular obstacles encountered by EA remains a challenge. During flight, critical challenges include meeting flight energy demands with batteries that have a lower energy density compared to conventional fuel aircraft, managing thermal conditions to ensure optimal battery performance, and reducing the weight of the battery system without compromising functionality. The integration of battery thermal management systems (BTMS) to maintain battery efficiency further increases the energy requirements and weight of the battery system. This paper aims to optimize mass energy density and Battery Thermal Management System ( BTMS ) energy consumption under the condition of meeting the energy, power and temperature requirements of large-scale battery packs for regional All-Electric Aircraft (AEA).
This thesis presents the design of an efficient and lightweight battery pack and its BTMS for pure electric aircraft. Based on a predefined flight cycle, the size of the battery required to meet the energy demands of the electric aircraft is calculated. Using experimental data on battery charging and discharging under varying temperatures and C-rates, an equivalent circuit model (ECM) of the battery cell is established. This ECM predicts the electrical and thermal behavior of the battery during flight. Subsequently, the material selection, specifications, dimensions, and structure of the battery module and BTMS are designed. With a focus on maintaining cell temperature and minimizing BTMS energy consumption, simulations of the packaged battery module are conducted. These simulations are used to explore and optimize various parameters. Finally, the modeling and simulation of the complete battery pack are performed. The simulation results demonstrate that the design effectively meets the operational requirements of pure electric aircraft in the targeted use scenario.
This thesis presents the design of an efficient and lightweight battery pack and its BTMS for pure electric aircraft. Based on a predefined flight cycle, the size of the battery required to meet the energy demands of the electric aircraft is calculated. Using experimental data on battery charging and discharging under varying temperatures and C-rates, an equivalent circuit model (ECM) of the battery cell is established. This ECM predicts the electrical and thermal behavior of the battery during flight. Subsequently, the material selection, specifications, dimensions, and structure of the battery module and BTMS are designed. With a focus on maintaining cell temperature and minimizing BTMS energy consumption, simulations of the packaged battery module are conducted. These simulations are used to explore and optimize various parameters. Finally, the modeling and simulation of the complete battery pack are performed. The simulation results demonstrate that the design effectively meets the operational requirements of pure electric aircraft in the targeted use scenario.
The rapid adoption of electric vehicles (EVs) poses significant challenges to low-voltage distribution grids, particularly in regions with high penetration rates like the Netherlands. As EVs increasingly draw power from and feed power back into the grid through technologies such as Vehicle-to-Grid (V2G) and mobile V2G, the stability and reliability of low-voltage grids are put to the test. This thesis investigates how uncoordinated charging behaviors, combined with real-world factors like commuting patterns, impact grid performance. The study focuses on key technical aspects such as grid congestion, voltage fluctuations, and transformer loading, aiming to understand the potential stress points in the grid.
Through a series of detailed simulations, the research explores different operational scenarios involving smart charging, V2G, and mobile V2G technologies. These simulations assess the grid’s response to varying levels of V2G penetration, seasonal demand shifts, and commuting behaviors, providing a realistic analysis of the challenges that low-voltage grids face. The study models suburban Dutch grids, emphasizing real-world conditions such as the asynchronous nature of charging and discharging patterns and how they can lead to localized imbalances.
This research reveals the complex interactions between EV integration and grid performance, emphasizing that user-driven charging behaviors and the growing penetration of V2G solutions can lead to significant grid instability without proper coordination. The findings highlight the necessity for advanced grid management strategies, infrastructure reinforcements, and innovative charging solutions to mitigate these risks. By offering insights into the technical challenges of grid integration under various real-world conditions, this thesis contributes to a deeper understanding of the infrastructure requirements and operational strategies needed to support the transition to electrified transportation on a large scale. ...
Through a series of detailed simulations, the research explores different operational scenarios involving smart charging, V2G, and mobile V2G technologies. These simulations assess the grid’s response to varying levels of V2G penetration, seasonal demand shifts, and commuting behaviors, providing a realistic analysis of the challenges that low-voltage grids face. The study models suburban Dutch grids, emphasizing real-world conditions such as the asynchronous nature of charging and discharging patterns and how they can lead to localized imbalances.
This research reveals the complex interactions between EV integration and grid performance, emphasizing that user-driven charging behaviors and the growing penetration of V2G solutions can lead to significant grid instability without proper coordination. The findings highlight the necessity for advanced grid management strategies, infrastructure reinforcements, and innovative charging solutions to mitigate these risks. By offering insights into the technical challenges of grid integration under various real-world conditions, this thesis contributes to a deeper understanding of the infrastructure requirements and operational strategies needed to support the transition to electrified transportation on a large scale. ...
The rapid adoption of electric vehicles (EVs) poses significant challenges to low-voltage distribution grids, particularly in regions with high penetration rates like the Netherlands. As EVs increasingly draw power from and feed power back into the grid through technologies such as Vehicle-to-Grid (V2G) and mobile V2G, the stability and reliability of low-voltage grids are put to the test. This thesis investigates how uncoordinated charging behaviors, combined with real-world factors like commuting patterns, impact grid performance. The study focuses on key technical aspects such as grid congestion, voltage fluctuations, and transformer loading, aiming to understand the potential stress points in the grid.
Through a series of detailed simulations, the research explores different operational scenarios involving smart charging, V2G, and mobile V2G technologies. These simulations assess the grid’s response to varying levels of V2G penetration, seasonal demand shifts, and commuting behaviors, providing a realistic analysis of the challenges that low-voltage grids face. The study models suburban Dutch grids, emphasizing real-world conditions such as the asynchronous nature of charging and discharging patterns and how they can lead to localized imbalances.
This research reveals the complex interactions between EV integration and grid performance, emphasizing that user-driven charging behaviors and the growing penetration of V2G solutions can lead to significant grid instability without proper coordination. The findings highlight the necessity for advanced grid management strategies, infrastructure reinforcements, and innovative charging solutions to mitigate these risks. By offering insights into the technical challenges of grid integration under various real-world conditions, this thesis contributes to a deeper understanding of the infrastructure requirements and operational strategies needed to support the transition to electrified transportation on a large scale.
Through a series of detailed simulations, the research explores different operational scenarios involving smart charging, V2G, and mobile V2G technologies. These simulations assess the grid’s response to varying levels of V2G penetration, seasonal demand shifts, and commuting behaviors, providing a realistic analysis of the challenges that low-voltage grids face. The study models suburban Dutch grids, emphasizing real-world conditions such as the asynchronous nature of charging and discharging patterns and how they can lead to localized imbalances.
This research reveals the complex interactions between EV integration and grid performance, emphasizing that user-driven charging behaviors and the growing penetration of V2G solutions can lead to significant grid instability without proper coordination. The findings highlight the necessity for advanced grid management strategies, infrastructure reinforcements, and innovative charging solutions to mitigate these risks. By offering insights into the technical challenges of grid integration under various real-world conditions, this thesis contributes to a deeper understanding of the infrastructure requirements and operational strategies needed to support the transition to electrified transportation on a large scale.
As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting increased pressure on the grid. One way to decrease the congestion in the grid is to make use of smart charging schedules for electric vehicles, with the objective of reducing peak demand and preventing the overloading of cables and transformers while reducing the cost of charging for electric vehicle owners.
The recent increase in the availability of real-life data has allowed the in-depth study of smart charging dynamics on a large scale through modeling and simulation. Mathematical optimization is a method that is often used to generate smart charging plans in state-of-the-art smart charging applications. However, while mathematical optimization can be very effective, as the objective function expands and more parameters are taken into account, the optimization becomes more complex and therefore require faster and smarter optimization algorithms. In addition, the recent availability of a vast amount of real-life data sets has made efficient data handling more important.
Machine learning is known to be an effective way of introducing Artificial Intelligence to smart charging algorithms. The use of reinforcement learning algorithms, a subset of machine learning could help overcome the disadvantages of mathematical optimization as trained algorithms are generally fast when predicting outcomes and have the potential to be accurate at the same time.
Therefore, the purpose of this work is to research the feasibility and additional benefit of machine learning to mathematical optimization-based smart charging algorithms. This is done through the development of end-to-end Q-learning and Double Deep Q Network reinforcement learning smart charging algorithms. The performance of both algorithms is evaluated on three separate case studies as well as on multiple different random cases and is compared to the charging performance of the average rate charging and mixed-integer programming algorithm benchmarks.
As a result, it becomes clear that for individual case studies the Q-learning and Double Deep Q Network agent are able to find cheap charging moments while charging the vehicle to 100\% battery capacity without violating charging constraints. However, when testing the performance of the Q-learning and Double Deep Q Network agents it becomes clear that the average charging performance is significantly worse than using the method of mixed-integer programming as the algorithms do not learn to generalize well.
Finally, the advantages and disadvantages of replacing mixed-integer programming with reinforcement learning are discussed as well as some limitations and recommendations for future work and improvement are given.
...
The recent increase in the availability of real-life data has allowed the in-depth study of smart charging dynamics on a large scale through modeling and simulation. Mathematical optimization is a method that is often used to generate smart charging plans in state-of-the-art smart charging applications. However, while mathematical optimization can be very effective, as the objective function expands and more parameters are taken into account, the optimization becomes more complex and therefore require faster and smarter optimization algorithms. In addition, the recent availability of a vast amount of real-life data sets has made efficient data handling more important.
Machine learning is known to be an effective way of introducing Artificial Intelligence to smart charging algorithms. The use of reinforcement learning algorithms, a subset of machine learning could help overcome the disadvantages of mathematical optimization as trained algorithms are generally fast when predicting outcomes and have the potential to be accurate at the same time.
Therefore, the purpose of this work is to research the feasibility and additional benefit of machine learning to mathematical optimization-based smart charging algorithms. This is done through the development of end-to-end Q-learning and Double Deep Q Network reinforcement learning smart charging algorithms. The performance of both algorithms is evaluated on three separate case studies as well as on multiple different random cases and is compared to the charging performance of the average rate charging and mixed-integer programming algorithm benchmarks.
As a result, it becomes clear that for individual case studies the Q-learning and Double Deep Q Network agent are able to find cheap charging moments while charging the vehicle to 100\% battery capacity without violating charging constraints. However, when testing the performance of the Q-learning and Double Deep Q Network agents it becomes clear that the average charging performance is significantly worse than using the method of mixed-integer programming as the algorithms do not learn to generalize well.
Finally, the advantages and disadvantages of replacing mixed-integer programming with reinforcement learning are discussed as well as some limitations and recommendations for future work and improvement are given.
...
As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting increased pressure on the grid. One way to decrease the congestion in the grid is to make use of smart charging schedules for electric vehicles, with the objective of reducing peak demand and preventing the overloading of cables and transformers while reducing the cost of charging for electric vehicle owners.
The recent increase in the availability of real-life data has allowed the in-depth study of smart charging dynamics on a large scale through modeling and simulation. Mathematical optimization is a method that is often used to generate smart charging plans in state-of-the-art smart charging applications. However, while mathematical optimization can be very effective, as the objective function expands and more parameters are taken into account, the optimization becomes more complex and therefore require faster and smarter optimization algorithms. In addition, the recent availability of a vast amount of real-life data sets has made efficient data handling more important.
Machine learning is known to be an effective way of introducing Artificial Intelligence to smart charging algorithms. The use of reinforcement learning algorithms, a subset of machine learning could help overcome the disadvantages of mathematical optimization as trained algorithms are generally fast when predicting outcomes and have the potential to be accurate at the same time.
Therefore, the purpose of this work is to research the feasibility and additional benefit of machine learning to mathematical optimization-based smart charging algorithms. This is done through the development of end-to-end Q-learning and Double Deep Q Network reinforcement learning smart charging algorithms. The performance of both algorithms is evaluated on three separate case studies as well as on multiple different random cases and is compared to the charging performance of the average rate charging and mixed-integer programming algorithm benchmarks.
As a result, it becomes clear that for individual case studies the Q-learning and Double Deep Q Network agent are able to find cheap charging moments while charging the vehicle to 100\% battery capacity without violating charging constraints. However, when testing the performance of the Q-learning and Double Deep Q Network agents it becomes clear that the average charging performance is significantly worse than using the method of mixed-integer programming as the algorithms do not learn to generalize well.
Finally, the advantages and disadvantages of replacing mixed-integer programming with reinforcement learning are discussed as well as some limitations and recommendations for future work and improvement are given.
The recent increase in the availability of real-life data has allowed the in-depth study of smart charging dynamics on a large scale through modeling and simulation. Mathematical optimization is a method that is often used to generate smart charging plans in state-of-the-art smart charging applications. However, while mathematical optimization can be very effective, as the objective function expands and more parameters are taken into account, the optimization becomes more complex and therefore require faster and smarter optimization algorithms. In addition, the recent availability of a vast amount of real-life data sets has made efficient data handling more important.
Machine learning is known to be an effective way of introducing Artificial Intelligence to smart charging algorithms. The use of reinforcement learning algorithms, a subset of machine learning could help overcome the disadvantages of mathematical optimization as trained algorithms are generally fast when predicting outcomes and have the potential to be accurate at the same time.
Therefore, the purpose of this work is to research the feasibility and additional benefit of machine learning to mathematical optimization-based smart charging algorithms. This is done through the development of end-to-end Q-learning and Double Deep Q Network reinforcement learning smart charging algorithms. The performance of both algorithms is evaluated on three separate case studies as well as on multiple different random cases and is compared to the charging performance of the average rate charging and mixed-integer programming algorithm benchmarks.
As a result, it becomes clear that for individual case studies the Q-learning and Double Deep Q Network agent are able to find cheap charging moments while charging the vehicle to 100\% battery capacity without violating charging constraints. However, when testing the performance of the Q-learning and Double Deep Q Network agents it becomes clear that the average charging performance is significantly worse than using the method of mixed-integer programming as the algorithms do not learn to generalize well.
Finally, the advantages and disadvantages of replacing mixed-integer programming with reinforcement learning are discussed as well as some limitations and recommendations for future work and improvement are given.
In this thesis, a closer look will be taken into the design of a propulsion system of an eVTOL (electric Vertical Take-off and Landing aircraft). The primary goal of the eVTOL is to maximize the payload for a given maximum take-off weight and the eVTOL has to go into service in 2030. To make an as light weight as possible propulsion system, an investigation will be done for the propeller length and the number of rotors, which is present on a given eVTOL design. In addition to this, different drive choices will be discussed and an optimal drive choice will be made. For this drive, a closer look will be taken in efficiency optimisation. From these parameters a design example will be given using the current state-of-the-art drive technology. In addition to this possible future drive, developments will be taken into account.
...
In this thesis, a closer look will be taken into the design of a propulsion system of an eVTOL (electric Vertical Take-off and Landing aircraft). The primary goal of the eVTOL is to maximize the payload for a given maximum take-off weight and the eVTOL has to go into service in 2030. To make an as light weight as possible propulsion system, an investigation will be done for the propeller length and the number of rotors, which is present on a given eVTOL design. In addition to this, different drive choices will be discussed and an optimal drive choice will be made. For this drive, a closer look will be taken in efficiency optimisation. From these parameters a design example will be given using the current state-of-the-art drive technology. In addition to this possible future drive, developments will be taken into account.
Master thesis
(2019)
-
Ghanshyam Chandrashekar, Bert van Wee, Jan Anne Annema, Gautham Ram Chandra Mouli, Benjamin Sprecher
Electric Vehicles are shaping the clean energy goals and have become the poster for climate change and the energy transition. Its not only cars that is making an impact but more importantly, micro-electric vehicles spearheading first/last mile solutions. These vehicles reduce net emissions on the environment. This market needs further literature and information to integrate and have better solutions. This thesis explored the ecosystem surrounding the micro-mobility market just to underpin the lack of literature available and the need for further research on this topic. All stakeholders were studied, vehicles were investigated, business cases were discussed and environmental impacts were approximated. The policy side to this market is very crucial to its growth and this has been highlighted in this thesis. The safety impacts indicate that the market has taken a huge hit in many countries the necessity for regulatory structure has become more integral. The vehicles will get better with time and safer, and if implemented in cleaner ways, can have a huge impact on short distance mobility space. The market is poised for growth but in the Netherlands there are many roadblocks and these need further exploration. The study concluded that these vehicles are bound to stay and grow and implementation depends on regulatory climate, enforcement of safety standards, technology standards and infrastructure. Technology trends also project better vehicles in the future. Innovation in types of implementation of shared micro-mobility is still a big question. When these factors are addressed, micro-electric mobility can grow and have a huge impact in the creation of Smart Cities and better, cleaner, more efficient mobility options.
...
Electric Vehicles are shaping the clean energy goals and have become the poster for climate change and the energy transition. Its not only cars that is making an impact but more importantly, micro-electric vehicles spearheading first/last mile solutions. These vehicles reduce net emissions on the environment. This market needs further literature and information to integrate and have better solutions. This thesis explored the ecosystem surrounding the micro-mobility market just to underpin the lack of literature available and the need for further research on this topic. All stakeholders were studied, vehicles were investigated, business cases were discussed and environmental impacts were approximated. The policy side to this market is very crucial to its growth and this has been highlighted in this thesis. The safety impacts indicate that the market has taken a huge hit in many countries the necessity for regulatory structure has become more integral. The vehicles will get better with time and safer, and if implemented in cleaner ways, can have a huge impact on short distance mobility space. The market is poised for growth but in the Netherlands there are many roadblocks and these need further exploration. The study concluded that these vehicles are bound to stay and grow and implementation depends on regulatory climate, enforcement of safety standards, technology standards and infrastructure. Technology trends also project better vehicles in the future. Innovation in types of implementation of shared micro-mobility is still a big question. When these factors are addressed, micro-electric mobility can grow and have a huge impact in the creation of Smart Cities and better, cleaner, more efficient mobility options.