Project Details
Project Description
This project brings together a team of Monash leading researchers, one of the most successful student teams in Australia, Monash MotorSport, and two well-known international leading companies in electrical machine design and autonomous vehicles, Regal Beloit and Bosch, to develop an innovative powertrain for future electric vehicles. With a focus on educating future engineers for the Australian automotive industry, the CIs, in collaboration with the industry partners, will work very closely with the Monash MotorSport alumni as postgraduate students to transfer invaluable automotive-related skills and knowledge to them.
With increasing concerns about drastic impacts of climate change and greenhouse gas emissions, electric vehicles (EVs) are gaining significant attention to replace conventional internal combustion engine (ICE) vehicles. A typical powertrain for an EV consists of a battery energy storage system (BESS) that feeds an electric motor via a DC/AC power electronic inverter. The overall efficiency and cost of the EV largely depends on the individual efficiency and cost of these parts. This project aims at developing a cost-effective and highly efficient powertrain for future EVs via 1) adopting alternative BESS technologies, 2) integrating the DC/AC inverter and the motor into one entity, and 3) developing a drive-assist system to modulate the driver’s input to a vehicle’s powertrain based on additional information gathered by external sensors to boost the overall efficiency.
For many years, Li-ion batteries have been the dominant choice of automotive industry for electric vehicles compared to other existing technologies such as lead-acid batteries mainly due to their higher power/energy density, longer cycle life, faster charging, and higher usable capacity. However, with the introduction of Lithium-Sulfur (LiS) batteries with a theoretical sulfur cathode capacity of 1670 mAh/g, a paradigm shift is happening in energy storage technologies. Having a potential capacity of up to 5 times more than their rivals, LiS batteries could provide a safer, cheaper alternative with higher energy density. However, to date, no commercial LiS battery suitable for EV applications (with required energy/power density and cycle life) has been introduced. At Monash, we have developed an innovative LiS battery technology, which has achieved a superior cycling capability compared to Li-ion batteries, while it can outperform its competitors in terms of energy/power density in the lab environment. This project will build upon the existing knowledge and expertise at Monash University to further develop LiS batteries for EVs to replace Li-ion batteries with a cheaper, safer alternative with superior dynamic performance.
EVs use a battery pack consisting of series-connected cells to achieve a given voltage required for their powertrain. Since the battery cells are not identical, they may behave differently while in operation, and their voltage levels may not be balanced. Additionally, various variables of the cells including their state of charge, state of health, and temperature must be monitored/incorporated in the charge/discharge process to ensure safe and efficient operation of the pack. A Battery Management System (BMS) plays a vital role to fulfil these requirements and to manage/protect the battery pack. For Li-ion batteries, well-established BMSs are available, which usually charge the pack with a constant-current followed by constant-voltage, and discharge them by a discontinuous current mode. Additionally, the Li-ion BMSs estimate the SoC and SoH via various algorithms such as coulomb count method, open-circuit voltage method, battery-model-based method, Kalman-filter-based methods, etc. However, due to inherent differences between Li-ion and LiS batteries, these methods are not necessarily applicable to or optimal for LiS battery packs. This projects will study the existing Li-ion BMSs and investigates their suitability for LiS battery packs for EV applications. It will also seek innovative alternative to the existing BMSs for safe and efficient operation of LiS batteries to provide the required energy/power for the EV’s inverter and electric motor.
In addition to the BESS, the EV’s powertrain performance and efficiency heavily rely on a power electronic inverter that drives the electric motor. AC motors have established their positions as the dominant choice of various manufacturers, while permanent magnet synchronous motors (PMSM) are more commonly utilised, e.g., in Nissan Leaf and Chevy Volts. Conventionally, the inverter and motor are two independent entities of the powertrain that are separately designed and packaged. To enable widely affordable EVs, the combined cost of the inverter/motor must be reduced to less than $12/kW (less than $660 for a 55 kW system) as recommended by the US Department of Energy. This project aims to develop an innovative integrated inverter/PMSM-motor set based on single rotor axial flux motors and SiC power switches with a focus on increasing efficiency and reducing cost.
Last but not least, utilising artificial intelligence and deep learning algorithms, a drive-assist system with an ultimate goal of more efficient operation of the whole powertrain chain by adopting external sensors such as cameras, laser scanners, ultrasonics and radars will be developed. The developed system will receive external information such as surrounding traffic status, distance to nearby cars, traffic lights status, etc., to optimise/modulate the requested torque from the drivetrain such that a more efficient operation is ensured. The driver, however, will be still able to override the commands of this drive-assist system if needed.
With increasing concerns about drastic impacts of climate change and greenhouse gas emissions, electric vehicles (EVs) are gaining significant attention to replace conventional internal combustion engine (ICE) vehicles. A typical powertrain for an EV consists of a battery energy storage system (BESS) that feeds an electric motor via a DC/AC power electronic inverter. The overall efficiency and cost of the EV largely depends on the individual efficiency and cost of these parts. This project aims at developing a cost-effective and highly efficient powertrain for future EVs via 1) adopting alternative BESS technologies, 2) integrating the DC/AC inverter and the motor into one entity, and 3) developing a drive-assist system to modulate the driver’s input to a vehicle’s powertrain based on additional information gathered by external sensors to boost the overall efficiency.
For many years, Li-ion batteries have been the dominant choice of automotive industry for electric vehicles compared to other existing technologies such as lead-acid batteries mainly due to their higher power/energy density, longer cycle life, faster charging, and higher usable capacity. However, with the introduction of Lithium-Sulfur (LiS) batteries with a theoretical sulfur cathode capacity of 1670 mAh/g, a paradigm shift is happening in energy storage technologies. Having a potential capacity of up to 5 times more than their rivals, LiS batteries could provide a safer, cheaper alternative with higher energy density. However, to date, no commercial LiS battery suitable for EV applications (with required energy/power density and cycle life) has been introduced. At Monash, we have developed an innovative LiS battery technology, which has achieved a superior cycling capability compared to Li-ion batteries, while it can outperform its competitors in terms of energy/power density in the lab environment. This project will build upon the existing knowledge and expertise at Monash University to further develop LiS batteries for EVs to replace Li-ion batteries with a cheaper, safer alternative with superior dynamic performance.
EVs use a battery pack consisting of series-connected cells to achieve a given voltage required for their powertrain. Since the battery cells are not identical, they may behave differently while in operation, and their voltage levels may not be balanced. Additionally, various variables of the cells including their state of charge, state of health, and temperature must be monitored/incorporated in the charge/discharge process to ensure safe and efficient operation of the pack. A Battery Management System (BMS) plays a vital role to fulfil these requirements and to manage/protect the battery pack. For Li-ion batteries, well-established BMSs are available, which usually charge the pack with a constant-current followed by constant-voltage, and discharge them by a discontinuous current mode. Additionally, the Li-ion BMSs estimate the SoC and SoH via various algorithms such as coulomb count method, open-circuit voltage method, battery-model-based method, Kalman-filter-based methods, etc. However, due to inherent differences between Li-ion and LiS batteries, these methods are not necessarily applicable to or optimal for LiS battery packs. This projects will study the existing Li-ion BMSs and investigates their suitability for LiS battery packs for EV applications. It will also seek innovative alternative to the existing BMSs for safe and efficient operation of LiS batteries to provide the required energy/power for the EV’s inverter and electric motor.
In addition to the BESS, the EV’s powertrain performance and efficiency heavily rely on a power electronic inverter that drives the electric motor. AC motors have established their positions as the dominant choice of various manufacturers, while permanent magnet synchronous motors (PMSM) are more commonly utilised, e.g., in Nissan Leaf and Chevy Volts. Conventionally, the inverter and motor are two independent entities of the powertrain that are separately designed and packaged. To enable widely affordable EVs, the combined cost of the inverter/motor must be reduced to less than $12/kW (less than $660 for a 55 kW system) as recommended by the US Department of Energy. This project aims to develop an innovative integrated inverter/PMSM-motor set based on single rotor axial flux motors and SiC power switches with a focus on increasing efficiency and reducing cost.
Last but not least, utilising artificial intelligence and deep learning algorithms, a drive-assist system with an ultimate goal of more efficient operation of the whole powertrain chain by adopting external sensors such as cameras, laser scanners, ultrasonics and radars will be developed. The developed system will receive external information such as surrounding traffic status, distance to nearby cars, traffic lights status, etc., to optimise/modulate the requested torque from the drivetrain such that a more efficient operation is ensured. The driver, however, will be still able to override the commands of this drive-assist system if needed.
| Status | Finished |
|---|---|
| Effective start/end date | 1/05/19 → 31/05/22 |