TY - GEN
T1 - Definition and optimization of the drive train topology for electric vehicles
AU - Pesce, Thomas
AU - Lienkamp, Markus
PY - 2012
Y1 - 2012
N2 - Due to the limited range of battery electric vehicles, a low energy consumption is more desirable, than it is in conventional vehicles. To accomplish this objective the paper focuses on an increased efficiency of the drive train, its topologies and its components, as this is one of the most promising approaches. With a set of basic characteristics of the desired vehicle (such as maximum speed, acceleration, climbing ability, class and range) an optimal fitted drive train according to the energy consumption should be found. This includes number, type and power of electric machines, transmission ratios, dynamic running radius, axle load distribution and battery capacity. The general approach uses a method consisting of a developed optimization routine and a specific simulation model. The developed optimization algorithm reduces the value ranges or even the design parameters to minimize the number of iterations. This intelligent algorithm is compared to conventional optimizers like pattern search or genetic algorithms. For the vehicle model valid results are important. To ensure validity for all possible topologies, vehicle and power classes an appropriate method is presented. Each relevant component model and its respective scaling concept are validated. After validation of a vehicle model with these component models, the scalability is transferable to the entire vehicle model. Some exemplary results of the model are shown, such as the influence of axle load distribution, choice of high-energy or high-power cells and potential of longitudinal torque-vectoring for multi-motor topologies.
AB - Due to the limited range of battery electric vehicles, a low energy consumption is more desirable, than it is in conventional vehicles. To accomplish this objective the paper focuses on an increased efficiency of the drive train, its topologies and its components, as this is one of the most promising approaches. With a set of basic characteristics of the desired vehicle (such as maximum speed, acceleration, climbing ability, class and range) an optimal fitted drive train according to the energy consumption should be found. This includes number, type and power of electric machines, transmission ratios, dynamic running radius, axle load distribution and battery capacity. The general approach uses a method consisting of a developed optimization routine and a specific simulation model. The developed optimization algorithm reduces the value ranges or even the design parameters to minimize the number of iterations. This intelligent algorithm is compared to conventional optimizers like pattern search or genetic algorithms. For the vehicle model valid results are important. To ensure validity for all possible topologies, vehicle and power classes an appropriate method is presented. Each relevant component model and its respective scaling concept are validated. After validation of a vehicle model with these component models, the scalability is transferable to the entire vehicle model. Some exemplary results of the model are shown, such as the influence of axle load distribution, choice of high-energy or high-power cells and potential of longitudinal torque-vectoring for multi-motor topologies.
KW - BEV (battery electric vehicle)
KW - Modeling
KW - Optimization
KW - Powertrain
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=84873298957&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84873298957
SN - 9781622764211
T3 - 26th Electric Vehicle Symposium 2012
SP - 194
EP - 205
BT - 26th Electric Vehicle Symposium 2012
T2 - 26th Electric Vehicle Symposium 2012
Y2 - 6 May 2012 through 9 May 2012
ER -