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Scalability - An approach for simulation and optimization of vehicular electric drives

机译:可扩展性 - 车辆电动驱动器仿真和优化方法

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There is a lot of experience developing electric and hybrid electric drive trains so far. But engineers are still looking for a uniform procedure for preliminary component design. For such a procedure, developers need powerful simulation tools supporting them in understanding the interdependent influences of drive train component dimensioning and control. Scalability in drive train simulation together with optimization is an extensive approach to address this current engineering challenge. Therefore, the goal of the presented work is to derive scalable models for steady-state simulations of electrical machines, frequency inverters and energy storages for vehicular applications and use them for optimization. The scalable modeling of the electric drive is realized by a geometric scaling. Based on a designed reference machine the operating range can be enlarged or diminished. The machine simulation is facilitated by equivalent circuit models for asynchronous induction machines with squirrel cage rotor and for permanent magnet synchronous machines. The inverter model is automatically adapted to the machine scaling. The inverter's loss behavior depends on the particular operating point and is modeled by analytic equations. The scaling of the energy storage components is realized by a variation of the cell number in parallel and serial connection. Lithium-ion battery technologies as well as double-layer capacitors and hybrid energy storage systems are considered. The operating point dependent losses are modeled by dint of equivalent circuits. The scalable models are combined realizing an entire electric drive train simulation. A control strategy is derived and parameterized. The usability of the scalable models for optimization procedures is shown. The selection of an adequate optimization algorithm is presented and the algorithm is integrated in the simulation tool. Finally, the optimization procedure is used for several vehicular electric drive train configurations.
机译:到目前为止,有很多经验开发电气和混合动力电动驱动火车。但工程师仍在寻找初步成分设计的统一程序。对于这样的程序,开发人员需要强大的仿真工具,支持他们了解传动系元件尺寸和控制的相互依赖性影响。传动列车模拟的可扩展性以及优化是一种广泛的方法,可以解决这一当前工程挑战。因此,所提出的工作的目标是推导出用于电机,频率逆变器和能量存储器的稳态模拟的可扩展模型,并使用它们进行优化。通过几何缩放实现电驱动器的可扩展建模。基于设计的参考机器,操作范围可以放大或减少。采用灰鼠笼式转子的异步电路机和永磁同步机等效电路型号,促进了机器仿真。逆变器模型自动适应机器缩放。逆变器的损耗行为取决于特定的操作点,并由分析方程式建模。通过并行和串行连接的单元数的变化来实现能量存储部件的缩放。考虑锂离子电池技术以及双层电容和混合能储能系统。操作点相关损耗由DINT等效电路建模。可扩展模型组合实现了整个电动传动系仿真。派生控制策略和参数化。显示了可扩展模型用于优化过程的可用性。提出了适当优化算法的选择,算法集成在模拟工具中。最后,优化过程用于多个车辆电动传动系配置。

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