Recent ARC Speakers at TARDEC Innovation Seminars

Dr. StefanopoulouDr. Hofmann, photo credit to EECS DepartmentOn April 6th and 27th 2015, Drs. Heath Hofmann and Anna Stefanopoulou, respectively, presented their research at U.S. Army TARDEC in Warren, MI.

The TARDEC Innovation Seminar is a weekly event where TARDECians hear about new reasearch, technologies and approaches to solving Army problems. The talks may be given by researchers from within TARDEC or from outside institutions.

Dr. Hofmann's talk was on "Computationally-Efficient Electric Machine Modelling Tools for Powertrain Design".         Electric machines are a key component of vehicle powertrains. Thus, computationally efficient yet accurate models for electric machines are essential for powertrain-level design, simulation, and optimization. In this presentation, I will discuss two modelling tools we have developed that are particularly helpful for powertrain analysis.
        The performance of an electric machine is significantly constrained by temperature. Hence, in order to determine the torque and power capabilities of an electric machine, knowledge of internal temperatures is required. We have developed a technique for computationally-efficient thermal models for electric machines that can be used for vehicle powertrain-level simulation and optimization. The technique is based upon simulating eigenmodes of the thermal dynamics as determined by 3D finite element analysis (FEA). The order of the finite element model is then dramatically reduced in two ways. First, the dynamic system is decomposed into two parts by using the orthogonality property of the eigenmodes, and only eigenmodes that are significantly excited are included in the dynamic model; other eigenmodes are treated as static modes. Second, only the temperatures in a few “hot spots” are calculated. By using the proposed model order reduction techniques, a large 3D FEA model can be reduced to a small reduced-order model without the necessity of calculating all the eigenmodes. Therefore, the simulation time of the model can be dramatically reduced compared with the full-order model while maintaining sufficient accuracy. Experimental results show good agreement between simulation results and measured data.
        The ability to quickly generate and predict the performance of new electric machine designs is also crucial for powertrain–level design and optimization. A finite-element-based method for quickly generating torque–speed curves and efficiency maps for electric machines will be presented. First, 2D magnetostatic (MS) finite element analysis (FEA) is conducted on a “base” machine design. This analysis produces torque, flux linkage, losses, and magnetic field intensity for the operating points of interest. These values are then scaled based upon changing the size of the machine and the effective number of turns of the machine windings to quickly generate a variety of new machine designs and their corresponding efficiency maps. Results suggest that, by avoiding re-solving the FEA for the scaled designs, the proposed techniques can be used to quickly generate efficiency maps, and thus are useful for powertrain-level simulation and optimization.

Abstract: Electric machines are a key component of vehicle powertrains. Thus, computationally efficient yet accurate models for electric machines are essential for powertrain-level design, simulation, and optimization. In this presentation, I will discuss two modelling tools we have developed that are particularly helpful for powertrain analysis.
        The performance of an electric machine is significantly constrained by temperature. Hence, in order to determine the torque and power capabilities of an electric machine, knowledge of internal temperatures is required. We have developed a technique for computationally-efficient thermal models for electric machines that can be used for vehicle powertrain-level simulation and optimization. The technique is based upon simulating eigenmodes of the thermal dynamics as determined by 3D finite element analysis (FEA). The order of the finite element model is then dramatically reduced in two ways. First, the dynamic system is decomposed into two parts by using the orthogonality property of the eigenmodes, and only eigenmodes that are significantly excited are included in the dynamic model; other eigenmodes are treated as static modes. Second, only the temperatures in a few “hot spots” are calculated. By using the proposed model order reduction techniques, a large 3D FEA model can be reduced to a small reduced-order model without the necessity of calculating all the eigenmodes. Therefore, the simulation time of the model can be dramatically reduced compared with the full-order model while maintaining sufficient accuracy. Experimental results show good agreement between simulation results and measured data.
        The ability to quickly generate and predict the performance of new electric machine designs is also crucial for powertrain–level design and optimization. A finite-element-based method for quickly generating torque–speed curves and efficiency maps for electric machines will be presented. First, 2D magnetostatic (MS) finite element analysis (FEA) is conducted on a “base” machine design. This analysis produces torque, flux linkage, losses, and magnetic field intensity for the operating points of interest. These values are then scaled based upon changing the size of the machine and the effective number of turns of the machine windings to quickly generate a variety of new machine designs and their corresponding efficiency maps. Results suggest that, by avoiding re-solving the FEA for the scaled designs, the proposed techniques can be used to quickly generate efficiency maps, and thus are useful for powertrain-level simulation and optimization.

Dr Stefanopoulou spoke on "Battery M&S for Enhanced Monitoring and Utilization"

Abstract: Real-time estimation of internal battery states is critical for predicting and controlling the complex electrochemical, thermal and mechanical behavior of lithium-ion batteries. In this presentation, I will highlight key accomplishments in:

  • Inner-cell temperature estimation and associated monitoring of cell aging using non-uniform forgetting factors to capture the long term resistance growth (patent app with TARDEC).
  • Investigations for thin-film sensor placement in large prismatic cells and pack thermal monitoring.
  • Optimal control for fast warm-up from sub-zero temperatures (patent app with TARDEC).
  • Real-time power limit to improve battery utilization and safety from thermal runaways.
  • State of Charge (SOC) estimation augmented by bulk stress measurements during lithium intercalation and thermal expansion (patent app).
  • Observability of individual cell SOC under cluster voltage measurements

If time allows, I will also show snapshots from our neutron imaging for in situ measurement of the lithium concentration inside a commercial Lithium Iron Phosphate (LFP) pouch cell battery to define the validity limits of various model simplifications such as the electrode-averaged assumption in single-particle battery models.