Andrej Ivanco

Finite-Element-Based Computationally Efficient Scalable Electric Machine Model Suitable for Electrified Powertrain Simulation and Optimization

Electric machines are a key component of electric/hybrid electric vehicle (EV/HEV) powertrains. Thus, computationally efficient models for electric machines are essential for powertrain-level design, simulation, and optimization. In this paper, a finite-element-based method for quickly generating torque-speed curves and efficiency maps for electric machines is presented. First, magnetostatic finite-element analysis (FEA) is conducted on a “base” machine design. This analysis produces torque, normalized losses, flux linkage, and the maximum magnetic field intensity in the permanent magnets for a wide range of current magnitudes and phase angles. 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 using postprocessing techniques. Results suggest that, by avoiding resolving the FEA for the scaled designs, the proposed techniques can be used to quickly generate efficiency maps, and thus are useful for EV/HEV powertrain-level simulation and optimization.

Naturalistic driving cycle synthesis by Markov chain of different orders

This paper evaluates the performance of using Markov chain of different orders to synthesise real-world representative drive cycles from numerous naturalistic drive cycles. The representative drive cycles can be a valuable input into the design of powertrains, especially for plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV). Their onboard cost-sensitive electric components, such as battery, require an appropriate sizing by understanding how people drive in naturalistic settings. Applying representative drive cycles instead of federal certification drive cycles provides flexibility of drive cycle length and ensures realistic cycle aggressiveness. Even though Markov chain has been widely used to synthesise representative drive cycles, the effects of different orders have not been systematically compared. Based on a publicly accessible portion of GPS-enhanced regional household travel survey, after statistical hypothesis tests, the results show that higher degree of representativeness can be achieved with a 3-order Markov chain compared to a 2-order Markov chain. These findings help to improve the accuracy of cycle synthesis for PHEV and EV analysis.

Synthesis and Experimental Validation of Battery Aging Test Profiles Based on Real-World Duty Cycles for 48-V Mild Hybrid Vehicles

Electrified powertrains are gaining a larger market share thanks to stricter emission regulation requirements and customer preferences. Ranging from micro hybrid to all-electric powertrains, however, their success highly depends on satisfactory life-long performance of onboard energy storage systems. In order to guide the battery pack design and management, real-world representative cell aging tests are essential for a successful product development. Focusing on cycle aging, this study proposes a novel methodology based on Welchs power spectral density estimation to characterize real-world cell duty cycles and synthesize representative profiles for cell aging tests. A 48-V mild hybrid vehicle model is developed in MATLAB/Simulink to relate the real-world vehicle-level drive cycles to cell-level duty cycles. Compared to existing test profiles found in literature, the newly designed profiles take the impacts of road conditions and driver styles into consideration to account for real-world resemblance. Experimental aging test results with the proposed aging profiles on nickel manganese cobalt lithium-ion cells not only hint at the real-world aging scenarios, but also lay the foundation for future work such as aging modeling. Repeatability of testing results is also investigated in this study.

Finite-element-based computationally-efficient electric machine model suitable for use in electrified vehicle powertrain design optimization

Electric machines and their corresponding power electronic drives are key components of electric/hybrid electric vehicle (EV/HEV) powertrains. Thus, computationally-efficient models for electric machines and drives are essential for powertrain-level design, simulation, and optimization. In this paper, a finite-element-based method for quickly generating torque-speed curves and efficiency maps for electric machines and drives is presented. First, magneto-static finite element analysis (FEA) is conducted on a “base” machine design. This analysis produces normalized torque, flux linkage, current, and losses for the operating points of interest. These values are then adjusted 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 the proposed techniques can be useful for EV/HEV powertrain design and optimization.