Design Optimization of Induction Motors with Different Stator Slot Rotor Bar Combinations Considering Drive Cycle

\ua9 2023 by the authors. In this paper, a sequential Taguchi method for design optimization of an induction motor (IM) for an electric vehicle (EV) is presented. First, a series of empirical and mathematical relationships is systematically applied to reduce the number of possible stator slot rotor bar (SSRB) combinations. Then, the admissible optimal combinations are investigated and compared using finite element (FE) simulation over the NEDC driving cycle, and the three best combinations are selected for further analysis. Each topology is optimized over the driving cycle using the k-means clustering method to calculate the representative working points over the NEDC, US06, WLTP Class 3, and EUDC driving cycles. Then, using the Design of Experiment (DOE)-based Taguchi method, a multi-objective optimization is carried out. Finally, the performance of the optimized machines in terms of robustness against manufacturing tolerances, magnetic flux density distribution, mechanical stress analysis, nominal envelope curve and efficiency map is carried out to select the best stator slot rotor bar combination. It is also found that the K-means clustering method is not completely robust for the design of electric machines for electric vehicle traction motors. The method focuses on regions with high-density working points, and it is possible to miss the compliant with the required envelope curve

Multi-Objective Drive-Cycle Based Design Optimization of Permanent Magnet Synchronous Machines

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this thesis, a vehicle dynamics model for a direct-drive machine and its associated vehicle parameters is implemented for the urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, power, and energy. K-means clustering and Gaussian mixture modeling (GMM) are two clustering techniques used to reduce the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle. These methods offer high computational efficiency and low computational time cost while optimizing an electric machine. Differential evolution (DE) is employed to optimize the baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM). A computationally efficient finite element analysis (CEFEA) technique is developed to evaluate the machine at the representative drive-cycle points elicited from the clustering approaches. In addition, a steady-state thermal model is established to assess the electric motor temperature variation between optimization design candidates. In an alternative application, the drive-cycle cluster points are utilized for a computationally efficient drive-cycle system simulation that examines the effects of inverter time harmonics on motor performance. The motor is parameterized and modeled in a PSIM motor-inverter simulation that determines the current excitation harmonics that are injected into the machine during drive-cycle operation. These current excitations are inserted into the finite element analysis motor simulation for accurate analysis of the harmonic effects. The analysis summarizes the benefits of high-frequency devices such as gallium nitride (GaN) in comparison to insulated gate bipolar transistors (IGBT) in terms of torque ripple and motor efficiency on a drive-cycle

Nonlinear optimal control of interior permanent magnet synchronous motors for electric vehicles

At present time, research in the field of Electric Vehicles (EV) is significantly intensifying around the world due to the ambitious goals of many countries, including the UK, to prohibit the sale of new gasoline and diesel vehicles, as well as hybrid vehicles, in the near future around 2030-35. The primary goal of this Ph.D. research is to improve the propulsion system of electric vehicles' powertrains through improvements in the control of Interior Permanent Magnet Synchronous Motors (IPMSM), which are commonly used in EV applications. The proposed approaches are supported by simulations in Matlab, Matlab-Simulink and laboratory-based experiments. The research initially proposes an analytical solution in implicit view for a combined Maximum Torque per Ampere (MTPA) and Maximum Efficiency (ME) control, allowing to determine the optimal d-axis current, based on the concept of minimisation of the fictitious electric power loss. With the exception of two parameters, the equation is identical to that of the ME control. Therefore, upgrading the ME control to the combined MTPA/ME control is relatively easy and doesn't require any change in hardware beyond a few minors of controller code in the software. The presented research demonstrates an easy-to-apply combined MTPA/ME control leading to the ‘Transients Optimal and Energy-Efficient IPMSM Drive’ providing smooth transitions to the MTPA control during transients and to the ME control during steady states. A concept of ‘Nonlinear Optimal Control of IPMSM Drives’ is also introduced in this Ph.D. research. The velocity control loop develops nonlinearities when energy consumption optimisation methods like MTPA, ME, or combined MTPA/ME are added. In addition, the control system's parameters can be inaccurate and fluctuate depending on the operating point or possible uncertainties in real-time operation. In the proposed method, the control structure is the same as in the Field Oriented II Control (FOC), with the close velocity and two current loops, but the Proportional-Integral (PI) controllers are replaced by Nonlinear Optimal (NO) Controllers. The linear part of the controller is designed as a Linear Quadratic Regulator (LQR) with integral action for each loop separately. This is, in fact, a PI controller with optimal gain parameters for a specific operating point. The nonlinear part takes the required fluctuations of the control system’s optimal gain parameters in real-time operation as new control actions to improve a robust control structure. The design procedure for the nonlinear part is similar to that of the LQR, but the criterion of A. Krasovsky's generalised work is used, and the analytical derivations lead to an explicit control solution for the nonlinear optimal part. The nonlinear part emulates the adjustments for updating the linear part’s optimal LQR gains based on operating conditions, instead of employing extensive look-up tables or complicated estimation algorithms. The proposed control is robust in the allowed range of the system’s parameters. In conclusion, upgrading existing industrial IPMSM drives into a robust and optimal energy-efficient version that can be used for electric vehicle applications is the main advantage of the novel control concept described in this Ph.D. research. For this upgrade, only a small portion of the software that is related to the PI controllers needs to be changed; no new hardware is needed. Therefore, it is cost-effective and simple to transform existing industrial IPMSM drives into a better version with the proposed method. This feature also leads to the design of more adequate IPMSM drives to meet the demands of Electric Vehicle (EV) operating cycles

Development of automated and connected testing processes for electric vehicles

Electric vehicles provide a practical transportation solution to overcome emission and energy deficiencies posed by combustion vehicles. However, high product costs driven by the price of components and immaturity of the processes to create them reduce the product’s financial competitiveness. Manufacturers need to adapt their processes to develop cars more economically while adhering to emission requirements by legislative bodies. This EngD determined the estimated R&D cost saving made through innovating automated and connected technologies into the development process to reduce the development costs of vehicles holistically. The research targeted physical testing costs due to the potential increase in demand for testing to improve the characterisation of virtual models while the automotive industry transitions to vehicle electrification. The research established objectives to target human, capital and facility costs as significant cost drivers for physical testing. Three applications of automation and connected systems were ideated and investigated to evaluate the saving potential of each cost driver. Firstly, an automated dynamometer was designed and experimentally tested to demonstrate its capability in reducing man-hours for powertrain component testing. Secondly, a distributed test network was virtually modelled to understand the opportunities to supplement physical prototype vehicles by utilising connected component test facilities. Finally, an automated test management system with test case generation capability was proposed and evaluated to determine its capability to improve testing productivity. Using the results from each technology innovation and Jaguar Land Rover’s historical strategy, a numerical model identified an estimated saving of £225m across 12 vehicle models representing a net change of 1.71%. Changes in human resources demand were the most significant contributor toward total development cost savings. DTS and automated dynamometer innovations provided 90% and 9% of human resource cost-saving, respectively. The results suggested that these technological innovations would make only a marginal impact on saving for customers. Ultimately, a combination of further developing of these technologies to maximise application and saving made on other portions of the vehicle development process is necessary to bridge the gap between combustion and electric vehicle. However, the savings proposed would benefit manufacturers financially and allow them to also gain additional revenue by providing opportunities to release vehicle models marginally earlier

Second Conference on Artificial Intelligence for Space Applications

The proceedings of the conference are presented. This second conference on Artificial Intelligence for Space Applications brings together a diversity of scientific and engineering work and is intended to provide an opportunity for those who employ AI methods in space applications to identify common goals and to discuss issues of general interest in the AI community