1,721,232 research outputs found
Battery State-of-health Adaptive Energy Management of Hybrid Electric Vehicles
Full text linkEffectively adapting the hybrid electric vehicle (HEV) powertrain operation as a function of the battery state-of-health (SOH) can lead to significant energy savings over the entire vehicle lifetime. This paper demonstrates the potential of a heuristic HEV energy management strategy (EMS) that is calibrated to adaptively minimize fuel consumption as the battery loses capacity due to ageing. A power-split HEV powertrain is used for this case study, and a heuristic EMS approach which does not adapt to battery ageing is used as the baseline. Particle swarm optimization (PSO) is implemented to tune the parameters of the baseline EMS to minimize fuel consumption. Then, the battery SOH adaptive HEV EMS is developed with parameters which are a function of battery SOH. The parameters are tuned with PSO as well. A battery SOH dependent model is created from experimental data from an ageing test which continued until SOH reached 16%. Simulation results demonstrate that the proposed battery SOH adaptive EMS achieves as much as 20% better fuel economy than the non-adaptive controller as the battery ages. An EMS for HEVs which controls the vehicle powertrain as a function of battery SOH is therefore critical to maintaining minimal fuel consumption throughout the life of the vehicle
Effect of coordinated control on real-time optimal mode selection for multi-mode hybrid electric powertrain
No full textAn online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and prospects of such transmissions. A novel architecture topology of the aforementioned class of transmission is selected through rigorous screening process whose workflow is presented here with brevity. The article systematically delineates the steps for deriving dynamics associated with all the feasible operating modes facilitated by the selected topology. The dynamics associated with all the feasible mode-shift events are also heeded judiciously. One of the legitimate concern of multi-mode transmission is its proclivity to contribute discontinuity of power-flow downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Many scholars in literature have substantiated the advent of dynamic coordinated control as a technique for ameliorating such discontinuity. Hence, a system-level coordinated control is employed within the energy management system (equivalent consumption minimization strategy), which governs the mode schedule of the multi-mode powertrain in real-time simulation. Simulation results corroborate the effect of coordinated control on the equivalent consumption minimization strategy in generating optimal mode schedule
Generalized Analytical Solution for N-Segment Axial Flux Halbach Arrays
No full textA generalized analytical solution for the 3-dimensional flux density field of an axial flux cylindrical Halbach Array is developed, based on existing work for 4-segment Halbach arrays. It is designed to reduce time, expense and knowledge associated with the traditional method of performing finite element analysis (FEA) to characterize such an array. It is applicable to the following applications: magnetic bearings, gears, couplings, generators and motors for electric vehicles. Halbach arrays of 4, 6 and 8-segment configurations are analyzed using the improved analytical method, then validated using FEA. The analytical equation is used to perform a sensitivity analysis on the 8-segment array
Estimation of equivalent thermal conductivity of impregnated slots in electric machines using Artificial Neural Network Surrogate Model
No full textThe accurate prediction of temperature within the slot of an electric motor stands as a crucial yet intricate task. It presents a challenge due to its computational demands, particularly when numerous iterations are requisite to identify the optimal configuration for a specific application. In response to this challenge, our study delves into the utilization of an Artificial Neural Network (ANN) as a tool to predict thermal conductivity within the motor slot with a high degree of accuracy. Our approach involves training the ANN using data derived from Finite Element Analysis (FEA)-based numerical simulations, which provide a robust foundation for modeling the thermal behavior of the motor slot. By harnessing the power of machine learning techniques embedded within the ANN, we aim to achieve a more efficient and effective means of temperature prediction compared to conventional methods. One of the key advantages of our proposed model is its ability to adapt and learn from complex and nonlinear relationships inherent in thermal conductivity estimation. This adaptability is especially beneficial in scenarios where traditional analytical models, as commonly found in existing literature, may fall short in capturing the intricacies of thermal behavior within the motor slot. Through rigorous testing and comparison with established analytical models, we demonstrate the superiority of our ANN-based approach in terms of accuracy and reliability. Our findings not only contribute to advancing the field of thermal management in electric motors but also highlight the potential of Artificial Neural Networks as a powerful tool for predictive modeling in complex engineering systems
Multi-Objective Hybrid Electric Vehicle Control for Maximizing Fuel Economy and Battery Lifetime
Full text linkHigh voltage batteries are a fundamental component of hybrid electric vehicles (HEVs). Energy management strategies (EMSs) for HEVs generally aim at maximizing fuel economy solely, yet the method of hybrid powertrain control has a strong impact on the battery lifetime. This paper proposes a multiobjective formulation of dynamic programming, a popular offline optimization tool, which is capable of maximizing both fuel economy and battery lifetime. Obtained numerical results allow correlation of predicted fuel economy with the corresponding predicted battery lifetime. The developed tool can thus help engineers account for battery lifetime during both the HEV powertrain architecture design and the EMS calibration processes
Structural Design Evaluation of Integrated Rotor Hub and Shaft for a High-Speed Surface Mounted Radial Flux Permanent Magnet Synchronous Motor
No full textIncreasing the reliability and power density of a surface-mounted permanent magnet synchronous machine (SPMSM) is crucial due to the broader applications in the automotive and aerospace sectors. Concerns with such machines are that the overall rotating assembly experiences significant mechanical loads due to the rapid rotational speeds, making it exceptionally challenging to design the structural integrity of these components. This study's main objective is to offer a scientific justification for designing an integrated rotor hub and shaft through efficient Finite Element Modeling (FEM) and integration strategies to maximize the rotating assembly durability of a 150kW radial flux SPMSM spinning at 20,000 rpm. The optimization of integrated topology is evaluated based on a multiphysics platform, along with studies conducted on motor assembly eigen frequency. The integrated approach combining the shaft and rotor hub made of AISI 4340 solely saves 1.84kg, removing the necessity of standard components such as balancing end clamp plates, locknuts, and washers. Lower masses are proportional to lower centrifugal forces, reducing radial stress and promoting component/assembly stiffness
Rotor Durability Optimization by Means of Finite Element Multiphysics Analysis for High-Speed Surface Permanent Magnet Electric Machines
No full textTransport electrification is pushing the automotive and aerospace industries to enhance the power density of their powertrains further and further. One of the technologies currently pursued by some companies is high-speed electric motors. For instance, the new Model S Plaid motor by Tesla has a carbon-fiber wrapped IPM (Interior Permanent Magnet) rotor which can exceed 20,000rpm. The SPX88-120 made by Helix company shows a power density of about 18kW/kg at 50,000rpm. However, such high rotating speeds result is huge mechanical stresses in the entire rotating assembly, thus making the structural design of these parts extremely challenging. The primary goal of this paper is to provide a scientific rationale for the effective Finite Element Modeling (FEM) and integration strategies to maximize the rotating assembly durability of a 150kW radial flux SPMSM (surface-mounted permanent magnet synchronous motor) considered as a case-study. A non-linear simulation requires the input of a stress-strain curve and modified power law hardening study is conducted. The secondary goal of the paper is to analyze the thermal stress risers for multiphysics optimization of components. An analytical methodology to estimate the fatigue life for fully reverse cyclic loading is expressed. An extensive study on the eigen mode shape and frequency was performed to understand the dominant frequency of the system. A comparative performance study is conducted on shaft critical speeds, modal analysis, and stiffness interaction between components. Multiphysics optimization of topology is undertaken, the principal stresses in significant load-bearing components are reduced by 10 to 33%
Bearing Current Modelling and Investigation in Axial Flux Permanent Magnet Synchronous Motors for Aerospace Applications
No full textThis paper investigates the bearing current issue of the Axial Flux Permanent Magnet Synchronous Motors (AFPMSM) used in aerospace. The case-study examined in this work is an eVTOL propulsion motor. The paper focuses on calculating the various parasitic components that relate to the bearing current phenomena in the AFPMSM. The simulation work presented in this paper provides a good understanding of the bearing current phenomenon and the sensitivity of the different parameters on the bearing current. According to the authors' best knowledge, this is the first time investigating the bearing current problem in AFPMSM. The paper also proposes a model for the bearing current simulation in this machine. Once the importance of the problem had been assessed, the bearing current possibility and the different solutions to overcome this problem have been investigated. The study also shows the pros and cons of each of these suggested solutions from the electrical point of view
Real-Time Optimal Energy Management of Electrified Powertrains with Reinforcement Learning
No full textReinforcement learning (RL)algorithm is employed in solving energy management problem for electrified powertrain in real-world driving scenarios and the application process is streamlined. A near-global optimal control policy is articulated for the energy management system (EMS) using Q-learning algorithm which is real-time implementable. The core of the EMS is an updating optimal control policy in the form of a changing look-up table comprising near-global optimal action value function (Q-values) corresponding to all feasible state-action combinations. Using the updating control policy, the EMS can optimally decide power-split between electric machines (EMs) and internal combustion engine (ICE) in real-world driving situations
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