Control development for hybrid vehicle powertrain with magnetic continuously variable transmission

This paper presents a control development for a hybrid vehicle powertrain with magnetic continuously variable transmission-MAGSPLIT. This is shown that in comparison with conventional continuously variable transmission (CVT) hybrid powertrain, a MAGSPLIT-based powertrain can provide similar functionality but with a number of advantages including efficiency improvement due to neglecting mechanical losses, inherent torsional vibration attenuation, and simplification of mechanical arrangement leading to improvement on powertrain safety and reliability. The proposed control strategy is developed with a MotoHawk hybrid transmission control unit (HTCU) and validated by both hardware in the loop (HIL) study and measurements from a MAGSPLIT-based powertrain brassboard

Active damping control of HEVs using Ansys and Matlab/Simulink software

This paper presents Parallel Hybrid Electric Vehicles (HEVs) powertrain design as well as a motor-based control approach that is designed to control or reduce driveline oscillations by introducing a Proportional-Integral-Derivative (PID) controller and a Fuzzy logic sliding mode controller. Because the torque of the electric motor can be decreased or increased more quickly than that of the Internal Combustion Engine (ICE), the vibration increases significantly. To solve this problem, an electric motor control-based Active Damping Control (ADC) strategy is employed to assure smooth driveline function and provide seamless driving experience for the driver. First, the basic level modeling of a hybrid electric powertrain in Ansys Simplorer environment is created and the performance was studied during the certification drive cycle. Thus, the main components of the powertrain– traction motor, battery and ICE – are researched, and basic models were built. The components were developed based on the Ansys software by using an automotive system level behavioral HEV library with VHDL-AMS language built in Ansys Simplorer environment. In addition, comparison of both controllers was presented. The simulation results show that using the ADC reduces more than 30 % of the driveline oscillations, thereby improving the drivability of HEVs

Review on hardware-in-the-loop simulation of wave energy converters and power take-offs

This paper reviews the state-of-the-art on Hardware-In-The-Loop simulation methodologies and technologies applied in the research field of wave energy converters. It reveals important issues, such as an unclear taxonomy and representations of these methodologies, which are critical for the success of the approach, mostly during the design of experiments and presentation of results. Moreover, a classification approach to these methodologies is not found in the literature. Thus, a generic taxonomical and classification framework is developed to support the review process. This framework is built based on three taxonomic subsystems that the review shows to be effective in organizing the reviewed methodologies: simulated, real and interface subsystems. In particular, the definition of the interface subsystem is key to overcoming the limitations found in the methodological representations. Furthermore, this review borrows the term actionability to this approach to better describe the nuances and gaps between the reviewed case studies. It is found that the different technical implementations are easily organized with the proposed framework, and the results cover a wide range of wave energy converter development phases. Likewise, this review shows opportunities for improvements in the methodology and application to a wider number of new case studies.info:eu-repo/semantics/publishedVersio

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor–generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor–generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ

Development of battery management system for hybrid electric propulsion system.

Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.PhD in Aerospac

Hybrid Electric Powertrain Design and Control with Planetary Gear Sets for Performance and Fuel Economy

Planetary gear sets (PGs) play a key role in hybrid electric vehicle (HEV) design by enabling a variety of unique architectures using a limited number of powertrain components. Leveraging the capability of this mechanical device, this study introduces an automated design process for PG-based HEV systems focusing on both fuel economy and performance, while also deriving the necessary analysis and synthesis tools. First, the design process generates all possible modes in an HEV design with a given set of powertrain components. The data structure and the derivation method of speed and torque relationships of each mode enable an exhaustive search of the large design space that grew with all the component topology and PG gear ratio combinations. Second, all powertrain types realizable with a given set of components are mathematically shown, and each feasible mode is classified under one of these powertrain types. Third, computationally efficient linear programming solvers suitable for vector operations are developed for each powertrain type to assess the forward- and backward-speed gradeability, long-hauling torque, and acceleration time of each mode for all PG gear ratio combinations. Fourth, the combination of modes that meets the performance requirements, along with the number and location of clutches that make these mode transitions possible, are identified. As a result, each potent mode combination, the clutches necessary for the mode transition, and the auxiliary modes established through all clutch state combinations constitute a design that meets the performance criteria. Last, the fuel economy improvement potential of each design is evaluated using an algorithm that approximates dynamic programming optimization. The results show that light-duty truck performance requirements can be met by many two-PG HEV designs without sacrificing fuel economy if the right analysis and synthesis techniques for exploring the entire design space are developed. In addition to the design process, the feasibility of mode transitions and the effect of mode transitions on the fuel economy simulation results are investigated. For this purpose, the dynamics of mode transition is analyzed, and control algorithms achieving the transitions without interrupting the desired vehicle torque are developed. Then, these analysis and synthesis techniques are automated so that they can be integrated into the fuel economy simulation algorithm. The simulation results reveal that some mode transitions have a negative effect on fuel economy and the assumption of mode transition feasibility at any operating point is not valid.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144111/1/oguzhada_1.pd

Design optimisation and real-time energy management control of the electrified off-highway vehicle with artificial intelligence

Targeting zeros-emissions in transportation, future vehicles will be more energy-efficient via powertrain electrification. This PhD research aims to optimise an electrified off-highway vehicle to achieve the maximum energy efficiency by exploring new artificial intelligence algorithms. The modelling study of the vehicle system is firstly performed. Offline design optimisation and online optimum energy management control methodologies have been researched. New optimisation methods are proposed and compared with the benchmark methods. Hardware-in-the-Loop testing of the energy management controller has been carried out for validation of the control methods. This research delivers three original contributions: 1) Chaos-enhance accelerated particle swarm optimisation algorithm for offline design optimisation is proposed for the first time. This can achieve 200% higher reputation-index value compared to the particle swarm optimisation method. 2) Online swarm intelligent programming is developed as a new online optimisation method for model-based predictive control of the vehicle energy-flow. This method can save up to 17% energy over the rule-based strategy. 3) Multi-step reinforcement learning is researched for a new concept of ‘model-free’ predictive energy management with the capability of continuously online optimisation in real-world driving. It can further save at least 9% energy