Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)

In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy

Supervisory Controller Validation For A Plug-In Parallel-Through-The-Road Hybrid Electric Vehicle By Software-In-The-Loop Testing

The goal of this research is to develop an operational supervisory controller for Wayne State University Hybrid Warriors\u27 hybrid electric vehicle architecture that can be transitioned easily to a hardware-in-the-loop testing environment for the 2011-2014 EcoCAR2 competition. It serves to demonstrate how model-based design, specifically software-in-the-loop testing, is effective for the initial steps in design, verification, and validation of a supervisory control strategy. Overall, the supervisory controller aims to meet all safety and functional requirements while reducing fuel consumption. The thesis starts by presenting a plug-in parallel-through-the-road architecture and its powertrain hardware components. Next, characteristics and capabilities of all significant powertrain components are explained along with the implementation of the vehicle plant model. Initial stages and preparations for the development of supervisory controller begin with applying the Design Failure Mode and Effects Analysis and identifying the functional vehicle requirements. Control strategies implemented within the supervisory controller are discussed in detail. Finally, results from the software-in-the-loop testing as well as safety critical fault mitigation are shown, to demonstrate the end product of a supervisory controller that has reached a high level of functionality and safety and therefore is ready for hardware-in-the-loop testing. Outlines are provided for extending the current work into next phases of hardware-in-the-loop testing, optimization using vehicle-in-the-loop results, and special applications such as cold-start

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

Operation Simulation and Control of a Hybrid Vehicle Based on a Dual Clutch Configuration

Today, the world thrives on making more fuel-efficient vehicles that consume less energy, emit fewer emissions and have enhanced overall performance. Hybrid Electric Vehicles (HEVs) offer the advantages of improved fuel economy and emissions without sacrificing vehicle performance factors such as safety, reliability and other features. The durability and performance enhancements of HEVs have encouraged researchers to develop various hybrid power-train configurations and improve associated issues, such as component sizing and control strategies. HEVs with dual clutch transmissions (HDCT) are used in operation modes to improve fuel efficiency and dynamic performance for both diesel engines and high-speed gas engines. Dual clutch transmissions (DCTs) are proved to be the first automatic transmission type to provide better efficiency than manual transmissions. DCTs also provide reduced shift shocks and shift time that result in better driving experience. In addition, advanced software allows more simplistic approaches and tunable launch strategies in HDCT development. In this dissertation, an innovative approach to develop a desired mode controller for a HDCT configuration is proposed. This mode controller allows the driver to select the desired driving style of the vehicle. The proposed controller was developed based on adaptive control theory for the overall HDCT system. The proposed Model Reference Adaptive Control (MRAC) was applied to a parallel hybrid electric vehicle with dual clutch transmission (HDCT), and yielded good performance under different conditions. This implies that the MRAC is adaptive to different torque distribution strategies. The current study, which was performed on adaptive control applications, revealed that the Lyapunov method was effective and yielded good performance. The MRAC method was also applied to the mode transition of an HDCT bus. The simulation results confirmed that the MRAC outperformed the conventional operation method for an HDCT with reduced vehicle jerk and the torque interruption for the driveline and with improved fuel efficiency.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145173/1/Final Dissertation Elzaghir.pdfDescription of Final Dissertation Elzaghir.pdf : Dissertatio

Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

As regulations on the emission of greenhouse gasses continue to tighten on the automotive industry, the production of hybrid electric vehicles has gained significant popularity in recent years. With the increase in production, there has been a parallel demand in the advancement of both mechanical hardware and control system implementation used in these vehicles. A critical factor in the efficient operation of a hybrid electric vehicle is the energy management strategy where the goal is to maximize the efficient use of fuel energy to propel the vehicle. Designing a fuel-efficient control system is a complex challenge due to the degrees of freedom that exist in the control of a hybrid electric vehicle. Several methods exist for the real-time implementation of control strategies that employ heuristic or optimization-based algorithms; however, these control strategies typically rely on the results of offline optimization as a benchmark against which the control strategies are evaluated. Offline energy management optimization strategies require a pre-defined driving schedule for which the operation of the powertrain can be evaluated to determine the globally optimal control policy. The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determine control trends that can be used to improve existing algorithms. The optimal combined CS fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6L 2019 Chevrolet Blazer

MODELING, SIMULATION AND CONTROL OF HYBRID ELECTRIC VEHICLE DRIVE WHILE MINIMIZING ENERGY INPUT REQUIREMENTS USING OPTIMIZED GEAR RATIOS

This project was conducted to analyze (model and simulate) and optimize an electric motor based drive system to propel a typical passenger vehicle in an urban driving environment. Although there are many HEV and EV type systems on the market today, this paper chose the Toyota Prius HEV system as a baseline using a brushless AC motor. Although a vehicle can be driven many ways, a more standardized Urban Dynamometer Driving Schedule, UDDS, was chosen to simulate real driving conditions. This schedule is determined by the US Environmental Protection Agency, EPA, and is intended to represent the city driving conditions for a typical passenger vehicle in a city environment. A high level modeling and simulation approach for vehicle and motor drive was taken to focus on motor operation and gear ratios from the electric to the mechanical drive system. Vehicle battery being the limiting factor in the range of the HEV vehicle, the energy usage of the battery was optimized to ensure lowest energy dissipation, thus gaining the most mileage out of the vehicle. How to maximize the drive mileage for a given battery size? There are multiple dynamic factors that affect the battery usage and efficiency. Factors such as road conditions, vehicle speed, weather, weight, and aerodynamics are amongst the many that govern battery mileage. Gear ratios and selection also play a crucial role in the loading and efficiency of the motor, thus affecting the battery mileage. In this project, the gear ratios between the electric motor and the vehicle drive shaft were the focus for this optimization. As part of the overall system model, gears and gear ratios were modeled and simulated to determine their optimum ratios for finding the minimum energy usage point for the battery

Intelligent energy management agent for a parallel hybrid vehicle

This dissertation proposes an Intelligent Energy Management Agent (IEMA) for parallel hybrid vehicles. A key concept adopted in the development of an IEMA is based on the premise that driving environment would affect fuel consumption and pollutant emissions, as well as the operating modes of the vehicle and the driver behavior do. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver, and the operating mode (and trend) of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to improved fuel economy and reduced emissions