SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears

International audienceThis paper proposes a Sensorless Direct Torque Control (SDTC) neural network traction control approach of an Electric vehicle (EV) without differential gears (electrical differential system). The EV is in this case propelled by two induction motor (one for each wheel). Indeed, using two electric in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed that is different from wheels speed characterized by slip in the driving mode, as an input. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed SDTC neural network approach operates satisfactorily

A Sensorless Direct Torque Control Scheme Suitable for Electric Vehicles

International audienceIn this paper a sensorless control is proposed to increase the efficiency of a Direct Torque Control (DTC) of an induction motor propelling an Electric Vehicle (EV). The proposed scheme uses an adaptive flux and speed observer that is based on a full order model of the induction motor. Moreover, it is evaluated on an EV global model taking into account the vehicle dynamics. Simulations were first carried out on a test vehicle propelled by a 37-kW induction motor to evaluate the consistency and the performance of the proposed control approach. The commonly used European drive cycle ECE-15 is adopted for simulation. The obtained results seem to be very promising. Then, the proposed control approach was experimentally implemented, on a TMS320F240 DSP-based development board, and tested on 1-kW induction motor. Experimental results show that the proposed control scheme is effective in terms of speed and torque performances. Indeed, it allows speed and torque ripple minimization. Moreover, the obtained results show that the proposed sensorless DTC scheme for induction motors is a good candidate for EVs propulsion

Torque vectoring to maximize straight-line efficiency in an all-electric vehicle with independent rear motor control

BEVs are a critical pathway towards achieving energy independence and meeting greenhouse and pollutant gas reduction goals in the current and future transportation sector [1]. Automotive manufacturers are increasingly investing in the refinement of electric vehicles as they are becoming an increasingly popular response to the global need for reduced transportation emissions. Therefore, there is a desire to extract the most fuel economy from a vehicle as possible. Some areas that manufacturers spend much effort on include minimizing the vehicle’s mass, body drag coefficient, and drag within the powertrain. When these values are defined or unchangeable, interest is driven to other areas such as investigating the control strategy of the powertrain. If two or more electric motors are present in an electric vehicle, Torque Vectoring (TV) strategies are an option to further increase the fuel economy of electric vehicles. Most of the torque vectoring strategies in literature focus exclusively on enhancing the vehicle stability and dynamics with few approaches that consider efficiency or energy consumption. The limited research on TV that addresses system efficiency have been done on a small number of vehicle architectures, such as four independent motors, and are distributing torque front/rear instead of left/right which would not induce any yaw moment. The proposed research aims to address these deficiencies in the current literature. First, by implementing an efficiency-optimized TV strategy for a rear-wheel drive, dual-motor vehicle under straight-line driving as would be experienced in during the EPA drive cycle tests. Second, by characterizing the yaw moment and implementing strategies to mitigate any undesired yaw motion. The application of the proposed research directly impacts dual-motor architectures in a way that improves overall efficiency which also drives an increase in fuel economy. Increased fuel economy increases the range of electric vehicles and reduces the energy demand from an electrical source that may be of non-renewable origin such as coal

Control of an Independent 4WD electric vehicle by DYC method

Current advances in the application of control systems to vehicle dynamics has made it practicable to improve the vehicle’s longitudinal, lateral and vertical dynamics. Some of the examples of application of these systems to vehicle control are traction control (longitudinal dynamics) to prevent wheel slip, ESP (lateral dynamics) to prevent loss of stability, and active suspension (vertical dynamics) to increase ride comfort. In this paper, the vehicle lateral motion is controlled by direct yaw control (DYC) method. This uses the yaw moment produced by the longitudinal forces of the tyres, for stabilising the vehicle motion during critical cornering conditions. The system is been designed to give substantially enhanced active safety and dynamic handling control. The vehicle dynamics control algorithm is developed for a FOX vehicle by controlling couple traction/braking torque of the four in-wheel motors, from basic driving slogans. These are the steering angle, position of the accelerator pedal and brake by the position of the brake pedal, as shown in Figure 1

Nonlinear Direct Torque Control of Interior Permanent Magnet Synchronous Motor Drive

This paper presents a nonlinear direct torque control (NDTC) strategy of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) propulsion. The proposed NDTC scheme applies a nonlinear model of IPMSM to dynamically determine the optimal switching states that optimize the EV drivers’ decision to reduce the workload. Moreover, the proposed NDTC method has a simple control structure and can explicitly handle system constraints and nonlinearities. The performance evaluation is conducted via a prototype IPMSM test-bed with a TMS320F28335 DSP. Comparative experimental results provide the evidence of improvements of the proposed NDTC strategy over the conventional DTC strategy by indicating a fast torque response and an accurate speed tracking even under rapid speed change conditions

Design of Outrunner Eectric Machines for Green Energy Applications

Interests in using rare-earth free motors such as switched reluctance motors (SRMs) for electric and hybrid electric vehicles (EV/HEVs) continue to gain popularity, owing to their low cost and robustness. Optimal design of an SRM, to meet specific characteristics for an application, should involve simultaneous optimization of the motor geometry and control in order to achieve the highest performance with the lowest cost. This dissertation firstly presents a constrained multi-objective optimization framework for design and control of a SRM based on a non-dominated sorting genetic algorithm II (NSGA-II). The proposed methodology optimizes SRM operation for high volume traction applications by considering multiple criteria including efficiency, average torque, and torque ripple. Several constraints are defined by the application considered, such as the motor stack length, minimum desired efficiency, etc. The outcome of this optimization includes an optimal geometry, outlining variables such as air gap length, rotor inner diameter, stator pole arc angle, etc as well as optimal turn-on and turn-off firing angles. Then the machine is manufactured according to the obtained optimal specifications. Finite element analysis (FEA) and experimental results are provided to validate the theoretical findings. A solution for exploring optimal firing angles of nonlinear current-controlled SRMs is proposed in order to minimize the torque ripple. Motor torque ripple for a certain electrical load requirement is minimized using a surrogate-based optimization of firing angles by adjusting the motor geometry, reference current, rotor speed and dc bus voltage. Surrogate-based optimization is facilitated via Neural Networks (NN) which are regression tools capable of learning complex multi-variate functions. Flux and torque of the nonlinear SRM is learned as a function of input parameters, and consequently the computation time of design, which is crucial in any micro controller unit, is expedited by replacing the look-up tables of flux and torque with the surrogate NN model. This dissertation then proposes a framework for the design and analysis of a coreless permanent magnet (PM) machine for a 100 kWh shaft-less high strength steel flywheel energy storage system (SHFES). The PM motor/generator is designed to meet the required specs in terms of torque-speed and power-speed characteristics given by the application. The design challenges of a motor/generator for this architecture include: the poor flux paths due to a large scale solid carbon steel rotor and zero-thermal convection of the airgap due to operation of the machine in vacuum. Magnetic flux in this architecture tends to be 3-D rather than constrained due to lack of core in the stator. In order to tackle these challenges, several other parameters such as a proper number of magnets and slots combination, number of turns in each coil, magnets with high saturated flux density and magnets size are carefully considered in the proposed design framework. Magnetic levitation allows the use of a coreless stator that is placed on a supporting structure. The proposed PM motor/generator comprehensive geometry, electromagnetic and mechanical dimensioning are followed by detailed 3-D FEA. The torque, power, and speed determined by the FEA electromagnetic analysis are met by the application design requirements and constraints for both the charging and discharging modes of operation. Finally, the motor/generator static thermal analysis is discussed in order to validate the proposed cooling system functionality

Well-to-Wheel Energy, Emissions, and Cost Analysis of Electricity and Fuel Used in Conventional and Electrified Vehicles, and Their Connection to a Sustainable Energy Infrastructure

Recent legislation by the United States Environmental Protection Agency (EPA) requires record low vehicle tailpipe emissions, necessitating research and development in the areas of lowering conventional (i.e., internal combustion engine) vehicle emissions rates while facilitating the widespread introduction of electrified vehicles. Currently, the EPA views Battery Electric Vehicles as having zero emissions. However, a number of studies illustrate this is not the case when considering the emissions produced in creating the electricity through a full Life Cycle Analysis. As a result, proper comparison of electrified and conventional vehicles must include a complete Well-to-Wheel (WtW) study including the emissions generated through production and use of liquid petroleum and biofuels. As a result, this work provides a full WtW investigation into fuel, electricity, and production analysis of conventional and electrified vehicles. This is supported by a thorough literature review of current and projected future technology, extrapolating to a fleet analysis, as well as applying the technology to an advanced electricity infrastructure. In the following effort, the first chapter simply provides a background into these different areas in order to help set the stage. Chapter 2 explores conventional vehicle emissions profiles predicting future requirements of engine and catalytic exhaust aftertreatment technologies. Findings illustrate that low temperature climates and aging both adversely affect a vehicle's ability to perform proper emissions reductions. This chapter additionally demonstrates an improvement in the fuel use emissions profiles of Argonne National Laboratories' Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model through the update of embedded time-sheet emissions lookup tables using EPA's Motor Vehicle Emissions Simulator (MOVES). This simulation package utilizes a statistical database of over 3000 counties in the continental United States in calculating the emissions profile of various vehicle and fuel type combinations, updating the current tables utilized in GREET. Chapter 3 utilizes these efforts in performing a life cycle analysis of a 1974 Volkswagen Super Beetle converted to a plug-in series hybrid. This work utilizes GREET in exploring the WtW fuel use emissions profile, as well as estimating the energy and emissions savings through reusing a number of stock vehicle components in the conversion. A vehicle dynamics model supports this analysis, calculating the average fuel use in a typical city/highway drive cycle. The fourth chapter expands upon this work, analyzing an 800+ vehicle fleet in a comparative analysis between electrified vehicles and their conventional counterparts. This work utilizes four simplified vehicle dynamics models, focusing on ten vehicles with various powertrains and fuel use algorithms. These models calculate the average fuel consumption of these vehicles, employing the GREET model in calculating the emissions profiles on a per-mile and yearly total basis. Furthermore, a full cost analysis of fuel and vehicle combinations demonstrates the economic impacts of electrifying the vehicle fleet. Finally, Chapter 5 seeks to support future research into electrified vehicles for vehicle-to-grid technology, energy storage, and infrastructure control through the design and construction of a small-scale smart grid in collaboration with a previous University of Kansas EcoHawks senior design team. This design consists of a renewable and conventional energy source, a grid load, bulk and dynamic grid storage, and a full sensory and control system. The final design meets the two requirements of a smart grid set forth by the Department of Energy: decentralization of energy production and storage, and providing two-way communication from end users or appliances and the energy network

Analytical formulation of selected activities of the remote manipulator system

Existing analysis of Orbiter-RMS-Payload kinematics were surveyed, including equations dealing with the two body kinematics in the presence of a massless RMS and compares analytical explicit solutions with numerical solutions. For the following operational phases of the RMS numerical demonstration, problems are provided: (1) payload capture; (2) payload stowage and removal from cargo bay; and (3) payload deployment. The equation of motion provided accounted for RMS control forces and torque moments and could be extended to RMS flexibility and control loop simulation without increasing the degrees of freedom of the two body system