In-wheel Electric Motors Research Articles

Torque Vectoring Control Strategies Comparison for Hybrid Vehicles with Two Rear Electric Motors

In today’s automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and control strategies. This paper analyzes different design possibilities for a torque vectoring system applied to an originally compact front-wheel drive hybrid electric vehicle with one internal combustion engine for the front axle and two added electric motors integrated in the wheels of the rear axle. A 14 degrees of freedom vehicle model is present o accurately reproduce the nonlinearities of vehicle dynamic phenomena and exploited to obtain high-fidelity numerical simulation results. Different control methods are compared, a PID, an LQR, and four different sliding mode control strategies. All controllers achieve sufficiently good results in terms of lateral dynamics compared with the basic hybrid version. The various aspects and features of the different strategies are analyzed and discussed. Chattering reduction strategies are developed to improve the performance of sliding mode controllers. For a complete overview, control systems are compared using a performance factor that weighs control accuracy and effort in different driving maneuvers, i.e., ramp and step steering maneuvers performed under quite different conditions ranging up to the limits.

Experimental thermal characterization of the in-wheel electric motor with loop heat pipe thermal management system

Current trends in technology toward the elimination of fossil fuels are increasing the value of electric vehicles (EVs). In turn, one of the main components of EVs is an electric motor (EM). The lifetime, reliability and output power of the EMs depend on the performance of their cooling systems. The cooling systems that are based on two-phase technologies of heat transfer, such as loop heat pipes (LHPs), have important advantages over traditional air- and liquid systems. On the other hand, LHP EM thermal control application is slowed down by the complication of their manufacturing and high production expenses. However, in recent years, new technologies are constantly being developed that can reduce the customization of LHP and, therefore, the cost of its production. In this paper the technology based on modular standardized approach for LHP design and manufacturing is applied to demonstrate the cooling performance of LHP in an in-wheel motor of a personal mobility vehicle (electric bike, wheelchair etc.) and compare it to the standard cooling solution. For this purpose, a double-capillary pump unit evaporator LHP was produced and coupled with an EM. EM thermal characteristics were studied and compared to the values obtained prior to LHP installation. Studies have shown that the use of two-phase cooling can increase motor performance up to 2.5 times.

Optimal design and power management control of hybrid biofuel–electric powertrain

This paper presents a multi-objective optimization procedure for a hybrid biofuel–electric vehicle (HBEV) powertrain design and fuzzy logic control. The vehicle concept is based on an electric drivetrain, while the battery is recharged by an onboard generator coupled to an ethanol-powered engine. The optimization procedure is done by the interactive adaptive-weight genetic algorithm method, aiming for the minimization of the fuel consumption, tailpipe emissions, powertrain size, and charging time, while the battery lifespan is improved. The powertrain parameters are defined as design variables, along with the membership functions, rules, and weights of the fuzzy logic controllers, responsible for the power split among the four in-wheel electric motors, the engine and generator operation. The optimization is done under the combination of four standard driving cycles combined in a 4 times loop, resulting in a 281.8 km path. As compared to a conventional vehicle with the same engine data, the best HBEV solution was able to save 17.78% fuel, while decreasing the emissions by 52.38% HC, 22.85% CO, and 28.57% NOx, considering a battery life expectation of 23,907 h. Moreover, this optimum HBEV was also evaluated under two combinations of real-world driving cycles, reaching fuel savings up to 30% and lower emissions as compared to the conventional vehicle in all analyzed scenarios. Finally, a cost analysis is performed to compare the proposed HBEV powertrain concept with other hybrid vehicle configurations presented in the literature.

DESIGN OF GEAR REDUCER FOR VEHICLE POWERED BY IN-WHEEL ELECTRIC MOTOR

Electric vehicles with cleaner, cheaper, and more efficient powertrain technologies are being developed by the automotive industry to reduce emissions and make cities more liveable. This imposes weight and size restrictions on the electric powertrain to lower the energy consumption, to leave enough room for the batteries, and to minimize the unsprung mass, in the case of vehicles driven by in-wheel electric motors. This paper presents the design of gear reducer for in-wheel electric motor as small and light as possible.

Study of Viscoelastic Rubber Mounts on Vehicle Suspensions with In-Wheel Electric Motors.

Rubber bushings and mounts are vastly used in automotive applications as support and interface elements. In suspension systems, they are commonly employed to interconnect the damping structure to the chassis. Therein, the viscoelastic nature of the material introduces a desirable filtering effect to reduce mechanical vibrations. When designing a suspension system, available literature often deals with viscoelastic mounts by introducing a linear or nonlinear stiffness behavior. In this context, the present paper aims at representing the rubber material using a proper viscoelastic model with the selection of different in-wheels motors. Thus, the mount dynamic behavior’s influence in a suspension is studied and discussed thoroughly through numerical simulations and sensitivity analyses. Furthermore, guidelines are proposed to orient the designer when selecting these elements.

Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario

A battery has normally a high energy density with low power density, while an ultracapacitor has a high power density but a low energy density. Therefore, this paper has been proposed to associate more than one storage technology generating a hybrid energy storage system (HESS), which has battery and ultracapacitor, whose objective is to improve the electric vehicle (EV) driving range. The HESS parameters have been evaluated in a configuration of EV powered by two in-wheel electric motors, coupled straight into the front wheels, and by a unique EM, connected to a differential transmission to drive the rear wheels. Moreover, this paper considers a real-world drive cycle based on the urban driving behavior of Campinas city, one of the most populous cities in Brazil. Aiming to minimize the HESS size and enhance the EV driving range, an optimization problem was formulated and solved using a genetic algorithm technique, in which the EV drivetrain parameters and HESS components and control are optimized. Finally, the obtained Pareto frontier defines the optimum EV configurations, in which the best-selected configurations were able to perform up to 188 km with a 418 kg HESS (maximum drive range solution), or 82.75 km with a 146.58 kg HESS (minimum HESS solution) and 319 km with a 188.43 kg HESS (best trade-off solution), without presenting performance losses.

Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation

Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV’s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV’s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV’s design parameters are optimized such as in-wheel electric motors’ torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a real-world driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.

Research on Dynamic Performance Simulation of In-wheel Motor Electric Vehicle Based on CarSim-Simulink

In order to reduce test costs and improve work efficiency, on the basis of the existing in-wheel-motor electric vehicle platform, CarSim software is used to establish a complete in-wheel-motor electric vehicle model that matches the actual vehicle. Based on Simulink, the dynamic model of the in-wheel motor is established. By defining the interface between the vehicle and the motor drive system, a joint simulation platform for in-wheel motor electric vehicles is established based on CarSim-Simulink, and simulation verification is performed on it. The results show that the established simulation model has high accuracy, can better simulate the existing experimental vehicle, accurately reflect the dynamic performance response of the vehicle, and provide a basis for further research and verification of control algorithms.

A Virtual Driveline Concept to Maximize Mobility Performance of Autonomous Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">In-wheel electric motors open up new prospects to radically enhance the mobility of autonomous electric vehicles with four or more driving wheels. The flexibility and agility of delivering torque individually to each wheel can allow significant mobility improvements, agile maneuvers, maintaining stability, and increased energy efficiency. However, the fact that individual wheels are not connected mechanically by a driveline system does not mean their drives do not impact each other. With individual torques, the wheels will have different longitudinal forces and tire slippages. Thus, the absence of driveline systems physically connecting the wheels requires new approaches to coordinate torque distribution. This paper solves two technical problems. First, a virtual driveline system (VDS) is proposed to emulate a mechanical driveline system virtually connecting the e-motor driveshafts, providing coordinated driving wheel torque management. The VDS simulates power split between driving wheels. Conceptually, VDS is founded on generalized tire and vehicle parameters. Generalized slippages are utilized to determine virtual gear ratios from a virtual transfer case to each wheel. The virtual gear ratios serve as signals to the electric motors. Secondly, a new velocity-based mobility performance index is used as the ratio of the actual velocity of a vehicle, with individual wheel management, to the theoretical velocity of the same vehicle equipped with a mechanical driveline without controllable gear ratios. Using the index as the objective function, a maximization problem of vehicle mobility is formulated and solved. Optimal virtual gear ratios are determined for maximal mobility in a given terrain condition. Simulations of a 4x4 tactical vehicle in stochastic soil conditions demonstrated a 17% increase in mean values of the velocity-based mobility performance index when the vehicle is electrically driven by the optimal virtual gear ratios as compared to the mechanical driveline system with non-controlled differentials.</div></div>

Electric Vehicle Powertrain and Fuzzy Control Multi-Objective Optimization, Considering Dual Hybrid Energy Storage Systems

The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass.

An Embedded Controller Application with Regenerative Braking for the Electric Vehicle

Regenerative braking is very important for increasing the total range of an electric vehicle. In this study, an embedded controller, including regenerative braking, is designed and implemented for an electric vehicle. Experimental studies are carried out on an electric vehicle driven by two in-wheel electric motors. In-wheel electric motors are preferred in light electric vehicles, since they are both highly efficient and supports regenerative braking. In our embedded controller application, the in-wheel electric motor is operated in both the motor mode and the regenerative braking mode. The in-wheel electric motor control embedded software is developed in the Matlab/Simulink environment. The developed software is embedded in the DSP STM32F407 microcontroller, which has ARM Cortex-M4 core. The in-wheel electric motor is controlled by a fuzzy logic controller in the motor mode, the in-wheel electric motor - in the regenerative braking mode. Different PWM (Pulse Width Modulation) ratios are applied to the wheel electric motor in the regenerative braking mode. The experimental data are recorded in real-time by transferring to a PC on the electric vehicle. The performance of the study is proven with experimental tests.

Robust Performance Improvement of Lateral Motion in Four-Wheel Independent-Drive Electric Vehicles

In this article a robust two-layer control scheme is proposed to improve the performance of Four-Wheel Independent-Drive (4-WID) electric vehicles. The main objective is to enhance the lateral motion performance of electric vehicle under various manoeuvres and in the existence of parameter uncertainties and exterior disturbances. The upper-layer control law is proposed according to a novel terminal sliding mode approach formulated using the longitudinal velocity and yaw rate dynamics of the electric vehicle. It aims at generating the desired driving forces of each tire along with the vehicle's yaw momentum while guaranteeing chattering-free motion and finite-time stability in the existence of parametric uncertainties and disturbances. The lower-layer control law is formulated in the light of a constrained optimization problem to generate the optimal forces and torques for the in-wheel electric motors and achieve the desired longitudinal force and yaw momentum. The efficacy of the suggested approach is confirmed using CARSIM; a high-fidelity simulation package. Its performance analysis considered three standard driving manoeuvres and included a comparative analysis of other designs. The obtained results show that the offered methodology ensures the finite-time stability of the vehicle and improves its manoeuvrability and tracking performance under various driving conditions.

Simulating in-Wheel Motors

Coupled electromagnetic and acoustic simulation of an in-wheel electric motor is key to an increasingly automated and user-friendly noise, vibration and harshness workflow

Velocity Optimization for Braking Energy Management of In-Wheel Motor Electric Vehicles

This paper presents two braking regenerative energy optimization controllers for in-wheel motor electric vehicles. The first one is a velocity-tracking controller based on a model predictive control (MPC) method to recover the braking energy. It takes the front and rears in-wheel motor efficiencies into account to distribute the hydraulic and in-wheel motor braking torque of the front and rear wheels. As the vehicle information and intelligence have brought new opportunities for energy management, another velocity optimization controller is designed by considering the restricted condition of terminal distance and terminal velocity. In this strategy, a receding-horizon MPC method is proposed to solve the restricted nonlinear optimal problem. Furthermore, this optimization algorithm is transformed from the time horizon to the distance horizon to satisfy the terminal distance constraint. AMESim/Simulink co-simulations are carried out to evaluate the effectiveness of the proposed controllers. The simulation results indicate that the velocity optimization method can achieve the braking requirement as well as effectively promote regenerative efficiency.

Suppression of Switched Reluctance Motor Vibration of In-Wheel Motor Electric Vehicle

Switched reluctance motor (SRM) has got great attention in in-wheel motor electric vehicle (IWM-EV), but SRM vertical force, the vertical component of SRM unbalanced radial force, yields SRM vertical vibration and does harm to dynamic performance of IWM-EV. In order to reduce the SRM vertical vibration, electromagnetic active suspension and a linear quadratic Gaussian (LQG) controller were used to suppress the unbalanced radial force in this paper. All the models and the controller were constructed in Matlab/Simulink R2015b. The controller considers five performance indexes: vehicle body acceleration, SRM airgap eccentricity, SRM stator acceleration, suspension dynamic deflections, and tyre deformation. Analytic Hierarchy Process (AHP) was used to calculate the weighted coefficients of performance indexes. Simulations indicate that this electromagnetic active suspension can reduce SRM vertical vibration obviously and improve dynamic performance of IWM-EV.

Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control

ABSTRACTCollision avoidance is a crucial function for all ground vehicles, and using integrated chassis systems to support the driver presents a growing opportunity in active safety. With actuators such as in-wheel electric motors, active front steer and individual wheel brake control, there is an opportunity to develop integrated chassis systems that fully support the driver in safety critical situations. Here we consider the scenario of an impending frontal collision with a stationary or slower moving vehicle in the same driving lane. Traditionally, researchers have approached the required collision avoidance manoeuver as a hierarchical scheme, which separates the decision-making, path planning and path tracking. In this context, a key decision is whether to perform straight-line braking, or steer to change lanes, or indeed perform combined braking and steering. This paper approaches the collision avoidance directly from the perspective of constrained dynamic optimisation, using a single optimisation procedure to cover these aspects within a single online optimisation scheme of model predictive control (MPC). While the new approach is demonstrated in the context of a fully autonomous safety system, it is expected that the same approach can incorporate driver inputs as additional constraints, yielding a flexible and coherent driver assistance system.

Teaching a vehicle to autonomously drift: A data-based approach using Neural Networks

This paper presents a novel approach to teach a vehicle how to drift, in a similar manner that professional drivers do. Specifically, a hybrid structure formed by a Model Predictive Controller and feedforward Neural Networks is employed for this purpose. The novelty of this work lies in a) the adoption of a data-based approach to achieve autonomous drifting along a wide range of road radii and body slip angles, and b) in the implementation of a road terrain classifier to adjust the system actuation depending on the current friction characteristics. The presented drift control system is implemented in a multi-actuated ground vehicle equipped with active front steering and in-wheel electric motors and trained to drift by a real test driver using a driver-in-the-loop setup. Its performance is verified in the simulation environment IPG-CarMaker through different open loop and path following drifting manoeuvres.

Multiple Fault-Tolerant In-Wheel Vehicle Control Based on High-level Control Reconfiguration

The paper deals with the fault-tolerant reconfigurable control design of a four-wheel independently-actuated (4WIA) electric vehicle. The purpose of the proposed method is to ensure the velocity and path tracking of the 4WIA vehicle in the event of a fault or performance degradation in one or even several in-wheel electric motors. The novelty of the presented method is the high-level control reallocation method based on the Linear Parameter Varying (LPV) control framework, by which the torque vectoring and steering of the vehicle are modified in order to deal with the impact of faulty in-wheel motors. The operation of the designed fault-tolerant method is validated in CarSim simulation environment.

Robust Yaw Stability Control for In-Wheel Motor Electric Vehicles

This study investigated robust yaw moment control for motion stabilization in four-wheel electric vehicles. A two-degree-of-freedom direct yaw moment control scheme is proposed. An engineering weighting function with embedded engineering specifications is included in the proposed approach. The controller was synthesized and its corresponding properties were studied. A rear-wheel drive in-wheel motor electric vehicle was employed for a practical evaluation of the scheme. Because of the robust control framework, the presented system can overcome model uncertainties, side wind disturbances, and parameter variation problems. Experiments were conducted to illustrate the feasibility and effectiveness of the proposed controller.

Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

In order to improve the driving performance and the stability of electric vehicles (EVs), a new multimachine robust control, which realizes the acceleration slip regulation (ASR) and antilock braking system (ABS) functions, based on nonlinear model predictive (NMP) direct torque control (DTC), is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives’ decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.