Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Electric vehicles with independently controlled drivetrains allow torque vectoring, which enhances active safety and handling qualities. This article proposes an approach for the concurrent control of yaw rate and sideslip angle based on a single-input single-output (SISO) yaw rate controller. With the SISO formulation, the reference yaw rate is first defined according to the vehicle handling requirements and is then corrected based on the actual sideslip angle. The sideslip angle contribution guarantees a prompt corrective action in critical situations such as incipient vehicle oversteer during limit cornering in low tire-road friction conditions. A design methodology in the frequency domain is discussed, including stability analysis based on the theory of switched linear systems. The performance of the control structure is assessed via: 1) phase-plane plots obtained with a nonlinear vehicle model; 2) simulations with an experimentally validated model, including multiple feedback control structures; and 3) experimental tests on an electric vehicle demonstrator along step steer maneuvers with purposely induced and controlled vehicle drift. Results show that the SISO controller allows constraining the sideslip angle within the predetermined thresholds and yields tire-road friction adaptation with all the considered feedback controllers

A single input single output formulation for yaw rate and sideslip angle control via torque-vectoring

Many torque-vectoring controllers are based on the concurrent control of yaw rate and sideslip angle through complex multi-variable control structures. In general, the target is to continuously track a reference yaw rate, and constrain the sideslip angle to remain within thresholds that are critical for vehicle stability. To achieve this objective, this paper presents a single input single output (SISO) formulation, which varies the reference yaw rate to constrain sideslip angle. The performance of the controller is successfully validated through simulations and experimental tests on an electric vehicle prototype with four drivetrains

On the experimental analysis of single input single output control of yaw rate and sideslip angle

Electric vehicles with individually controlled drivetrains allow torque-vectoring, which improves vehicle safety and drivability. This paper investigates a new approach to the concurrent control of yaw rate and sideslip angle. The proposed controller is a simple single input single output (SISO) yaw rate controller, in which the reference yaw rate depends on the vehicle handling requirements, and the actual sideslip angle. The sideslip contribution enhances safety, as it provides a corrective action in critical situations, e.g., in case of oversteer during extreme cornering on a low friction surface. The proposed controller is experimentally assessed on an electric vehicle demonstrator, along two maneuvers with quickly variable tire-road friction coefficient. Different longitudinal locations of the sideslip angle used as control variable are compared during the experiments. Results show that: i) the proposed SISO approach provides significant improvements with respect to the vehicle without torque-vectoring, and the controlled vehicle with a reference yaw rate solely based on the handling requirements for high-friction maneuvering; and ii) the control of the rear axle sideslip angle provides better performance than the control of the sideslip angle at the centre of gravity

A gain scheduled robust linear quadratic regulator for vehicle direct yaw moment control

Yaw moment control systems improve vehicle stability and handling in severe driving manoeuvres. Nevertheless, the control system performance is limited by the unmodelled dynamics and parameter uncertainties. To guarantee robustness of the control system against system uncertainties, this paper proposes a gain scheduling Robust Linear Quadratic Regulator (RLQR), in which an extra control term is added to the feedback of a conventional LQR to limit the closed-loop tracking error in a neighbourhood of the origin of its state-space, despite of the uncertainties and persistent disturbances acting on the plant. In addition, the intrinsic parameter-varying nature of the vehicle dynamics model with respect to the longitudinal vehicle velocity can jeopardize the closed-loop performance of fixed-gain control algorithms in different driving conditions. Therefore, the control gains optimally vary based on the actual longitudinal vehicle velocity to adapt the closed-loop system to the variations of this parameter. The effectiveness of the proposed RLQR in improving the robustness of classical LQR against model uncertainties and parameter variations is proven analytically, numerically and experimentally. The numerical and experimental results are consistent with the analytical analysis proving that the proposed RLQR reduces the ultimate bound of error dynamics

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

On pre-emptive vehicle stability control

Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview.This work was supported in part by the Horizon 2020 Framework Programme of the European Commission under grant agreements no. 769944 (STEVE project) and no. 824311 (ACHILES project)

Assessment of the Energy Consumption and Drivability Performance of an IPMSM-Driven Electric Vehicle Using Different Buried Magnet Arrangements

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)This study investigates the influence of the buried magnet arrangement on the efficiency and drivability performance provided by an on-board interior permanent magnet synchronous machine for a four-wheel-drive electric car with two single-speed on-board powertrains. The relevant motor characteristics, including flux-linkage, inductance, electromagnetic torque, iron loss, total loss, and efficiency, are analyzed for a set of six permanent magnet configurations suitable for the specific machine, which is controlled through maximum-torque-per-ampere and maximum-torque-per-voltage strategies. Moreover, the impact of each magnet arrangement is analyzed in connection with the energy consumption along four driving cycles, as well as the longitudinal acceleration and gradeability performance of the considered vehicle. The simulation results identify the most promising rotor solutions, and show that: (i) the appropriate selection of the rotor configuration is especially important for the driving cycles with substantial high-speed sections; (ii) the magnet arrangement has a major impact on the maximum motor torque below the base speed, and thus on the longitudinal acceleration and gradeability performance; and (iii) the configurations that excel in energy efficiency are among the worst in terms of drivability, and vice versa, i.e., at the vehicle level, the rotor arrangement selection is a trade-off between energy efficiency and longitudinal vehicle dynamics.Peer reviewedFinal Published versio

Energy-efficient torque-vectoring control of electric vehicles with multiple drivetrains

The safety benefits of torque-vectoring control of electric vehicles with multiple drivetrains are well known and extensively discussed in the literature. Also, several authors analyze wheel torque control allocation algorithms for reducing the energy consumption while obtaining the wheel torque demand and reference yaw moment specified by the higher layer of a torque-vectoring controller. Based on a set of novel experimental results, this study demonstrates that further significant energy consumption reductions can be achieved through the appropriate tuning of the reference understeer characteristics. The effects of drivetrain power losses and tire slip power losses are discussed for the case of identical drivetrains at the four vehicle corners. Easily implementable yet effective rule-based algorithms are presented for the set-up of the energy-efficient reference yaw rate, feedforward yaw moment and wheel torque distribution of the torque-vectoring controller

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer

A fast and parametric torque distribution strategy for four-wheel-drive energy-efficient electric vehicles

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies