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

Feasibility of a neural network-based virtual sensor for vehicle unsprung mass relative velocity estimation

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity

Ride Blending Control for Electric Vehicles

Vehicles equipped with in-wheel motors (IWMs) feature advanced control functions that allow for enhanced vehicle dynamics and stability. However, these improvements occur to the detriment of ride comfort due to the increased unsprung mass. This study investigates the driving comfort enhancement in electric vehicles that can be achieved through blended control of IWMs and active suspensions (ASs). The term &ldquo ride blending&rdquo , coined in a previous authors&rsquo work and herein retained, is proposed by analogy with the brake blending to identify the blended action of IWMs and ASs. In the present work, the superior performance of the ride blending control is demonstrated against several driving manoeuvres typically used for the evaluation of the ride quality. The effectiveness of the proposed ride blending control is confirmed by the improved key performance indexes associated with driving comfort and active safety. The simulation results refer to the comparison of the conventional sport utility vehicle (SUV) equipped with a passive suspension system and its electric version provided with ride blending control. The simulation analysis is conducted with an experimentally validated vehicle model in CarMaker&reg and MATLAB/Simulink co-simulation environment including high-fidelity vehicle subsystems models. Document type: Articl

Nonlinear Model Predictive Control for Integrated Energy-Efficient Torque-Vectoring and Anti-Roll Moment Distribution

This study applies nonlinear model predictive control (NMPC) to the torque-vectoring (TV) and front-to-total anti-roll moment distribution control of a four-wheel-drive electric vehicle with in-wheel-motors, a brake-by-wire system, and active suspension actuators. The NMPC cost function formulation is based on energy efficiency criteria, and strives to minimize the power losses caused by the longitudinal and lateral tire slips, friction brakes, and electric powertrains, while enhancing the vehicle cornering response in steady-state and transient conditions. The controller is assessed through simulations using an experimentally validated high-fidelity vehicle model, along ramp steer and multiple step steer maneuvers, including and excluding the direct yaw moment and active anti-roll moment distribution actuations. The results show: 1) the substantial enhancement of energy saving and vehicle stabilization performance brought by the integration of the active suspension contribution and TV; 2) the significance of the power loss terms of the NMPC formulation on the results; and 3) the effectiveness of the NMPC with respect to the benchmarking feedback and rule based controllers

Ride blending control for electric vehicles

Vehicles equipped with in-wheel motors (IWMs) feature advanced control functions that allow for enhanced vehicle dynamics and stability. However, these improvements occur to the detriment of ride comfort due to the increased unsprung mass. This study investigates the driving comfort enhancement in electric vehicles that can be achieved through blended control of IWMs and active suspensions (ASs). The term “ride blending”, coined in a previous authors’ work and herein retained, is proposed by analogy with the brake blending to identify the blended action of IWMs and ASs. In the present work, the superior performance of the ride blending control is demonstrated against several driving manoeuvres typically used for the evaluation of the ride quality. The effectiveness of the proposed ride blending control is confirmed by the improved key performance indexes associated with driving comfort and active safety. The simulation results refer to the comparison of the conventional sport utility vehicle (SUV) equipped with a passive suspension system and its electric version provided with ride blending control. The simulation analysis is conducted with an experimentally validated vehicle model in CarMaker® and MATLAB/Simulink co-simulation environment including high-fidelity vehicle subsystems models

STATE-OF-THE-ART AND FUTURE DEVELOPMENTS IN INTEGRATED CHASSIS CONTROL FOR GROUND VEHICLES

Many modern ground vehicles feature state-of-the-art powertrain, braking and suspension control systems. These technologies are rapidly filtering through to heavy and off-road vehicles. The drawback of many of these systems is that their operation is largely considered only in stand-alone mode. The paper introduces up-to-date and coming ground vehicle technology related to the integration and advanced control of active chassis control systems. In particular, addressing the task of coordinated subsystems control can provide simultaneous enhancements in traction and braking performance, handling, off-road mobility, driving comfort and energy efficiency. A special focus in the paper is given to coordinated operation of brake control, active suspension, and dynamic tyre pressure management. The influence of each particular subsystem on the vehicle safety, off-road mobility and ride comfort is evaluated in simulation. It is further described and confirmed in simulation how the integrated chassis control (ICC) can simultaneously improve each of these vehicle characteristics. From the hardware viewpoint, a variant of ground vehicle architecture with aforementioned integrated active chassis systems is introduced. This architecture and its corresponding implementation on a sport utility vehicle are currently investigated within the European Union-funded Horizon 2020 consortium EVE. The work presented is a collaborative effort among several ISTVS members across the globe

On the model-based design of front-to-total anti-roll moment distribution controllers for yaw rate tracking

In passenger cars active suspensions have been traditionally used to enhance comfort through body control, and handling through the reduction of the tyre load variations induced by road irregularities. However, active suspensions can also be designed to track a desired yaw rate profile through the control of the anti-roll moment distribution between the front and rear axles. The effect of the anti-roll moment distribution relates to the nonlinearity of tyre behaviour, which is difficult to capture in the linearised vehicle models normally used for control design. Hence, the tuning of anti-roll moment distribution controllers is usually based on heuristics. This paper includes an analysis of the effect of the lateral load transfer on the lateral axle force and cornering stiffness. A linearised axle force formulation is presented, and compared with a formulation from the literature, based on a quadratic relationship between cornering stiffness and load transfer. Multiple linearised vehicle models for control design are assessed in the frequency domain, and the respective controllers are tuned through optimisation routines. Simulation results from a nonlinear vehicle model are discussed to analyse the performance of the controllers, and show the importance of employing accurate models of the lateral load transfer effect during control design

Advanced electric vehicle components for long-distance daily trips

This paper introduces a holistic engineering approach for the design of an electric sport utility vehicle focused on the reliable capability of long-distance daily trips. This approach is targeting integration of advanced powertrain and chassis components to achieve energy-efficient driving dynamics through manifold contribution of their improved functions. The powertrain layout of the electric vehicle under discussion is designed for an e-traction axle system including in-wheel motors and the dual inverter. The main elements of the chassis layout are the electro-magnetic suspension and the hybrid brake-by-wire system with electro-hydraulic actuators on the front axle and the electro-mechanical actuators on the rear axle. All the listed powertrain and chassis components are united under an integrated vehicle dynamics and energy management control strategy that is also outlined in the paper. The study is illustrated with the experimental results confirming the achieved high performance on the electric vehicle systems level

Connected and shared X-in-the-loop technologies for electric vehicle design

The presented paper introduces a new methodology of experimental testing procedures required by the complex systems of electric vehicles (EV). This methodology is based on real-time connection of test setups and platforms, which may be situated in different geographical locations, belong to various cyber-physical domains, and are united in a global X-in-the-loop (XIL) experimental environment. The proposed concept, called XILforEV, allows exploring interdependencies between various physical processes that can be identified or investigated in the process of EV development. The paper discusses the following relevant topics: global XILforEV architecture; realization of required high-confidence models using dynamic data driven application systems (DDDAS) and multi fidelity models (MFM) approaches; and formulation of case studies to illustrate XILforEV application