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An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.

The coupling effect between road excitation and an in-wheel switched reluctance motor (SRM) on vehicle ride comfort is numerically analysed. A hybrid control system consisting of fault tolerant H∞ suspension controller and SRM controller for an in-wheel SRM driven electric vehicle is proposed to improve the vehicle ride comfort and motor operation performance. By conducting numerical simulations based on the developed quarter-car active suspension model and switched reluctance motor model, it is observed that the road roughness is highly coupled with SRM airgap eccentricity and unbalanced residual vertical force. The SRM airgap eccentricity is influenced by the road excitation and becomes time-varying such that a residual unbalanced radial force is induced; which is one of the major causes of SRM vibration. To suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort, a fault tolerant controller based on output feedback H∞ control method is designed to reduce the sprung mass acceleration. Moreover, an SRM controller is adapted by using the combined Current Chopping Control (CCC) and Pulse Width Modulation control (PWM) to further improve the SRM performance. A comparison of passive suspension and suspensions with hybrid control method on the vehicle and SRM dynamic response under stochastic road excitation and bump road excitation is illustrated. The results indicate that the proposed hybrid control method can effectively reduce the SRM airgap eccentricity, residual unbalanced radial force and achieve better vehicle ride comfort.

This paper proposes a novel 2-step global sensitivity analysis algorithm to provide an in-depth sensitivity analysis of the vehicle parameters on the system responses. A 9 degree-of-freedom...

This paper presents a new approach for vibration mitigation based on a dynamic vibration absorbing structure (DVAS) for electric vehicles (EVs) that use in-wheel switched reluctance motors (SRMs). The proposed approach aims to alleviate the negative effects of vibration caused by the unbalanced electromagnetic force (UMEF) that arises from road excitations. The analytical model of SRMs is first formulated using Fourier series, and then a model of the coupled longitudinal-vertical dynamics is developed taking into consideration the external excitations consisting of the aerodynamic drag force and road unevenness. In addition, numerical simulations for a conventional SRM-suspension system and two novel DVASs are carried out for varying road levels specified by ISO standards and vehicle velocities. The results of the comparison reveal that a 35% improvement in ride comfort, 30% improvement of road handling, and 68% improvement in air gap between rotor and stator can be achieved by adopting the novel DVAS compared to the conventional SRM-suspension system. Finally, multi-body simulation (MBS) is performed using LMS Motion to validate the feasibility of the proposed DVAS. Analysis of the results shows that the proposed method can augment the effective application of SRMs in EVs.

This paper presents novel algorithms for vibration control of the in-wheel motor (IWM) driven electric vehicles to improve its ride comfort and reduce IWM vibration. A quarter vehicle model is first developed based on a dynamic vibration absorbing structure (DVAS) driven by a switched reluctance motor (SRM). This model considers the coupled longitudinal-vertical dynamics and the unknown road profile as well as the unbalanced electromagnetic force induced by the SRM are treated as the excitation. The dynamics and boundary models of two commercially available semi-active dampers are then presented, which are used as the actuators of both the suspension and the DVAS structure. Based on the developed model, a hybrid controller with a hybrid acceleration driven damping algorithms is proposed to reduce the vibration of the sprung mass and the SRM. The controller parameters are subsequently determined by solving the multi-objective optimization problem with a multi-objective evolutionary optimization method. Numerical simulation results for random road and bumpy excitations are analyzed, and multi-body simulation is finally performed to validate the feasibility of the proposed controllers. Results indicate that the proposed hybrid controllers can effectively improve ride comfort and reduce the SRM vibration compared with the traditional suspension system with IWM.

Changes in the structure and operating conditions of hybrid vehicles have caused new vibration and noise problems. In order to save energy and reduce emission, the hybrid vehicle will start or stop the engine selectively under different road conditions. The annoying vibration of vehicles during engine start and stop process is not expected by the drivers, whose influences for ride comfort can not be ignored. This paper studies the excitation source of the engine, analyzes the pumping resistance, inertial resistance and combustion torque of the engine, and establishes an exhaustive engine model based on the analysis results in ADAMS. Finally, co-simulation results show that using the coordinated torque control of the large and small motors, the vibration of the whole vehicle in the mode-switch between engine start and stop is reduced significantly.

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards ...

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

System modeling is one of the most important tasks of dynamic analysis and prediction systems, and imprecise model may lead to high bias. The presence of noise in sample data can make it more difficult to obtain precise system models. A new modeling algorithm called ANFIS-GMDH is presented in this paper, which builds upon the traditional ANFIS structure and utilizes the self-organizing mechanisms of GMDH. The aim of ANFIS-GMDH is to improve upon the traditional ANFIS method and prevent overfitting of noisy data. The well-studied Box-Jenkins gas furnace data is utilized to validate the algorithm, with results showing that the proposed algorithm performs better than traditional ANFIS, GMDH and subtractive clustering for both noisy and noiseless data, without any significant increase in execution time.

A novel road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the road profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the road profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.

In this paper, a review of the literature on vehicle suspension control with an emphasis on learning based algorithms is presented. An elaborated discussion on the potentials of learning based controllers is also given. Some of the most well-known active and semi-active suspension controllers are elicited from the literature, and their pros and cons are reported. By categorizing the existing suspension control techniques and considering their functionalities, it is tried to make a ground for indicating the high potential of learning strategies for this task. In this context, several advantageous features of statistical learning and computational intelligence methods are enumerated, which can play a key role in the future of vehicle suspension control. In the authors’ view, the necessity of considering learning strategies for suspension control lies in the fact that, over the past two decades, tremendous effort has been exerted on developing smart and autonomous vehicles to reduce the need for human-machine interaction. Given the fact that the final goal of automotive industrialists is to design efficient and safe autonomous vehicles, it is impossible to neglect the pivotal role of probabilistic artificial intelligence and statistical learning algorithms. In short, by reading this review paper, one can find out (1) the state-of-the-art of the conducted researches on suspension controllers, (2) the potential of learning strategies and their applicability to vehicle suspension control, and (3) the important open questions which deserve further investigation to come up with robust, stable and accurate learnable controllers. The outcome of this paper can be of use for practitioners working on designing smart and autonomous vehicles.

This paper presents a comprehensive comparison and analysis for the effect of time delay on the five most representative semi-active suspension control strategies, and refers to four unsolved problems related to semi-active suspension performance and delay mechanism that existed. Dynamic characteristics of a commercially available continuous damping control (CDC) damper were first studied, and a material test system (MTS) load frame was used to depict the velocity-force map for a CDC damper. Both inverse and boundary models were developed to determine dynamic characteristics of the damper. In addition, in order for an improper damper delay of the form t+τ to be corrected, a delay mechanism of controllable damper was discussed in detail. Numerical simulation for five control strategies, i.e., modified skyhook control SC, hybrid control (HC), COC, model reference sliding mode control (MRSMC), and integrated error neuro control (IENC), with three different time delays: 5 ms, 10 ms, and 15 ms was performed. Simulation results displayed that by changing control weights/variables, performance of all five control strategies varied from being ride comfort oriented to being road handling oriented. Furthermore, increase in delay time resulted in deterioration of both ride comfort and road handling. Specifically, ride comfort was affected more than road handling. The answers to all four questions were finally provided according to simulation results.

To meet the requirements of excitation information for semi-active suspension control, a new road classification method with application of Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented. Due to distinct system responses for different road levels, the sprung mass acceleration signal was utilized for classification. To analyze the properties of various road inputs from different perspectives, the acceleration signal was first decomposed into 5 categories via wavelet transform, and 11 statistic features were calculated for each category. Then, an improved distance evaluation technique was applied to remove irrelevant features. With the extracted superior features, a new 2-layers ANFIS classifier was implemented to calculate overall road level. Simulation results revealed that the proposed classifier had significantly improved performance compared to all 1-layer ANFIS classifiers for individual category, and can accurately classify road level with negligible time delay.

A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.

The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN) is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario.

A new method for road profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.Copyright © 2014 by ASME

The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.