The method of kinematic excitation reconstruction based on measured suspension dynamic responses – experimental verification

The paper presents the method of kinematic road excitation reconstruction based on measured suspension dynamic responses and its reconstruction with use of estimated displacements of unsprung mass as a preliminary approximation of kinematic excitation and tracking control system with a PID controller that allows for faithful reconstruction of unsprung mass accelerations and, in turn, kinematic excitations. The authors performed an experimental verification of the method with use of one axle car trailer and measurements of road profile and acquiring signals of suspension dynamics responses. The signal processing methodology and obtained results are presented for random and determined excitations. The necessary requirements to use the method effectively were defined and its limitations were listed.

Design and test of hydro-pneumatic ISD suspension in heavy multi-axle vehicles

Aiming to improve the road friendliness so as to reduce the road damage caused by heavy multi-axle vehicles, and to enhance the ride comfort, we propose a kind of hydro-pneumatic ISD suspension structure, which is equivalent to a two-stage ISD structure integrating a traditional hydro-pneumatic suspension and a fluid inerter. Firstly, based on the 1/4 model, a genetic algorithm is used to optimize the key structural parameters of hydro-pneumatic ISD suspension. Secondly, the AMESim dynamic model of heavy multi-axle vehicles is built for the performance comparison between the traditional hydraulic and hydro-pneumatic ISD suspensions. Finally, this paper machines a hydro-pneumatic ISD suspension to replace the traditional hydraulic one in a heavy multi-axle vehicle to carry out a road test. Test results indicate that the proposed suspension can effectively restrain the vibrations of sprung and unsprung mass and improve ride comfort as well as road friendliness. The hydro-pneumatic ISD suspension can be applied to engineering.

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.

SEARCH FOR RATIONAL EBGINEERING SOLUTIONS OF DIESEL LOCOMOTIVES FOR THE EASTERN RANGE

The purpose of the study is to search for rational engineering solutions for the main autonomous locomotive for the Eastern range of OAO Russian Railways. Research methods: methods of physical and field experiments, analytical methods for calculating the dynamics of the rolling stock. Research results and novelty: it is established that the use of a four-axle truck of a diesel locomotive TEM7 does not allow to create a locomotive that meets all the requirements of OAO Russian Railways, and the design of truck TEM7 is irrational for mainline locomotives; it is proved that the one-sided arrangement of brake blocks leads to deterioration in the locomotive braking properties. The bearing and axial towline, despite the use of an asynchronous engine, has an unsprung mass 1.5 times greater than that of the previously produced domestic analogue with a collector engine and almost the same mass of the wheel-motor unit; dynamic moments in the drive due to the lack of elastic elements during prolonged operation can reach 56% of the traction torque, which worsens the traction properties of the locomotive. Conclusion: it is advisable to carry out a design study of the drive variant with a support-frame asynchronous traction engine and an axial gearbox, as well as to conduct a technical and economic analysis and design study of a cheaper version of a diesel locomotive with sections on three two-axle bogies, maximally unified with electric locomotives, with a booster tanker module and AC-DC transmission with axial regulation of eight-pole collector motors with support-frame suspension.

In-Wheel Two-Speed AMT with Selectable One-Way Clutch for Electric Vehicles

To improve the efficiency of the electric vehicle (EV) drive systems and EV performance, the use of multi-speed transmissions and distributed drives has been studied extensively. In addition, to develop efficient and compact drive systems, new clutch solutions are needed. In this paper, we propose an in-wheel two-speed automatic mechanical transmission (IW-AMT) with a selectable one-way clutch (SOWC). The IW-AMT consists of a high-speed motor and a mechanical shift actuator, and it can realize shifting without power interruption, thus effectively reducing the unsprung mass and the technical specifications of the motor. We established a virtual prototype model of the IW-AMT to show the shifting process and evaluate the quality of shifting. The simulation results of the upshifting process indicated that the vehicle torque and velocity changed smoothly, and the maximum jerk is less than 10 m/s3. Furthermore, to improve the jerk induced by the downshifting process, we analyzed the momentary state of the SOWC struts that are dropped and attempted to improve the jerk from two aspects: improving the wet multi-plate clutch (WMPC) combination curve and improving the SOWC structure. The results indicated that the downshift-induced jerk can be reduced to 13 m/s3.

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

With the automotive industry moving towards automated driving, sensing is becoming an increasingly important part of 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 (BiLSMT) 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 was 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 for the estimation of vehicle unsprung mass relative velocity.

Study on adverse effect suppression of hub motor driven vehicles with inertial suspensions

With increasingly severe energy supply and environmental pollution, Hub Motor Driven Vehicle (HMDV) have become the ideal configuration for future electric vehicles. The adverse effect caused by the significant increase in HMDV unsprung mass directly affects the vehicle ride comfort and handling stability, becomes a technical bottleneck for theoretical research and industrial development to be solved urgently. The HMDV adverse effect should be suppressed by the vehicle suspension system, which can be classified as vertical motion inertia instability caused by the increase of unsprung mass, but the lack of inertial elements in the conventional suspension “damping-spring” structure restricts the overall performance of the suspension. Therefore, the inertial suspension containing the inertial element “inerter” is utilized to effectively distribute the HMDV vertical motion inertia to suppress its adverse effects. First, the adverse effects of increased unsprung mass on vehicle ride comfort and road friendliness are studied. Then, the inertial suspension models of different structures are established and their impedance expressions are derived. Thirdly, the impact of the inertance in the inertial suspension on the suppression of the HMDV adverse effect is studied. Finally, the particle swarm optimization algorithm is utilized to optimize the parameters of the inertial suspension to improve the performance of the suspension. The results show that the inertial suspension can reduce the RMS value of body acceleration and dynamic tire load. The RMS value of body acceleration and dynamic tire load of the L4 structure is reduced by 8.1% and 16.38% respectively. It shows that the inertial suspension can effectively suppress the HMDV vertical adverse effect, improve the HMDV ride comfort and road friendliness, and lay the foundation for the subsequent suppression research.

Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles

This paper compares the energy economy and vertical vibration characteristics of in-wheel drive electric vehicles (IEVs), in-wheel drive electric hydraulic hybrid vehicles (IHVs) and centralized drive electric vehicles (CEVs). The dynamic programming (DP) algorithm is used to explore the optimal energy consumption of each vehicle. The energy economy analysis shows that the IEV consumes more energy than the CEV due to its relatively lower electric motor efficiency, even with fewer driveline components. The IHV consumes much more energy than the IEV and CEV because of the energy loss in the hydraulic driveline. The vertical vibration analysis demonstrates that both IEV and IHV degrade the vehicle driving comfort due to increased unsprung mass. Taking the advantage of high power density of the hydraulic motor, IHV have less unsprung mass when compared with the IEV, which helps to mitigate the vibration problems caused by increased unsprung mass.

Design of a Lightweight Multifunctional Composite Railway Axle Utilising Coaxial Skins

The rising economic and environmental pressures associated with the generation and consumption of energy necessitates the need for lightweighting of railway vehicles. The railway axle is a prime candidate for lightweighting of the unsprung mass. The reduction of unsprung mass correlates to reduced track damage, energy consumption and total operating costs. This paper presents the design of a lightweight multifunctional hybrid metallic-composite railway axle utilising coaxial skins. The lightweight axle assembly comprises a carbon fibre reinforced polymer composite tube with steel stub axles bonded into either end. The structural hybrid metallic-composite railway axle is surrounded by coaxial skins each performing a specific function to meet the secondary requirements. A parametric sizing study is conducted to explore the sensitivity of the design parameters of the composite tube and the stub axle interaction through the adhesive joint. The optimised design parameters of the axle consist of a; composite tube outer diameter of 225 mm, composite tube thickness of 7 mm, steel stub axle extension thickness of 10 mm and a bond overlap length of 100 mm. The optimised hybrid metallic-composite railway axle design concept has a mass of 200 kg representing a reduction of 50% over the solid steel version.

ANALYSIS OF 3D PRINTED VS CONVENTIONALLY MACHINED FSAE UPRIGHTS

With the advancement in technologies for additive manufacturing, it is now possible to create complex parts which once required hours of machining and produced considerable amount of scrap. Upright is an important component of a car as it connects the wheel assembly to suspension components. It has to tolerate force due to braking, steering and bump and it also holds brake caliper. Reducing unsprung mass is a major aim to build any race car as it reduces the inertia and helps to keep tires in contact with the road. Upright being considered an unsprung component makes for a perfect object for analysis. Aim of this report is to compare and analyse the CAD model designed for conventional machining and 3D printing, find the difference in mass and compare the stresses generated due to forces mentioned above. The work from this project will result in a lighter, more complex upright without compromising on the strength