Effect of the Change of Inertial, Elastic and Dissipative Parameters on the Ride Comfort of a Road Vehicle

The comfort on a vehicle is of great interest as it is related with the driver perception and hence with ride safety conditions. Road infrastructure in Colombia presents unique characteristics; for that reason, there is interest in how road vehicles can be adapted to perform properly when they are subjected to the road conditions of the country. This work is centred on the study of the effect that a change on a set composed by vehicle’s inertial, elastic and dissipative parameters has on the ride comfort of a driver. The ride comfort of the driver is analysed in terms of exposition to vibrations induced by the road unevenness to the vehicle body. A computational approach is implemented, by means of a previously validated multibody model with seven degrees of freedom (DOF). Two road input configurations are considered, both of them generated by isolated bumps. The effect that a variation on several vehicle parameters has on the driver comfort was analysed using comfort indexes defined by the standard ISO 2631-1

Optimum suspension design for non-linear half vehicle model using particle swarm optimization (PSO) algorithm

This paper is considered with a non-linear suspension design for half vehicle model by using particle swarm optimization technique. To analyze the ride comfort, a five-degree of freedom system is built, and it is integrated with the Particle Swarm Optimization (PSO) for optimizing the vehicle vibrations. A multi-objective function is proposed as the sum of the minimum seat and vehicle body acceleration, the minimum suspension deflection and the constraints and the design variables of the optimization problem are selected as the spring and damping coefficients of the front and rear suspension and the non-linear spring and the linear damping coefficients of the seat. The simulations are carried out and the results are compared with the non-optimized values. It is demonstrated that the vehicle vibration is decreased significantly with the help of the optimum values of the suspension parameters

Optimal design parameters of air suspension systems for semi-trailer truck. Part 1: modeling and algorithm

The purpose of this paper is to improve the performance of air suspension systems for a semi-trailer truck in the direction of reducing the dynamic wheel load acting on road surface (Part 1: modeling and algorithm). To achieve the goal of finding the optimal design parameters for the air suspension systems, a half-vehicle dynamic model under the road-vehicle interaction with 12 degrees of freedom (d.o.f) is established for searching the optimal design parameters of vehicle suspensions using genetic algorithm (GA). Dynamic load coefficient (DLC) is considered as a target function. Two optimal conditions: optimal design of geometrical parameters of air spring suspension systems (Case 1) and optimal design of parameters of air suspension systems (Case 2) are selected in this study. The results of this paper are the basis for optimization and discussion in Part 2 as the results and discussion

A novel multi-objective quantum particle swarm algorithm for suspension optimization

In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-knownsingle objectiveQPSOby substantialmodifications in the core equations and implementation of new multi-objectivemechanisms. The novel algorithmMOQPSO and the long-establishedNSGA-II andCOGA-II (Compressed-ObjectiveGenetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found byMOQPSO,which presented a Pareto front noticeably wider for the same number of objective function calls

Robust multi-objective design of suspension systems

This thesis presents a robust multi-objective optimal design of four-degree-of-freedom passive and semi-active suspension systems. The passive suspension system is used in a racing car and the semi-active suspension is implemented on a passenger car. Mathematical models of the commercial and racing vehicle suspension systems are used in the computer simulations. A robust multi-objective design of the suspension systems is carried out by considering the minimization of three objectives: passenger’s head acceleration (HA), suspension deflection (SD), and tire deflection (TD). The first objective is concerned with the passenger’s health and comfort. The suspension stroke is described by SD and the tire holding is characterized by TD. The optimal design of the passive suspension involves tuning the coefficients of the sprung spring and damper, tire stiffness, and inertance of the inerter. Suspension systems’ parametric variations are very common and cannot be avoided in practice. To this end, a robust multi-objective optimization method that takes into consideration small changes in the design parameters should be considered. Unlike traditional multi-objective optimization problems where the focus is placed on finding the global Pareto-optimal solutions which express the optimal trade-offs among design objectives, the robust multi-objective optimization algorithms are concerned with robust solutions that are less sensitive to perturbations of decision variables. As a result, the mean effective values of the fitness functions are used as design objectives. Constraints on the design parameters and goals are applied. Numerical simulations show that the robust multi-objective design (RMOD) is very effective and guarantees a robust behavior as compared to that of the classical multi-objective design (MOD). The results also show that the robust region is inside the feasible search space and avoids all of its boundaries. The decision parameter space of the semi-active suspension includes both passive and active components. The passive components include the stiffness of the sprung spring, damping coefficient of the shock absorber, and stiffness of the tire. The active elements are the design details of the LQR algorithm. During the design, global sensitivity analysis is conducted to determine the elements of the suspension system that have high impact on the design objectives. The mass of the passenger’s head and upper body, the mass of the passenger’s lower body and cushion, passenger and cushion’s elastic properties, and the sprung mass of the vehicle are selected for the sensitivity analysis. Results show that the design goals are more sensitive to the variations in the sprung mass than the other parameters. As a result, parametric variations in the sprung mass of the vehicle and passive elements of the suspension system are considered. Similar to the design of the passive suspension, the mean effective values of SD, TD, and HA are used as design objectives. Also, constraints are applied on the objectives in compliance with the requirements of ISO 2631-1 on the design of car suspension systems. The optimization problem is solved by the NSGA-II (non-dominated sorting genetic algorithm) and robust Pareto front and set are obtained

Hybrid Electromagnetic Vibration Isolation Systems

Traditionally, dynamic systems are equipped with passive technologies like viscous shock absorbers and rubber vibration isolators to attenuate disturbances. Passive elements are cost effective, simple to manufacture, and have a long life span. However, the dynamic characteristics of passive devices are fixed and tuned for a set of inputs or system conditions. Thus in many applications when variation of input or system conditions is present, sub-optimal performance is realized. The other fundamental flaw associated with passive devices is that they expel the undesired kinetic energy as heat. Recently, the introduction of electromagnetic technologies to the vibration isolation systems has provided researchers with new opportunities for realizing active/semi-active vibration isolation systems with the additional benefit of energy regeneration (in semi-active mode). Electromagnetic vibration isolators are often suffer from a couple of shortcomings that precludes their implementations in many applications. Examples of these short comings include bulky designs, low force density, high energy consumption (in active mode), and fail-safe operation problem. This PhD research aims at developing optimal hybrid-electromagnetic vibration isolation systems to provide active/semi-active and regenerative vibration isolation for various applications. The idea is to overcome the aforementioned shortcomings by integrating electromagnetic actuators, conventional damping technologies, and stiffness elements into single hybrid packages. In this research, for both semi-active and active cases, hybrid electromagnetic solutions are proposed. In the first step of this study, the concept of semi-active hybrid damper is proposed and experimentally tested that is composed of a passive hydraulic and a semi-active electromagnetic components. The hydraulic medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and energy regeneration to the hybrid design. Based on the modeling and optimization studies, presented in this work, an extended analysis of the electromagnetic damping component of the hybrid damper is presented which can serve as potent tool for the designers who seek maximizing the adaptability (and regeneration capacity) of the hybrid damper. The experimental results (from the optimized design) show that the damper is able to produce damping coefficients of 1300 and 0-238 Ns/m through the viscous and electromagnetic components, respectively. In particular, the concept of hybrid damping for the application of vehicle suspension system is studied. It is shown that the whole suspension system can be adjusted such that the implementation of the hybrid damper, not only would not add any adverse effects to the main functionally of the suspension, but it would also provide a better dynamics, and enhance the vehicle fuel consumption (by regenerating a portion of wasted vibration energy). In the second step, the hybrid damper concept is extended to an active hybrid electromagnetic vibration isolation systems. To achieve this target, a passive pneumatic spring is fused together with an active electromagnetic actuator in a single hybrid package. The active electromagnetic component maintains a base line stiffness and support for the system, and also provides active vibration for a wide frequency range. The passive pneumatic spring makes the system fail-safe, increases the stiffness and support of the system for larger masses and dead loads, and further guarantees a very low transmissibility at high frequencies. The FEM and experimental results confirmed the high force density of the proposed electromagnetic component, comparing to a voice coil of similar size. In the proposed design, with a diameter of ~125 mm and a height of ~60 mm, a force variation of ~318 N is obtained for the currents of I=±2 A. Furthermore, it is demonstrated that the proposed actuator has a small time constant (ratio of inductance to resistance for the coils) of less than 5.2 ms, with negligible eddy current effect, making the vibration isolator suitable for wide bandwidth applications. According to the results, the active controllers are able to enhance the performance of the passive elements by up to 80% and 95% in terms of acceleration and force transmissibilities, respectively

Integrated optimisation for dynamic modelling, path planning and energy management in hybrid race vehicles

Simulation software has for many years been developed to enhance the research and development phase of new vehicle introductions. With the introduction of the testing embargo in most forms of world championship motorsport, model validation is a necessity. To optimise the unknown vehicle and tyre parameters and to reduce the error between measured and simulated data in such a multi-input multi-output non-convex optimisation problem, a novel multi-objective particle swarm optimisation (PSO) technique is applied to ensure a fully validated vehicle model is developed and analysed for speed and performance. These optimisation algorithms are further developed to explore the trajectory planning problem to improve the lap time for the shortest path, minimum curvature and a combined approach, producing optimal racing line pathways and vehicle dynamic inputs and output responses by exploring trajectories and vehicle traction circle limits. Finally, a hybrid electric vehicle transient dynamics model for the control of energy management is presented. The hybrid powertrain contains an internal combustion engine, kinetic energy recovery system and heat energy recovery system with deployment and harvesting control parameters. The performance of single-objective and multi-objective particle swarm optimisation algorithms are compared and analysed. The proposed simulation model and optimisation techniques are applied to address an array of problems, including model validation, racing line trajectory design, fastest lap time problem, and energy management strategies. All results are validated and optimised with respect to the experimental data collected on the real track in Silverstone to ensure the results can be applied to physical real-world scenarios

Otimização robusta multiobjetivo por análise de intervalo não probabilística : uma aplicação em conforto e segurança veicular sob dinâmica lateral e vertical acoplada

Esta Tese propõe uma nova ferramenta para Otimização Robusta Multiobjetivo por Análise de Intervalo Não Probabilística (Non-probabilistic Interval Analysis for Multiobjective Robust Design Optimization ou NPIA-MORDO). A ferramenta desenvolvida visa à otimização dos parâmetros concentrados de suspensão em um modelo veicular completo, submetido a uma manobra direcional percorrendo diferentes perfis de pista, a fim de garantir maior conforto e segurança ao motorista. O modelo multicorpo possui 15 graus de liberdade (15-GDL), dentre os quais onze pertencem ao veículo e assento, e quatro, ao modelo biodinâmico do motorista. A função multiobjetivo é composta por objetivos conflitantes e as suas tolerâncias, como a raiz do valor quadrático médio (root mean square ou RMS) da aceleração lateral e da aceleração vertical do assento do motorista, desenvolvidas durante a manobra de dupla troca de faixa (Double Lane Change ou DLC). O curso da suspensão e a aderência dos pneus à pista são tratados como restrições do problema de otimização. As incertezas são quantificadas no comportamento do sistema pela análise de intervalo não probabilística, por intermédio do Método dos Níveis de Corte-α (α-Cut Levels) para o nível α zero (de maior dispersão), e realizada concomitantemente ao processo de otimização multiobjetivo. Essas incertezas são aplicáveis tanto nos parâmetros do problema quanto nas variáveis de projeto. Para fins de validação do modelo, desenvolvido em ambiente MATLAB®, a trajetória do centro de gravidade da carroceria durante a manobra é comparada com o software CARSIM®, assim como as forças laterais e verticais dos pneus. Os resultados obtidos são exibidos em diversos gráficos a partir da fronteira de Pareto entre os múltiplos objetivos do modelo avaliado Os indivíduos da fronteira de Pareto satisfazem as condições do problema, e a função multiobjetivo obtida pela agregação dos múltiplos objetivos resulta em uma diferença de 1,66% entre os indivíduos com o menor e o maior valor agregado obtido. A partir das variáveis de projeto do melhor indivíduo da fronteira, gráficos são gerados para cada grau de liberdade do modelo, ilustrando o histórico dos deslocamentos, velocidades e acelerações. Para esse caso, a aceleração RMS vertical no assento do motorista é de 1,041 m/s² e a sua tolerância é de 0,631 m/s². Já a aceleração RMS lateral no assento do motorista é de 1,908 m/s² e a sua tolerância é de 0,168 m/s². Os resultados obtidos pelo NPIA-MORDO confirmam que é possível agregar as incertezas dos parâmetros e das variáveis de projeto à medida que se realiza a otimização externa, evitando a necessidade de análises posteriores de propagação de incertezas. A análise de intervalo não probabilística empregada pela ferramenta é uma alternativa viável de medida de dispersão se comparada com o desvio padrão, por não utilizar uma função de distribuição de probabilidades prévia e por aproximar-se da realidade na indústria automotiva, onde as tolerâncias são preferencialmente utilizadas.This thesis proposes the development of a new tool for Non-probabilistic Interval Analysis for Multi-objective Robust Design Optimization (NPIA-MORDO). The developed tool aims at optimizing the lumped parameters of suspension in a full vehicle model, subjected to a double-lane change (DLC) maneuver throughout different random road profiles, to ensure comfort and safety to the driver. The multi-body model has 15 degrees of freedom (15-DOF) where 11-DOF represents the vehicle and its seat and 4-DOF represents the driver's biodynamic model. A multi-objective function is composed by conflicted objectives and their tolerances, like the root mean square (RMS) lateral and vertical acceleration in the driver’s seat, both generated during the double-lane change maneuver. The suspension working space and the road holding capacity are used as constraints for the optimization problem. On the other hand, the uncertainties in the system are quantified using a non-probabilistic interval analysis with the α-Cut Levels Method for zero α-level (the most uncertainty one), performed concurrently in the multi-objective optimization process. These uncertainties are both applied to the system parameters and design variables to ensure the robustness in results. For purposes of validation in the model, developed in MATLAB®, the path of the car’s body center of gravity during the maneuver is compared with the commercial software CARSIM®, as well as the lateral and vertical forces from the tires. The results are showed in many graphics obtained from the Pareto front between the multiple conflicting objectives of the evaluated model. The obtained solutions from the Pareto Front satisfy the conditions of the evaluated problem, and the aggregated multi-objective function results in a difference of 1.66% for the worst to the best solution. From the design variables of the best solution choose from the Pareto front, graphics are created for each degree of freedom, showing the time histories for displacements, velocities and accelerations. In this particular case, the RMS vertical acceleration in the driver’s seat is 1.041 m/s² and its tolerance is 0.631 m/s², but the RMS lateral acceleration in the driver’s seat is 1.908 m/s² and its tolerance is 0.168 m/s². The overall results obtained from NPIA-MORDO assure that is possible take into account the uncertainties from the system parameters and design variables as the external optimization loop is performed, reducing the efforts in subsequent evaluations. The non-probabilistic interval analysis performed by the proposed tool is a feasible choice to evaluate the uncertainty if compared to the standard deviation, because there is no need of previous well-known based probability distribution and because it reaches the practical needs from the automotive industry, where the tolerances are preferable