Abstract

At least two different actuators work in cooperation in regenerative braking for electric and hybrid vehicles. Torque blending is an important area, which is responsible for better manoeuvrability, reduced braking distance, improved riding comfort, etc. In this paper, a control method for electric vehicle blended antilock braking system based on fuzzy logic is promoted. The principle prioritizes usage of electric motor actuators to maximize recuperation energy during deceleration process. Moreover, for supreme efficiency it considers the battery’s state of charge for switching between electric motor and conventional electrohydraulic brakes. To demonstrate the functionality of the controller under changing dynamic conditions, a hardware-in-the-loop simulation with real electrohydraulic brakes test bed is utilized. In particular, the experiment is designed to exceed the state-of-charge threshold during braking operation, what leads to immediate switch between regenerative and friction brake modes. Document type: Part of book or chapter of book

Abstract

The trajectory phase–plane method provides an increased significance to the analysis of stability and performance of the controllers for complex nonlinear plants. The aim of this study is the stability and performance analysis of a fuzzy logic controller for anti–lock braking system applying the trajectory phase–plane technique. The controller’s main task is to hold optimal braking performance by recognizing road surface. The stability and performance of the anti–lock braking system controller are examined with respect to the conventional friction brake and electric motors separately. The applied methodology allows for visual assessment of the stability and performance of studied nonlinear systems. The work refers to hardware–in–the–loop simulations conducted at the Technische Universität Ilmenau. The results demonstrate that the fuzzy logic controller is stable independently of the employed actuator. Moreover, the electric actuators allow for faster convergence and more accurate tracking of the optimal wheel slip.

Abstract

The plug-in hybrid electric vehicle allows vehicle propulsion from multiple internal power sources. Electric energy from the grid can be utilized by means of the plug-in connection. An on-line energy management (EM) strategy is proposed to minimize the costs for taking energy from each power source. Especially in a dynamical energy market, an on-line optimization algorithm is desirable since energy prices change over time. By construction, the proposed EM system can operate with, and without prediction information. If predictions are available, an electronic horizon is applied to anticipate on up-coming events and further optimize the strategy. Illustrative examples are given to explain the added value for both solutions. Also the situation where energy is transferred back to the grid is considered. © 2008 IEEE.

Abstract

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off. © 2008 IEEE.

Abstract

The purpose of this study is to suggest an optimal multi-objective fuzzy fractional-order PIλDμPIλDμ controller (MOFFOPID) for the speed control of EV systems with time-delay. It is presumed that while the EV is in movement, the armature winding resistance of the direct current (DC) motor varies with time due to temperature changes and this makes it impossible to develop an accurate dynamic model for an EV system. Consequently, in order to have a fast and accurate tracking of the set-point, besides a smooth control signal, a new multi-objective stochastic optimisation is used for the online adjustment of the parameters of MOFFOPID controller. Moreover, a comparison is made between the results of the current study and those of some of the most recent studies on the same topic, which have used online multi-objective PI and online multi-objective fuzzy PI, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are implemented on a DC motor to verify the effectiveness of the proposed MOFFOPID controller in controlling the speed of the DC motor which has non-linear features. The results of the simulation confirm the desirable performance of suggested controller.

Abstract

In recent years, the development of advanced driver assistance systems (ADAS) - mainly based on lidar and cameras - has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations - in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicles actuators - brake and throttle pedals - were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicles actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging. © 2012 Elsevier Ltd. All rights reserved. Peer Reviewed Document type: Article