Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.<br /

Control Strategies for Hybrid Vehicles in Mountainous Areas

AbstractThis paper presents control strategies for a Hybrid Electric Vehicle (HEV) aiming at fuel and battery consumption reduction in real life conditions. For years, car manufacturers have modeled and simulated control strategies using standardized driving cycles based on theoretical speed values such as the NEDC in Europe, leaving important external parameters out of the equation. Establishing driving cycles made out of GPS acquisitions and segmenting them into road sections, classified in different categories depending on the input parameters, including slope, allows the creation of logic rules defining the driving mode to adopt in each situation. Using Fuzzy Logic, those rules can be interpreted and used to adapt the control strategy to road conditions, resulting in many strategies covering every kind of road segment and offering different opportunities of energy savings

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.<br /

FUZZY LOGIC CONTROL FOR ENERGY MANAGEMENT SYSTEM OF A HYBRID ELECTRIC VEHICLE

The Hybrid Electric Vehicle (HEV) electric motor is typically powered by a battery pack through power electronics. The fuel consumption in HEV is already lower compared to conventional vehicle. However, there will be a need to control the distribution of torque between the engine and electric motor to further minimize the fuel consumption. With reference to this issues, the purpose of this project is to create a complete HEV using a MATLAB/Simulink tool. From the model created, it will be equipped with a controller for energy management system. The method used is by taking the driver command, the state of charge (SOC) of the battery, the vehicle speed, percentage of throttle and engine efficiency as inputs, a fuzzy logic control for parallel HEV has been developed in a controller to effectively control the torque distribution between Internal Combustion Engine (ICE) and electric motor which is known as In-Wheel Motor (IWM). This research also discusses the methodology for designing a base vehicle model using MATLAB/Simulink. Prior to modelling HEV model, the base vehicle model was validated in terms of the fuel consumption to verify the model. The verified built base model will then be modified to become HEV model by virtually installing IWM at the rear wheels together with a controller inside the trunk. The proposed energy management strategy is implemented on a parallel HEV model and it is then simulated to a selected drive cycles. Since the distribution of torque in HEV model is varied according to the rules set, the fuel consumption is reduced significantly as compared with conventional base vehicle model. The simulation results reveal that, the HEV model built from conventional vehicle model has a significant improvement of 23% in terms of fuel economy as well as maintaining battery SOC within its operation range

Fuzzy Logic Controller for Parallel Plug-in Hybrid Vehicle

Hybrid electric vehicles combine two methods for propelling a vehicle. In a parallel hybrid vehicle, the two propulsion methods work in parallel to meet the total power demand. Among different combination of power sources, internal combustion engine and electric motor drive system are the most popular because of their availability and controllability. Plug-in hybrid vehicle is the latest version in hybrid vehicle family. In plug-in hybrid vehicle, battery is directly recharged from the electrical power grid and it can be used for a long distance with higher efficiency. Most of the hybrid vehicles on the road are parallel in nature and battery is recharged directly by the engine. If it is possible to convert existing hybrid vehicle into plug-in hybrid vehicle, it will lead to significant improvements in fuel economy and emissions.In this thesis, two fuzzy logic controllers have been developed for the energy management system of the hybrid vehicle. For the first controller, it is assumed that the vehicle will work like a plug-in hybrid vehicle. For the second controller it is assumed that the battery will always recharged by the engine. It is found that with the help of developed fuzzy logic controller, the plug-in hybrid vehicle can run up to 200 miles with high efficiency. Both controllers are developed and their performance is tested on the highly reliable vehicle modeling and simulation software AUTONOMIE. The main objective of developing the controllers is increasing the fuel economy of the vehicle. The results from the both developed controllers are compared with the default controller in AUTONOMIE in order to show performance improvements

Optimization of Hybrid Electric Bus Driving System's Control Strategy

AbstractThe popularity of hybrid electric bus (HEB) is a most realistic way to solve emission and energy problem currently, so it's important to improve the HEB's fuel economy and efficiency. This paper optimizes the HEB's driving system to satisfy the conditions of this city. We applied the fuzzy logic control of modern control theory to the driving system's control of parallel-HEB, and optimized the driving system's control strategy of this city's hybrid bus based on this theory. We adopted the ADVISOR2002 for HEB's driving system's re-development, namely established the driving system's simulation model for this city's hybrid bus, then we tested the simulation model on the HEB urban driving cycle which had been developed in our preparatory work. The simulation results of our new control strategy and the simulation model proposed in this paper can further enhance the fuel economy and improve the driving system's efficiency, thus the results provided important reference for the upgrading of this type HEB's driving system

STUDY OF CONTROL SCHEMES FOR SERIES HYBRID-ELECTRIC POWERTRAIN FOR UNMANNED AERIAL SYSTEMS

Hybrid-Electric aircraft powertrain modeling for Unmanned Aerial Systems (UAS) is a useful tool for predicting powertrain performance of the UAS aircraft. However, for small UAS, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic in addition to the subsequent impact on the performance of powertrain components. Small UAS aircraft utilize small-displacement engines with poor thermal efficiency and, therefore, could benefit from a hybridized powertrain by reducing fuel consumption. This study uses a dynamic simulation of a UAS, representative flight profiles, and powertrain control logic approaches to evaluate the performance of a series hybrid-electric powertrain. Hybrid powertrain component models were developed using lookup tables of test data and model parameterization approaches to generate a UAS dynamic system model. These models were then used to test three different hybrid powertrain control strategies for their ability to provide efficient IC engine operation during the charging process. The baseline controller analyzed in this work does not focus on optimizing fuel efficiency. In contrast, the other two controllers utilize engine fuel consumption data to develop a scheme to reduce fuel consumption during the battery charging operation. The performance of the powertrain controllers is evaluated for a UAS operating on three different representative mission profiles relevant to cruising, maneuvering, and surveillance missions. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of each controller. The first fuel efficiency-focused controller is the ideal operating line (IOL) strategy. The IOL strategy uses performance maps obtained by engine characterization on a specialized dynamometer. The simulations showed the IOL strategy produced average fuel economy improvements ranging from 12%-15% for a 30-minute mission profile compared to the baseline controller. The last controller utilizes fuzzy logic to manage the charging operations while maintaining efficient fuel operation where it produced similar fuel saving to the IOL method but were generally higher by 2-3%. The importance of developing detailed dynamic system models to capture the power variations during flight with fuel-efficient powertrain controllers is key to maximizing small UAS hybrid powertrain performance in varying operating conditions