Towards enabling predictive optimal energy management systems for hybrid electric vehicles with real world considerations

2021 Spring.Includes bibliographical references.In the pursuit of greater vehicle fleet efficiency, Predictive Optimal Energy Management Systems (POEMS) enabled Plug-in Hybrid Electric Vehicles (PHEV) have shown promising theoretical results. In order to enable the practical development of POEMS enabled PHEV technology, if must first be determined what method and what data is needed is for providing optimal predictions. Research performed at Colorado State University and partner institutions in 2019 and 2020 pursued a novel course in considering the widest range of possible data and methods of prediction currently available including a survey of all feasible Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Advance Driver Assistance Systems (ADAS), and Ego vehicle CAN data streams with classical and novel machine learning methods. Real world vehicle operation data was collected in Fort Collins Colorado, processed, and used in the development of optimal prediction methods. From the results of this research, concrete conclusions on the relative value of V2I, V2V, and ADAS information for prediction, and high fidelity predictions were obtained for 10 second horizons using specialized Artificial Neural Networks

Analysis of Emission and Fuel Economy in a Plug in Hybrid Vehicle Using Various Control Strategies

Plug-in hybrid electric vehicles (PHEVs) differ from hybrid vehicles (HEVs) with their capability to use off-board electricity generation to recharge their battery. Electric vehicles are highly emerging for transportation purpose, which have been developed over the past several decades due to various environmental concerns. Pure electric vehicles currently do not have adequate range when powered by batteries alone and also recharging of it requires several hours. The shortcoming raised with the standalone energy source powered electric vehicle made to think about an alternative option for an electric vehicle and motivated towards the hybridization of energy sources in electric vehicle. This paper analyzes the equivalent power circuit and operation principles of a PHEV using UDDS and NEDC driving pattern. Regenerative braking also provides an effective way of extending the driving range of battery powered electric vehicles. Conventional automobiles use Internal Combustion Engines (ICEs) to operate with the energy source from fossil fuels. However, the conventional vehicle system provides limited fuel economy, as well as producing harmful air pollutants. A Plug-in Hybrid Electric Vehicle (PHEV) has been introduced which operates within its all electric range. Which have high capacity of energy storage system. PHEVs used to charge the battery from electricity grid, which differs from the traditional Hybrid Electric Vehicles (HEVs). The plug in hybrid electric vehicle is instigated for enhancing the vehicle performance by improving the fuel economy, effectively capturing the regenerative braking energy by controlling the Battery State of Charge (SoC) level within the optimal upper and lower bound that would improve the battery life, eliminating the fuel starvation problem and maximizing the drivability of the vehicle through an optimized distribution of the required power to the load. The proposed work is focused on designing a gasoline based Hybrid Electric Vehicle includes the modelling of hybrid energy sources and other interfacing structures

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

Impacts of plug-in hybrid vehicles and combined heat and power technologies on electric and gas distribution network losses

Distribution network operators (DNOs) require strategies that can offset the tradeoffs new embedded technologies have on their assets. This paper employs modelling to show that through control device manipulation, gas and electric (G&E) network operators can influence savings in energy losses under the presence of plug-in hybrid vehicles (PHEVs) and combined heat and power technologies (CHPs). An integrated gas and electric optimal power flow (OPF) tool is introduced to undertake various case studies. The OPF tool evaluates the technical impacts experienced in the networks when DNOs apply a "plug and forget" operation strategy and then compares the results against a "loss minimisation" strategy. Results show the benefits in applying different strategies are more considerable in electric networks than in gas networks. The study corroborates that an integrated G&E analysis offers a fresh perspective for stakeholders in evaluating energy service networks performance under different operation strategies

Operational Cost Minimization of Grid Connected Microgrid System Using Fire Fly Technique

oai:oai.jieee.a2zjournals.com:article/1Present time, green energy sources interfacing to the utility grid by utilizing microgrid system is very vital to satisfy the ever increasing energy demand. Optimal operation of the microgrid system improved the generation from the distributed renewable energy sources at the lowest operational cost. Large amount of constraints and variables are associated with the microgrid economic operation problem. Thus, this problem is very complex and required efficient technique for handing the problem adequately. This research utilized the fire fly optimization technique for solving the formulated microgrid operation problem. Fire fly algorithm is based on the behaviour and nature of the fire flies. A microgrid system modelling which incorporated various distributed energy sources such as solar photo voltaic, wind turbine, micro tur-bine, fuel cell, diesel generator, electric vehicle technology, etc.. Energy storage system is utilized in this research for supporting renewable energy sources’ integration in more reliable and qualitative way. Further, the electric vehicle technology i.e. battery electric vehicle, plug-in hybrid electric vehicle and fuel cell electric vehicle are utilized to support the microgrid and utility grid systems with respect to variable demands. Optimal operational cost-minimization problem of the developed microgrid system is solved by fire fly algorithm and compared with the grey wolf opti-mization and particle swarm optimization techniques. By comparative analysis it is clear that the fire fly algorithm provides the minimum operational cost of microgrid system as compared to the GWO and PSO. MATLAB software is utilized to model the microgrid system and implementation of the optimization techniques

Exclusive Operation Strategy for the Supervisory Control of Series Hybrid Electric Vehicles

Supervisory control systems (SCSs) are used to manage the powertrain of hybrid electric vehicles (HEV). This paper presents a novel SCS called Exclusive operation strategy (XOS) that applies simple rules based on the idea that batteries are efficient at lower loads while engines and generators are efficient at higher loads. The XOS is developed based on insights gained from three conventional SCSs for series HEVs: Thermostat control strategy (TCS), Power follower control strategy (PFCS) and Global equivalent consumption minimization strategy (GECMS). Also, recent technological developments have been considered to make the XOS more suited to modern HEVs than conventional SCSs. The resulting control decisions are shown to emulate the operation of approximate global optimal solutions and thus achieve significant improvement in fuel economy as compared to TCS and PFCS. In addition, the generally linear relationship between required power and engine power for the XOS provides auditory cues to the driver that are comparable to conventional vehicles, thus reducing barriers to adopting HEVs. The simplicity and effectiveness of the XOS makes it a practical SCS