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 /

Machine learning approach to investigate EV battery characteristics

The main factor influencing an electric vehicle’s range is its battery. Battery electric vehicles experience driving range reduction in low temperatures. This range reduction results from the heating demand for the cabin and recuperation limits by the braking system. Due to the lack of an internal combustion engine-style heat source, electric vehicles\u27 heating system demands a significant amount of energy. This energy is supplied by the battery and results in driving range reduction. Moreover, Due to the battery\u27s low temperature in cold weather, the charging process through recuperation is limited. This limitation of recuperation is caused by the low reaction rate in low temperatures. Technology developments for battery electric vehicles are mostly focused on maintaining the vehicle battery package temperature and state of charge. For battery management systems, state of charge and battery temperature estimations are important since they prevent over charge, over discharge, and thermal runaway. Estimation and controlling battery temperature and the state of charge guarantees safety, it will also increase the vehicle\u27s life cycle. This study analyzes the effects of ambient and battery temperature on heating system energy demand and regenerative braking parameters. Moreover, different machine learning methods for estimating the battery temperature and its state of charge are compared and presented. The analysis is based on the BMW i3 winter trips dataset which includes data for 38 different drive cycles. Results show that every 3 degrees of ambient temperature drop results in a 1% increase in the heating energy share. Furthermore, the ability of machine learning methods such as LSTM and GRU has been demonstrated to successfully forecast battery temperature and state of charge

Development of a neural network-based energy management system for a plug-in hybrid electric vehicle

The high potential of Artificial Intelligence (AI) techniques for effectively solving complex parameterization tasks also makes them extremely attractive for the design of the Energy Management Systems (EMS) of Hybrid Electric Vehicles (HEVs). In this framework, this paper aims to design an EMS through the exploitation of deep learning techniques, which allow high non-linear relationships among the data characterizing the problem to be described. In particular, the deep learning model was designed employing two different Recurrent Neural Networks (RNNs). First, a previously developed digital twin of a state-of-the-art plug-in HEV was used to generate a wide portfolio of Real Driving Emissions (RDE) compliant vehicle missions and traffic scenarios. Then, the AI models were trained off-line to achieve CO2 emissions minimization providing the optimal solutions given by a global optimization control algorithm, namely Dynamic Programming (DP). The proposed methodology has been tested on a virtual test rig and it has been proven capable of achieving significant improvements in terms of fuel economy for both charge-sustaining and charge-depleting strategies, with reductions of about 4% and 5% respectively if compared to the baseline Rule-Based (RB) strategy

Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

VEHICLE-INFRASTRUCTURE INTEGRATION (VII) ENABLED PLUG-IN HYBRID ELECTRIC VEHICLES (PHEVS) FOR TRAFFIC AND ENERGY MANAGEMENT

Vehicle Infrastructure Integration (VII) program (also known as IntelliDrive) has proven the potential to improve transportation conditions by enabling the communication between vehicles and infrastructure, which provides a wide range of applications in transportation safety and mobility. Plug-in hybrid electric vehicles (PHEVs) that utilize both electrical and gasoline energy are a commercially viable technology with potential to contribute to both sustainable development and environmental conservation through increased fuel economy and reduced emissions. Considering positive potentials of PHEVs and VII in ITS, a framework that integrates PHEVs with VII technology was created in this research utilizing vehicle-to-vehicle and vehicle-to-infrastructure communications for transmitting real time and predicted traffic information. This framework aims to adjust the vehicle speed at each time interval on its driving mission and dynamically optimize the total energy consumption during the trip. Equivalent Consumption Minimization Strategy (ECMS) was utilized as the control strategy of PHEVs energy management for minimization of the equivalent energy. It was found that VII traffic information has the capability to benefit energy management, as presented in this thesis, while supporting the broader national transportation goals of an active transportation system where drivers, vehicles and infrastructure are integrated in a real time fashion to improve overall traffic conditions

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Energy management for electric vehicles in smart cities: a deep learning approach

International audienceWe propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajec-tory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy managemen

Impactos del Cambio Climático en la Generación de Energía Renovable y Evaluación de Escenarios de Generación Energética

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 26-04-2022This Thesis was titled Climate Change Impacts on Renewable Energy Generation and Energy Generation Scenarios.Climate change is attributed, among other factors, to greenhouse gas emissions produced by the energy sector (including the transport). At the same time, climate change is expected to affect this sector by changing the availability of resources, altering its enabling conditions and transforming demand patterns. This thesis addresses climate change impacts on renewable generation and electricity demand by providing an overview of the most relevant transformations projected in literature and by developing methodologies and quantitative analysis to ascertain the specific infuence in three case studies.The first and second chapters are focus on estimating climate change impacts in wind and photovoltaic generation in specific plants. Both provide physical and economic projections of expected changes, along with conclusions for the development of energy policies. The last chapter delves into how climate change and the scenarios proposed to curb it, can affect the demand for electricity in a region, due to the expected changes in the generation infrastructure and changes on the demand side such as a high penetration of electric vehicles...Esta Tesis se tituló Impactos del Cambio Climático en la Generación de Energía Renovable y Escenarios de Generación de Energía. El cambio climático se atribuye, entre otras variables, a las emisiones de gases de efecto invernadero producidas por el sector energético (incluyendo el transporte). Al mismo tiempo, el cambio climático se espera que pueda afectar a este sector cambiando la disponibilidad de sus recursos, alterando sus condiciones habilitantes y transformando los patrones de la demanda. Esta Tesis aborda los impactos del cambio climático en la generación renovable y cambios en el comportamiento de la demanda de electricidad, proporcionando una introducción a las transformaciones más relevantes proyectadas por la literatura y desarrollando metodologías y análisis cuantitativos que determinan el impacto específico en tres casos de estudio. El primer y el segundo capítulo se centran en determinar los cambios esperados en la generación eólica y fotovoltaica en plantas específicas, con especial atención en el calentamiento global. Ambos proporcionan proyecciones físicas y económicas de los cambios esperados, junto con conclusiones para el desarrollo de políticas energéticas. El último capítulo profundiza en cómo el cambio climático y los escenarios propuestos para frenarlo, pueden afectar a la demanda de electricidad de una región, debido a los cambios esperados en las infraestructuras de generación y en cambios por el lado de la demanda como sería una elevada penetración de los vehículos eléctricos...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu