Implementation of a P&E management system for a dual-source EV powered by different batteries

This paper introduces Smart Grid and associated technical, environmental and socio-economic, and other non-tangible benefits to society, and articulates the need for the concept and the fact that it is a dynamic interactive, real-time infrastructure that responds to the challenges of designing and building the power system of the future, rather than being simply a marketing term. To illustrate the diversity of terminology, the paper compares an Electric Power Research Institute (EPRI) definition with that suggested by a study group of the International Electrotechnical Commission (IEC). Next, a paper sponsored by the Canadian Electricity Association (CEA) that cites three example definitions to highlight the diversity of views of Smart Grid is briefly reviewed. Early misconceptions and characterizations of Smart Grid are discussed as a prelude to addressing challenging issues that motivate developing and implementing related innovative technologies, products and services. The paper then discusses the potential promise of the Smart Grid, which is embedded in its often-cited attributes of efficiency, accommodating, quality focus, enabling and self-healing to name some. The paper then addresses some of the often-cited impediments to accepting Smart Grid which are based on concerns and issues confronting its forward progress, adoption and acceptance. Distribution Automation (DA) and embedded intelligence are discussed emphasising self-healing, optimizing operation and facilitating recreation and recovery from abnormal events. Functional and integration requirements of Distributed Energy Resources (DER,) are detailed. Smart Consumption Infrastructure elements of Distribution Management Systems (DMS,) Automated Metering Infrastructure (AMI,) Smart Homes (SH), and Smart Appliances (SA,) are discussed. Following the introductory section, this paper summarizes contributions included in the double issue 42(3–4) of the Electric Power Components and Systems Journal. To begin, papers are offered discussing smart grid activities in China, India, and the development of a Smart Grid roadmap for the US State of Kentucky. The approaches of each of these cases reflect the diversity of policy initiatives in these jurisdictions. Two state of the art reviews are given next. The first considers distribution network active management and future development trends in technologies and methods, where centralized and decentralized management frameworks and applying agent-based coordination are discussed. The second offers a review of smart home technologies and the goals of an energy management system (SHEMS). This section is concluded by a letter providing an overview of recent and expected advances nanotechnology applications in Smart Grid. Following the state of the art review section, ten papers offering new and innovative research approaches and results are included. These papers cover Smart Grid topics such as real-time energy control approach for smart home energy management systems, optimal operation of energy- efficient buildings with Combined Heat and Power (CHP) systems, energy management and control of Electric Vehicle (EV) charging stations, voltage-frequency control of a voltage source inverter (VSI) in a smart islanded microgrid, smart generation scheduling for wind-thermal-pumped storage systems, optimized power system restoration, robust data transmission upon compressive sensing, data fusion for wide-area oscillation monitoring, satellite based GPS synchronized monitoring systems, stability in Smart Grid with emerging renewable energy techno- logies.

Efficient use of electric energy by users is one of the critical ways for increasing energy efficiency, reducing cost of power generation and supply, and environmental concerns. As part of remedial measures to address issues of energy use, many technologies for demand reduction ranging from energy saving devices through a variety of energy management systems have emerged to help users manage and control their consumption. In this paper, we present the design and implementation of a home energy and power management system that enables users to monitor, regulate, and manage their demand response dynamically through scheduling and controlling of their appliances using a mobile application. At the crux of the power management system is an embedded microcontroller which is encoded with schemes for monitoring, managing, scheduling and controlling operations. The user interface on the mobile device directly communicates with the power management system via integrated Raspberry Pi server that uses Wi-Fi communication protocol while communication between the Raspberry Pi and the embedded microcontroller is based on Bluetooth communication. A numerical simulation of the proposed design was first developed and a prototype of the design was implemented and tested. The test results show that the system was able to effectively monitor, control and regulate the power usage in the home. The flexibility offered by the system allows the user to self-regulate the amount of power usage, which translates into cost savings with the overall effect of flattening of the demand peaks.

The electrification of the aviation industry is a major challenge to realizing net-zero in the global energy sector. Fuel cell (FC) hybrid electric aircraft (FCHEV) demonstrate remarkable competitiveness in terms of cruise range and total economy. However, the process of simply hybridizing different power supplies together does not lead to an improvement in the aircraft economy, since a carefully designed power system topology and energy management scheme are also necessary to realize the full benefit of FCHEV. This paper provides a new approach towards the configuration of the optimal power system and proposes a novel energy management scheme for FCHEA. Firstly, four different topologies of aircraft power systems are designed to facilitate flexible power flow control and energy management. Then, an equivalent model of aircraft hydrogen consumption is formulated by analyzing the FC efficiency, FC aging, and BESS aging. Using the newly established model, the performance of aircraft can be quantitatively evaluated in detail to guide FCHEA design. The optimal aircraft energy management is realized by establishing a mathematical optimization model with the reduction of hydrogen consumption and aging costs as objectives. An experimental aircraft, NASA X-57 Maxwell, is used to provide a detailed performance evaluation of different power system topologies and validate the effectiveness of the energy management scheme. The new approach represents a guide for future power system design and energy management of electric aircraft.

The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

The electric energy consumption in passenger vehicles is rapidly increasing. To limit the associated increase in fuel consumption, an energy management system has been developed. This system exploits the fact that the losses in the internal combustion engine vary with the operating point, and uses the possibility to temporarily store electric energy in a battery, such that electric energy is only produced at moments when it is cheap to generate. To come to a practically applicable solution, a vehicle model is derived, containing only the component characteristics relevant for this application. The energy management problem is formulated as an optimization problem. The fuel consumption over a driving cycle is minimized, while respecting physical limitations of the components and maintaining an acceptable energy level of the battery. Several optimization methods are studied to come to a solution. The dynamic optimization problem is solved using Dynamic Programming. After rewriting it as a static optimization problem and approximating the cost function by a quadratic function, the problem is solved using Quadratic Programming, which requires less computation time. A real-time implementable strategy has been derived from the Quadratic Programming problem, that does not require a prediction of the future driving cycle. This strategy compares the current cost of producing electric energy with the estimated average cost. By adapting the average cost based on the energy level of the battery, it is ensured that the battery energy level will remain around the desired value. Simulations show that a fuel reduction up till 2% can be obtained on a conventional vehicle without major hardware changes. Higher reductions are possible on the exhaust emissions. To predict and explain the amount of fuel reduction that can be obtained with a given vehicle configuration, a set of engineering rules is derived based on typical component characteristics. Their results correspond reasonably well with the simulations. An advanced power net topology is studied which contains both a battery and an ultracapacitor that are connected by a DC-DC converter and a switch. Because of the increased complexity, this system is modeled using linear and piecewise linear approximations of the component characteristics, such that the energy management problem can be casted as a Linear Programming problem. The discrete switch makes it a Mixed Integer Linear Programming Problem. A realtime strategy, similar to the strategy for the conventional power net, has been derived. The addition fuel reduction that is obtained with the dual storage power net is small, because the maximum profit that can be obtained with an ideal lossless battery is not much higher than with a normal battery. Subsequently, hybrid electric vehicles are studied that use an Integrated Starter Generator which can be used for generating electric power and for vehicle propulsion. Several conSummary figurations are studied with respect to their potential fuel reduction. Configurations that enable start-stop operation of the engine obtain a much higher fuel reduction, up to 40%. The controllers are tested in real-time on a Hardware-in-the-Loop environment, where a vehicle simulation model is combined with existing electric components. It is shown that the electric power setpoints provided by an energy management strategy can be realized in practice.

Clean power generation from various renewable energy sources has been gaining much attention due to sustainable and environmental reasons. As renewable energy sources and power demand vary, a hybrid power system and proper energy management are required. This study is focused on the development of an energy management system (EMS) in the hybrid renewable energy system consisting of photovoltaic cell (PV), proton exchange membrane fuel cell (PEMFC), and proton exchange membrane electrolysis cell (PEMEC). A vanadium redox flow battery (VRFB) as energy storage for long-term operation is integrated into such a hybrid system. The models of each subsystem are developed based on conservative equations, electrochemistry theory, and power balances used to evaluate the efficiency of the developed EMS. During the system operation, the electric power from PV is supplied to the load, and the excess power is used to charge the battery and then run PEMEC for hydrogen production. When power shortages occur in the system, PEMFC using stored hydrogen or battery is operated. Firstly, the performance of the hybrid energy system using VRFB is investigated and compared with that using a traditional battery (i.e., lead-acid battery). The simulation results show that under the same energy supply and demand situation, the hybrid power system with VRFB outperforms, which leads to a more extended battery operating period. Furthermore, the use of VRFB can decrease the operational time of the fuel cell and electrolysis cell by 18.35% and 14.97%, respectively. Then, the effect of changes in solar energy according to weather conditions on the performance of each unit in the hybrid power system is analyzed in terms of the amount of hydrogen consumed by PEMFC and produced by PEMEC and the amount of dump load power. In the case of high solar irradiance such as in the summer, the hybrid power system is stable due to the balance between hydrogen usage and production, and low dump load power is observed. In the winter and arbitrarily weather, although there is remaining excess power after supplying to the load, it is not sufficient for hydrogen production; taking electricity from the main grid or importing hydrogen from other sources is required to make the system stable for the long-term operation. Finally, the two new energy management strategies of the hybrid power system in which the minimum power of PEMEC operation is differently managed are presented. A suitable energy management strategy for each scenario of the irradiance profiles is identified. In the first strategy, the excess power that is lower than the minimum power for PEMEC operation will distribute to the dump load. This strategy is suitable for the summer scenario due to much excess energy. In the second strategy, it is determined that there is enough power from battery discharging to operate PEMEC at the minimum power point. It is found that this strategy is suitable for the situation with low excess power such as in the winter and arbitrarily weather.

Batteries, ultracapacitors (UCs), and fuel cells are widely being proposed for electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) as an electric power source or an energy storage unit. In general, the design of an intelligent control strategy for coordinated power distribution is a critical issue for UC-supported PHEV power systems. Implementation of several control methods has been presented in the past, with the goal of improving battery life and overall vehicle efficiency. It is clear that the control objectives vary with respect to vehicle velocity, power demand, and state of charge of both the batteries and UCs. Hence, an optimal control strategy design is the most critical aspect of an all-electric/plug-in hybrid electric vehicle operational characteristic. Although much effort has been made to improve the life of PHEV energy storage systems (ESSs), including research on energy storage device chemistries, this paper, on the contrary, highlights the fact that the fundamental problem lies within the design of power-electronics-based energy-management converters and the development of smarter control algorithms. This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies. Finally, this paper summarizes the benefits of the various techniques and suggests the most viable solutions for on-board power management, more specific to PHEVs with multiple/hybrid ESSs.

The growing integration of renewable energy sources and electric vehicles (EVs) into grid systems poses serious challenges to energy forecasting, power management, and system stability. The conventional energy management frameworks fall short in responding to the uncertainty of renewables and dynamic load behavior of EVs. In this context, the current research suggests a novel hybridized energy management and power forecasting scheme to enhance grid resilience, sustainability, and efficiency. A two‐way communication model is established to facilitate real‐time electricity price and power coordination among solar photovoltaic (PV), wind turbines, energy storage devices, EVs, and industrial loads. The new kernel‐based nonparametric energy mode optimizer model, which is trained on historical energy and weather patterns, strongly enhances the accuracy of power forecasting and system response. Experimental outcomes confirm peak PV generation of 17 kW at noon with inverter output of 16 kW, and voltage across microgrids (MG1: 0.94–1.06 V, MG2: 0.90–1.025 V) within the stable range. Battery state of charge varies between 180 and 200%, providing stable energy supply and load balancing. This research fills core energy forecasting and grid interfacing gaps and provides a scalable solution for future smart grid and EV‐integrated renewable systems.

A long-term goal of future naval shipboard power systems is the ability to manage energy flow with sufficient flexibility to accommodate future platform requirements such as, better survivability, continuity, and support of pulsed and other demanding loads. To attain this vision of shipboard energy management, shipboard power and energy management systems must coordinate operation of all major components in real-time. The primary components of a shipboard power system are the generators, energy storage modules, and increasingly power electronics that interface those sources and main load centers to the system. Flexible management of energy flow throughout shipboard distribution systems can be realized by automated coordination of multiple power electronic converters along with storage and generation systems. Use of power converters in power distribution systems has continuously increased due to continued development of the power electronics building blocks (PEBB) concept which reduces cost and increasing reliability of converters. Recent developments in SiC power devices are yielding PEBBs with far greater switching frequencies than Si based devices resulting in an order of magnitude reduction of the time scales as compared to converter systems utilizing conventional IGBT based PEBBs. In addition there have also been advancements in highly modularized converter systems with hundreds of PEBBs such as the Modular Multilevel Converter. Both of those trends have resulted in the continued evolution of the Universal Controller Architecture which attempts to standardize control interfaces for modular power electronic systems. Further development of interface definitions and increasing communication and computational capabilities of new FPGA based controllers provides opportunities beyond simply supporting SiC PEBBs. Fast control coordination across the system using an appropriate communication architecture provides a degree of energy management not previously realizable in shipboard power systems. The paper will present recent research results in networked control architectures for power electronic converter coordination and control. It will demonstrate that current FPGA and gigabit speed serial communication technologies allow for a very high degree of energy flow control.

Automated power and energy management systems (APEMS) are unifying control systems that coordinate individual power system components and present appropriate information to system operators. This paper presents ongoing work to establish a methodology and framework for evaluating such systems. As the design space offers many degrees of freedom including functional decomposition, choice of sensors, means of communication, and controls, systematic exploration and comparison of viable options is critical to designing a solution that maximizes overall system performance. This paper presents on initial progress towards a new unified, flexible, and scalable methodology that effectively evaluates performance and objectively contrasts candidate APEMS solutions.

Power systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.

Energy management of renewable energy-based combined heat and power systems: A review

10 - DC microgrids for electric vehicle wireless charging

In the modern electronic power system, energy management and load forecasting are important tasks. Energy management systems are designed concerning monitoring and optimizing the energy requirement in smart systems. This research work is divided into two parts. The first part will contain load forecasting and energy management in a smart grid. Load forecasting in the smart grid can be divided into three parts long-term, mid-term, and short-term load forecasting. The second part will describe energy usage optimization for the electric vehicle. Here we will show grids to vehicle energy demand management and optimization. This chapter will first introduce different deep learning techniques and then discuss their applications related to smart-grid and smart vehicle.

As a new energy vehicle, range-extended electric tractor have the advantages of long driving range and low pollutant emissions. However, at present, most extended battery have problems of insufficient energy management and high consumption.Therefore, it is necessary to study the energy management strategy of the extended-range electric vehicle battery. The additional battery provides power and power for the whole vehicle, realizes on-board charging through the expansion field, provides charging function when parking, and can be connected to the power grid; High voltage power supply safety management function; High voltage battery charger, high voltage power supply device, high voltage circuit current detection function, manual maintenance switch, battery system thermal management system, monitoring system operation status detection function.In order to construct the advanced energy management strategy and carry out the effective simulation analysis, based on the Cruise platform, this paper studies the power system matching and energy management control strategy of the extended battery, and completes the power system matching according to the vehicle dynamic parameters and the basic parameters of the vehicle. In this paper, a Cruise vehicle model of an extended-range extended battery is established on the Cruise professional simulation software platform, and joint simulation experiments are performed based on the energy management strategy model built in Matlab / Simulink. Simulation results show that the control algorithm designed in this paper satisfies the vehicle's driving needs,improves the economy of the vehicle by 6.84%, better controls the change of battery SOC. So as to verify the dynamics and economics of the power system and to evaluate the advantages and disadvantages of using this energy management strategy in different operating conditions, this paper studies the dynamics and economics under different cycling conditions. The vehicle power system matching and energy management strategies designed in this article have better economic characteristics when driving in this cycle.