Proposed Energy Management Control Research Articles

Energy Management Considering both Efficiency Optimization and Lifetime Balance of Multi-stack FCS

Abstract With the increase of energy transformation and environmental protection awarenesses, fuel cells, as a clean and efficient energy conversion technology, have been widely adopted in transportation and stationary power supply applications. A single fuel cell has a limited power capability. On the other hand, the multiple fuel cells are combined into a multi-stack fuel cell system (MFCS), which provides a high power output and modular design. However, the energy management of such multi-stack systems faces challenges such as uneven power distribution, inconsistent stack life, and low overall efficiency. Current multi-stack fuel cell control systems often optimize single objectives, either efficiency or lifetime balance produce the optimized energy management strategy. This may not suffice for the overall system benefit. For example, if only efficiency is optimized by energy management, the failure to consider lifetime balance may result in uneven life degradation for each fuel cell, and vice versa. To address the above issues, this paper proposes an energy management strategy for multi-stack fuel cell systems that considers dual objectives, which can simultaneously ensure the optimal co-optimization of the output efficiency and stack life for each stack, thereby improving the overall operating efficiency and lifetime of the system. The effectiveness and practicality of the proposed energy management control strategy were verified on a 60kW dual-stack PEMFC fixed power generation system developed by SeeEx.

Evaluation of technical and financial benefits of battery energy storage system control strategies

The recent increase in renewable energy generation can balance consumption and reduce carbon emissions. With battery energy storage optimizing supply and demand, it is more important than ever to manage charge control to the benefit of all stakeholders. In this paper, the developed and proposed energy management control methods based on the technical operating criteria of battery energy storage (BESS) and considering self-consumption rate (SCR), self-supply rate (SSR) and curtailment rate are compared in terms of environmental index and economics for daily and annual demand profiles for various household prosumer demand profiles in Istanbul and Antalya. Considering the supply-demand matching based on demand profile, feed-in damping, fixed feed-in, schedule mode, schedule mode with constant charging power and self-consumption control methods are proposed for optimum operation for each prosumer profile. The results show that feed-in damping and fixed feed-in methods can reduce household prosumer costs by up to 22.3% in the daily analysis. Moreover, similar control methods can increase SCR by up to 29.5% and reduce costs by up to 10.62% for higher irradiances in the annual analysis. Proper management of BESS charge control can facilitate sustainable development goals by assisting plans of many stakeholders.

A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles

Abstract Hybrid energy storage systems have attracted more and more interests due to their improved performances compared with sole energy source in system efficiency and battery lifetime. This study aims to propose a real-time energy management control strategy for achieving these goals. The strategy is based on a combination of wavelet transform, neural network and fuzzy logic. Wavelet transform is an effective tool in extracting different frequency components of load power demand to match the characteristics of battery and supercapacitor. However, it is hard to be directly applied in a real-time system. For this, a neural network model, which is offline trained using the dataset obtained from the wavelet transform decomposition, is developed to online predict the low frequency power demand for the battery. Accordingly, the high frequency power demand is online calculated and distributed to the supercapacitor. In addition, a fuzzy logic based supervisory controller is further developed for controlling the supercapacitor voltage within a certain suitable range. Finally, a 72 V battery and 96 V supercapacitor hybrid energy storage system real-time hardware platform has been developed to validate the effectiveness of the proposed energy management control strategy.

Cost Minimization Energy Control Including Battery Aging for Multi-Source EV Charging Station

A Multi-Source Electric Vehicle Charging Station (MS-EVCS) is a local entity that combines the grid energy with Distributed Energy Resources (DERs) with the aim of reducing the grid impact due to electric vehicles (EVs) charging events. The integration of stationary and in-vehicle Energy Storage Systems (ESSs) in MS-EVCSs has gained increasing interest thanks to the possibility of storing energy at off-peak hours to be made available at peak-hours. However, the ESS technology and the vehicle-to-grid (V2G) concept show several issues due to cost, battery life cycle, reliability, and management. The design of the MS-EVCS energy management system is of primary importance to guarantee the optimal usage of the available resources and to enhance the system benefits. This study presents a novel energy management strategy for Real-Time (RT) control of MS-EVCS considering DERs, stationary ESS, and V2G. The proposed energy management control allows defining the MS-EVCS control policy solving several cascaded-problems with the aim of achieving the minimum operating cost when the battery degradation and the stochastic nature of the sources are considered. The key feature of the proposed methodology is the lower computational effort with respect to traditional optimal control methodologies while achieving the same optimal solution.

On Energy Management Control of a PV-Diesel-ESS Based Microgrid in a Stand-Alone Context

This paper deals with the energy management control of a PV-Diesel-ESS-based microgrid in a stand-alone context. In terms of control, an Isolated Mode Control (IMC) strategy based on a resonant regulator is proposed. In Parallel Mode Control (PMC) conditions, the diesel generator (DG) is controlled to operate at its nominal power. In this context, a supervisory algorithm optimizing the power flow between the microgrid’s various components ensures switching between the two modes for different possible scenarios. To prove the effectiveness of the proposed control strategies, the energy management control (EMC) is tested first using a standard state of charge (SOC) profile emulating the microgrid different states. Then real data are used to simulate the load and solar radiations. An experimental validation on a reduced scale test bench is carried out to prove the feasibility and the effectiveness of the proposed energy management control strategies.

Intelligent energy management control for independent microgrid

This work presents a new adaptive scheme for energy management in an independent microgrid. The proposed energy management system has been developed to manage the utilization of power among the hybrid resources and energy storage system in order to supply the load requirement based on multi-agent system (MAS) concept and predicted renewable powers and load powers. Auto regressive moving average models have been developed for predicting the wind speed, atmospheric temperature, irradiation, and connected loads. The structure proposed in this paper includes renewable sources as primary source and storage system as secondary source. A wind generator and solar PV array system together acts as primary source, which supplies power to the local load most of the time in this energy management strategy. When they fail to meet the load demand, the secondary source present in the system will assist the primary source and help to attain the goal of satisfying load demand without interruption. If the primary source and secondary source together are not able to meet the load demand then load shedding will be executed according to the priority set. Thus the developed MAS algorithm co-ordinates the hybrid system components and achieves energy management among renewable energy sources, storage units, and load under varying environmental conditions and varying loads. STATCOM based compensation has been implemented to balance the reactive power demand and to mitigate the voltage fluctuations and harmonics on the AC bus. The proposed microgrid has been simulated with MAS concept in Matlab/Simulink environment. The results presented in this paper show cases the effectiveness of the proposed energy management controller.

Online Optimizing Plug-In Hybrid Energy Management Strategy for Autonomous Guidance and Drive-aware Scenarios

Recent advances in autonomous driving and vehicle connectivity help to ensure safety and comfort in various driving conditions. These trends have widenened the system boundary conditions for hybrid powertrain operation with driving trajectory planning hence offering potential to improve powertrain operational efficiency. This paper presents an energy management (EM) controller for a plug-in hybrid vehicle exploiting predicted velocity trajectory together with its integration in both autonomous longitudinal guidance and driver-aware scenarios. The driver-aware scenario uses Markov chain based stochastic modelling of driving characteristics. The proposed EM controller solves online, a discretized version of the fuel consumption minimization problem using direct methods transcribing the problem into a finite dimensional mixed boolean quadratic problem with polytopic constraints. The convex part of the resulting problem is solved using an active set method. Simulation results from different driving situations based on standard driving cycle and real world driving scenarios demonstrate the functionality of the controller and its flexbility to handle varying control objectives.

Smart Energy Management controller for a Micro Grid

This paper adopts a proposed energy management controller in a micro grid. This micro grid integrates both a renewable energy source and energy storage systems. Photovoltaic (PV) is adopted as a main power source, a valve regulated lead acid battery, and Super-capacitor are the energy storage system. This hybrid energy storage integrates the advantages of a high energy density of the battery and a high power density of the super-capacitor to decrease the stress on the battery and to decrease the voltage change at transient conditions. The proposed controller consists of two control units. The First unit is used for maximum power point tracking (MPPT), and the Second is used to drive the bidirectional converters for charging and discharging modes of the battery and the super capacitor; according to the load demand and the environmental conditions. The simulation results show the effectiveness and the efficiency of the proposed control strategy.

An Energy Management Scheme with Power Limit Capability and an Adaptive Maximum Power Point Tracking for Small Standalone PMSG Wind Energy Systems

Due to its high energy generation capability and minimal environmental impact, wind energy is an elegant solution to the growing global energy demand. However, frequent atmospheric changes make it difficult to effectively harness the energy in the wind because maximum power extraction occurs at a different operating point for each wind condition. This paper proposes a parameter-independent intelligent power management controller that consists of a slope-assisted maximum power point tracking (MPPT) algorithm and a power limit search (PLS) algorithm for small standalone wind energy systems with permanent synchronous generators. Unlike the parameter-independent perturb & observe algorithms, the proposed slope-assisted MPPT algorithm preempts logical errors attributed to wind fluctuations by detecting and identifying atmospheric changes. The controller's PLS is able to minimize the production of surplus energy to minimize the heat dissipation requirements of the energy release mechanism by cooperating with the state observer and using the slope parameter to seek the operating points that result in the desired power rather than the maximum power. The functionality of the proposed energy management control scheme for wind energy systems is verified through simulation results and experimental results.

Trip-Oriented Energy Management Control Strategy for Plug-In Hybrid Electric Vehicles

This paper presents a trip-oriented energy management control strategy for plug-in hybrid electric vehicle (PHEV). The proposed strategy provides system optimization and control methods to improve real-world fuel economy (FE) by optimizing the power demand distribution between fuel and battery electricity and the power delivery split between the mechanical and electrical paths in a PowerSplit PHEV architecture. A two degree of freedom system model is established to characterize the actuation dynamics and the power delivery properties of the powertrain. This paper achieves three important contributions to PHEV energy management control research: 1) the optimal control problem is solved considering both the nonlinearity of battery efficiency and the complexity of PowerSplit architecture; 2) a novel trip-oriented energy consumption preplanning method is proposed using a driving pattern-based dynamic programming approach; and 3) a feedback control system is designed to realize the optimal energy consumption process in real applications. The proposed energy management control strategy has been shown to improve FE in Ford Escape PHEVs.

A near-optimal rule-based energy management strategy for medium duty hybrid truck

This paper covers the design and implementation of a rule-based energy management strategy for a medium duty hybrid truck. In this paper, a procedure for the design of a near-optimal energy management strategy is presented. The procedure utilises the dynamic programming (DP) algorithm to find the optimal control strategy that minimises the fuel consumption over a given driving mission. Through the analysis of the behaviour of DP control actions, near-optimal rules are extracted and tuned to design a rule-based strategy for charge sustaining operation which, unlike DP control signals, is implementable on-board of the vehicle. Drivability metrics such as frequent clutching and engine on/off behaviour are also included in the control design based on the implementation of the DP under different drivability scenarios. The performance of the proposed energy management control strategy is studied by using a proposed longitudinal vehicle model of a pre-transmission parallel medium duty hybrid truck with a clutch. The proposed near-optimal rule-based strategy, benchmarked against the optimal DP solution, shows performance within 3% of the global optimal one.