Optimal Energy Management Strategy Research Articles

Energy Management Strategy Considering Fuel Economy and Life of Fuel Cell for Fuel Cell Electric Vehicles

The optimization of energy management strategy (EMS) for fuel cell electric vehicles (FCEV) is mainly applied to reduce fuel consumption. However, serious power fluctuations affect the durability of the fuel cell, which is still a challenge for the FCEV. In order to optimize the fuel cell’s service life and economy, this paper proposes an EMS scheme based on fuzzy control. In the proposed EMS, the first layer uses the power following strategy to address the output power distribution issue between the battery and the fuel cell. In order to limit the rapid shift in fuel cell power, the second layer employs a fuzzy control method. Considering the variety of constraints, such as fuel cell power fluctuation and hydrogen consumption, the particle swarm optimization algorithm is used to optimize the membership function of the fuzzy controller. The performance and the operating characteristics of the fuel cells are then evaluated using a test bench. Afterward, the proposed EMS is verified by AMEsim joint simulation. The optimized strategy increased the driving range by 4.7% and 5.9% in world light vehicle test cycle (WLTC) and new European driving cycle (NEDC) conditions, respectively. It also reduces the fuel cell degradation by 21.4% and 19.8%.

Adaptive Model-Predictive-Control-Based Real-Time Energy Management of Fuel Cell Hybrid Electric Vehicles

To compete with battery electric vehicles, fuel cell (FC) hybrid electric vehicles (FCHEVs) are required to offer better performance in fuel economy and FC durability. To this end, this article proposes a novel real-time adaptive model predictive control (AMPC)-based energy management strategy (EMS) for FCHEVs to improve their fuel efficiency and mitigate the degradation of their onboard FC hybrid systems. First, a linear parameter-varying (LPV) prediction model of the FC hybrid system that considers the system parameter variation is developed. The model offers sufficient accuracy while enabling the real-time implementation capability of the AMPC. Then, an AMPC strategy is proposed to optimally distribute the load current of the FCHEV between the FC and the battery in real time. In each control interval of the AMPC, the LPV prediction model is updated online to adapt to the variations of the battery state of charge. The constrained optimization problem of the AMPC is then formulated to achieve a desired tradeoff among four performance metrics and is further transformed into a quadratic programming problem, which can be solved in real time. Hardware-in-the-loop tests are performed on a downscaled FC hybrid system with the proposed AMPC-based EMS, a commonly used rule-based EMS, an equivalent consumption minimization strategy, and an improved MPC-based EMS, respectively. Results show that among the four real-time EMSs, the AMPC-based EMS achieves the best performance in reducing hydrogen consumption and FC current fluctuation and the smallest optimality gap with respect to an offline dynamic programming-based optimal EMS.

Optimal Management of an Energy Community with PV and Battery-Energy-Storage Systems

Most of current prosumer-energy-management approaches are focused on economic optimization by self-consumption maximization. Nevertheless, a lack of energy management strategies (EMS) that tackle different interaction possibilities among community-clustered solar plus battery prosumers has been detected. Furthermore, such active prosumer EMS may include participation in ancillary service markets such as automatic frequency restoration reserves (aFRR) through an optimized battery-energy storage-system (BESS) operation, as well as incorporating community-level energy management. In this study, an optimal EMS that includes aggregated aFRR-market participation of five solar plus battery prosumers participating in an energy community (EC), with the aim of reducing total costs of ownership for each individual prosumer is proposed. For its validation, different scenarios have been analyzed. The results show that the proposed EMS allows a levelized cost of energy (LCOE) reduction for all community members with respect to the base-case scenario. Moreover, the most profitable scenario for all prosumers is still the only PV.

An optimal energy management strategy for a photovoltaic/li-ion battery power system for DC microgrid application

The purpose of this paper is to propose an energy management strategy (EMS) based on flatness control method for a standalone hybrid photovoltaic-battery system. The goal of the proposed method is to use non-linear flatness theory to develop an efficient EMS in order to provide a stable DC bus voltage and an optimal power sharing process between the solar array and the battery. The suggested EMS is responsible for balancing the power reference for the PV system and the battery while keeping the DC bus voltage steady and performing at its reference value. In order to maximize the PV’s power, a perturb and observe with a variable step size (VSSP and P&O) based maximum power point tracking (MPPT) method with a DC/DC boost converter was used. In addition, a DC/DC bidirectional converter was developed to control the charging and discharging process of the battery. Moreover, the proposed EMS strategy was verified in a MATLAB®/Simulink-based simulation environment by subjecting it to a variety of scenarios, including those with varying degrees of irradiation and sudden changes in load. The obtained results show that the presented EMS method was able to keep the bus voltage stable despite changes in load or solar radiation. Furthermore, the EMS By minimizing bus voltage spikes, the technique also ensured excellent power quality which helped the battery’s operation in terms of lifetime and efficiency. Finally, the suggested strategy has a minimum overshoot rate in the bus voltage and higher tracking efficiency compared with the classical load following (LF) strategy under various load conditions.

Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

Adaptation and optimization of a photovoltaic system for a country house

This chapter provides the rationale for monitoring photovoltaic (photovoltaic) systems, its purpose, the need for proper measurement, and the frequency required to obtain meaningful results. The need for system monitoring falls into three groups: user feedback, performance review, and system evaluation. Each group is briefly summarized; other related topics include calibration, data storage, and data transfer. The text covers various monitoring modes such as performance testing, system evaluation, data collection and storage, as well as data analysis and reporting. The evaluation standards given by the International Electrotechnical Commission (IEC) are noted in the Data Analysis and Reporting section. The figures are provided to illustrate several types of displays that are an integral part of monitoring. Optimized modes of operation of the power part of a household photovoltaic system consisting of photovoltaic panels, an MRRT controller, an inverter and an electric energy storage unit, with Autonomous and network connection of the load. To build an optimal and cost-effective energy management strategy, it is necessary to take into account the energy profile of the resident, the characteristics of electricity production based on photovoltaic cells, and the electricity tariff for utilities. A photovoltaic system can be connected to the grid in order to receive energy from the grid or enter it into the network to eliminate the discrepancy between the generated, consumed and stored energy.

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

This paper presents a comprehensive literature review of the state-of-the art modeling and optimization methods for the power and propulsion systems of ships. Modeling is a tool to investigate the performance of actual systems by running simulations in the virtual world. There are two main approaches in modeling: physics-based and data-driven, which are both covered in detail in this survey paper. The output from the simulations might not be optimal in terms of certain performance criteria such as energy consumption, fuel cost etc. Hence, it is vital to optimize the systems considering the efficient interaction between the components, to yield the optimal performance for the integrated vessel’s powertrain. In this paper, the optimization case studies, for the ship energy systems, will be divided in terms of a) optimal design (topology and sizing), b) optimal control and energy management strategies, c) combined optimal design and control. Tables that summarize the literature review outcomes will also be presented at the end of each section. The main outcome is that limited literature is available for optimizations of ship powertrains using data-driven models, especially surrogate models. Surrogate-assisted optimizations for integrated ship energy systems can yield optimal solutions at fast computational speeds, with sufficient accuracy, even for complex, nested, multi-level, multi-objective optimizations.

An Energy Management System for the Optimal Operation of BESS in DC Microgrids: A Robust Convex Programming Approach

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US$ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US$ 5243.23 (19.79%), US$ 5072.23 (15.88%), and US$ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.

Energy management strategy based on convex optimization for fuel cell/battery/ultracapacitor hybrid vehicle

For a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell (FC), a battery (BAT) and an ultracapacitor (UC), an improved convex-optimization-based energy management strategy (EMS) is proposed in this article. To protect the fuel cell and the battery from peak power, an adaptive fuzzy filter is used to complete the frequency decoupling of the power demand. Subsequently, an adaptive equivalent consumption minimization strategy (A-ECMS) is introduced to improve fuel economy. To improve the optimality of the EMS obtained, an improved convex optimization method is utilized by introducing a slack variable and a barrier function. Finally, based on experimental data and an improved convex optimization algorithm, the optimal EMS is calculated and verified by a series of simulations under different driving cycles. Simulation results confirm that, compared with the traditional ECMS-based EMS, the proposed EMS can improve fuel economy and achieve lower power fluctuation and optimal FC efficiency for the FCHEV.

Energy Management and Control in Multiple Storage Energy Units (Battery-Supercapacitor) of Fuel Cell Electric Vehicles.

This paper presents a new approach of energy management for a fuel cell electric vehicle traction system. This system includes a supercapacitor, a traction battery of valve-regulated sealed lead-acid type, a high-performance permanent magnet traction system, and a power electronics converter. Special attention was placed on the coordination for managing the flow of energy from several sources to treat the concerns of prolonged electric vehicle mileage and battery lifetime for drivetrains of electric vehicles. Connection to a supercapacitor in parallel with the electric vehicle's battery affects electric vehicle battery lifetime and its range. The paper used a study case of an all-electric train, but the used methods can be applied on hybrid or electric train cases. Fuzzy logic control and proportional integral control methods were used to control the electric vehicle system. The results of these two control methods were examined and compared. The simulation results were compared between the proposed electric vehicle system and the traditional system to show the effectiveness of the proposed method. Comparison of waveforms was made with and without the supercapacitor. The proposed optimized energy management strategy could improve the overall performance of the hybrid system and reduce the power consumption.

Design Space Exploration of Lithium-Ion Battery Packs for Hybrid-Electric Regional Aircraft Applications

Distributed hybrid and electric propulsion systems are one of the most promising technologies to reduce aircraft emissions, resulting in research efforts to investigate new architectures and the design of optimal energy management strategies. This work defines the optimal requirements in terms of battery pack sizing and cell technology for a hybrid-electric regional wing-mounted distributed propulsion aircraft through the application of a design space exploration method. The propulsion system considered in this study is a series-parallel hybrid turboelectric power train with distributed electric fans. A set of six lithium-ion battery cell technologies was identified and experimentally characterized, including both commercially available and prototype cells at different combinations of specific energy and power. A model of the aircraft was developed and used to define the optimal energy management strategy for the hybrid turboelectric propulsion system, which was solved using dynamic programming. The design space exploration was conducted by varying the cell technology and battery storage system size; and the effects on fuel consumption, energy management strategy, and thermal management were compared.

Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition

The energy management in fuel cell vehicles (FCVs) is crucial to maintain the economical operation of FCVs and the fuzzy logic control (FLC) is mainly used to manage the energy split between the fuel cell and other energy sources. To overcome the limitation of traditional FLC, the dependence on expert knowledge leading to the insufficient energy split, this paper proposes strategy optimization based on FLC with driving cycles recognition achieve near-optimal fuel economy and stable battery charge sustenance. Initially, the whole FCV model is established, which includes electrical system, vehicle dynamic system, energy management system. Additionally, with the objective function which is the minimum equivalent hydrogen consumption of four typical driving cycles, the centers and widths of FLC membership function are optimized by genetic algorithm (GA), respectively. Finally, the driving cycles recognition is achieved based on K-means clustering method, and characteristic parameters are extracted and classified. Compared with GA-optimized fuzzy EMS and the traditional fuzzy EMS, the simulation results demonstrate that the equivalent hydrogen consumption based on proposed strategy is reduced by 16.55% and 40.50%, respectively. Therefore, the proposed strategy can effectively smooth the output of proton exchange membrane fuel cell (PEMFC) and enhance the total fuel economy.

An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system

Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.

Optimization Based Adaptive Cruise Control and Energy Management Strategy for Connected and Automated FCHEV

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.

Optimal home energy management strategy: A reinforcement learning method with actor-critic using Kronecker-factored trust region

The global development trend of building decentralized and low-carbon energy systems has given rise to challenges in the demand side energy management. With the overwhelming increase in the domestic electricity demand and distributed energy penetration, scheduling residential energy consumption has become essential for decreasing environmental impact. To this end, the paper proposes an energy scheduling strategy optimization framework for a home energy management system devised with photovoltaic and storage to realize the optimal scheduling of household appliances. Uncertainties in energy usage behaviour and real-time electricity prices were considered and described using a Markov decision process model. Subsequently, to search for the optimal appliance scheduling policy in a complex and changing environment, a model-free energy scheduling approach was proposed based on actor-critic using the Kronecker-Factored Trust Region (ACKTR). Unlike conventional deep reinforcement learning methods, the proposed ACKTR-based approach has high sampling efficiency and can deal with both discrete and continuous control actions to jointly optimize the scheduling strategies of various types of appliances. Finally, the numerical results of case studies validate the effectiveness and demonstrate that the proposed approach can significantly reduce costs while maintaining satisfaction, with a remarkable 25.37% cost saving on a typical test day.

Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation

To enhance the hydraulic energy utilization and torque output stability, a novel mechatronics-electro-hydraulic power coupling electric vehicle (MEH-PCEV) is proposed, integrating a hydraulic pump/motor and a motor into a single device for mutual energy conversion. For MEH-PCEVs equipped with multiple energy sources, a cluster analysis method is used to classify the actual road test dataset and provide guiding ideas for designing rule-based energy management strategies (RB-EMS). Simultaneously, for the output torque anomaly phenomenon in RB-EMS, an inverse thinking fuzzy logic optimization energy management strategy (FLO-EMS) conside ring multi-parameter objectives as input is used to adjust the electromagnetic torque in real-time and reasonably allocate the energy flow. The simulation results demonstrate that the electric and total torque output are more stable. The electric peak torque is relieved, with a corresponding increase in the percentage of electrical energy recovery. With the equal power demand, the overall efficiency of the motor working point is substantially improved, and the energy consumption rate is decreased by 24.42%. Under the active regulation of FLO-EMS, hydraulic energy is more reasonably utilized to meet the vehicle demand power while avoiding energy dissipation and waste. Moreover, this work is expected to reference the development and engineering applications of electro-hydraulic coupling systems.

Intelligent and optimal energy management strategy to control the Micro-Grid voltage and frequency by considering the load dynamics and transient stability

Maximizing the use of renewable energy resources in the context of the micro-grid (MG) idea has posed a variety of technical and economic challenges to current distribution networks. The most important challenge is the control of vital and essential factors such as voltage/frequency (V/F) in milliseconds and seconds timescales. This article presents, the intelligent control platform of V/F control in the primary and secondary based on communication system. The proposed control platform is programmed using an adaptive droop controller and considering load dynamics and the fluctuations of distributed generation sources. In the primary, proper detection and reaction in V/F control are considered. In the secondary, in addition to controlling the deviation from the V/F reference, to increase the level of stability and prevent V/F fluctuations, power exchange management between MG and the main grid are considered. Implementing an intelligent control strategy utilizing the features of the particle swarm optimization (PSO) algorithm in inverter-based MGs significantly decreases overshoot, settling time in the face of load fluctuations, power generation fluctuations, and switching risk. Fast damping, increasing the accuracy in error detection and fast reaction are other features of the proposed control platform. The proposed control platform is simulated and analyzed in MATLAB / Simulink software.

Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications

An optimal techno-economic multi-level energy management strategy (EMS) is designed for a DC microgrid applied to a commercial building. The proposed power system is a grid-connected microgrid composed of a fuel cell (FC) system, a photovoltaic (PV) array, and a battery storage unit. The interaction between various types of power sources may lead to stability problems. Moreover, providing an uninterruptible and high-quality power supply, reducing operating costs, and maximizing system efficiency are essential factors. This paper presents a techno-economic EMS, one of the most significant objectives for the following years. The proposed EMS comprises two levels for minimizing the operating cost and supplying the load and another control level based on flatness control theory to improve the power quality. The first level is based on one of the following EMSs: State Machine Control (SMC), Equivalent Consumption Minimization Strategy (ECMS), and External Energy Maximization Strategy (EEMS) to improve efficiency and reduce the required power. The second level is based on the economic dispatching algorithm (EDA) to reduce the operating cost and ensure a UPS. Finally, the system performance is elaborated; the simulation results prove the superiority of the EEMS-EDA combination, where the cost-saving can be enhanced by 3.47%, and the efficiency is about 80.44%.

Optimal management of electric hotel loads in mild hybrid heavy duty truck

The problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. This work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

Energy Management Based on Neural Networks for a Hydraulic Hybrid Wheel Loader

This paper presents a method to derive optimised energy management strategies for a hydraulic hybrid wheel loader. Energy efficiency is a key aspect for the sustainability of off-road mobile machines. Energy management strategies for on-road hybrid vehicles cannot be directly applied to off-road hybrid machines. One significant reason is that there are added degrees of freedom with respect to how power can be recovered, exchanged and reused in the different functions, such as drivetrain or work functions. This results in more complex energy management strategies being derived. This paper presents an analysis and preliminary conclusions for a proposed method to derive optimised online energy management strategies for a hydraulic hybrid wheel loader. Dynamic programming is used to obtain optimal offline energy management strategies for a series of drive cycles. The results are used as examples to train a neural network. The trained neural network then implements the energy management strategy and is used to make optimised control decisions. Through simulation, the neural network’s ability to learn the dynamic programming decision-making process is shown, resulting in the machine operating with fuel consumption similar to that of the offline optimal energy management strategy. Aspects of simplicity to model these machines for dynamic programming optimisation, the data necessary to train the network, the training process, variables used to learn the dynamic programming decision-making process and the robustness of the network when facing unseen operational conditions are discussed. The paper demonstrates the simplicity of the method for taking into account variables that affect the control decisions, therefore achieving optimised solutions.