Rule-based Energy Management Strategy Research Articles

Optimal SOC Control and Rule-Based Energy Management Strategy for Fuel-Cell-Based Hybrid Vessel including Batteries and Supercapacitors

Around the world, the development of electric vehicles is underway, including in maritime transportation. However, the development of clean energy vessels still has a long way to go. Fuel cells (FCs) are a relevant choice among the many clean energy sources to power clean energy vessels. However, due to the complex and drastic change in the shipload power, FCs need to be equipped with dynamic fast-response energy storage equipment to make up for it. For multiple energy storage devices connected in parallel, the state of charge (SOC) is not balanced, which affects their service life and the stability of the vessel microgrid, as well as slowing the speed and lowering the accuracy of SOC equalization. This paper proposes a distributed variable sag slope control strategy for vessels to improve SOC equalization, with a FC as the energy source and a battery and supercapacitor as the energy storage system (ESS). For the output power distribution problem of energy storage equipment caused by shipload power variation, a power distribution strategy with a variable filter time constant is used to improve the reasonableness of the output power distribution of energy-based lithium batteries and power-based supercapacitors. Meanwhile, this paper considers the power generation equipment’s service life and energy cost as the optimization objectives, considering the discharge depth of the energy storage equipment. Finally, a method based on the combination of the lithium battery SOC rule (the rule formulated according to the state of charge and load power change in energy storage equipment) and particle swarm optimization algorithm is proposed to solve this problem. The simulation results show that the proposed strategy improves the equalization speed and accuracy of the SOC of energy storage devices, fully realizes the characteristics of different energy storage devices, and reduces the life loss of energy storage devices.

Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEV). A Markov velocity predictor optimization method and its applications in PHEV energy management is studied in this paper. The initial Markov velocity predictor is constructed using complete driving cycle information and the state space of the Markov velocity predictor is then optimized for specified driving conditions using simulated annealing algorithm (SAA). The practical driving conditions are identified using a multi-feature driving condition recognition unit by using the support vector machine (SVM) method. Based on the driving conditions identified, velocities are predicted using the proposed method and optimized using dynamic programming (DP) algorithm in conjunction with the state of charge (SOC) reference and vehicle state. The energy management strategy derived is then implemented in the vehicle controllers. Comparing with the traditional rule-based energy management strategy, simulation results indicate that the PEM strategy proposed herein can reduce fuel consumption.

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.

Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Design of cost-based sizing and energy management framework for standalone microgrid using reinforcement learning

The standalone photovoltaic-battery energy storage (PV-BES) microgrid has gained substantial interest recently due to its ability to provide uninterrupted power to consumers in remote areas. In such microgrids, components must be precisely sized and energy must be supplied most cost-effectively at all times. This paper presents a cost-based framework for determining the optimal size and energy management of standalone microgrids using reinforcement learning. Fundamental to this framework is two essential phases; the first is finding the best size of PV-BES using an analytical and economic sizing (AES) model based on minimum levelized cost of energy (LCOE). The AES phase is then followed by optimizing the energy management strategy (EMS) of the microgrid using reinforcement learning to provide optimum cost savings. The novelty in this work can be outlined as optimizing both the size and EMS of a standalone PV-BES microgrid using the AES model and Q-learning in an integrated framework. This can lead to improved performance demonstrated in reducing the LCOE, decreasing diesel generator working hours, and enhancing PV utilization and system efficiency. The results show an advantageous reduction in total cost while meeting load requirements. Additionally, the proposed framework is evaluated using several metrics to measure the impact of employing Q-learning against the AES-finite automata model. For instance, a decrease of 22% in diesel generator working hours and an increase of 6% in PV utilization while a reduction of 11% in the LCOE is accomplished. On the other hand, the proposed framework is examined against two rule-based EMSs, load following strategy (LFS) and cycle charging strategy (CCS), and outperforms these two EMSs in terms of LCOE, PV utilization, and system efficiency.

Multi-objective optimum energy management strategies for parallel hybrid electric vehicles: A comparative study

In this study, multi-objective optimum energy management strategies are developed for pre- and post-transmission parallel hybrid electric vehicles. The first strategy aims to improve fuel economy and electric system efficiency, while the second strategy adds the improvement in battery performance and life. Multi-objective genetic algorithm is adopted to solve the formulated optimum energy management problems, hence generates optimum control inputs for each drivetrain configuration. Mathematical models of vehicle dynamics and drivetrain components are integrated with the developed strategies in a simulation environment. Results for both strategies are compared to a baseline rule-based energy management strategy, commonly used as a benchmark in literature. Additionally, both drivetrain configurations are compared and analyzed through different standard driving cycles. The results show the significant improvement in battery performance due to its consideration in the energy management strategy, with either null or positive effect on fuel consumption. Additionally, the results of pre-transmission drivetrain configuration show noticeable improvement in battery performance as compared to the post-transmission results.

A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.

Sizing Optimization Algorithm for Vehicle-to-Grid System Considering Cost and Reliability Based on Rule-Based Scheme

The most widely two Renewable Energy Sources (RESs) used are solar and wind as naturally found sources due to the provided merits as clean, free of charge, and environmentally friendly. However, they are facing limitations in intermittency. This article aims to utilize natural resources integrated with the utility grid and Electric Vehicle (EV) to provide a hybrid system with a minimum of two objectives namely Cost of Electricity (COE) and reliability using the Losses Power Supply Probability (LPSP) method. The two mentioned objectives are considered to satisfy the residential load demand with EV in terms of Vehicle-to-Grid (V2G) as this article considered. The mentioned objective has been addressed by the Improved Antlion Optimization Algorithm (IALO) and coupled with a high supervisory control method called Rule-Based Energy Management Strategy (RB-EMS) to guarantee to spread the power among the system component. Optimization results show the hybrid integration of the utilized RESs with the Battery (BT) gives the most economic and reliable system for the study area integrated with the EV battery. The gained result was validated with a natural-inspired metaheuristic algorithm called Particle Swarm Optimization (PSO). This article assesses the effect on the RESs generators to achieve an economic and reliable system.

Battery Hybrid Energy Storage Systems for Full-Electric Marine Applications

The high cost of Lithium-ion battery systems is one of the biggest challenges hindering the wide adoption of electric vessels. For some marine applications, battery systems based on the current monotype topologies are significantly oversized due to variable operational profiles and long lifespan requirements. This paper deals with the battery hybrid energy storage system (HESS) for an electric harbor tug to optimize the size of the battery system. The impact of battery hybridization was investigated on three key performance indicators inclusive of cost, system efficiency, and battery weight. The design life of the battery system is considered to be 10 years, and NMC and LTO cell technologies are used as high-energy (HE) and high-power (HP) battery cells. The HESS design is based on a parallel full-active architecture with a rule-based energy management strategy. The results of this research indicate that battery hybridization can reduce the system cost by around 28% and 14% in comparison with a monotype battery with LTO and NMC cells, respectively. Although no noticeable difference in system efficiency is observed between the monotype system and HESS, battery hybridization reduces the total weight of the battery cells by more than 30% compared to monotype topology. This study implies that the hybridization of battery systems could be a promising solution to reduce the cost and weight of large battery packs in electric vessels.

Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems

To fully exploit the potential of decarburization in the transport sector (e.g., electric vehicles (EV)) and energy sector (e.g., building energy system), this paper proposes a new concept of ‘Vehicle-to-Micro-Grid (V2μG) network’ that incorporates the off-grid building energy system with flexible power storage/supply provided by battery EVs (BEVs) and fuel cell EVs (FCEVs). The work is conducted with three main contributions: 1) a rule-based energy management strategy is proposed to study the impact of the V2μG interactions on the EV battery degradation; 2) a scenario analysis based on four working modes is conducted to evaluate the energy efficiency, costing, environmental impacts, and component ageing of the proposed V2μG network; 3) the optimum settings for system configuration, capacity, and operation strategy of the V2μG network are obtained by the NSGA-II algorithm. The study suggested that the degradation of lithium-ion batteries in BEV can be reduced by 13% compared to the network without FCEVs. In a community with 160 households and 200 EVs, the optimal V2μG network can reduce carbon dioxide emissions by 515.56 tons annually compared to the conventional off-grid building energy system powered by internal combustion engines.

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.

A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies.

Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution

For the energy management, the energy conversion usually attracts focus of the researches in the control strategy design of hybrid electric vehicle (HEV), but the computational efficiency and dynamic coordination problem are often ignored, especially for the multi-mode HEV. Thus, this paper proposes a model predictive control (MPC)-based predictive energy management strategy for dual-mode HEV. In this strategy, the future vehicle speed is predicted in the given horizon, and then, an improved sequence quadratic programming algorithm (ISQP) that combines the deep Q-learning is designed to solve MPC problem, which effectively improves the computational efficiency and optimality of original SQP in iterative optimization. Meanwhile, a dynamic process coordination control algorithm is developed to take the torque coordination problem and balance relationship of mode shift dynamic process into the energy management problem. Eventually, the DP, SQP-MPC and rule-based energy management strategy are designed as the benchmark strategies to compare with the proposed method, and they are conducted in the three different test cycles. The results verify that the proposed strategy presents the desirable performance in fuel saving, real-time capability and robustness.

Development of a comprehensive transient fuel cell-battery hybrid system model and rule-based energy management strategy

ABSTRACT In the study, a comprehensive transient fuel cell-battery hybrid power system model is established to explore the output performances and dynamic responses of fuel cell electric vehicles (FCEVs). A fuel cell system sub-model, a one-dimensional transient Li-ion battery sub-model, two direct current/direct current (DC/DC) sub-models as well as a vehicle load sub-model are integrated. The coupled heat and mass transfer processes are considered inside fuel cell system and Li-ion battery. After rigorous model validation, the overall performances under several typical China standard vehicle road conditions are investigated, including power distribution between different power units, battery state of charge (SOC), battery charge/discharge rate, fuel cell operating duration, temperature, and other key parameters. During the 100 km h−1 acceleration processes under the preliminary rule-based energy management strategy (EMS), the accelerated speed is found to be the largest in the beginning, resulting in the fact that the discharge power for battery increases sharply. The shorter the acceleration time, the larger the maximum power demand for Li-ion battery. During the combined 1300 s high-speed operating conditions under the on-off switch EMS, the total operating duration of fuel cell system is calculated to be about 531 s, and the total output power is calculated to be 11,556 kJ. In comparison, the vehicle saves about 1044 kJ energy and increases the fuel cell operation duration by 333.7% under the power-following EMS. The present study provides important guidance in terms of dynamic simulation analysis and energy management strategy for FCEVs.

Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain

Electrification is the future trend for construction machinery due to its advantage of zero-carbon emission. The battery of electric construction machinery has an inevitable degradation phenomenon, which will increase the maintenance cost. In this paper a parallel electric-hydraulic hybrid powertrain is proposed to extend battery lifetime of an electric wheel loader by utilizing a hydraulic powertrain to provide and capture power during launch and braking. Different operation modes are analyzed and a rule-based energy management strategy is developed to determine the holding and switching conditions of each mode. Powertrains and battery aging models are introduced in detail and dynamic simulation is developed in Simulink. Results show that the parallel electric-hydraulic hybrid powertrain with the proposed strategy can reduce the battery full equivalent cycles, thereby achieving a 15.64% improvement in the battery lifetime compared with the pure electric powertrain. Moreover, influences of the strategy parameters on extending the battery lifetime are further discussed, which gives a parametric design guideline to the energy management strategy.

DEVELOPMENT OF COST-EFFECTIVE ENERGY MANAGEMENT STRATEGY FOR STAND-ALONE HYBRID SYSTEM

Deployment of Photovoltaic (PV) and Wind Turbine (WT) as a stand-alone system are the most affordable solution to the electrification problem in the rural areas. However, the main challenge that hinder the sustainability of PV and WT is the output fluctuation. Thus, the battery and the non- Renewable Energy (RE) sources are required to make sure the continuous supply to the load is met. Therefore, the purpose of this paper is to propose the rule-based Energy Management Strategy (EMS) for the stand-alone hybrid system in order to ensure the continuity of supply and the minimum utilization of non-RE can be achieved. The continuous monitoring is designed to avoid the degradation of battery performance while minimizing the non-RE cost. The efficient Stage of Charge (SoC) limit of battery is investigated and applied in this study to store the excess RES power effectively. In this paper, the diesel fired Microturbine (MT) is selected as a non-RE source to integrate into the stand-alone hybrid system that consists of PV, WT and battery. The result shows that the proposed rule-based EMS for the in the stand-alone hybrid system is able to reduce the cost of MT operation to a minimum. Restricting the SoC limits effects the use of MT.

An Electric-Hydrostatic Energy Storage System for Hydraulic Hybrid Wheel Loader

As a typical energy storage in hydraulic hybrid powertrain, the hydraulic accumulator has high power density but low energy density. There are some efforts in improving the energy density of hydraulic energy storage to achieve balanced performance. Therefore in this study an electric-hydrostatic energy storage system is proposed to replace hydraulic accumulator in a hydraulic hybrid wheel loader. Through active control of proposed energy storage, constant system pressure is possible to provide good vehicle drivability. A rule-based energy management strategy without estimating final drive power is proposed. The vehicle performance and fuel consumption with different constant system pressures are studied. Results show the vehicle fuel consumption is reduced by 5.9% with constant system pressure reduced from 33 to 27 MPa. The energy density and power density of proposed energy storage are calculated, showing a much higher energy density and slightly lower power density than gas-charged accumulator.

Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles

The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.

ADAPTIVE ENERGY MANAGEMENT STRATEGY FOR SUSTAINABLE OPERATION OF HYBRID ELECTRIC VEHICLE

Hybrid electric vehicles (HEVs) in the transportation sector is spearheading the industry towards embracing green technology to ensure the sustainability of the environment. HEVs offer significant reduction of exhaust emissions while retaining vehicle performance achieved via a revolutionary energy management strategy (EMS) which reforms the management of power flow from the dual energy sources of a HEV. However, researchers are faced with challenges to extract the maximum performance out of HEVs due to the contradicting nature between its main objectives, namely vehicle performance, fuel consumption and cost. In this study, an investigation focusing on the fuel-saving potential of a power-split type HEV using a fuzzy logic-based EMS is conducted. The purpose of this research is to explore methods to improve fuel efficiency of a HEV through a smart and adaptive EMS. The power flow in the proposed model is decided based on its current vehicle speed and the global discharge rate value derived from the real-time battery state-of-charge and remaining trip distance. From simulations over standard drive cycles, the proposed controller is able to outperform a rule-based EMS by an improvement of up to 65.4% in terms of fuel consumption which subsequently reduces the volume of pollutants released to the atmosphere.

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.