Energy Management Control Strategy Research Articles

Novel Current Source Converter for Integrating Multiple Energy Storage Systems

The increasing penetration of renewable energy sources (RESs) in transmission and distribution systems presents several challenges for grid operators. In particular, the unpredictable behavior of RESs can disrupt the balance between energy production and load demand, potentially affecting the stability of the entire system. Grid-connected energy storage systems (ESSs) offer a possible solution to manage the uncertainty associated with RESs. In fact, ESSs exchange power with the grid through the adoption of suitable energy management strategies, which are typically implemented by power electronics-based grid interfaces. Unlike other current source converter (CSC) solutions described in the literature, which only interface with a single energy storage device, this paper introduces a novel topology for a three-phase delta-type current source converter (D-CSC), which is capable of integrating three independent ESSs using the same number of semiconductors as traditional CSC solutions. Thus, it considerably enhances the flexibility of a pcs without increasing the number of converter components. In addition, an innovative energy management control strategy is also introduced. This strategy enables the D-CSC to compensate for energy imbalances arising between the three ESSs, which might be caused by several factors, such as different aging characteristics, converter component tolerances, operating conditions, and temperature drifts. Hence, the D-CSC-based interface is capable of proper grid operation even if the three ESSs have different characteristics, thus opening the possibility of employing this converter to integrate both first and second-life devices. First, the topology of the proposed D-CSC is introduced, followed by a detailed mathematical description of its control strategy. The proper grid operation of the D-CSC was tested under different scenarios, considering the grid integration of three independent superconducting magnetic energy storage systems in a marine vessel. The proposed D-CSC is compared to traditional CSC solutions, highlighting the superior performances of the novel converter topology in terms of efficiency, total harmonic distortion of the output currents, and overall cost reduction for the pcs.

Simulation and experimental research on energy management control strategy for fuel cell heavy-duty truck

Energy Management Control Strategies (EMCS) play a significant role in improving the economy of Fuel Cell Heavy-duty Truck (FCHT). In this paper, a Single Fuzzy (S-Fuzzy) control strategy is introduced for the FCHT by considering the output characteristics of the fuel cell as well as the charging and discharging power characteristics of the power battery at the current state of charge (SOC). On this basis, the real-time difference between the current SOC and the target SOC of the power battery during the actual operation of the vehicle was further considered, the real-time correction coefficient of the fuel cell output power was introduced, and a Compound Fuzzy (C-Fuzzy) control strategy suitable for FCHT was developed. Meanwhile, the established vehicle model was verified dynamically and economically to ensure its accuracy, with the maximum speed error lower than 2%, the SOC error within 4.9%, and the relative dynamic simulation error of 1.3%. Utilizing co-simulation platforms Cruise and Matlab, the research demonstrates that the C-Fuzzy control strategy significantly outperforms the S-Fuzzy strategy, reducing Equivalent Hydrogen Consumption (EHC) by 1.63% under C-WTVC conditions and by 3.12% under CHTC-TT conditions. To counteract efficiency loss from fuel cell load fluctuations, a real-time EMCS based on C-Fuzzy control and multi-level power output was developed. Validated over a 122-km route, the real-time EMCS reduced the equivalent hydrogen consumption per 100 km of the FCHT to 10.14 kg, maintained the average fuel cell efficiency at 51.02%, and ensured stable SOC of the power battery within the predetermined target range.

Energy management control strategies addressing the rSOC degradation phenomena in a polygeneration microgrid

Economically competitive long-term energy provision and performance stability are becoming increasingly strategic for the upcoming energy market. Despite hydrogen-based technologies representing a viable way for the aforementioned targets, such systems are affected by performance reduction over time. This paper is aimed at presenting energy management mitigation policies for a reversible solid oxide-based (rSOC) polygeneration microgrid. Particularly, interest has been paid towards the rSOC degradation phenomena and its mitigation by applying strategies allowing meeting target performance and satisfying economics. In a multilevel framework, the rSOC degradation mitigation has been pursued proposing two central-level strategies, namely Grid support (GS) and Limited Grid support (LGS), which act between the power split targets coming from the supervisory level and working set points actuated by the low-level controllers. The former aims at compensating for the energy gap caused by degradation relying on external contributions: power grid network and boilers. Alike, the goal of the latter is to keep the rSOC turned on as much as possible, therefore, complying with the energy management goals given by supervisory level. The state of charge of the hydrogen storage tank has been chosen as a metric to best show the results obtained implementing the mitigation strategies, as it directly displays the rSOC active management and boundaries applied on the storage systems. Considering a 3 years scenario, the LGS strategy in comparison to the GS one has resulted to be more effective concerning: costs, microgrid self-sufficiency, reliability and overall robustness. Quantitatively, for the LGS strategy, considering degradation degrees in the range 0.25–1 %/kh, relative additional integration costs with respect to the case unaffected by degradation are seen to be in the range 30–105 %.

双电机驱动电动拖拉机能量管理策略的研究

At present, electric tractors experience significant battery energy loss during operation, resulting in a short continuous running time. Therefore, in order to reduce the power consumption of the tractor drive system, minimize battery energy loss, and extend the operating time under various conditions, this paper presents a method for driving an electric tractor based on dual-motor coupling. Based on the characteristics of the transmission structure, an online torque distribution strategy for dual-motor coupling-driven electric tractors using a fuzzy control approach is proposed. First, an enhanced genetic algorithm is utilized to optimize the fuzzy rule table. Simultaneously, it is compared with the offline optimization strategy of dynamic programming. Subsequently, a method that integrates test data models and theoretical models is employed to establish an efficiency model of key components of the electric tractor drive system and a longitudinal dynamics model of the entire machine. The performance of the entire vehicle was simulated and analyzed under plowing conditions. Finally, on the experimental bench, conduct steady-state load tests and dynamic performance tests on the dual-motor coupled drive system. The results show that the State of Charge (SOC) change trends of the fuzzy control strategy based on the improved genetic algorithm and the dynamic programming strategy are similar. The SOC change values are close, which enhances the adaptability of the electric tractor in various operating conditions. Compared with the fuzzy control strategy, the improved strategy reduced average power consumption by 8.8%, demonstrating that the fuzzy control energy management strategy based on the enhanced genetic algorithm is both economical and superior. The bench experiment demonstrated that the dual-motor drive system can adapt to load changes to achieve power distribution between the two motors, meeting the required workload while reducing power consumption.

Research on Mode Switching and Energy Management of Hybrid Electric Vehicle

Aiming at the optimization of mode switching control and energy management strategy for parallel hybrid electric vehicles, a mode switching control method and A-ECMS steady-state energy management strategy is proposed. Firstly, in order to effectively suppress the impact caused by mode switching, and a fuzzy controller is designed for clutch engagement displacement. Then, on the basis of ensuring the smoothness of mode switching, A-ECMS control strategy based on SOC feedback is established. Finally, Isight and Cruise are used to build a co-simulation platform, and multi-island genetic algorithm is used to study the influence of initial value selection of equivalent factor on the controller. The results show that the proposed mode switching control and energy management control strategy, combined with A-ECMS strategy, can effectively improve the vehicle fuel economy on the basis of ensuring the mode switching smoothness.

Energy management control strategies for energy storage systems of hybrid electric vehicle: A review

AbstractContinuous efforts to preserve the environment and to reduce gaseous emissions due to the massive growth of urban economic development and heightened concerns over crude oil depletion have accelerated researchers to find long‐term solutions, particularly in the transportation sector with the focus on powertrain electrification. This article delivers a comprehensive overview of electric vehicle architectures, energy storage systems, and motor traction power. Subsequently, it emphasizes different charge equalization methodologies of the energy storage system. This work's contribution can be identified in two points: first, providing an overview of different energy management methods to researchers and scholars. Second, to highlight the state‐of‐the‐art leanings in major components and to highlight promising approaches to hybrid electric vehicle future development.

A real‐time energy management control strategy of hybrid energy system for a pure electric loader

SummaryTo enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system.

Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%.

NTNU-SINTEF SolarNet: A solar irradiation monitoring network at high latitudes

This study presents a monitoring network for solar irradiation at high latitudes, called NTNU-SINTEF SolarNet. The network collects, with a time resolution ranging from seconds to hours, solar irradiance data, e.g. global horizontal irradiation, diffuse horizontal irradiation, direct normal irradiation, global tilted irradiation, solar energy generation, which are required in solar irradiation modelling in built environments. The network will be used for specific applications, such as (i) anomalies detection, (ii) influences of ground albedo, and (iii) ageing/degradation of solar modules, that are described in this paper. Some characteristics that make the NTNU-SINTEF SolarNet relevant for solar energy research at high latitudes are identified: short distances among the sensors, the ease of data accessibility, the use of the same sensor typologies, and different solar module technologies. The research holds the potential to boost the solar energy digitalization, impacting on several aspects such as predictive and adaptive control strategies for energy management, design of renewable energy system, multi-scale optimization and efficient exploitation of solar energy.

IoT-Based Smart Energy Management in Hybrid Electric Vehicle Using Driving Pattern

Driving patterns involve multiple starts, stops, and goes, requiring both average and momentary demands on power. Batteries function better for the average power demand. An ultracapacitor is used as an auxiliary system with a longer life cycle and higher power density for the vehicle’s momentary power needs. The challenging part of hybridization is smart energy management among the sources. An energy management control strategy decides the energy share among the ESSs. Most of the researchers used standard driving patterns for analyzing vehicle performance. However, a real-time driving pattern is needed to investigate the vehicle’s performance. An IoT-based smart decision-making module can better regulate the energy across the multisource based on sensor unit data about vehicle driving speed. The experimental findings illustrate the effect of the suggested smart energy management control on the hybridization of ESSs to split the power among ESSs to meet the demand for power, maintain the charges in ESSs, and extend the lifespan of the battery. Based on the calculations, the LM approach has a minimal MAD of 0.046781 for training data and 0.043859 for testing data. It has been said that the LM algorithm has the lowest MSE, with values of 0.006397 and 0.005781 for training and testing, respectively. Even in RMSE, the training and testing values of 0.079355 and 0.075893 for the LM Algorithm are the lowest. When the LM algorithm is being trained, its MAPE score is 0.022489, but when it is being tested, it is 0.004846.

Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid

Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage.

Energy Management Strategy of Hybrid Ships Using Nonlinear Model Predictive Control via a Chaotic Grey Wolf Optimization Algorithm

Reducing energy consumption and carbon emissions from ships is a major concern. The development of hybrid technologies offers a new direction for the rational distribution of energy. Therefore, this paper establishes a torque model for internal combustion engines and motors based on first principles and fitting the data collected from the test platform; in turn, it develops a model for fuel consumption and carbon emissions. Furthermore, the effect of irregular waves using an extended Kalman filter is estimated as well as feedback to the controller as a disturbance variable. Then, a parallel hybrid ship energy management strategy based on a new real-time nonlinear model of predictive control is designed to achieve energy conservation and emission decrease. A hybrid algorithm of chaotic optimization combined with grey wolf optimization is utilized to solve the nonlinear optimization problem in the nonlinear model predictive control strategy and a local refined search is performed using sequential quadratic programming. Through the comparison of fuel consumption, carbon emissions, real-time performance, and the engine load path, the superiority of the nonlinear model predictive control energy management strategy based on the chaotic grey wolf optimization algorithm is verified.

A Forward-Type Single-Stage Multi-Input Inverter With Series-Time-Overlapping Power Supply

A Forward type single-stage multi-input inverter with series-time-overlapping power supply is proposed. The circuit structure and topological family, energy management control strategy (EMCS), high frequency (HF) switching process analysis, the relationship between multiple duty cycles and power decoupling, common mode interference suppression between multiple inputs are fully studied. The circuit structure is composed of multi-input selection switches in series and an isolated bidirectional Forward type single input single output inverter. This topological family includes 6 circuits such as full-bridge type. A master-slave energy management SPWM compound control strategy with independent control of input source power based on input voltage feedforward and output voltage feedback is proposed. It realizes the energy management of the input source by controlling the instantaneous value of the output voltage and the output power ratio of the multiple inputs and achieves the smooth switching between the two power supply modes. The designed and developed 3kVA inverter prototype has excellent performances such as single-stage power conversion, HF electrical isolation, high conversion efficiency, small size and weight, wide duty cycle adjustment range, high output waveform quality, and strong load adaptability.

Assessment of control strategies for energy management in vapor compression cycles

Assessment of control strategies for energy management in vapor compression cycles

Optimal control study of home energy management with cooperative dispatch of electric vehicles and energy storage devices

In order to solve the problem that the charging and discharging characteristics of electric vehicles (EVs) are not fully utilized by household users with distributed photovoltaic (DPV), which leads to low photovoltaic utilization efficiency and poor household electricity cost and economy, this paper proposes an optimal control strategy for household energy management based on cooperative scheduling of EVs and energy storage devices. In this paper, we firstly classify the household electric devices based on the load characteristics, and then formulate the control strategies of electric vehicles and energy storage devices based on the relationship between the total load power and the PV output, and propose the objectives of household side, environment and grid side based on this, and solve the multi-objective problem by using the improved gray wolf optimization (GWO) algorithm based on Tent mapping chaos optimization. The​ effectiveness of the model and algorithm is verified by simulating and analyzing a household customer under real-time electricity price. The results show that the system operating conditions can be improved to achieve the expected goals of minimizing household electricity cost and maximizing PV consumption under the conditions of energy management optimal control.

Optimized Energy Management Control of a Hybrid Electric Locomotive

Hybrid electric propulsion, using batteries for energy storage, is making significant inroads into railway transportation because of its potential for notable fuel savings and the related reductions in greenhouse gases emissions of hybrid railway traction over non-electrified railway lines. Due to the inherent complexity of hybridized powertrains, combining different power conversions and energy storage capabilities, the corresponding operation of their energy management needs to be precisely optimized in order to achieve the minimum possible fuel consumption. Having this in mind, this paper proposes a real-time energy management control strategy for a diesel–electric hybrid locomotive based on the optimization results obtained by means of a dynamic programming optimization algorithm aimed at fuel consumption minimization while honoring the battery state-of-charge constraints and powertrain physical constraints. The final optimization result, expressed in terms of the optimal battery state-of-charge reference (target), is used as an additional input into the state-of-charge controller within the real-time energy management system. The subsequent simulation analysis shows clear fuel economy improvement with 22.9% of fuel savings obtained for the locomotive featuring a hybrid powertrain equipped with batteries over the conventional one.

Hierarchical energy management control based on different communication topologies for hybrid electric vehicle platoon

To address the problem of longitudinal control and energy management of nonlinear hybrid electric vehicle platoon with different unidirectional communication topologies, a novel hierarchical energy management control strategy based on different communication topologies for platoon is proposed. Firstly, the upper platoon controller is based on the established nonlinear platoon longitudinal dynamics model. The vehicle-to-vehicle communication is applied to obtain the preceding vehicle information under different unidirectional communication topologies. The distributed model predictive control is used to realize the platoon longitudinal control and obtain the desired demand torque. Secondly, the lower energy management controller is based on desired demand torque and combines with current battery state of charge, the equivalent factor-based Deep Q-Learning is adopted to reasonably allocate the power component torque. Then, the platoon control effects of different unidirectional communication topologies are compared and verified under the given driving cycle. The preceding and leading following topology is chosen that satisfies real-time and optimality. The topology is also used to verify the effectiveness and working condition adaptability for this strategy. Finally, the simulation results under Chongqing actual working condition show that the strategy can well meet the requirements of platoon following, safety and comfort. The average fuel economy is improved by 2.61% and 7.58% compared with traditional deep Q-learning and Q-Learning, respectively, which can achieve global optimal similar to dynamic programming.

Multi-agent-based control strategy for centerless energy management in microgrid clusters

Interconnecting microgrids with similar geographical environment and related characteristics electrically and communicatively, this constitutes a microgrid cluster, which is a higher-level distributed power structure and an effective way to improve the utilization rate of distributed energy and local absorption level. However, the stable operation of microgrid clusters is determined by the cluster structure and its control strategy, which is also the focus and difficulty of current research. To address this challenge, this paper proposes a ring-based multi-agent microgrid cluster energy management strategy, which realizes the centerless coordinated autonomous operation of microgrid clusters with high stability. In addition, based on the multi-agent control strategy, this paper designs a variety of control strategies that can be switched autonomously for different control objectives of the microgrid cluster to realize smooth grid connection and off-grid of the microgrid cluster. Finally, based on Matlab platform simulation, the experimental results show that the control structure and energy management strategy proposed in this paper can realize the centerless coordination and autonomy of the microgrid cluster on the basis of stable operation, minimize the energy interaction between the cluster and the distribution network, and greatly improve the utilization rate and local absorption level of distributed energy.

A Review on BLDC Motor Application in Electric Vehicle (EV) using Battery, Supercapacitor and Hybrid Energy Storage System: Efficiency and Future Prospects

The automotive industry has rapidly introduced pollution-free vehicles such as Electric Vehicle (EV). The development and improvement of the EV to replace the conventional vehicle become crucial to obtain the customer satisfaction and high technology achievements. The main systems in EV that are improvise to be switch from the conventional engine with a fuel source to an electric type drive system, include the electric motor and the energy/power storage called battery. There are several types of electric motors that suitable for EV and the best solution was Brushless Direct Current (BLDC) motor in terms of power, speed, torque and low maintenance. Meanwhile, the fuel source replacement is the electrical energy/power storage such as batteries. The aims were to study the best Energy Storage System (ESS) in EV which leads to introducing Battery Energy Storage System (BESS), but the drawbacks of the system give the opportunity improvement, in replacement using Supercapacitor Energy storage System (SESS) and Hybrid Energy Storage System (HESS). SESS is a reliable source, but the stand-alone Supercapacitor also has a minimum operation time. With several adjustments in the energy management control strategy, the discharge rate of energy from a supercapacitor can be minimized to prolong its operation.

Machine learning-based energy harvesting for wearable exoskeleton robots

Wearable exoskeleton robot can provide additional movement assistance for the wearer in many fields and enhance their movement ability. Reduce the degree of long-term exercise fatigue and avoid injury during exercise. However, due to the limitation of battery technology and battery capacity, it is difficult for exoskeleton robots to work continuously for a long time. Therefore, the energy management and optimization of wearable exoskeleton robot become an important way to break through the bottleneck of its future development. In order to optimize the energy management system of exoskeleton robot, an energy management control strategy based on machine learning and a SOC11SOC: State of Charge. value prediction model were proposed. Based on the structure of composite power supply, this paper selects Q-learning in machine learning to build the battery energy management strategy system, and introduces the time difference reinforcement learning algorithm. The experimental results show that the energy management optimization system can effectively shorten the response time of the robot system, reduce the prediction error value of SOC value, and provide more accurate electricity information for the management and control system. At the same time, the system in this paper shows good energy management control under different working conditions, and can realize dynamic energy management and improve energy utilization according to the actual power demand and power supply operation of the exoskeleton robot.