Optimal Energy Management Strategy Research Articles

Optimal dispatching of an energy system with integrated compressed air energy storage and demand response

The integrated energy system is considered to be an important way to avoid energy supply risks by virtue of advantages in meeting diversified energy demand and improving energy utilization efficiency. Energy storage enables microgrid operators to respond to variability or loss of generation sources. In view of the difficulty of battery to fully improve the energy utilization efficiency and solve the problems of clean energy power large-scale consumption, and the difficulty of single demand response to significantly improve the operational reliability and economy of the integrated energy system, a rolling optimization planning framework and model of an integrated energy system considering compressed air energy storage and sliding time window-based electric and heating integrated response demand is proposed, which can obtain both optimal resource configuration and energy management strategy. The planning problem is to coordinate the output of various dispatching resources on the basis of satisfying the constraints, so as to minimize the total cost. Taking into account the uncertainty of wind power and photovoltaic output and the unplanned outage risk of the unit, a stochastic optimization model is constructed based on the probability density function of the risk variable, and finally the cost probability distribution is obtained. The results verify the universality of the planning model. The comparison of five different energy system configuration schemes and multiple time window length schemes verifies the validity and superiority of the model. The simulation results show that the integrated energy system scheme proposed by this planning model has better economy than the scheme without compressed air energy storage, and the operating cost decreases by 11.9% and the total cost decreases by 4.5%. In addition, the results show that through the introduction of demand response, the operating economy and flexibility of the integrated energy system are improved, and as the sliding window length increases, the total cost gradually decreases.

Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle

To reduce large peak torque and improve the vehicle's dynamic performance during frequent stops and starts, a novel mechatronics-electro-hydraulic power coupling system (MEH-PCS) is proposed. It combines a conventional electric motor and a piston pump/motor into a single unit that converts electrical, mechanical, and hydraulic energy. After a theoretical analysis of the system power flow, the paper establishes a rule-based dynamic optimal energy management strategy to control energy distribution and the dynamic switching of working modes in real-time. Moreover, a prototype vehicle model (MEH-PCEV) based on the MEH-PCS is developed by analyzing the working modes of the system. Thereafter, the feasibility and superiority of MEH-PCEV are verified by comparing it with AMESim certified pure electric vehicles. The verification results indicate that the MEH-PCEV's battery consumption rate is reduced by 14.418% and 21.174%, respectively, in the new European driving cycle and the American city cycle. Furthermore, the time for the vehicle to reach the start-up speed threshold is enhanced, and the electric peak torque is substantially reduced. Ultimately, the paper proposes a fuzzy controller with vehicle speed and pressure of the accumulator as variables to optimize the logic threshold control strategy. The electric torque is substantially reduced while the overall efficiency of the motor operating point is enhanced. The consumption rate of electric energy has been improved somewhat. Under the drive strategy's control, the MEH-PCS is well suited for frequent stops and starts conditions. As a development, this research is expected to provide a reference for improving electric torque.

Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle

This paper deals with the comparison of architecture and adaptive energy management strategy (EMS) for hybrid powertrain system (HPS), including one or two electric motor, an engine and a battery, for a plug-in hybrid electric logistics vehicle (PHELV). The most attractive advantage deriving from HPSs is the possibility of reducing emission and improving fuel economic. For comparison purposes, the series, parallel, and series-parallel hybrid powertrain system are examined by dynamic programming (DP) algorithm using the same vehicular parameters. The approach of adaptive EMS is driving pattern recognition (DPR) to obtain optimum estimation of EMS parameters under different driving cycle. A back propagation (BP) neural network DPR optimized model by an improved genetic algorithm (IGA) has been proposed. Taking the costs of fuel consumption, the parameters of the fuzzy logic controller (FLC) and equivalent consumption minimization strategy (ECMS) are optimized. The comparative results show that the series PHELV fuel economy improvements are 7.60% and 6.53%, compared with parallel and series-parallel PHELV. The difference between the optimal fuzzy energy management strategy and the global optimization is 4.74%, and the ECMS is 4.66%.

The effect of the energy interchange dynamics on the zero-energy hydrogen economy of households with FC hybrid electric vehicles

The effect of the energy interchange dynamics on the Zero-Energy Hydrogen Economy (ZEH2E) of households with Fuel-cell Hybrid Electric Vehicles (FCHEV) is investigated in this work. An isolated grid constituted of a household, a battery bank, a PEMFC, a PEM electrolyzer, and PV panels were used as a rig for the study together with an optimal energy management strategy, which guaranteed ≥100% PV self-sufficiency index of the household load and hydrogen harvesting for electrical transportation. Global horizontal irradiance and temperature data, measured every 10 min for 365 days in three different locations of Mexico and Hardware-in-the-loop experiments were used in the analysis. Short and long-term effects of the energy storage system and their importance for achieving ZEH2E conditions were identified. Proposed solar, and battery utilization indexes revealed the short-term effect of the battery storage, imperceptible even in daily average values. Hydrogen production and vehicle autonomy values strongly depended on the season of the year, location, vehicle characteristics, driving cycle, and household load, therefore two indexes were proposed to compare the different scenarios: the hydrogen production-to - average household power load ratio (mH2-to-load) and the average vehicle power per traveled distance. These indexes allowed us to reveal low effective load-location pairs for hydrogen production and low effective driving cycle-vehicle pairs for hydrogen utilization. Our results revealed that the average data to analyze ZEH2E conditions is not enough and that detailed data is necessary.

Robust Combined Design and Control Optimization of Hybrid-Electric Vehicles Using MDSDO

Identifying a true, system-level optimal solution for hybrid-electric vehicle (HEV) powertrains requires methods such as combined design and control optimization (co-design) to account for the coupling between powertrain design and optimal energy management strategies. Despite the widespread application of co-design techniques to HEV powertrains in recent years, limited research has been done to account for the inherent uncertainties present in these systems. This is problematic as such uncertainties may not only impact HEV powertrain component sizing and control strategies, but also overall vehicle performance and cost-both of which are critically-important in the fiercely-competitive automotive industry. One way to address this issue is to apply a stochastic dynamic system optimization technique known as robust multidisciplinary dynamic system design optimization (R-MDSDO) to the HEV powertrain co-design problem. This method ensures that the HEV powertrain co-design solution, including its system performance and cost, explicitly accounts for random variations within the system and its problem formulation using concepts from robust design optimization. Therefore, in this study, we formulate and solve a robust co-design problem for a power-split HEV powertrain using R-MDSDO to identify the optimal component designs, state trajectories, and control trajectories such that the vehicle powertrain cost is minimized. The robust solution is then compared to the solution from the associated deterministic problem, with the results indicating that accounting for system uncertainty has a significant impact on HEV powertrain co-design.

An integrated optimal operating strategy for a grid-connected AC microgrid under load and renewable generation uncertainty considering demand response

The challenge of a modern energy system is to be reliable, robust, easily accessible, economically viable, and environment-friendly. This paper addresses the above issue by proposing a novel integrated optimal energy management scheme for a grid-connected AC microgrid. The objectives of the energy management scheme are to minimize the cost of operation and reduce the emission. A group of measures like incentive-based demand response (DR) program economic-emission load dispatch, optimal battery scheduling, and network reconfiguration is simultaneously implemented in the energy management scheme to enhance the steady-state performance of the microgrid. Uncertainties are modeled using a stochastic approach in this work. The bi-objective optimization is carried out in the fuzzy domain employing the “fuzzy max–min” satisfaction criterion. Further, the incentive value of the DR program is optimized, and a sensitivity analysis is carried out to investigate the impact of the incentive value on the operating cost. Simulation studies on a thirty-three node test system demonstrate that the operating cost can be reduced by 18.90%, and emission by 45.44% for the given test system. If both cost and emission are to be reduced simultaneously, then the operating cost and emission are reduced by 9.08% and 34.85%, respectively.

Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.

Multi-objective optimization of hybrid PEMFC/Li-ion battery propulsion systems for small and medium size ferries

Hybrid Polymer Electrolyte Membrane Fuel Cells/Lithium-ion battery powertrains are a promising solution for zero-local-emissions marine propulsion. The present study aims to optimize the design and operation of such hybrid powertrain for small-size passenger ferries, taking into account the performance degradation of both fuel cells and batteries. A Mixed-Integer Linear-Programming approach and a hierarchical method are adopted to concurrently minimize the fuel cells degradation, the capital expenditure and the operating expenditure, while constraints are included in the model to limit the battery degradation. The results show that the proposed multi-objective optimization can lead to a reduction of fuel cells degradation by up to 65% compared to a cost-minimization only. However, this can imply an increase in the battery capacity by up to 136%. The proposed method has general validity, and it is a useful tool for both preliminary design and choice of the optimal energy management strategy for ships energy systems.

Optimal Energy Management Strategy for an Islanded Microgrid with Hybrid Energy Storage

Due to the randomness and volatility of light intensity and wind speed, renewable generation and load management are facing new challenges. This paper proposes a novel energy management strategy to extend the life cycle of the hybrid energy storage system (HESS) based on the state of charge (SOC) and reduce the total operating cost of the islanded microgrid (MG). In this paper, a demand response (DR) optimization model of independent MG is introduced considering the total cost of MG operation, carbon emission and new energy consumption penalty. Meanwhile, by analyzing the SOC of the HESS, starting and stopping criteria (SSC) of diesel generator (DG), residential load classification, an optimal energy management strategy for island grid operation of HESS based on DR is proposed. Then, the particle swarm optimization (PSO) algorithm with improved acceleration factor is applied to solve the optimization scheme. The simulation results show that a novel energy management strategy has improved the economical and reliable performance of the independent MG via setting the charging and discharging criteria of HESS and SSC of DG. Moreover, flexible scheduling of part controllable loads can be used to improve the renewable energy consumption rate and reduce the loss of load rate.

Adaptive real-time optimal control for energy management strategy of extended range electric vehicle

The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.

Two-stage fuzzy-logic-based for optimal energy management strategy for SOFC/PV/TEG hybrid polygeneration system with electric charging and hydrogen fueling stations

Integration between supplies for stationary power and vehicles is potentially useful for increasing the efficiency and the reliability of energy generation systems. Solid oxide fuel cell is one matured technology, which is suitable for a polygeneration system and provides an integration of supply for stationary power and vehicles. However, a combination of solid oxide fuel cell with photovoltaic thermal and thermoelectric generation increases the complexity of a polygeneration system. The system needs a management strategy for dispatching the energies produced. Therefore, in this work, a fuzzy energy management strategy was applied for this polygeneration system by considering two different configurations: an off-grid system with electric vehicle supply and an on-grid system with hydrogen vehicle supply. A two-stage fuzzy energy management strategy considering optimization and management of multi-parameters of the polygeneration components was considered. The evaluation of the optimum fuzzy was analyzed based on energy, economic, and environmental criteria. From the results obtained, the optimal strategy increased the reliability, energy, and system cost savings by 22.05%, 22.4%, and 32.58%, respectively. Moreover, the optimum management reduced the power loss of the polygeneration system by about 48.82%, which was achieved by the configuration with electric vehicles supply and off-grid connection.

Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China

Energy management strategy (EMS) plays an important role in improving energy economy of plug-in hybrid electric vehicles (PHEVs). The frequently fluctuations of electricity and oil prices have significant impacts on EMS optimization. In this study, the influence law of the varying prices on the optimal control policy is revealed. The statistical analysis of electricity and fuel prices for typical cities in China is proposed as a case study, and then a varying prices-conscious EMS for PHEVs is developed. The presented control strategy is optimized based on the simulated annealing particle swarm optimization (SA-PSO) algorithm. Taking into account the above efforts, the price influence surfaces (PIS) are proposed and a PIS-based adaptive EMS is finally established. The presented control strategy can achieve better energy economy under real-world driving condition than traditional EMS that doesn't concern the impacts of price variations. The results indicate that the energy cost of PHEV can be further reduced by up to 9.88% under certain driving situations.

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

The object of this research came from the continuing need to analyze and identify effective methods of reducing pollution and optimizing the functions of electric vehicles. So, a classic analysis of the management strategies proposed in the literature was performed to identify the main research directions that can be used to design an optimal strategy. The performed analysis was validated using different counting and representation methods in VOSviewer and Excel. VOSviewer is a program that visually presents the best research approaches for understanding and developing applications. Thus, analyzing the results obtained, it is possible to identify where the research of energy management strategies for electric vehicles is focused. For example, the most important factors for increasing the performance of a strategy are identified as related to optimization, efficiency, degradation, fuel economy, and systemic development. We identified 127 energy management strategies in the studied articles, which were analyzed and classified by multiple criteria. For example, 21.59% of the strategies have as main goal to increase the vehicles efficiency, followed by the 10% on fuel economy, 7.8% hydrogen consumption strategy, 6.29% work for performance optimization and emission reduction, and so on. So, using this tool, researchers can explore their field of research to identify challenging topics to address in the coming years.

Energy management of heavy-duty fuel cell vehicles in real-world driving scenarios: Robust design of strategies to maximize the hydrogen economy and system lifetime

Energy management is a critical issue for the advancement of fuel cell vehicles because it significantly influences their hydrogen economy and lifetime. This paper offers a comprehensive investigation of the energy management of heavy-duty fuel cell vehicles for road freight transportation. An important and unique contribution of this study is the development of an extensive and realistic representation of the vehicle operation, which includes 1750 hours of real-world driving data and variable truck loading conditions. This framework is used to analyze the potential benefits and drawbacks of heuristic, optimal, and predictive energy management strategies to maximize the hydrogen economy and system lifetime of fuel cell vehicles for road freight transportation. In particular, the statistical evaluation of the effectiveness and robustness of the simulation results proves that it is necessary to consider numerous and realistic driving scenarios to validate energy management strategies and obtain a robust design. This paper shows that the hydrogen economy can be maximized as an individual target using the available driving information, achieving a negligible deviation from the theoretical limit. Furthermore, this study establishes that heuristic and optimal strategies can significantly reduce fuel cell transients to improve the system lifetime while retaining high hydrogen economies. Finally, this investigation reveals the potential benefits of predictive energy management strategies for the multi-objective optimization of the hydrogen economy and system lifetime.

Multi-objective benchmark for energy management of dual-source electric vehicles: An optimal control approach

This paper proposes a novel method to develop a multi-objective optimal energy management strategy (EMS) for hybrid battery/supercapacitor (SC) electric vehicles. The method is based on an alternative approach of using Pontryagin’s minimum principle (alt-PMP), which is superior to dynamic programming in terms of computational effort while obtaining better performance. The novel multi-objective EMS allocates the battery and SC powers to minimize the battery degradation and the SC subsystem losses. The proposed approach deduces transparent analytical forms of the optimal solutions, which have been rarely discussed in the literature. The results form a Pareto optimal (nondominated) front displaying the trade-offs associated with the objectives, which can serve as a benchmark to evaluate other real-time control strategies. Numerical investigations are carried out to validate the advantages of the proposed method. The benchmark role of the obtained nondominated front is illustrated by comparing it to the well-known filter-based strategy. Moreover, this study shows the conversion of the Pareto front to an “ultimate utopia point” corresponding to the ideal case of the SC subsystem efficiency. The proposed approach can be extended to dimensioning problems, to develop real-time EMS, and to more complex multi-source systems in future works.

An online optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on hybrid MPC Algorithm*

This paper proposed a global on-line optimisation energy management strategy based on the hybrid model predictive control (HMPC) algorithm. First, the non-linear models of a dual-mode power-split hybrid electric vehicle is built. Then, the dual-mode power-split hybrid electric vehicle is considered as a hybrid system which includes continuous and discrete states, and a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory. Thirdly, an HMPC energy management strategy is designed based on the MLD predictive model. The HMPC-based energy management strategy is compared with dynamic programming based and rule-based energy management strategies. The simulation results indicate that the HMPC-based energy management strategy is helpful for real-time control and improving fuel economy. good control performance. Finally, the control performance of the proposed HMPC-based energy management strategy is compared with that of the DP-based energy management strategy and rule- based energy management strategy. The simulation results indicated that the control performance of HMPC can achieve 80.6 % of that of DP.

Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation

In order to realize optimal power distribution between proton exchange membrane fuel cell and supercapacitor in hybrid electric tram, an online extremum seeking-based optimized energy management strategy is proposed in this work. Considering that the fuel cell is a complex nonlinear system, its performance will vary as the external parameters change, so it is necessary to consider the performance state of stack. An online extremum seeking algorithm is investigated in this work to seek the maximum power and maximum efficiency points by searching the variation in fuel cell performance. Besides, this work also updates its “safe operating zone” based on the results of the online extremum seeking. This process is achieved by the adaptive recursive least square algorithm. Furthermore, in order to limit the power dynamic of fuel cell, the degradation of the stack is considered in this study. To guarantee the stable and continued operation of the electric tram, the state of charge fluctuation range of supercapacitor is also limited. The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform. The proposed method is also compared with state machine control and equivalent consumption minimization strategy to further demonstrate that it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics.

Optimal energy management strategy for plug-in hybrid electric vehicles based on a combined clustering analysis

Based on the optimization results of dynamic programming, this paper proposes an optimal rule design method for a series-parallel plug-in hybrid electric vehicle mode division strategy combining K-means clustering and improved artificial bee colony. The improved artificial bee colony algorithm uses the average of the sum of the squares of the distances method to initialize the colony to overcome the randomness of initialization and best utilize the useful information, constructs a fitness function adapted to the clustering analysis, and introduces an acceleration factor into the search equation to dynamically adjusted search scope. Combining the improved artificial bee colony algorithm with the K-means clustering algorithm, the clustering performance is improved and the optimal rule-based mode division strategy is obtained. The simulation results show that the new strategy reduces fuel consumption by 4.78% and electricity consumption by 1.81% comparing with the original strategy.

Real-Time Optimization of Energy Management Strategy for Fuel Cell Vehicles Using Inflated 3D Inception Long Short-Term Memory Network-Based Speed Prediction

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the historical speed and image information to improve the accuracy of speed prediction. Meanwhile, the energy economy and powertrain system durability are the objectives of real-time optimization. For optimizing energy economy and powertrain system durability of FCVs, the real-time optimization of EMS using the Inflated 3D Inception LSTM network-based speed prediction is proposed. To do this, the mathematical models including energy economy and powertrain system durability of FCVs are developed at the beginning. Then, based on the predicted speed sequences, a real-time optimization method with sequential quadratic programming (SQP) algorithm is proposed to minimize the energy consumption and take into consideration powertrain system degradation in the prediction horizon. Simulation results show that the proposed EMS can significantly reduce the total cost of energy consumption and powertrain system degradation.

Optimal Energy Management for Connected Hybrid Electric Vehicles Based on Bayesian Optimization

Abstract Due to the limitations in computing power and information, globally optimal control for hybrid electric vehicle (HEV) is difficult to achieve. The development of connectivity technology brings new opportunities for vehicle control to achieve quasi-global optimization by using future information. In this paper, an optimal energy management strategy for a parallel HEV is proposed and applied in the cloud computing side, based on Bayesian optimization (BO) algorithm. By seeking for the minimal accumulated equivalent fuel consumption (FC), the parameters energy management is optimized iteratively. Simulation results show that the proposed method can can converge within 5 steps and reduce the equivalent FC by more than 10% over manually tuned controller. In addition, the impacts of prediction horizon and accuracy on the FC optimization is quantitively investigated.