Optimal Energy Management Strategy Research Articles

Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV.

Comparative analysis of photovoltaic/rechargeable batteries sizing-dependent configurations for optimal energy management strategies in microgrids

Comparative analysis of photovoltaic/rechargeable batteries sizing-dependent configurations for optimal energy management strategies in microgrids

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid

This study presents an optimal adaptive intelligent energy management strategy designed for a DC microgrid based on a fuel cell system (FC), photovoltaic array (PV), and battery bank. The main objective is to enhance the system's power saving. For this reason, an optimal adaptive FC-based energy management strategy (EMS) is designed. The optimizer updates the fuzzy membership functions according to the provided energy by the fuel cell and the main grid. The used optimizer is based on meta-heuristic optimization strategies such as particle swarm optimization (PSO); salp swarm algorithm (SSA); Archimedes optimization algorithm (AOA); marine predator algorithm (MPA); artificial ecosystem-based optimization (AEO); equilibrium optimizer (EO); political optimizer (PO) and tunicate swarm algorithm (TSA). Each one of the studied optimizers provides its proper performance depending on its optimization mechanism. To check the performance of both conventional and optimal adaptive FC based EMS, a comparative study was carried out utilizing Matlab Simulink. The obtained results prove the political optimizer's superiority (PO) over the other optimizers, where the obtained power saving using PO achieved 7.7% with a tracking efficiency of 99.571%. Statistical analysis using the Analysis of variance (ANOVA) test will be performed to approve the performance of the proposed energy management strategy.

Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations

Multi-energy multi-microgrid (MMG) networks are considered as a promising form of energy systems that can integrate various energy resources and improve energy utilization efficiency. Carbon emission limitation, regarded as a significant factor in energy management, has received increasing attention in recent years. By taking into account both economic and environmental factors, MMG networks can offer a great opportunity to reduce operation costs and carbon emissions. In this paper, we propose an optimal energy management strategy for minimizing the operation cost of an MMG network, considering operation constraints and carbon emissions. The energy management strategy is designed to consist of a day-ahead phase and an intra-day phase to overcome the uncertainty effects of renewable energy sources (RESs) generation and load demands. We first present a day-ahead scheduling strategy for the MMG network, in which microgrids operate in a distributed manner and share electricity while preserving their privacy. We then present an intra-day scheduling strategy for each microgrid, in which the operation costs and penalty costs caused by the adjustment of energy devices and energy procurement are minimized sequentially using a rolling horizon method. Simulation results demonstrate the effectiveness of the proposed energy management strategy in lowering operation costs and carbon emissions.

Multi-Objective Optimization of Distributed Generation Despite Energy Storage Systems for Optimal Management

The influence of distributed generation resources and energy storage units in distribution networks is increasing. Therefore, it is essential to examine their effect on network reliability. In this study, in order to present the optimal energy management strategy in the smart distribution network, the problem of multi-objective optimization of rearrangement of distribution feeders with the presence of distributed generation sources and energy storage units has been solved in a dynamic framework. Objective functions in this study include minimization of energy losses, redistributed energy and operating costs. In order to simultaneously optimize the reliability index and other objective functions, another optimal design for the production of distributed generation units, charging and discharging of energy storage systems as well as the topology of distribution network feeders is presented. In order to solve the multi-objective optimization problem in this study, a combination of particle social algorithms and frog mutation has been used. In order to show the ability of the method in providing an optimal energy management plan in a 95-bus network has been tested.

Energy management strategy of hybrid solar-hydro system with various probabilities of power supply loss

An optimal energy management strategy is difficult for an efficient operation of hybrid renewable energy systems due to the instability of energy resources. The present study analyzes a hybrid PV-battery integrated with the pump-hydro-storage system (PHSS) utilizing the rainfall potential at various probabilities of power supply loss (PPSL). Two configurations have been studied. Configuration-1 is a PV-battery system, and Configuration-2 is a hybrid PV-battery-PHSS. PPSL are varied between 0.00 and 0.10 in steps of 0.02. The particle swarm optimization technique is used for size optimization based on the Levelized Cost of energy minimization. Configuration-1 and Configuration-2 at PPSL of 0.00 are highly oversized. Configuration-2, compared to Configuration-1, has less excess power due to the integration of PHSS. A decrease in the power supply reliability by just 2% has caused the decrease in the Levelized Cost of energy by about 31.3% for Configuration-1 and 28.8% for Configuration-2. The power system efficiency for Configuration-1 at PPSL of 0.00, 0.02, 0.04, 0.06, 0.08, and 0.10 is 34%, 71%, 77%, 79%, 80%, and 80%, respectively. However, for the Configuration-2, the power system efficiency compared to Configuration-1 at PPSL of 0.02, 0.04, 0.06, 0.08, and 0.10 has increased by about 11.8%, 2.8%, 0.0%, 1.3%, 1.3%, and 1.3%, respectively.

Model-based fully coupled propulsion-aerodynamics optimization for hybrid electric aircraft energy management strategy

Hybrid electric aircraft concepts are high in future aviation agenda to enabling reduced fuel consumption and emissions. However, the additional weight of the introduced battery and electrical components and sizing cascading effects will impact aircraft mission analysis performance. Therefore, the potential benefit of adopting hybrid electric aircraft will highly depend on the applied energy management strategy (EMS). This paper presents a three-layer propulsion-mission analysis-EMS integrated multi-objective optimization scheme for hybrid electric aircraft identifying feasible design aspects considering fully coupled propulsion-aerodynamics effects. The ‘propulsion’ layer comprises the propulsion system modelling, integration approaches, and performance synthesis using an Artificial Neural Network (ANN)-based gas turbine surrogate model. The ‘mission-analysis’ layer is designed for multi-energy sources hybrid electric aircraft mission analysis considering fully coupled propulsion-aerodynamic effects. The ‘EMS’ layer utilizes non-dominated sorting genetic algorithm II (NSGA-II) addressing the design trade-off, i.e., block fuel burn, energy consumption, emissions. The proposed scheme is applied to a typical narrow-body aircraft, Boeing 737–800, equipped with mechanically integrated hybrid electric parallel propulsion configuration to explore flight electrification in civil aviation. Moreover, sensitivity analysis of battery technology level and flight mission definition is followed providing insights to hybrid electric aircraft application scenarios. The EMS optimization results indicate that for short/medium haul aircrafts which operate in high altitude with long flight durations, fuel as consumable energy source is prone to be used in initial stages to reduce aircraft weight and lift-dependent drag, while battery as non-consumable energy is optimally allocated in final flight stages of descent and landing. The design of hybrid electric aircraft is highly sensitive to both flight mission definition and battery specific energy projections. With battery specific energy projections of 1500 Wh/kg for the year 2035, optimal block fuel burn reduction by −44.62%, −31.47% and −21.86% can be obtained at the flight range design of 1000 nmi, 1250 nmi and 1500 nmi respectively.

A data-driven energy management strategy based on performance prediction for cascade refrigeration systems

The cascade refrigeration systems become the preferred choice in the field of cold storage due to its excellent performance under the condition of low evaporation temperature. Hence, a set of accurate performance prediction and energy management framework is essential for energy conservation and emission reduction. In this paper, a library of refrigeration components is established, based on which, two methods taking the data-driven model and the knowledge-driven model into account are presented to obtain optimal intermediate pressure in the cascade-condenser from the perspective of power and COP prediction respectively. Then, the effectiveness of the proposed optimal energy management strategy is demonstrated taking an actual CO2/NH3 cascade refrigeration system as a case study. Ten parameters are used to train GA-LSSVM model and 13014 on-site testing data points are randomly divided into the training set and the testing set. Two empirical formulas are used to calculate the isentropic efficiency of the NH3 and the CO2 compressors. A thermodynamic model is used as the knowledge-driven model to connect the calculation of the high-temperature stage with that of the low-temperature stage. The results show that these two methods are accurate enough for power and COP prediction. Based on the proposed methods, a set of data (1440 data points) from a typical workday is used to show the improvement by applying the optimal energy management strategy and the results show that the power consumption can be reduced by 10.52%.

A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Real-time optimal EMS of adaptive charge sustenance for fuel cell hybrid buses based on driving characteristics recognition

To improve the service lifetime and fuel economy of fuel cell hybrid buses (FCHB), it is extremely important to establish a reasonable energy management strategy (EMS). Therefore, an optimization-based EMS for dual-energy systems is proposed in this paper. Specifically, the proposed strategy is realized via pontryagin’s minimum principle (PMP), which can solve the problems that include inaccurate estimation of the motor output power, the difficulty of SOC adaptive sustenance and real-time application of the algorithm. First, we analyze the driving characteristics of bus routes based on real bus driving data, and solve the optimal co-state values of the historical driving segments (DSs) by dividing driving cycle (DC) and DS. Then, the motor output power and the density-based spatial clustering of applications with noise (DBSCAN) algorithm are employed to predict the co-state value of the next DS. Thereafter, the real-time application of the PMP algorithm is achieved by using predicted co-state and analyzing the SOC adaptive sustenance item during the FCHB runs. The comparisons between real bus experiments and the simulations of five EMSs are conducted to validate the effectiveness. Results demonstrate that our strategy can reduce hydrogen consumption by 6.6% on average versus the rule-based strategy, the fluctuation range of SOC is less than 2%, the mean absolute error (MAE) between the initial and final SOC is 0.10%, and the fuel cell has less lifetime decay. Moreover, our strategy has the same excellent performance compared with the strategy under known driving conditions. In addition, the online computation time per step of the proposed real-time strategy is averaged at 34.8 ms, less than the sampling time interval 1 s. Therefore, the real-time performance of our strategy will contribute to the promotion and application of FCHBs.

Hierarchical energy management of a hybrid propulsion system considering speed profile optimization

This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve a reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to achieve reliable real-time control. Finally, to verify the proposed method, real rail line data in China are used as a case study, and the results show that the proposed method offers the better energy and tramway efficiency.

High‐Voltage Topological Architecture‐Based Energy Management Strategy of the Plug‐In Hybrid Powertrain System

Hybrid technology (including plug‐in hybrid) integrates the advantages of traditional automobile technology and pure electric technology, which can greatly reduce fuel consumption and improve emissions. It has become one of the main technologies developed at present and in the next 15∼20 years. Energy management is the core algorithm of hybrid electric vehicle control strategy, and it is the focus of current research. However, these studies mainly focus on the high efficiency of control assembly, optimal management of power system energy, and maximum recovery of renewable energy but have not considered energy distribution management and optimization between the power battery and the low‐voltage battery. Hence, based on the high‐voltage topology of the plug‐in hybrid system, this paper proposes the optimal energy management strategy between the power battery and the low‐voltage battery under different working conditions. The charging and discharging characteristics of the power battery under different electric quantities are also combined. The experimental results show that based on the optimized energy management strategy, the pure electric driving range is increased by 6% under NEDC condition for a C‐class plug‐in hybrid car, and the energy‐saving effect of the vehicle is further improved.

Optimized Energy Management Strategy for Hybrid Fuel Cell Powered Drones in Persistent Missions using Real Flight Test Data

AbstractThis paper proposes an optimized energy management strategy (EMS) for hybrid electric fuel cell/battery-based drones focusing on fuel economy while extending sources lifespans in persistent missions. An off-the-shelf drone fed by a 650W fuel cell is selected as a case study, where the power splitting is conventionally managed by a simple rule-based method. Then, a multi-objective genetic algorithm is used to optimize the proposed EMS parameters considering three scenarios regarding battery state of charge, namely charge sustaining, depleting, and increasing. Therefore, advantages of rule-based strategy and genetic algorithm are combined in an online EMS to fit on drone applications. Extensive simulation results demonstrate that the proposed approach allows power sources to operate within their rated area, prolonging their service life, and leading to 5.1% of fuel consumption reduction. Thus, the autonomy will be increased depending on the carried hydrogen quantity, and the world record can be extended by about 37min. It may also lead to benefit in the operating cost achieving 1450C during one fuel cell stack lifecycle. In fleet tasks, the benefit can be further multiplied.

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.

Cabin Load Prediction Using Time Series Forecasting in Long-haul Trucks for Optimal Energy Management

Predicting the electrical loads experienced by a battery pack during the 10 hour hotel period of a long haul class 8 mild hybrid truck with a sleeper cab, and using the information to achieve an optimal energy management strategy and controlling the State of Charge (SOC) of battery pack can help in improving it's freight efficiency. In this work, Machine Learning (ML) based algorithm has been proposed to predict the driver activity during the hotel period. Hence, the power load demanded from the auxiliaries can be predicted. A special kind of Recurrent Neural Network (RNN) called Long and Short Term Memory (LSTM) is used for the prediction task because of its ability to store recurrent information of a small and a large time horizon. To train the LSTM algorithm, the synthetic load profles are synthesized using rules and observations derived from the existing baseline electrical power load profile of the hotel period. This paper entails the whole process of data synthesis to training the neural network on the synthesized data and the prediction and validation of the power load. The input to the network is a time series of 600 time steps. Dynamic Time Warping (DTW) is used to manipulate the time axis and point wise euclidean distance between the forecast and the test data is used to quantify the accuracy of the model. Then by performing hyper-parameter optimization we find the best combination for number of hidden units and the number of training days for the algorithm.

Optimal energy management strategy for fuel cell/battery/supercapacitor vehicles using wavelet transform and equivalent consumption minimization strategy

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.

Research on energy management strategy of fuel cell–battery–supercapacitor passenger vehicle

The energy management strategy of passenger vehicles that adopts the hybrid energy form of the fuel cell, battery and supercapacitor was complicated. Traditional power following-logic threshold filtering strategy could better use the power directly output by fuel cell(FC) and reduce the loss of charge and discharge. In some power ranges, the energy produced by the fuel cell lagged behind the demand power. In response to this problem, the power following-logic threshold filtering strategy is optimized based on dynamic allocation. According to the parameters of the vehicle, Simulink/Powertrain was used to build a power system model to verify the effectiveness of the current strategy under urban dynamometer driving schedule (UDDS) and then compared with the traditional control strategy. The optimized energy management strategy decreases hydrogen consumption by 1.9%; the proportion of power above 10 kW is reduced by about 20%; the battery end state of charge (SOC) values under the control strategy before and after the improvement are 51.64 and 50.91, respectively.

A cloud-based energy management strategy for hybrid electric city bus considering real-time passenger load prediction

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers. After analyzing the importance of battery aging and passenger load effects on an optimal energy management strategy, this study introduces the passenger load prediction into the hybrid-electric city buses energy management problem, which is not well studied in the existing literature. The average model, Decision Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, a dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage leveraging cloud techniques. Then, rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to the vehicle onboard controller to handle prediction errors and uncertainties. The proposed cloud-based Dynamic Programming and rule extraction framework with the passenger load prediction show 4% and 11% lower bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% of the dynamic programming with the true passenger load information.