Rule-based Power Management Strategy Research Articles

A distributed rule‐based power management strategy in a photovoltaic/hybrid energy storage based on an active compensation filtering technique

This paper proposes a distributed rule-based power management strategy for dynamic power balancing and power smoothing in a photovoltaic (PV)/battery-supercapacitor hybrid energy storage system. The system contains a PV system, a battery-supercapacitor hybrid energy storage system (HESS), and a group of loads. Firstly, an active compensation technique is proposed which improves the efficiency of the power smoothing filter. Then, a distributed supervisory control technique is employed that prevents the BESS and SC from SOC violation while maintaining the balance between generation and load. To this end, the system components are divided into three different reactive agents including an HESS agent, a PV agent, and a load agent. These agents react to the system changes by switching their operational mode upon satisfying a predefined rule. To analyse the hybrid dynamical behaviour of the agents and design the supervisory controllers, the agents are modelled in hybrid automata frameworks. It is shown that the proposed distributed approach reduces the complexity of the supervisory control system and increases its scalability compared to its equivalent centralized method. Finally, the performance of the proposed approach is validated using a test system simulated in MATLAB/Simulink.

A rule-based power management strategy for Vehicle-to-Grid system using antlion sizing optimization

Stable and reliable electricity is a prerequisite to the development of any nation and improving the living standard of the society. By exploiting Renewable Energy Sources (RESs) such as solar energy (PV) and wind energy (WT) as widely acknowledged RESs to be more environmentally friendly and address power difficulties. They are acknowledged to be an interesting option to charge Electric Vehicles (EVs) via Vehicle-to-Grid (V2G) technology. On the contrary, to the best knowledge of the authors, there are few numbers published articles on RESs (PV and WT) integrated with Electric Vehicle Charging Station (EVCS) to charge EVs. This research is aimed at implementing a metaheuristic optimization algorithm named Antlion Optimizer (ALO) algorithm for sizing the system components to meet the load demand and minimize the two objective functions - Losses Power Supply Probability (LPSP) and Cost of Electricity (COE) while maximizing the Renewable Energy Fraction (REF). In addition, the Rule-Based Energy Management Strategy (RB-EMS) is proposed to control the flow of power in the system. The integration of RESs to the utility grid with V2G technology and V2G services is discussed. Furthermore, the State-of-the-art for improving the stability and reliability of the grid-connected system is presented. As a result, the attained result from the proposed method shows better performance results as 0.0936 $/kWh, 0.1044%, and 0.9940% for COE, LPSP, and REF, respectively. The research is conducted in different microgrid configurations using a MATLAB environment.

Power Management Strategy for a Novel Hybrid Electric Bus with Dual Planetary Gears

Hybrid electric vehicles with power split system have been widely used during recent years due to its huge potential to reduce the fuel consumption and enhance the drivability. This paper presents a novel powertrain architecture for a hybrid electric bus (HEB) based on dual planetary gear. The configuration and operating modes are described elaborately in this paper and a rule-based power management strategy is adopted to the HEB. Meanwhile, dynamic programming (DP) algorithm is also used as a benchmark to verify the effectiveness of this system. In order to validate the fuel-saving ability, both the rule-based and DP-based power management strategy are simulated based on the same typical driving cycle. As a result, the low fuel consumption achieved by DP verifies the effectiveness of this novel powertrain architecture. Moreover, the fuel consumption of the rule-based strategy is merely 11.29% higher than that on DP, which proves the feasibility of the rule-based strategy.

Study on Power Management Strategy of HEV using Dynamic Programming

For hybrid electric vehicle, it is necessary to control power distribution among multiple power sources to improve fuel economy performance of vehicle. In this paper, power management strategy of hybrid electric vehicle using Dynamic programming is studied. Deterministic dynamic programming could present outstanding fuel economy, while its application as real time control of vehicle is limited. Thus, different kinds of power management strategy using dynamic programming are studied. Stochastic dynamic programming, artificial neural networks and rule-based power management strategy using results from dynamic programming are studied. Simulations using parallel type hybrid electric vehicle model are conducted. Simulation results including fuel economy performance on diverse driving cycles are compared and analysed.

Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions

This paper proposes a novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions. To optimize the threshold parameters of the rule-based power management strategy under a certain driving cycle, the particle swarm optimization algorithm was employed, and the optimization results were used to determine the optimal control actions. To better implement the power management strategy in real time, a driving condition recognition algorithm was proposed to identify real-time driving conditions through a fuzzy logic algorithm. To adjust the thresholds of the rule-based strategy adaptively under uncertain driving cycles, a dynamic optimal parameters algorithm has been further established accordingly, and it is helpful for avoiding the problem that the thresholds of the rule-based strategy are very sensitive to the driving cycles. Finally, in combination with the above efforts, a detailed operational flowchart of the particle swarm optimization algorithm-based optimal power management through driving cycle recognition has been proposed. The results illustrate that the proposed strategy could greatly improve the control performance for different driving conditions. Especially for the uncertain driving cycles, the reduction in energy loss can be up to 1.76%.

Regularized MPC for Power Management of Hybrid Energy Storage Systems with Applications in Electric Vehicles

This paper examines the application of Regularized Model Predictive Control (RMPC) for Power Management (PM) of Hybrid Energy Storage Systems (HESSs). To illustrate, we apply the idea to the PM problem of a battery-supercapacitors (SCs) powertrain to reduce battery degradation in Electric Vehicles (EVs). While the application of Quadratic MPC (QMPC) in PM of HESS is not new, the idea to examine RMPC here is motivated by its capabilities to prioritize actuator actions and efficiently allocate control effort, as advocated by recent works in the control and MPC literature. Thorough simulations have been run over standard urban test drive cycles. It is found out that QMPC and RMPC, compared to rule-based PM strategies, could reduce the battery degradation over 70%. It is also shown that RMPC can slightly outperform QMPC in reducing battery degradation. Moreover, RMPC, compared to QMPC, could potentially extend the range of that SCs can be used, thus exploiting the degree of freedom of the powertrain to a larger extent. We also make some discussions on the feasibility issues and tuning challenges that RMPC faces, among others.

Predictive power management strategies for stand-alone hydrogen systems: Lab-scale validation

Power Management Strategies (PMSs) to control stand-alone energy systems affect the reliability of meeting load demand as well as the cyclic operation of various subsystems. The hybridisation of sources through the integration of hydrogen fuel cells with energy storage means optimising the PMS should be “intelligently” done unless relying on rule-based PMSs which are simplistic to use but subject to lack of optimisation. This paper presents the methodology and validation of a lab-scale (desktop) energy system controlled by a predictive PMS. Validation of the intelligently based PMS can be done in the lab-scale before (costly) full deployment in the field, but experiments to support this have not been reported in relation to hydrogen systems. The experimentally tested hybrid energy system consists of an emulated renewable power source which can represent solar-PV and/or wind generators, battery bank and PEM fuel cell integrated with metal hydride storage. Experimental testing as well as the use of real-time predictions using Neural Networks is utilised. The effects of several control parameters which are either hardware dependant or affect the predictive algorithm are investigated with system performance, under the predictive PMS, benchmarked against a rule-based PMS. The results reveal that a predictive PMS is impacted by the prediction horizon used to forecast the availability of renewables or load, the decision time interval used for updating the PMS as well as time lags resulting from hardware sensors used to convey system status to the decision algorithm responsible for updating the PMS. The maximum thresholds of the above mentioned control parameters are 120, 15 and 3 s, respectively. Beyond these limits, the ability of the predictive PMS to effectively control the system degrades significantly. This study demonstrates the feasibility of using real-time predictions of renewable resources and load demand to optimise a PMS in a stand-alone energy system and experimentally validates this, which has not been previously reported.

Development of PMP-based power management strategy for a parallel hybrid electric bus

To operate a hybrid electric vehicle efficiently, it is important how to manage energy components such as the engine, motor and battery of the vehicle. In this paper, the Pontryagin’s minimum principle (PMP) based power management strategy is developed and applied to a forward simulation program able to calculate the fuel economy of a vehicle with its specified driving cycle. Typical two other power management strategies, the Max state of charge (SOC) power management strategy and the power split ratio rule-based power management strategy are also developed and applied to verify the application possibility of the PMP-based power management strategy. A simulated comparison of the power management strategies using six driving cycles is conducted. As a result, considering the characteristics of buses which drive a routine cycle repeatedly, the PMP-based power management strategy, which shows the most efficient performance with respect to the fuel economy and the maintenance of the battery SOC, is more appropriate than the other power management strategies while the application of PMP-based power management strategy to a conventional vehicle has a difficulty because of the problem about maintenance of the battery SOC.

Fuel consumption of fuel cell hybrid vehicles considering battery SOC differences

Fuel cell hybrid vehicles (FCHVs) have become one of the most promising candidates for future transportation due to current energy supply problem and environmental problem. Fuel economy is an important factor in FCHVs. In order to properly evaluate the fuel economy of an FCHV, the initial battery state of charge (SOC) and the final battery SOC have to be identical so that the effect of the battery energy usage on the fuel economy is neglected. In the simulation or in the real driving, however, the final battery SOC is usually different from the initial battery SOC, and the final battery SOC often depends on the power management strategy. To consider the difference between the two battery SOC values, the concept of equivalent fuel consumption is presented by two methods. One is based on the relationship between delta SOC and delta fuel consumption, and the other is based on the optimal control theory. Two rule-based power management strategies for an FCHV are presented, and for each strategy, the fuel economy is evaluated based on the two methods. The characteristics of the two methods are discussed and compared, and the superior one is selected based on the comparison.

연료전지 하이브리드 자동차의 룰 베이스 전략과 최적 제어 전략의 비교

Fuel economy is an important factor in a vehicle owing to recent energy supply and environmental problems. This paper deals with fuel cell hybrid vehicles (FCHVs) and introduces a fuel economy evaluation method. The fuel economy of an FCHV depends on its power management strategy. Two rule-based power management strategies are applied to this paper and their fuel economy is evaluated based on the optimal control theory. The concept of the optimal line is also applied to this paper, which is used to compare the fuel consumption of a power management strategy to the optimal result. The two rule-based strategies are also compared to each other.

Fuel economy evaluation of fuel cell hybrid vehicles based on optimal control

The fuel economy of a fuel cell hybrid vehicle (FCHV) depends on its power management strategy because the strategy determines the power split between the power sources. Several types of power management strategies have been developed to improve the fuel economy of FCHVs. This paper proposes an optimal control scheme based on the Minimum Principle. This optimal control provides the necessary optimality conditions that minimize the fuel consumption and optimize the power distribution between the fuel cell system (FCS) and the battery during driving. In this optimal control, the final battery state of charge (SOC) and the fuel consumption have an approximately proportional relationship. This relationship is expressed by a linear line, and this line is defined as the optimal line in this research. The optimal lines for different vehicle masses and different driving cycles are obtained and compared. This research presents a new method of fuel economy evaluation. The fuel economy of other power management strategies can be evaluated based on the optimal lines. A rule-based power management strategy is introduced, and its fuel economy is evaluated by the optimal line.

Fuel economy evaluation of fuel cell hybrid vehicles based on equivalent fuel consumption

Fuel cell hybrid vehicles (FCHVs) have become a major topic of interest in the automotive industry owing to recent energy supply and environmental problems. Consequently, fuel economy evaluation methods of FCHVs have a popular research topic. The initial state of charge (SOC) and the final SOC of the battery have to be identical in an evaluation of the fuel economy of an FCHV. In an actual driving situation or during a forward simulation, however, the final SOC depends on the power management strategy, which is usually different from the initial SOC. To consider the effect of the difference between the initial and final SOC on fuel economy evaluation, the concept of equivalent fuel consumption, based on the optimal control, is introduced in this paper. A rule-based power management strategy is applied to an FCHV, and its fuel economy is evaluated in terms of the equivalent fuel consumption and compared to the optimal control result.

Power management strategies for a series hydraulic hybrid drivetrain

This paper presents implementation details for two optimisation-based energy management strategies for a series hydraulic hybrid drivetrain. The first strategy is a causal, real-time implementable instantaneous consumption minimisation strategy that requires minimal information about future loads on the system. The second strategy is a non-causal but formally trip-optimal power allocation scheme involving Dynamic Programming (DP). Both strategies are applied to a medium duty urban truck featuring independent hydrostatic wheel drives and fuel economy results are compared to those obtained with a commonly cited real-time implementable, rule-based power management strategy.