Optimization-based Energy Management Strategies Research Articles

Enhancing Efficiency in Hybrid Marine Vessels through a Multi-Layer Optimization Energy Management System

Abstract: Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices’ (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs’ SOCs are obtained.

Research on the adaptability of energy management to thermal management of PEMFCs

Abstract The stable output of power and the effective control of stack temperature play a very important role in the durability and stability of fuel cell hybrid vehicles. In actual working conditions, energy management of power system and thermal management of Proton Exchange Membrane Fuel Cells (PEMFCs) need to be coordinated with each other to jointly ensure the efficient and stable operation of vehicles, however, most of the research in these two directions is independent. In response to the research gaps mentioned above, the concept of adaptability has been proposed for the first time with the aim of combining these two systems for research. This paper takes the fuel cell hybrid vehicle as the research object and establishes the energy management system and thermal management system model based on Matlab/Simulink software. In order to investigate the adaptability of energy management strategies to the temperature control system, two representative types of rule-based and optimization-based energy management strategies (power-following strategy, PFS, and adaptive equivalent hydrogen consumption minimization strategy, A-EHMS) are designed to be numerically simulated in the driving cycle condition of the UDDS, and then combined with the temperature control system of the fuel cell to comparatively analyze the above two types of energy management strategies from the perspectives of power allocation and the impact on the thermal management system. The results show that the PFS provides better protection for the battery under the same operating conditions. And from the perspective of fuel economy and adaptability to the temperature control system, the fuel consumption of A-EHMS is reduced by 12.5% and the adaptability to the temperature control system is improved by about 13.5% compared to PFS.

An Enhanced Extremum Seeking-Based Energy Management Strategy with Equivalent State for Hybridized-Electric Tramway-Powered by Fuel Cell–Battery–Supercapacitors

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements’ SOCs at the end of a driving cycle are also regulated to be equal to the initial ones.

An energy management strategy for fuel cell hybrid electric vehicle based on a real-time model predictive control and pontryagin’s maximum principle

ABSTRACT In order to maintain the battery SOC, the fuel cell power will fluctuate dramatically, as well as frequent start-stop, which will greatly increase the life attenuation of the fuel cell and reduce the durability. An optimization-based energy management strategy with a real-time model predictive control and pontryagin’s maximum principle for FCHEV is proposed in this paper, both the fuel economy and the fuel cell durability are considered in the optimization. A novel model predictive control is studied to achieve energy distribution. After the calculation of predicted speed sequence through back propagation neural network, pontryagin’s maximum principle is introduced to solve the optimal control problem in each prediction horizon and obtain the ideal control strategy. In addition, the fuel cell degradation model is introduced in the modeling process, the minimum power point of the fuel cell system is designed to improve the fuel economy and durability of the fuel cell. Compared with the rule-based strategy, the proposed MPC strategy has better performance to reduce the total equivalent hydrogen consumption, which can save up to 8.44% in the test case of the mid-size fuel cell passenger car while maintaining the stability of the battery’s state of charge.

Energy management of an eVTOL aircraft with optimization based on the Equivalent Consumption Minimization Strategy (ECMS)

This article introduces an optimization-based energy management strategy developed and validated for electric vertical take-off and landing (eVTOL) aircraft. The primary objective of this strategy is to minimize hydrogen consumption while accounting for resource constraints, such as the battery and fuel cell, to enhance energy efficiency and sustainability while adhering to performance and safety requirements. The eVTOL employed in this research is an essential component of our “Viable” research project, featuring a hybrid propulsion system that combines batteries and fuel cells.To accomplish this, mathematical and computational techniques were employed using the equivalent consumption minimization strategy method to determine the optimal operational parameters for the eVTOL aircraft, considering the available energy resources. These techniques were implemented for the first time in MATLAB, enabling simulations of the aircraft’s performance under various conditions. The results demonstrate the efficacy of the energy management strategy in significantly reducing hydrogen consumption while maintaining optimal performance and safety. Graphs and comparative analyses are presented to highlight the evolution of hydrogen consumption compared to different parameters and to compare the approach with alternative methods.Furthermore, the article explores an alternative solution that offers related results and performance as MATLAB, utilizing the open-source software OpenModelica (OM). Energy management experiments using ECMS were conducted on OM, yielding highly satisfactory outcomes in terms of simulation accuracy and cost-effectiveness. The method was also evaluated in simulations of propulsive system resources, developed on OM, validating the results concerning energy distribution, compliance with constraints, and real-time feedback.In summary, this article presents a comprehensive strategy for effectively managing energy consumption in eVTOL aircraft. By leveraging simulations conducted in MATLAB and OM, the strategy’s effectiveness was assessed, with potential implications for advancing the sustainability of electric eVTOL aircraft.

An Intelligent Coot Bird Optimization-based Energy Management Strategy in Hybrid Electric Vehicle

An Intelligent Coot Bird Optimization-based Energy Management Strategy in Hybrid Electric Vehicle

Driving style-aware energy management for battery/supercapacitor electric vehicles using deep reinforcement learning

Driving style can significantly affect the energy consumption, battery lifespan, and driving economy of electric vehicles. In this context, this paper proposes a novel driving style-aware energy management strategy for electric vehicles with battery/supercapacitor hybrid energy storage systems based on deep reinforcement learning. Firstly, a semi-supervised support vector machine-based driving style recognition method is presented to recognize the driving style, where twenty features are extracted from limited labeled velocity/acceleration data and then reduced to six dimensions by locally linear embedding. The six dimension features are used to obtain accurate recognition results. Then a proximal policy optimization-based energy management strategy is proposed with the driving style as an additional input state, to optimize the power allocation and minimize the battery capacity loss cost. Extensive results illustrate the effectiveness of the proposed methods, e.g., the proposed driving style recognition method can recognize the real-time driving style with an accuracy of over 95%. Taking the recognized style as input, the proposed driving style-aware energy management strategy can reduce the battery capacity loss cost by 3.30–4.19% and 1.77–8.15%, compared with no driving style and incorrect driving style input energy management methods, respectively.

Incentive learning-based energy management for hybrid energy storage system in electric vehicles

Deep reinforcement learning has emerged as a promising candidate for online optimal energy management of multi-energy storage vehicles. However, how to ensure the adaptability and optimality of the reinforcement learning agent under realistic driving conditions is still the main bottleneck. To enable the reinforcement learning agent to efficiently learn the optimal power allocation strategies under diverse driving conditions, this paper proposes an incentive learning-based energy management strategy for battery-supercapacitor electric vehicles to minimize the battery capacity loss cost and power loss cost. First, an incentive reward function based on supercapacitor state-of-charge and vehicle acceleration is proposed for proximal policy optimization-based energy management strategy, which can stimulate the agent to learn for optimal power allocation policy under high load power conditions quickly. Second, a random sampling-based velocity transfer probability surface is constructed for pre-training to guarantee strategy optimality under unfamiliar driving cycles. Third, the generalized advantage estimation and layer normalization of neural networks are incorporated to improve the learning convergence. Results show that the proposed method can reduce the above costs by 5.8%–13.8% and 11.7%–38.8% compared with existing deep reinforcement learning methods under the pre-training driving cycle and test driving cycles, respectively, which yields closer results to offline dynamic programming.

Global Optimization-Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi-objective Optimization Algorithm

Global Optimization-Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi-objective Optimization Algorithm

Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Electric vehicles (EVs) are seen as a necessary component of transportation's future growth. However, the performance of batteries related to power density and energy density restricts the adoption of electric vehicles. To make the transition from a conventional car to a pure electric vehicle (PEV), a Hybrid Electric Vehicle's (HEV) Energy Management System (EMS) is crucial. The HEVs are often powered with hybrid electrical sources, therefore it is important to select the optimal power source to improve the HEV performance, minimize the fuel cost and minimize hydrocarbon and nitrogen oxides emission. This paper presents the Grey Wolf Optimization (GWO) algorithm for the control of the power sources in the HEVs based on power requirement and economy. The proposed GWO-based EMS provides optimized switching of the power sources and economical and pollution free control of HEV.

New Members Selection for the Expansion of Energy Communities

Energy communities are key enablers for end-users to actively participate in the energy transition in a more consumer-centric context. This paper focuses on the expansion of existing energy communities that may need to select new members among a pool of candidates. Selection is based on heuristic methods for better explainability and to promote a transparent selection process from end-users’ perspectives. The proposed methodology is further verified with an accurate optimization-based energy management strategy. The member selection is performed in an iterative process where the best potential candidate is added as a new member of the energy community before running the same procedure over successive iterations. Simulations were performed for a complete month with a real community of six houses and nine potential candidates. The proposed rule-based method achieves similar ranks among candidates for two investigated metrics and return the same results as the more accurate optimization. Furthermore, the results show a hint on how to identify the best location (i.e., member) to install new assets that can contribute best to the energy community since it can boost the value brought by the candidates to the community. In that sense, the proposed method also serves as an investment decision support tool as well as a selection strategy for inhabitants of an energy community.

Efficient Multidimensional Dynamic Programming-Based Energy Management Strategy for Global Composite Operating Cost Minimization for Fuel Cell Trams

Energy management is key to improve the performance of hybrid system. At present, the global optimization-based energy management strategy (EMS) is considered to have the best optimization effect under a known operating condition. However, the traditional global optimization-based EMS is often very time-consuming, which brings great inconvenience to debug and obtain the optimal result. Besides, in the literature, most works still introduce only the fuel consumption into the objective function while failing to consider the durability of fuel cells. In order to make up for these shortcomings, based on the modeling of hybrid system, this article first proposes a global composite operating cost minimization strategy (CMS) both considering fuel consumption and depreciation of purchase cost of fuel cells. Then, this article proposes computationally efficient multidimensional dynamic programming (EMDP) to resolve the optimal control trajectory of the studied issue. Finally, based on the hardware-in-the-loop platform, this article carries out comparative tests of the proposed EMDP algorithm and CMS strategy with traditional ones. Results show that the EMDP algorithm can effectively speed up the computation compared with the traditional dynamic programming. Besides, the proposed CMS strategy would lead to lower operating cost mainly by reducing the degradation cost of fuel cells so that the lifetime of fuel cells could also be extended.

Evaluation of energy management strategies for fuel cell/battery-powered underwater vehicles against field trial data

• Evaluation of Energy Management Strategies (EMS) against field trial data. • Fuel cell/battery-hybrid power system is modelled in Simulink®. • Performance criteria include energy efficiency, degradation and power reliability. • Choice of EMS depends on character of mission: high-autonomy missions require more complex EMS. This study combines high-fidelity simulation models with experimental power consumption data to evaluate the performance of Energy Management Strategies (EMS) for fuel cell/battery hybrid Autonomous Underwater Vehicles (AUV). The performance criteria are energy efficiency, power reliability and system degradation. The lack of standardized drive cycles is met by the cost-efficient solution of synthesizing power profiles from sampled AUV field trial data. Three power profiles are used to evaluate finite-state machine, fuzzy logic and two optimization-based EMS. The results reveal that there is a trade-off between the objectives. The rigidity of the EMS determines its load-following behavior and consequently the performance regarding the objectives. Rule-based methods are particularly suitable to design energy-efficient operations, whereas optimization-based methods can easily be tuned to provide power reliability through load-following behavior. Both classes of EMS can be best-choice methods for different types of missions.

Multi-objective robust energy management for environment powered unmanned surface vehicles

The environment powered unmanned surface vehicle (EPUSV) is capable of achieving large-range and long-term marine operations by harvesting abundant environment energy. However, considering the intermittency of environment energy, limitation on battery capacity and varieties of loads, it is necessary to control and coordinate the electric devices of EPUSV to exploit energy efficiently. To address this issue, an optimization-based energy management strategy (EMS) is proposed in this paper to achieve optimal management for the devices of EPUSV. The operation constraints and relevant objectives of different types of devices are firstly modeled through mix-integer nonlinear programming (MINLP) method. The uncertainty parameters in the model are addressed by robust optimization method. Then various task modes are defined based on the preference for different objectives. Simulations indicate that the proposed method is able to achieve optimal scheduling for each device while satisfying the underlying operation constraints. Besides, the adaptability for different kinds of tasks and the robustness to the fluctuation of uncertainty parameters of the proposed EMS is analyzed and discussed.

An Optimization-Based Energy Management Strategy for PEM Fuel Cell-Battery Hybrid Energy System for Locomotive Applications

An Optimization-Based Energy Management Strategy for PEM Fuel Cell-Battery Hybrid Energy System for Locomotive Applications

Nonlinear Model Predictive Control of Mild Hybrid Powertrains With Electric Supercharging

Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and the high interaction between the 48V mild hybrid powertrain and the electrical system pose a major challenge. Current research results show the fundamental potential of optimization-based energy management strategies. However there is a lack of optimization-based solutions that allow online direct control of the electrified air path and the electric motor. In this context, this study presents a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain with an electric supercharger, which is able to simultaneously improve the response behavior and energy consumption. The control concept is developed on the basis of a detailed system description. The special features of optimization-based control for the degrees of freedom of the powertrain, i.e., the belt starter generator (BSG) and the electrified air path through a throttle, waste gate, and electric supercharger (eC), are addressed. After an analysis and verification of the control properties, the NMPC is evaluated in dynamic driving cycle simulations through a comparison with a state-of-the-art rule-based control strategy. The investigations show that the NMPC is able to control the system well even under transient situations with a high influence of disturbance variables. In addition, the NMPC allows a specific and fuel saving use of the 48V system while improving the driving dynamics at the same time.

Energy Management of a Multi-Source Vehicle by λ-Control

This paper deals with the real-time energy management of a fuel cell/battery/supercapacitors energy storage system for electric vehicles. The association of the battery and the supercapacitors with the fuel cell aims to reduce the hydrogen consumption while limiting the constraints on the fuel cell and the battery. In this paper, a real-time optimization-based energy management strategy by λ-control is proposed. Simulation results on a standard driving cycle show that the hydrogen consumption is reduced by 7% in comparison with a fuel-cell-based electric vehicle without any secondary energy storage source. Moreover, the energy management strategy ensures the system safety while preserving the fuel cell and the battery. Experimental results show that the developed energy management strategy is well-suited for the real-time requirements, applicability, and safety.

A Lyapunov Optimization-Based Energy Management Strategy for Energy Hub With Energy Router

The energy crisis and increasing concerns about the environment in modern society have fostered the development of the integrated energy system (IES) in smart grid. The energy hub (EH) is a minimum construction of the IES. The chief purpose of this paper is to propose a Lyapunov optimization-based energy management strategy for the energy hub with an energy router. First, this paper describes a configuration of the EH with an energy router and sets the basic operating principles of the energy router. Second, a method is proposed to analyze the energy balance equations of the EH. Furthermore, the problem of energy management is formulated considering the constraints of energy balance equations, energy storage, flexible loads, and other constraints of operation. Finally, energy storage and flexible electric loads are modeled as stochastic processes, and three virtual queues are constructed to relax these constraints. The Lyapunov drift-plus-penalty function is applied to formulate a relaxed form of the energy management problem. Case studies of a single EH and two-connected EHs are performed to test the effectiveness of the proposed strategy, and the results are compared with a greedy algorithm.

A robust online energy management strategy for fuel cell/battery hybrid electric vehicles

Traditional optimization-based energy management strategies (EMSs) do not consider the uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the uncertain driving cycles. The energy consumption model of the FCHEV is built by considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear programming-based method is proposed to produce the benchmark solution. The ROEMS instantaneously minimizes the equivalent power of fuel cell and battery, where an equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming to battery energy. To control the state of charge of battery, two control coefficients are introduced to adjust the power of battery in objective function. Another penalty coefficient is used to amend the power of fuel cell, which reduces the load change of fuel cell so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has good performance in both fuel economy and load change control of fuel cell. The most important advantage of ROEMS is its robustness and adaptivity, because it almost produces the optimal solution without changing the control parameters when driving cycles are changed.

Optimization-Based Energy Management Strategy for a 48-V Mild Parallel Hybrid Electric Power System

Abstract The application of a 48-V mild hybrid powertrain system is one of the eco-friendly technologies for global CO2 reduction in the present sector of hybrid electric vehicles (HEVs). It is well known that the energy management strategy is crucial for improving the fuel economy of the HEVs. Therefore, the strengths and the limitations of dynamic programming (DP) global optimization strategy and adaptive equivalent consumption minimization strategy (ECMS) for this kind of powertrain system under new European driving cycle (NEDC) and federal test procedure (FTP75) driving cycles are discussed in this paper. The results of the DP global optimization solution have also been adopted as a reference for evaluating the degree of optimality of real-time controllers. The reference start of charge (SOC) was found to be a very important parameter for the real-time ECMS approach. Thus, an adaptive ECMS approach using the SOC obtained from DP global optimization algorithm as a reference SOC in real-time ECMS control was studied. The study results in this paper may provide some theoretical support for future energy management optimization of a 48-V mild hybrid parallel powertrain system.