Integrated Energy Management Strategy Research Articles

Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.

Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems

Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) concept. It uses a nested approach combining the best of the three EMS categories (optimization-based (OBS), rules-based (RBS), and learning-based (LBS) strategies) to overcome the real-time operating condition limitations of the fuel cell hybrid electric vehicle (FCHEV). Through a fuel cell/battery hybrid architecture, the purpose is to improve hydrogen consumption and manage the battery state of charge (SOC) under real-time driving conditions. The proposed iEMS approach is based on an OBS with optimal control to make the energy-optimal decision. However, it requires the adaptations of real-time operating conditions and a dynamic SOC horizon management. These requirements are supported by combining an RBS based on expert and fuzzy rules to compute the SOC target on each sliding window and an LBS based on fuzzy C-mean clustering to enhance the cooperative environment data processing and adapt it to the FHCEV topology. Our approach obtained simple and realistic system behaviors while having an acceptable computing time suitable for real time constraint. It was then designed and validated using a 27-h real-time measured database. The results show the effectiveness of the proposed iEMS concept with an excellent performance close to the optimal offline strategy (an under 2% consumption gap).

Integrated Energy Management Strategy Based on Finite Time Double Consistency Under Non-Ideal Communication Conditions

Collaborative energy management is a critical issue in integrated energy system (IES). Aiming at the problem, a novel finite time double consistency algorithm is proposed to solve the energy management problem of IES. By implementing this algorithm, the energy equipment units only need to perform local communication and local calculation, effectively solving the nonlinear coupling optimization problem in the system. Moreover, the algorithm enables the completion of optimal economic dispatch quickly in a finite time. The algorithm utilizes double consistency iteration of the incremental cost price and the estimated value of equipment output to effectively coordinate the relationship among energy equipment outputs. On this basis, an effective combined event trigger mechanism is designed by introducing measurement error, which enables energy equipment to communicate only when necessary, thereby avoiding the need for continuous information exchange. The proposed algorithm considers non-ideal com-munication environments such as communication delay and disturbance. To address these issues, the consistency theory and the model reduction theory are combined to provide a sufficient condition for the system's stability. Finally, the convergence and stability of the algorithm are verified using the Lyapunov method, and simulation experiments confirm the effectiveness of the designed algorithm.

Energy efficiency improvement of intelligent fuel cell/battery hybrid vehicles through an integrated management strategy

This paper investigates an integrated energy management strategy (IMS) to improve energy efficiency of proton exchange membrane fuel cell (FC) system in intelligent fuel cell/battery hybrid vehicles (FCHVs). Unlike other similar works which solely control output current from the FC system, stack temperature regulation is added in this research due to its impact on FC output power performance. For liquid-cooled FC systems, joint management of FC current and stack temperature is more difficult than air-cooled FC systems due to complicated thermal management systems. To address this, firstly, a control-oriented integrated model is established to provide the foundation for controller design. For the purpose of decreasing the strong nonlinearity of integrated model, the heat generation term is parameterized with a polynomial function. Secondly, in order to determine reference trajectories for controller, a data set consisting of nearly optimal operation points (FC current and stack temperature) is constructed offline, which provides reference trajectories based on simple rules in real time. Finally, a contraction-based control (CBC) approach is utilized to regulate FC current and stack temperature jointly. The bounded convergence between system states and reference trajectories is guaranteed by contraction theory. The effectiveness of the proposed IMS is validated via MATLAB simulation.

Integrated energy management of hybrid power supply based on short-term speed prediction

The energy management strategy and output limitation of energy storage system affect the actual regenerative braking recovery. In order to optimize the performance and energy efficiency of vehicle energy storage system in the process of braking energy recovery, an integrated energy management strategy based on short-term speed prediction is proposed in this paper. Firstly, support vector machine classification method is used for off-line training and on-line recognition of driving patterns, and then input-output hidden Markov model and Gaussian mixture model are fused to establish speed prediction models under urban, suburban, and high-speed conditions for real-time prediction of speed sequences. Then, the power is distributed by the dynamic programming algorithm according to the short-term speed prediction results, and the distribution coefficient is corrected through the integrated strategy of braking force distribution and power distribution. Finally, the simulation results under continuously changing driving conditions show that the pattern recognition with changeable span is conducive to improving its accuracy during the transition period, and the proposed integrated energy management strategy is helpful to optimizing the performance of the hybrid energy storage system. The renewable energy utilization efficiency index indicates the efficiency of the integrated energy management strategy.

Integrated energy management strategy of powertrain and cooling system for PHEV

ABSTRACTEnergy management strategy (EMS) is crucial in improving the fuel economy of plug-in hybrid electric vehicle (PHEV). Existing studies on EMS mostly manage powertrain and cooling system separately which cannot get the minimum total energy consumption. This paper aims to propose a novel EMS for a new type of dual-motor planetary-coupled PHEV, which considers cooling power demand and effect of temperature on fuel economy. Temperature-modified engine model, lithium-ion battery model, two motors, and cooling system models are established. Firstly, the separated EMS (S-EMS) is designed which manages powertrain and cooling system separately. Sequentially, after the analysis of thermal characteristics of the powertrain and cooling system, the thermal-based EMS (T-EMS) is then proposed to manage two systems coordinately. In T-EMS, cooling power demand and the charging/discharging energy of motors are calculated as equivalent fuel consumption and integrated into the object function. Besides, a fuzzy controller is also established to deicide the fuel-electricity equivalent factor with consideration of the effect of temperature and state of charge on powertrain efficiency. Finally, the hardware-in-loop experiment is carried out to validate the real-time effect of EMS under the New European Driving Cycle. The result shows that cooling power demand and temperature can significantly affect the fuel economy of the vehicle. T-EMS shows better performance in fuel economy than S-EMS. The equivalent fuel consumption of the cooling system of T-EMS decreases by 27% compared with that of S-EMS. The total equivalent fuel consumption over the entire trip of PHEV using T-EMS is reduced by 9.7%.

Adaptive Hierarchical Energy Management Design for a Plug-In Hybrid Electric Vehicle

To promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs), this paper proposes an adaptive hierarchical energy management strategy for a plug-in HEV. In this paper, deep learning (DL) and genetic algorithm (GA) are synthesized to derive the power split controls between the battery and internal combustion engine. First, the architecture of the multimode powertrain is founded, wherein the particular control actions, state variables, and optimization objective are explained. Then, the hierarchical framework for control actions generation is introduced. GA is utilized to search the global optimal controls based on the powertrain model provided in MATLAB/Simulink. DL is applied to train the neural network model that is connecting the inputs and control actions. Finally, the effectiveness of the presented integrated energy management strategy is validated via comparing with the original charge depleting/charge sustaining policy. Simulation results indicate that the proposed technique can highly improve the fuel economy. Furthermore, a hardware-in-the-loop is conducted to evaluate the adaptive and real-time characteristics of the designed energy management system.

Design of an integrated energy management strategy for a plug-in hybrid electric bus

The performance of Plug-in Hybrid Electric Vehicle (PHEV) depends on the energy management strategy (EMS). An optimal EMS guarantees the maximum use of the energy through electric power grid, coordinates power output of main power sources and exerts comprehensive advantages of both engine and motor. However, the current EMS cannot react to the dynamic driving cycles in the future and thus have a bad real-time performance. In order to solve these problems, a novel EMS based on rule-based energy strategy (RE), dynamic programming algorithm (DP) and equivalent fuel consumption algorithm (ECMS) is proposed. First, DP is used to extract the boundary of mode switching and shift schedule under three typical driving cycles in offline, especially, RE is corrected using the boundary of mode switching. Second, an instantaneous optimization strategy-ECMS is used to replace the Charge-Depleting Mode (CD) of RE for finding the real-time optimal solution in a wider range in online, in this part, owe to the fixed distance of urban public transport, a reference State of Charge (SOC) is formulated, and the Proportion-Integration (PI) algorithm is used to make the actual SOC always follow the reference SOC by adjusting the equivalent factor. Finally, combine all the above efforts, a real-time optimization EMS is proposed and validated through simulation and experiment. The results show that this approach can significantly enhance the vehicle drivability, with overall obvious improvement of the comprehensive performance qualification based on both fuel economy and drivability.

An Integrated Energy Management Strategy With Parameter Match Method for Plug-In Hybrid Electric Vehicles

A battery-ultracapacitor hybrid energy storage system (HESS) is a reliable candidate to overcome the drawbacks of a single power source system for its complementary features of power and energy in plug-in hybrid electric vehicle application. In this paper, an integrated energy management strategy procedure, which consists of three layers, is presented to distribute the power of the battery packs and the ultracapacitor packs. First, an HESS parameter match method is developed under different driving cycles with optimization objectives of cost and weight. Second, three-level wavelet transform (WT) algorithm is employed to isolate the different frequency components of power demand. The low-frequency components with less transients and sharps in power demand are provided by extending the battery lifetime, while an ultracapacitor satisfies the high-frequency components for its nice features of high specific power. Finally, fuzzy logic control is used to manage power flows between various components for guaranteeing the HESS performance. The simulation results show that compared with WT-based-only energy management strategy, the proposed procedure can always control the battery currents within 2C rate and reduce the energy consumption of about 6.54%.

Traction control–integrated energy management strategy for all-wheel-drive plug-in hybrid electric vehicle

Plug-in hybrid electric vehicle is one of the potential candidates to tackle the stringent fuel economy standards. Many previous studies focus on optimal fuel economy by coordinating primary power source and assistant power source in plug-in hybrid electric vehicles, which usually neglects vehicle dynamic performance. All-wheel-drive plug-in hybrid electric vehicle has advantages both in fuel economy and dynamic performance. However, when all-wheel-drive plug-in hybrid electric vehicle is traveling on low cohesive roads, how to balance the conflict between traction performance and fuel economy undoubtedly will be the crucial problem. To address the issue, an integrated control strategy is proposed for all-wheel-drive plug-in hybrid electric vehicle in this article, namely, traction control system–integrated energy management strategy. A charge depletion/charge sustaining strategy is employed to realize the torque distribution mainly according to the engine optimal working region to achieve better fuel eco...

Carbon footprint analysis as a tool for energy and environmental management in small and medium-sized enterprises

ABSTRACTThe reduction of carbon emissions has become a top priority in the decision-making process for governments and companies, the strict European legislation framework being a major driving force behind this effort. On the other hand, many companies face difficulties in estimating their footprint and in linking the results derived from environmental evaluation processes with an integrated energy management strategy, which will eventually lead to energy-efficient and cost-effective solutions. The paper highlights the need of companies to establish integrated environmental management practices, with tools such as carbon footprint analysis to monitor the energy performance of production processes. Concepts and methods are analysed, and selected indicators are presented by means of benchmarking, monitoring and reporting the results in order to be used effectively from the companies. The study is based on data from more than 90 Greek small and medium enterprises, followed by a comprehensive discussion of cost-effective and realistic energy-saving measures.

Analytical Solution to Energy Management Guaranteeing Battery Life for Hybrid Trucks

This paper considers a parallel hybrid electric truck where the tractive force delivered to the wheels can be provided by a conventional internal combustion engine (ICE) and/or a motor generator (MG). This hybrid truck is equipped with a clutch system that is capable of decoupling the ICE and the MG from the drivetrain to improve the fuel reduction performance, compared with a hybrid truck without a separate clutch for the MG. In the considered hybrid truck, the energy management strategy utilizes the battery to reduce the fuel consumption and the associated emissions. As the battery life is limited, the vehicle owner will suffer from extra costs for battery replacement. Henceforth, it is necessary to guarantee sufficient battery life. This paper develops a quasi-static battery cycle-life model and formulates a model-based integrated energy management (IEM) strategy. This IEM strategy optimizes the power split (between the ICE and the MG) and the operation of the clutch system to minimize the hybrid truck fuel consumption while guaranteeing the requested battery life. The problem of fuel minimization with battery life guarantee is analytically solved using optimal control theory. Mathematical and physical arguments are used to explain the compromise between fuel reduction and battery life preservation. Simulation results demonstrate that the IEM strategy will guarantee the requested battery life for different driving cycles while allowing appropriate hybrid powertrain operations for fuel minimization. Moreover, the results also show the fuel reduction improvement from decoupling the MG from the drivetrain using the clutch system.