Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Research on System Control and Energy Management Strategy of Flux-Modulated Compound-Structure Permanent Magnet Synchronous Machine

The flux-modulated compound-structure permanent magnet synchronous machine (CS-PMSM), composed of a brushless double rotor machine (DRM) and a conventional permanent magnet synchronous machine (PMSM), is a power split device for plug-in hybrid electric vehicles. In this paper, its operating principle and mathematical model are introduced. A modified current controller with decoupled state feedback is proposed and verified. The system control strategy is simulated in Matlab, and the feasibility of the control system is proven. To improve fuel economy, an energy management strategy based on fuzzy logic controller is proposed and evaluated by the Urban Dynamometer Driving Schedule (UDDS) drive cycle. The results show that the total energy consumption is similar to that of Prius 2012.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Energetic Macroscopic Representation of an Electrically Heated Building with Electric Thermal Storage and Heating Control for Peak Shaving

As a part of the Smart Grid concept, an efficient energy management at the residential level has received increasing attention in lately research. Its main focus is to balance the energy consumption in the residential environment in order to avoid the undesirable peaks faced by the electricity supplier. This challenge can be achieved by means of a home energy management system (HEMS). The HEMS may consider local renewable energy production and energy storage, as well as local control of some particular loads when peaks mitigation is necessary. This paper presents the modeling and comparison of two residential systems;one using conventional electric baseboard heating and the other one supported by Electric Thermal Storage (ETS);the ETS is employed to optimize the local energy utilization pursuing the peak shaving of residential consumption profile. Simulations of the proposed architecture using the Energetic Macroscopic Representation (EMR) demonstrate the potential of ETS technologies in future HEMS.

Power-balancing Instantaneous Optimization Energy Management for a Novel Series-parallel Hybrid Electric Bus

Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization（PBIO） energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus（SPHEB） dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope,elevation,speed and route distance preview to achieve optimal energymanagement of plug-in hybrid electric vehicles(PHEVs).Theapproach is to identify route features from historical and real-time traffic data,in which information fusion model and trafficprediction model are used to improve the information accuracy.Then,dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimalsolution for real-time energy management.The solution is thereference for PHEV energy management control along the route.To improve the system's ability of handling changing situation,the study further explores predictive control model in the real-time control of the energy.A simulation is performed to modelPHEV under above energy control strategy with route preview.The results show that the average fuel consumption of PHEValong the previewed route with model predictive control(MPC)strategy can be reduced compared with optimal strategy andbase control strategy.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Modular energy cost optimization for buildings with integrated microgrid

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants.The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energyprice profiles. This is a key element for technology separation,replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations,which are suitable for deployment as short-and long-term zero-energy building investments.

Distributed Real-time Temperature and Energy Control of Energy Efficient Buildings via Geothermal Heat Pumps

Geothermal heat pumps(GHPs)are a type of heating ventilation and air conditioning(HVAC)systems that use low-temperature resources from soil and groundwater for heating/cooling.In recent years,there has been an increasing interest in GHP systems due to their high energy efficiency and abundant geothermal resources.Thus,the optimization and control design of the GHP system has become a hot topic.On the other hand,as the GHP system is an ideal respon-sive load,mechanism design for the GHP system to realize demand response(DR)in a virtual power plant(VPP)without affecting user comfort is particularly essential.In this paper,we propose a distributed real-time temperature and energy management method via GHP systems for multi-buildings,where both floor and radiator heating/cooling distribution subsystems in multiple thermal zones are considered.We design an energy demand response mechanism for a single GHP to track the given energy consumption command for participating in VPP aggregation/disaggregation.Besides,a coordination mechanism for multiple GHPs is designed for the community-level oper-ator in joining VPP aggregation/disaggregation.Both designed schemes are scalable and do not need to measure or predict any exogenous disturbances such as outdoor temperature and heating disturbances from external sources,e.g.,user activity and device operation.Finally,four numerical examples for the simulation of two different scenarios demonstrate the effectiveness of the proposed methods.

基于深度强化学习的电-气区域综合能源系统安全校正控制决策方法

电–气区域综合能源系统电、气相互耦合与影响,使得其安全校正控制难度大且对快速性要求高,为此,提出一种基于深度确定性策略梯度(deepdeterministicpolicy gradient,DDPG)算法的安全校正控制决策方法。首先,进行系统多能流与变量分析,建立安全校正控制的目标与约束条件。然后,构建基于DDPG的安全校正控制模型,设计目标奖励和各种约束条件奖励,提出结合基于综合灵敏度的安全校正知识经验设计目标奖励函数,使调整具有方向性,且目标奖励考虑能量枢纽(energy hub,EH)的经济效益及其可再生能源消纳;通过智能体离线训练,使其能够在线做出实时最优的安全校正控制策略,预先产生专家经验数据集存放于经验回放池,提高训练速度和收敛性。最后,通过含EH电–气区域综合能源系统仿真算例验证了所提方法的有效性。

山西农业大学综合教学楼工程全过程管理与质量控制探讨

工程项目管理和质量控制一直是建筑工程建设关注的核心。以山西农业大学综合教学楼为例,对建筑工程项目质量管理和质量控制体系,以及工程质量控制策略及实践进行探讨,以丰富建筑工程项目管理和质量控制的实践。

基于模糊控制的燃料电池电动轻卡能量管理策略研究

以某款燃料电池电动轻型卡车作为研究对象,以提高燃料电池系统的经济性和耐久性为目的,利用Matlab/Simulink搭建了燃料电池电动轻卡动力系统仿真模型,对其能量管理策略进行优化。在原模糊控制策略的基础上进行改进,对燃料电池输出功率变化率进行约束,制定了一种改进后的模糊控制策略。将改进后的模糊控制策略与有限状态机控制策略和原模糊控制策略进行对比仿真验证。结果表明,在NEDC和UDDS循环工况下,改进后的模糊控制策略比原模糊控制策略氢耗量分别减少5.65%和8.29%;与有限状态机控制策略相比,改进后的模糊控制策略的氢耗量分别减少16.63%和10.64%,并且改进后的模糊控制策略的燃料电池输出功率波动幅度更小,变化更加稳定,提高了燃料电池系统的经济性和耐久性。

基于DBO-MPC的混合动力汽车能量管理策略

混合动力汽车(hybrid electrical vehicle,HEV)的能量管理策略直接决定了车辆的燃油经济性、驾驶性能和寿命,为解决HEV能量管理策略的最优性与实时行驶工况不确定性之间的矛盾,以混联式HEV为研究对象,提出一种基于模型预测控制(model predictive control,MPC)与蜣螂优化算法(dung beetle optimizer,DBO)的HEV能量管理策略。首先,该策略采用基于堆叠式长短时记忆神经网络(stacked long-short term memory neural network,Stacked LSTM-NN)的车速预测模型预测未来行驶车速。其次,根据预测车速将混合动力汽车的功率分配问题描述为MPC预测范围内的滚动优化问题,提出考虑燃料消耗和电池保护的成本函数,利用DBO算法对预测时域内发动机功率进行优化求解。最后,在城市道路循环(urban dynamometer driving schedule,UDDS)工况下分别对所提策略的车速预测精度和经济性与其他策略进行仿真对比验证。结果表明:与传统LSTM速度预测模型相比,Stacked LSTM速度预测模型的RMSE降低了13.9%,每步平均预测时间减少1 ms;与基于规则的策略相比,基于DBO-MPC的策略模型节油率达到25.3%,同时SOC状态波动更为平稳,对电池的保护效果更好。

基于多层ReLU网络的楼宇暖通空调系统能量管理策略

提升现代智能楼宇的能量管理能力,是电力紧平衡背景下促进电网节能增效的重要举措。文中提出一种数据驱动的楼宇暖通空调系统节能控制策略,避免了传统模型预测控制对精确热力学建模的依赖。首先,在搭建楼宇热网络仿真模型的基础上,采用延时嵌入法构造输入特征,建立基于修正线性单元的多层神经网络模型,实现室内温度时间序列的多步预测。然后,针对电网的分时电价,构建滚动时域优化模型,并将其重构为混合整数线性规划的形式,实现有限控制周期内优化模型的高效求解。最后,基于Simscape搭建楼宇热仿真模型,验证了所提控制策略的有效性。仿真结果表明,所提策略能够满足温度舒适度要求,并提高楼宇的经济运行水平。

基于需求功率预测的电动拖拉机能量管理策略

针对电动拖拉机在犁耕工况下电机需求电流波动比较大的特点,为了改善动力电池的输出电流过高或过低及电动拖拉机犁耕持续作业时间短的现象,利用超级电容高功率密度的特点,设计了一种锂电池+超级电容结构的双电源电动拖拉机,并建立了Amesim/Simulink联合仿真模型。以模型预测控制作为双电源系统的能量管理方法,基于长短期记忆神经网络建立电动拖拉机犁耕工况下的需求功率预测模型,使用动态规划算法求解最佳的锂电池输出电流。仿真结果表明:相比于模糊控制策略,基于模型预测控制策略有效降低了锂电池大电流放电的频率且峰值电流降低了40%,有效提高了锂电池的使用寿命;超级电容的SOC保持在比较高的范围内,且电动拖拉机在犁耕工况下的单位里程能量消耗降低了2.17%,实现了双电源电流分配最优,提高了电动拖拉机的动力性和经济性。

电动汽车BMS从控板热分析及散热优化

针对电动汽车电池管理系统从控板服役过程中因温度过高和不均影响整车动力性、安全性问题,本文基于CFD理论,采用Icepak软件建立并验证了某商用BMS从控板热分析模型。首次在车载服役条件下,基于热分析模型开展了温度场分析和热均匀性优化研究。BMS从控板热仿真分析表明,均衡模块及供电模块因局部积热温度均超过BMS的设计温度限值60℃,整个BMS从控板最大温差为21.0℃。为此,进一步开展了BMS从控板散热路径分析,并通过改变均衡电阻间距、布局、PCB基材以及增设导热硅胶垫进行散热优化设计。提高了BMS从控板的散热能力,使BMS从控板的最高温度控制在设计规定值60℃以下,同时整个电路板的温差降为6.9℃,提高了BMS从控板在实际车载服役条件下的安全性和可靠性,可望为BMS从控板热设计与优化提供理论方法。

混合动力拖拉机能量管理策略综述

对混合动力拖拉机进行研究,可有效提高燃料经济性,而能量管理策略的研究则是提高混合动力拖拉机燃油经济性的核心技术问题。通过文献整理及文献翻译,对能量管理的内涵进行分析。从基于规则和基于优化两个角度,对几种常见的能量管理策略进行分类。根据不同种类混合动力拖拉机的构型,分析其所适合的能量管理策略及其特点,并对之后能量管理控制策略应用于拖拉机上的研究方向做出了建议。

夏热冬暖地区办公建筑外遮阳百叶智能控制对比分析

随着建筑节能工作深入,建筑智能遮阳技术迅速发展,外遮阳智能控制系统成为有效降低建筑能耗、提高能源利用效率的新兴节能技术。通过建筑外遮阳百叶智能控制系统的对比设计,采用EnergyPlus的EMS模块进行模拟,比较东向百叶外遮阳不同智能控制方式对室内能耗的影响,对百叶最优倾角常开、入射角控制、冷负荷控制、综合控制进行能耗分析。研究结果发现:3种百叶控制方式与百叶最优倾角常开状态对比均有一定的节能效果,仅有入射角控制这一方式的总能耗最低,综合控制次之,仅有冷负荷控制的方式能耗最高。研究结果为建筑设计者提供了关于东向百叶外遮阳智能控制系统设置的参考,通过优化入射角控制设置降低能耗、提高建筑舒适性。