Analysis of Quality Control and Safety Management in Electrical Engineering

In the development process of the society nowadays,electrical engineering has been widely used with remarkable results,but there are still some problems in the course of actual management,which requires effective technical measures to control the quality in electrical engineering and the enhancement of safety management work.Therefore,in order to better ensure safety management requirements in electrical engineering and strengthen related trainings and thus better carried out safety management and quality control to meet respective requirements in the real world,in this paper,a brief analysis is conducted in quality control and safety management in electrical engineering.

基于A-ECMS的增程式电动汽车能量管理策略设计及应用

增程式电动汽车因其电驱效率高、无里程焦虑等优点,在新能源汽车市场备受青睐。为了进一步提升其综合性能,本研究在MATLAB/Simulink环境下构建了增程式电动汽车仿真模型,并利用等效自适应因子以改进燃油消耗最小化算法,形成了基于A-ECMS的能量管理策略。结果表明,在WLTC循环工况测试中,A-ECMS能量管理策略的百公里综合油耗为6.42 L,综合燃油消耗量为0.71 L,均低于其他方法。最终的电池充电状态(SOC)为31.1%,与目标SOC的偏差仅为1.1%。该策略显著提升了燃油经济性和电池使用寿命,为增程式电动汽车性能优化提供了新的参考方法。

The Application of Principle of Automatic Control in the site Management of Electric Power Construction Enterprise

It is need to establish site project department during the construction of electric power project.The head office should design and build the main structure,continually improve structure of project department,meantime,positively apply principle of automatic control to improve the site management of electric power construction.In this article,the application strategy of principle of automatic control is analyzed focusing on the site work of electric power construction.

混动车辆能量管理模块化ECMS框架

文中提出一种用于混合动力车辆能量管理实时控制的模块化等效消耗最小化策略(ECMS)框架,可实现能量管理实时控制的分布式开发。该框架采用Pontryagin极小值原理求解ECMS最优控制问题。首先,将最优控制问题分解为与各个子系统相关的优化问题;其次,通过Hamiltonian函数将与每个子系统有关的优化问题协调到全局最优;最后,在某款DHT混合动力车辆验证模块化ECMS,此车辆模型支持4种操作模式:2种电动模式、并联模式和串联模式。测试结果表明,模块化ECMS只需修改能量管理系统中功率平衡方程的连接矩阵,即可解决上述每种操作模式的最优控制问题。此框架不需要迭代过程,更适合实时控制的分布式开发。

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.

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.

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.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

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.

Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

ECMS系统的新型结构及其应用

针对火电厂厂用电气监控管理系统(ECMS)现有的系统结构(通讯+硬接线与全通讯方式)存在的问题,提出了一种与DCS系统相互独立的多机组ECMS系统结构,高压厂用电部分采用双100M以太网,低压厂用电及其他智能装置采用总线形式,通过虚拟局域网技术(VLAN),区分了不同机组对系统的监控权限,实现对厂用电源部分的监控及对其他厂用电部分的监测管理。现场试验数据显示,该系统遥信上传时间小于300 ms,站控层遥控指令发出至开关变位信号返回,时间小于1.8 s。

基于DP-ECMS的插电式混合动力城市客车能量管理策略研究

以一款气-电型插电式混合动力城市客车(PHEV)为研究对象,针对能量管理中的最小能耗问题分别应用电量消耗-电量维持(CD-CS)策略、动态规划(DP)、等效能耗最小化策略(ECMS)和自适应等效能耗最小化策略(A-ECMS)进行中国典型城市工况仿真。在对上述几种能量管理策略仿真结果分析的基础上,提出一种将动态规划与等效能耗最小化策略相结合的DP-ECMS策略。结果表明:DP-ECMS的能耗特性接近动态规划,同时具有等效能耗最小化策略的实时性特点,为PHEV的能量管理提供了参考。

基于动态规划算法的混合动力汽车改进型ECMS能量管理控制研究

为改善混合动力汽车的燃油经济性,以耦合器侧的输入扭矩为研究对象,以最优化扭矩分配为目标,应用动态规划求出已知工况下并联型混合动力汽车全局最优的动力源工作状态,以此作为输入求得等效因子,并提出一种改进型瞬时等效燃油消耗最低策略(ECMS)。将电池充放电过程等效为虚拟的发动机运转过程,利用虚拟等效燃油消耗,建立改进型ECMS能量管理整车仿真模型,并与功率跟随策略和传统ECMS进行仿真分析比较。结果表明:对于改进型ECMS,相较于功率跟随策略,SOC降低了3.36%,燃油经济性提高了10.95%;相较于传统ECMS,SOC提高了1.48%,燃油经济性提高了3.20%。

燃料电池汽车行驶里程自适应ECMS策略

为提高插电式燃料电池汽车的经济性,基于等效氢消耗最小策略,通过等效系数S与行驶里程的自适应规则调整目标价值函数,以控制动力电池电能消耗速率,同时引入参考SOC进一步修正等效系数S,使行驶过程中动力电池尽可能多地获取电网电能,同时避免动力电池过度放电,从而实现控制策略对行驶里程的自适应性。通过Matlab/Simulink建立插电式燃料电池汽车仿真模型,仿真结果表明,当行驶里程超过纯电动行驶里程后,该策略能控制动力电池SOC在行驶结束时到达目标值。硬件在环对比试验的结果表明,采用里程自适应的ECMS策略时,总里程为100,150和200 km的氢气消耗量分别比基于CD-CS的ECMS策略降低8.75%,14.21%和16.63%。

基于驾驶意图云模型识别的混合动力汽车A-ECMS的构建

基于驾驶意图云模型识别的自适应等效燃油消耗最小策略(A-ECMS),构建了一种混合动力汽车能量管理策略,并对该策略进行了仿真研究。采用云模型算法,识别驾驶意图,获取了典型行驶工况下的意图识别结果;以加权平均的方法,求得各意图下对应的等效因子;建立了等效因子查询表。结果表明:在新欧洲行驶循环(NEDC)工况下,相比于基于充电状态(SOC)反馈的A-ECMS策略,本策略的燃油经济性提高了1.3%;在自定义组合工况下也能取得近似ECMS策略的燃油经济性控制效果,且具有更好的SOC稳定性控制效果。因而,该A-ECMS策略提高了原来的等效燃油消耗最小策略(ECMS)的实用性,体现驾驶意图。

Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles

In this paper,a model predictive control(MPC)based on back propagation neural network(BPNN)prediction model was proposed for compressor speed control of air conditioning system(ACS)and battery thermal management system(BTMS)coupling system of battery electric vehicle(BEV).In order to solve the problem of high cooling energy consumption and inferior thermal comfort in the cabin of the battery electric vehicle thermal management system(BEVTMS)during summer time,this paper combines the respective superiorities of artificial neural network(ANN)predictive modeling and MPC,and creatively combines the two methods and uses them in the control of BEVTMS.Firstly,based on ANN and heat transfer theory,BPNN prediction model,ACS and BTMS coupling system were established and verified.Secondly,a mathematical method of MPC was established to control the speed of the compressor.Then,the state parameters of the coupled system were predicted using a BPNN prediction model,and the predicted values were passed to the MPC,thus achieving accurate control of the compressor speed using the MPC.Finally,the effects of PID control and MPC based on BPNN prediction model on thermal comfort of cabin and compressor energy consumption at different ambient temperatures were compared in simulation under New European Driving Cycle(NEDC)conditions.The results showed for the constructed BPNN prediction model predicted and tested values of the selected parameters the mean squared error(MSE)ranged from 2.498%to 8.969%,mean absolute percentage error(MAPE)ranged from 4.197%to 8.986%,and mean absolute error(MAE)ranged from 3.202%to 8.476%.At ambient temperatures of 25℃,35℃ and 45℃,the MPC based on the BPNN prediction model reduced the cumulative discomfort time in the cabin by 100 s,39 s and 19 s,respectively,compared with the PID control.Under three NEDC conditions,the energy consumption is reduced by 1.82%,2.35%and 3.48%,respectively.When the ambient temperature was 35℃,the MPC based on BPNN prediction model can make the ACS and BTMS coupling system have better thermal comfort,and the energy saving effect of the compressor was more obvious with the temperature.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.

基于ECMS算法的能量管理策略研究

控制策略是混合动力汽车的核心技术之一,直接影响了混合动力汽车的经济性、动力性等。本文以Prius混联式混合动力汽车为研究对象,进行ECMS算法的研究,对算法中的等效系数的确定以及等效系数的主动修正进行研究分析,先根据行驶工况确定参考等效系数,再根据电池SOC对等效系数进行调整,并在AVL/CRUISE中建立整车模型,在SIMULINK中设计了基于ECMS算法的控制策略,进行整车经济性的仿真分析,仿真结果表明所设计的控制策略能够在实现较好的燃油经济性的同时维持电池SOC的平衡。