考虑锂离子电池老化的纯电动汽车能量管理策略

为改善电动汽车的电池使用寿命,提出了一种电池老化管理策略,使汽车在综合考虑了车辆的行驶里程、充电时间、驾驶性能等指标的情况下来控制电池容量退化,将多目标、多输出的优化问题转变为单一性能指标.该策略以电池充放率和放电深度为控制变量,采用粒子群优化算法对模型进行离散化迭代,选择出每一步的最优控制变量数值.经过20万km的NEDC驾驶工况循环验证,电池退化减少了0.2%,证明所提电池老化管理策略可以减少电池老化,改善汽车性能.     

基于GPR代理模型和GA-APSO混合优化算法的软基水闸底板脱空反演EI北大核心CSCD

软基水闸底板脱空是水闸在长期服役期间受水流侵蚀等环境因素影响所产生的一种危害极大且难以察觉的病害。由于其病害部位于水下,传统方法难以检测,该研究提出一种基于高斯过程回归(Gaussian process regression,GPR)代理模型和遗传-自适应惯性权重粒子群(genetic algorithm-adaptive particle swarm optimization,GA-APSO)混合优化算法的水闸底板脱空动力学反演方法,用于检测软基水闸底板脱空。首先,构建表征软基水闸底板脱空参数和水闸结构模态参数之间非线性关系的GPR代理模型;其次,基于GPR代理模型与水闸实测模态参数建立脱空反演的最优化数学模型,将反演问题转化为目标函数最优化求解问题;最后,为提高算法寻优计算的精度,提出一种GA-APSO混合优化算法对目标函数进行脱空反演计算,并提出一种更合理判断反演脱空区域面积和实际脱空区域面积相对误差的指标—面积不重合度。为验证所提方法性能,以一室内软基水闸物理模型为例,对两种不同脱空工况开展研究分析,结果表明,反演脱空区域面积和模型实际设置脱空区域面积的相对误差分别为8.47%和10.77%,相对误差值较小,证明所提方法能有效反演出水闸底板脱空情况,可成为软基水闸底板脱空反演检测的一种新方法。     

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector. The capability of online fuzzy tracking systems is maximum power, resistance to radiation and temperature changes, and no need for external sensors to measure radiation intensity and temperature. However, the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing. The controller used in the maximum power point tracking (MPPT) circuit must be able to adapt to the new radiation conditions. Therefore, in this paper, to more accurately track the maximum power point of the solar system and receive more electrical power at its output, an adaptive fuzzy control was proposed, the parameters of which are optimized by the whale algorithm. The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm (P&O) method, which is a practical and high-performance method. To evaluate the performance of the proposed algorithm, the particle swarm algorithm optimized the adaptive fuzzy controller. The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system. Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method, which distinguishes it from other methods. On the other hand, the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm, which confirms the higher accuracy of the proposed algorithm.     

基于BSO-BPNN模型的电能计量装置异常诊断方法研究

电网运维人员主要根据用电信息采集系统采集到的巡检数据对电能计量装置进行人工异常检测。针对人工诊断存在的漏报、误报、判断标准不一、准确度低等问题,文章提出一种天牛须搜索算法(beetle antennae search)和粒子群算法(particle swarm optimization)结合的天牛群算法(beetle swarm optimization),并将其用于优化BP神经网络(back propagation neural network)电能计量装置异常诊断模型。文章利用天牛群算法迭代寻优BP神经网络权阈值,根据诊断准确率对天牛群算法优化性能进行评价,并和粒子群优化的BP神经网络模型诊断结果进行对比。实验分析表明,天牛群算法优化的BP神经网络模型对于电能计量装置的异常诊断具有更高的准确度以及稳定性。     

基于工况识别的IWM-EV主动悬架MOPSO模糊滑模控制

轮毂电机电动汽车(in-wheel motor electric vehicle,IWM-EV)的电机激励与车辆系统的耦合特性严重的恶化车辆的动力学性能以及电机的工作稳定性,针对这种振动负效应问题,建立了考虑机电耦合的车辆动力学耦合模型,并设计了工况识别的主动悬架多目标粒子群(multi-objective particle swarm optimization,MOPSO)模糊滑模控制器。基于傅里叶级数法建立了轮毂电机的垂向不平衡激励与电机转矩的电机模型;将电机模型与车辆动力学模型结合建立了电机与悬架联合的垂向-驱动非线性动力学耦合模型。基于耦合模型分析了车辆的机电耦合振动负效应特性,针对模型强非线性的特点,设计了耦合模型的非线性控制器。仿真结果表明,控制器能既能有效的减小电机的相对偏心率,抑制电机不平衡电磁力,又能提升车辆动力学性能,有效的抑制了轮毂电机电动汽车的振动负效应。     

潮流分析和鸟群优化在风水协同运行中的应用

风力发电的随机性导致弃风。考虑在实际发电约束以及电网络约束条件下,运用水电的调峰调频特性,最大化平抑风电出力的波动性,使风电出力与水电出力之和按照计划输出,将风电处理成可供电力系统实施调度计划的稳定发电节点。为了提高风水协同发电总输出功率平稳性,提出了基于鸟群算法的风水协同运行控制方法。在控制系统寻优前进行系统潮流计算,在保证系统潮流安全条件下,有效地控制水轮机组出力,快速追踪期望输出功率。算例验证了上述方法的有效性,并且分析了高弃风惩罚和高功率波动惩罚对风电水电协同运行的影响。     

多臂机提综臂辅助旋铆并联机器人优化设计

提综臂是多臂机上实现凸轮开口运动的关键核心部件.为满足多臂机提综臂的自动化加工需求,设计了一种新型辅助旋铆并联机器人,以实现在提综臂旋铆加工过程中对零部件的高速、高精度抓取和摆放,显著提升生产效率和保证加工质量.首先,介绍了新型辅助旋铆并联机器人机构的拓扑结构特征,并结合旋量理论及修正的G-K(Grübler-Kutz...     

A demonstration of the improved efficiency of the canonical coordinates method using nonlinear combined heat and power economic dispatch problems

Economic dispatch is the short-term determination of the optimal output from a number of electricity generation facilities to meet the system load while providing power. As such, it represents one of the main optimization problems in the operation of electrical power systems. This article presents techniques to substantially improve the efficiency of the canonical coordinates method (CCM) algorithm when applied to nonlinear combined heat and power economic dispatch (CHPED) problems. The improvement is to eliminate the need to solve a system of nonlinear differential equations, which appears in the line search process in the CCM algorithm. The modified algorithm was tested and the analytical solution was verified using nonlinear CHPED optimization problems, thereby demonstrating the effectiveness of the algorithm. The CCM methods proved numerically stable and, in the case of nonlinear programs, produced solutions with unprecedented accuracy within a reasonable time.     

计及蓄电池寿命的冷热电联供型微电网多目标经济优化运行

冷热电联供（combined cooling,heating and power,CCHP）型微电网不仅能为清洁能源的开发利用提供良好的平台,降低能源消费带来的环境污染,而且能够改善供电电能质量,降低系统损耗。为使CCHP型微电网经济计算更加符合实际运行工况,考虑将蓄电池寿命损耗带来的经济损失加入经济调度计算模型,同时考虑CCHP型微电网的经济性和环保性,建立CCHP型微电网的多目标优化模型。根据最大满意度的原则将多目标优化模型转化为单目标优化模型,利用改进型遗传算法,优化日内各微源的出力。通过算例对比分析多目标优化和各个单目标优化对微电网中各微源出力的影响,结果表明：多目标优化模型能够兼顾CCHP型微电网的经济性和环保性,更加接近CCHP型微电网的实际运行工况。