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.

Novel flexible hybrid electric system and adaptive online-optimal energy management controller for plug-in hybrid electric vehicles

In order to achieve the improvement of the driving comfort and energy efficiency,an new e-CVT flexible full hybrid electric system(E2FHS) is proposed,which uses an integrated main drive motor and generator to take the place of the original automatic or manual transmission to realize the functions of continuously variable transmission(e-CVT).The design and prototype realization of the E2FHS system for a plug-in hybrid vehicle(PHEV) is performed.In order to analyze and optimize the parameters and the power flux between different parts of the E2FHS,simulation software is developed.Especially,in order to optimize the performance of the energy economy improvement of the E2FHS,the effect of the different energy management controllers is investigated,and an adaptive online-optimal energy management controller for the E2FHS is built and validated by the prototype PHEV.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus

Recently,plug?in hybrid electric bus has been one of the energy?e cient solutions for urban transportation. However,the current vehicle e ciency is far from optimum,because the unpredicted external driving conditions are di cult to be obtained in advance. How to further explore its fuel?saving potential under the complicated city bus driving cycles through an e cient control strategy is still a hot research issue in both academic and engineering area. To realize an e cient coupling driving operation of the hybrid powertrain,a novel coupling driving control strategy for plug?in hybrid electric bus is presented. Combined with the typical feature of a city?bus?route,the fuzzy logic inference is employed to quantify the driving intention,and then to determine the coupling driving mode and the gear?shifting strategy. Considering the response deviation problem in the execution layer,an adaptive robust controller for electric machine is designed to respond to the transient torque demand,and instantaneously compensate the response delay and the engine torque fluctuation. The simulations and hard?in?loop tests with the actual data of two typical driving conditions from the real?world city?bus?route are carried out,and the results demonstrate that the pro?posed method could guarantee the hybrid powertrain to track the actual torque demand with 10.4% fuel economy improvement. The optimal fuel economy can be obtained through the optimal combination of working modes. The fuel economy of plug?in hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.

向电动车看齐 PRIUS PLUG-IN HYBRID

单从环保方面来说,纯电动车无疑是最优秀的,但是充电设施的不完善以及自身续航里程有限等因素依然制约着纯电动车的发展,而单纯的混合动力车型在控制CO2排放方面能力有限,于是一种名为"PHEV"的车型诞生了。

Analysis of Hybrid Rechargeable Energy Storage Systems in Series Plug-In Hybrid Electric Vehicles Based on Simulations

In this paper, an extended analysis of the performance of different hybrid Rechargeable Energy Storage Systems (RESS) for use in Plug-in Hybrid Electric Vehicle (PHEV) with a series drivetrain topology is analyzed, based on simulations with three different driving cycles. The investigated hybrid energy storage topologies are an energy optimized lithium-ion battery (HE) in combination with an Electrical Double-Layer Capacitor (EDLC) system, in combination with a power optimized lithium-ion battery (HP) system or in combination with a Lithium-ion Capacitor (LiCap) system, that act as a Peak Power System. From the simulation results it was observed that hybridization of the HE lithium-ion based energy storage system resulted from the three topologies in an increased overall energy efficiency of the RESS, in an extended all electric range of the PHEV and in a reduced average current through the HE battery. The lowest consumption during the three driving cycles was obtained for the HE-LiCap topology, where fuel savings of respectively 6.0%, 10.3% and 6.8% compared with the battery stand-alone system were achieved. The largest extension of the range was achieved for the HE-HP configuration (17% based on FTP-75 driving cycle). HP batteries however have a large internal resistance in comparison to EDLC and LiCap systems, which resulted in a reduced overall energy efficiency of the hybrid RESS. Additionally, it was observed that the HP and LiCap systems both offer significant benefits for the integration of a peak power system in the drivetrain of a Plug-in Hybrid Electric Vehicle due to their low volume and weight in comparison to that of the EDLC system.

大佬的环保生活 NEW BENZ S 500 PLUG-IN HYBRID

众所周知，以丰田为代表的日系车企在混合动力及插电式混合动力领域一直处于行业领先水平，但是其他车企并未坐以待毙，奔驰公司曾以老款S级轿车为基础研发混合动力及插电式混合动力技术，并于2009正式推出S 400 Hybrid车型以及S 500 Plug-in Hybrid概念车。如今，经过4年的进化和发展，插电式混合动力技术也更趋成熟，于是今年的法兰克福车展就成了奔驰S 500 Plug-in Hybrid量产车型首发亮相的最佳舞台。

OPEL GRANDLAND X PLUG-IN HYBRID4 能“混”才行

'随着各家车企都在新能源领域进行着自己的努力,似乎再不行动的汽车品牌就会被这个时代抛弃。日前,在中国已很少见却在欧洲混得风生水起的欧宝推出了旗下首款插电混合动力车型——Grandland X Plug-in Hybrid4,正如其名称所写的那样,新车型是基于Grandland X打造而来。'

混出精彩 VOLVO XC60 PLUG-IN HYBRID CONCEPT

凭借着高大宽敞的空间和底盘的高通过性,在自驾游越来越盛行的今天,SUV车型慢慢成了市场上的＂香馍馍＂,其市场热度毫不逊色于小排量车型。但高油耗和高排放的环保问题成为制约其进一步发展的瓶颈,于是开发混合动力的SUV车型成为汽车厂商研发的一个重点。

Plug-in Hybrid启示录

2009年的法兰克福和东京车展中，奔驰、宝马、丰田、铃木、三菱等都展出了他们的插电式混合动力车，这是否代表这种技术已经是新一代混合动力车的趋势？

起亚Ray Plug-In Hybrid

全新Ray概念车乃由Kia美国设计中心操刀，旨在将Kia新一代设计元素，融合在Plug—in Hybrid插电式油电混合车款之中。目前正在研究替代动力的方案。而Kia也计划在今年内，将LPG Hybrid液化石油气混合动力车款导入市售量产阶段，并在韩国开售。

Performance Analysis of Plug-in Hybrid Passenger Vehicles

PHEVs （passenger plug-in hybrid electric vehicles） have shown significant fuel reduction potential. Furthermore, PHEVs can also improve longitudinal vehicle dynamics with respect to acceleration and engine elasticity. The objective of this study is to investigate potential of concurrent optimization of fuel efficiency and driving performance. For the studies, a backward vehicle model for a parallel PHEV was designed, where the power flow is calculated from the wheels to the propulsion units, the conventional ICE （internal combustion engine） and the EMG （electric motor/generator） unit. The hybrid drive train is according to a P2 layout, consequently the EMG is situated between the shifting clutch and the ICE. The implemented operation strategy distributes the power to both propulsion units depending on the vehicle speed, requested driving torque, the battery＇s SOC （state of charge） and SOP （state of power）. Additional information, such as the slope of the road, can be taken into account by the operation strategy. In the paper, the fuel saving potential as well as the longitudinal dynamics change of different PHEV configurations is presented as a function of battery capacity and EMG power. Consequently, applicable hybrid components can be defined. By using additional information of the environment like various sensor data, road slope amongst others, the fuel saving potential can be improved even more. By studying the dynamic model, the overall results of the backward model are confirmed. In conclusion, this study shows that it is possible to concurrently reduce fuel consumption and increase driving performance in PHEVs. The potential depends strongly on the configuration of the electric components and the implemented operation strategy. Consequently, the hybrid system configuration has to be chosen carefully and aligned to the vehicle performance.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles.While fuel economy is a key factor in the design of plug-in hybrid electric vehicles,a fuel economy label can educate customers about the economic advantage of purchasing a particular car.The fuel economy label of a PHEV consists of parameters like driving range,electrical energy consumption,fuel economy for city,highway,and combined use,battery recharge time,and fuel consumption rates.The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs.Firstly,the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model.The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model.The output variables simulated were engine size in litres;estimated driving range when the battery is fully charged in km,battery recharged time in hours,city fuel consumption(L/100 km),highway fuel consumption(L/100 km),combined fuel consumption(L/100 km),estimated driving range when the tank is full,carbon dioxide(CO_(2))emission in grams/km,electric motor power in kW,number of cylinders,and electrical charges consumed in kWh/100 km.Different cases of input variables were considered for the inverse function model.The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术治疗多节段脊髓型颈椎病临床疗效分析

目的:分析颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术(EODL)治疗多节段脊髓型颈椎病的疗效,探讨多节段脊髓型颈椎病患者手术方式的选择。方法:对2017年7月—2020年7月在首都医科大学附属北京中医医院手术治疗的70例多节段脊髓型颈椎病患者进行回顾性分析,根据手术方式不同,分为前路组35例和后路组35例,前路组患者行Hybrid手术[颈椎前路椎间盘切除融合术(ACDF)联合人工颈椎间盘置换术(ACDR)],后路组患者行EODL。记录2组患者住院时间、手术时间、术中出血量和术后引流量,通过日本骨科协会(JOA)评分、JOA改善率、颈椎残障功能指数(NDI)、疼痛视觉模拟评分(VAS)和术后满意度评分进行疗效评价,统计2组患者术后并发症发生情况。结果:与后路组比较,前路组患者术中出血量、术后引流量、住院时间和手术时间均明显减少(P<0.01),术前各项评分差异无统计学意义(P>0.05)。末次随访时,与后路组比较,前路组患者JOA评分和JOA改善率明显升高(P<0.01),NDI评分和VAS评分明显降低(P<0.01)。与术前比较,末次随访时2组患者JOA评分明显升高(P<0.01),NDI和VAS评分均明显降低(P<0.01)。按术后满意度评分评价,2组患者术后满意度均较高。2组患者术后并发症发生率比较差异无统计学意义(P>0.05)。结论:颈椎前路Hybrid手术和EODL在治疗多节段脊髓型颈椎病方面均取得了较为满意的疗效。Hybrid手术具有出血量少和手术时间短等优点,临床上应根据患者实际情况选择最适宜的术式。

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

基于Hybrid A^(*)算法的变压器声级巡检系统研究与设计

随着国内变电站数目逐步增加,采用固定式声级监测终端对变压器声级检测的方式已经满足不了日常检测的需求。为解决固定式声级监测终端方式成本高、维护复杂、设备利用率低等问题,该文率先提出了一种变压器声级巡检系统,并设计了最优声级巡检路径。通过场地定位传感器生成变压器场地栅格图信息,采用Hybrid A^(*)算法将场地栅格图信息生成符合国标所要求的最优声级巡检路径检测点;针对所开发的变压器声级巡检装置,采用生成的巡检路径对变压器进行声级测定作业,对测定的声级数据进行分析处理。测试结果表明,该文开发的系统与设计算法的变压器声级巡检时间、检测效率以及数据的采集准确性都优于固定式声级监测终端方式,系统完成变压器声级巡检全过程的成功率可达95%。

复杂建设环境下基于Hybrid A^(*)算法的铁路平面线形绿色优化设计

随着“双碳经济下绿色铁路”理念的兴起,将“绿色生态”融入到铁路平面线路优化已成为近年来的研究热点。本文以铁路建设成本与生态破坏成本的协同优化为目标,引入并改进了一种自动驾驶导航算法(Hybrid A^(*)算法),以适应复杂的铁路设计问题,同时考虑最小曲线半径、最大曲线半径、最短曲线长度、最短夹直线长度、缓和曲线长度等铁路线形约束。研究结果表明:(1)改进后算法以离散网格方式整合外部环境因素,实现渐进式全局探索,获取接近全局最优的铁路线路设计结果;(2)该方法在复杂外部环境约束下,无需预设水平交点位置和数量,可自动生成符合线路-环境耦合约束的优化平面线路方案。

p-d Orbital Hybridization Engineered Single-Atom Catalyst for Electrocatalytic Ammonia Synthesis

The rational design of metal single-atom catalysts(SACs)for electrochemical nitrogen reduction reaction(NRR)is challenging.Two-dimensional metal-organic frameworks(2DMOFs)is a unique class of promising SACs.Up to now,the roles of individual metals,coordination atoms,and their synergy effect on the electroanalytic performance remain unclear.Therefore,in this work,a series of 2DMOFs with different metals and coordinating atoms are systematically investigated as electrocatalysts for ammonia synthesis using density functional theory calculations.For a specific metal,a proper metal-intermediate atoms p-d orbital hybridization interaction strength is found to be a key indicator for their NRR catalytic activities.The hybridization interaction strength can be quantitatively described with the p-/d-band center energy difference(Δd-p),which is found to be a sufficient descriptor for both the p-d hybridization strength and the NRR performance.The maximum free energy change(ΔG_(max))andΔd-p have a volcanic relationship with OsC_(4)(Se)_(4)located at the apex of the volcanic curve,showing the best NRR performance.The asymmetrical coordination environment could regulate the band structure subtly in terms of band overlap and positions.This work may shed new light on the application of orbital engineering in electrocatalytic NRR activity and especially promotes the rational design for SACs.

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.