Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

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.

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.

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.

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.

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.

Non-radioactive Hybridization in Microwells Using Enzym eLinked Im m uneSorbentAssay forDetection ofRT-PCR-Am -plified CK19-and CEA-m RNA

PCR enzyme linked immune sorbent assay (ELISA) was developed for detection of RT PCR amplified cytokeratin 19 (CK19) mRNA and carcinoembryonic antigen (CEA) mRNA. The non radioactive hybridization was performed in a streptavidin coated microwell with digoxigenin labeled PCR products and with biotin labeled capture probe. PCR ELISA was proved to be expedient, simple, sensitive and safe for identification of CK19 , CEA RT PCR products. These results were proven by sequencing.

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。

Progress on Transferring Elite Genes from Non-AA Genome Wild Rice into Oryza sativa through Interspecific Hybridization

The progress of research on transferring elite genes from non-AA genome wild rice into Oryza sativa through interspecific hybridization are in three respects, that is, breeding monosomic alien addition lines （MAALs）, constructing introgression lines （ILs） and analyzing the heredity of the characters and mapping the related genes. There are serious reproductive barriers, mainly incrossability and hybrid sterility, in the interspecific hybridization of O. sativa with non-AA genome wild rice. These are the ＇bottleneck＇ for transferring elite genes from wild rice to O. sativa. Combining traditional crossing method with biotechnique is a reliable way to overcome the reproductive barriers and to improve the utilizing efficiency of non-AA genome wild rice.

Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method.Afterwards,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults adaptively.Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

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.

Decentralized PD Control for Non-uniform Motion of a Hamiltonian Hybrid System

In this paper, a decentralized proportional-derivative （PD） controller design for non-uniform motion of a Hamiltonian hybrid system is considered. A Hamiltonian hybrid system with the capability of producing a non-uniform motion is developed. The structural properties of the system are investigated by means of the theory of Hamiltonian systems. A relationship between the parameters of the system and the parameters of the proposed decentralized PD controller is shown to ensure local stability and tracking performance. Simulation results are included to show the obtained non-uniform motion.

Non-intrusive hybrid interval method for uncertain nonlinear systems using derivative information

This paper proposes a new non-intrusive hybrid interval method using derivative information for the dynamic response analysis of nonlinear systems with uncertain-but- bounded parameters and/or initial conditions. This method provides tighter solution ranges compared to the existing polynomial approximation interval methods. Interval arith- metic using the Chebyshev basis and interval arithmetic using the general form modified affine basis for polynomials are developed to obtain tighter bounds for interval computation. To further reduce the overestimation caused by the ＂wrap- ping effect＂ of interval arithmetic, the derivative information of dynamic responses is used to achieve exact solutions when the dynamic responses are monotonic with respect to all the uncertain variables. Finally, two typical numerical examples with nonlinearity are applied to demonstrate the effective- ness of the proposed hybrid interval method, in particular, its ability to effectively control the overestimation for specific timepoints.

Overview of one transistor type of hybrid organic ferroelectric non-volatile memory

Organic ferroelectric memory devices based on field effect transistors that can be configured between two stable states of on and off have been widely researched as the next generation data storage media in recent years.This emerging type of memory devices can lead to a new instrument system as a potential alternative to previous non-volatile memory building blocks in future processing units because of their numerous merits such as cost-effective process,simple structure and freedom in substrate choices.This bi-stable non-volatile memory device of information storage has been investigated using several organic or inorganic semiconductors with organic ferroelectric polymer materials.Recent progresses in this ferroelectric memory field,hybrid system have attracted a lot of attention due to their excellent device performance in comparison with that of all organic systems.In this paper,a general review of this type of ferroelectric non-volatile memory is provided,which include the device structure,organic ferroelectric materials,electrical characteristics and working principles.We also present some snapshots of our previous study on hybrid ferroelectric memories including our recent work based on zinc oxide nanowire channels.

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。