A universal charge-compensating strategy for high-energy-density pseudocapacitors

For pseudocapacitive electrode materials(PseEMs),despite much progress having been made in achieving both high power density and high energy density,a general strategy to guide the enhancement of intrinsic capacitive properties of PseEMs remains lacking.Here,we demonstrate a universal chargecompensating strategy to improve the charge-storage capability of PseEMs intrinsically:ⅰ) in the electrolyte with anion as charge carriers(such as OH-),reducing the multivalent cations of PseEMs into lower valences could create more reversible low-to-high valence redox cou ples to promote the intercalation of the anions;ⅱ) in the electrolytes with cation as charge carriers(such as H^(+),Li^(+),Na^(+)),oxidizing the multivalent cations of PseEMs into higher valences could introduce more reversible high-to-low valence redox couples to promote the intercalation of the cations.And we demonstrated that the improved intrinsic charge-storage capability for PseEMs originates from the increased Faradaic charge storage sites.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Injection Strategy in Natural Gas–Diesel Dual-Fuel Premixed Charge Compression Ignition Combustion under Low Load Conditions

Dual-fuel premixed charge compression ignition (DF-PCCI) combustion has been proven to be a viable alternative to conventional diesel combustion in heavy-duty compression ignition engines due to its low nitrogen oxides (NOx) and particulate matter (PM) emissions. When natural gas (NG) is applied to a DF-PCCI engine, its low reactivity reduces the maximum pressure rise rate under high loads. However, the NG–diesel DF-PCCI engine suffers from low combustion efficiency under low loads. In this study, an injection strategy of fuel supply (NG and diesel) in a DF-PCCI engine was investigated in order to reduce both the fuel consumption and hydrocarbon (HC) and carbon monoxide (CO) emissions under low load conditions. A variation in the NG substitution and diesel start of energizing (SOE) was found to effectively control the formation of the fuel–air mixture. A double injection strategy of diesel was implemented to adjust the local reactivity of the mixture. Retardation of the diesel pilot SOE and a low fraction of the diesel pilot injection quantity were favorable for reducing the combustion loss. The introduction of exhaust gas recirculation (EGR) improved the fuel economy and reduced the NOx and PM emissions below Euro VI regulations by retarding the combustion phasing. The combination of an NG substitution of 40%, the double injection strategy of diesel, and a moderate EGR rate effectively improved the combustion efficiency and indicated efficiency, and reduced the HC and CO emissions under low load conditions.

Analysis on the Mode and Competitive Strategy of Electricity Sales in the Background of Electric Power System Reform

A new round of reform of the power system conforms to the national development of the energy revolution and the reform of the electricity market system. As an important promoter of the energy revolution and power reform, a new round of power system reform has brought profound changes to the energy industry and the power industry and the electricity market system has changed dramatically. With the release of the electricity market, market competition intensified. This paper analyzes the competition strategy of electricity sales market, analyzes the characteristics of market participants such as power generation enterprises, power grid enterprises, electricity sales companies and users, and puts forward the corresponding coping strategies to help the market participants to deal with the opportunities and challenges brought by power system reform, provide guidance and reference for the market participants.

A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation

The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehiclemass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variableslope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicleshifting strategy was formulated according to the identification results. The co-simulation results showed that,compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-timevehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had thefollowing advantages, e.g., avoiding frequent shifting of vehicles up the hill, making full use ofmotor braking downthe hill, and improving the overall performance of vehicles.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

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.

The transcorneal electrical stimulation as a novel therapeutic strategy against retinal and optic neuropathy: a review of experimental and clinical trials

Transcorneal electrical stimulation （TES） is a novel therapeutic approach to activate the retina and related downstream structures. TES has multiple advantages over traditional treatments, such as being minimally invasive and readily applicable in a routine manner. Series of animal experiments have shown that TES protects the retinal neuron from traumatic or genetic induced degeneration. These laboratory evidences support its utilization in ophthalmological therapies against various retinal and optical diseases including retinitis pigmentosa （RP）, traumatic optic neuropathy, anterior ischemic optic neuropathy （AION）, and retinal artery occlusions （RAOs）. Several pioneering explorations sought to clarify the functional mechanism underlying the neuroprotective effects of TES. It seems that the neuroprotective effects should not be attributed to a solitary pathway, on the contrary, multiple mechanisms might contribute collectively to maintain cellular homeostasis and promote cell survival in the retina. More precise evaluations y/a functional and morphological techniques would determine the exact mechanism underlying the remarkable neuroprotective effect of TES. Further studies to determine the optimal parameters and the long-term stability of TES are crucial to justify the clinical significance and to establish TES as a popularized therapeutic modality against retinal and optic neuropathy.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

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.

Torque Distribution Strategy for Integrated Starter/ Generator Hybrid Bus Implemented by Fuzzy Algorithm

A torque distribution strategy was designed by using fuzzy logic to realize the optimal control. The vehicle load zones were dynamically divided into several zones by several torque lines to indicate the drivers demand and the high or low efficient operating areas of the diesel engine. The fuzzy logic controller with trapezoid membership function and Mamdani rule reference mechanism was utilized. There are over 100 rules used in this fuzzy-based torque distribution strategy which are sorted into four rule-bases. The fuel economy and acceleration tests were designed to test and validate the integrated starter/generator (ISG) bus perfor-mance using fuzzy-based torque distribution strategy. The fuel economy is improved 7.7% compared with the rule-based strategy. Finally the road test results reveal that there is about 15% improvement of fuel economy. And the 0-50 km/h acceleration time is 9.5% shorter than the original bus.

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.

Development and Verification of the Equilibrium Strategy for Batteries in Electric Vehicles

The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.

Shift scheduling strategy development for parallel hybrid construction vehicles

The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

High performance rotary triboelectric nanogenerator based on wool charge supplementation strategy with low wear

Triboelectric nanogenerator(TENG)is an emerging method for harvesting mechanical energy.In traditional rotary TENGs(RTENGs),the mutual friction between positive and negative friction materials significantly shortens their operational lifespan.The non-contact triboelectric nanogenerator addresses this issue effectively;however,its low output performance still limits practical applications.In this work,we introduce a novel charge supplementation strategy to enhance the performance of NCRTENGs.This strategy involves directly affixing wool between the Cu electrodes of the NCR-TENG,while the negative friction material is modified by doping with MXene,resulting in a substantial enhancement of output.The voltage,current,and charge transfer increased by 4.5,4.5,and 4.8 times,respectively,reaching 451 V,21.2μA and 47 nC.Furthermore,NCR-TENG demonstrates remarkable stability,maintaining 100%output characteristics after 33,600 cycles.The output power reaches2.3 mW when load resistance is 107Ω.It takes only 0.8 s to charge a 0.1μF capacitor to 10 V.This work not only improves the output performance of the NCR-TENG but also retains the capability of low-speed startup while maintaining high wear resistance.The simple and effective charge supplementation strategy proposed here provides a new perspective for further improving the output characteristics of NCR-TENGs.NCR-TENG has potential application prospects in harvesting wind energy to power traffic flow sensor networks,detecting environmental and vehicle information,and optimizing traffic signal control.

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.

Study on Ultracapacitors Parameter Design and Control Strategy of Electric Bus

Based on the analysis of ultracapacitors efficiency and vehicle driving character, three conditions restricting the ultracapacitors charge/discharge time are derived. The study focuses on ultracapacitors specific design rules decided by charge/discharge time and how to integrate with battery packs and converter through ultracapacitors＇voltage and current double regulators. Through BFC6100-EV electric bus test, it is shown that the proposed method and control strategy are feasible and the system can effectively improve the bus dynamic performance and ability to receive braking energy effectively.