Scenario-based Optimal Real-time Charging Strategy of Electric Vehicles with Bayesian Long Short-term Memory Networks

Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station(EVCS).Firstly,an optimization model for real-time EV charging strategy is proposed to address these challenges,which accounts for environmental uncertainties of an EVCS,encompassing EV arrivals,charging demands,PVG outputs,and the electricity price.Then,a scenario-based two-stage optimization approach is formulated.The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory(B-LSTM)network.Finally,numerical results substantiate the efficacy of the proposed optimization approach,and demonstrate superior profitability compared with prevalent approaches.

Overview of multi-stage charging strategies for Li-ion batteries

To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global problems.Lithium-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive price.However,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced weight.For fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle life.However,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each stage.This paper provides a review of these problems by compiling information from the literature.An overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and analyzed.The impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.

Empowering highway network:Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures,electric vehicles(EVs)have emerged as an effective measure to reduce reliance on fossil fuels,combat climate change,uphold sustainable energy and environmental development,and strive towards carbon peaking and neutrality goals.This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes(DWCLs)and EV charging strategy joint optimization in highway networks.Taking into account established charging resources in highway service areas(HSAs),the nonlinear charging characteristics of EV batteries,and the traffic capacity constraints of DWCLs.The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs.By ensuring that EVs maintain an acceptable state of charge(SoC),the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment,thus reducing the construction cost of wireless charging facilities and user charging expenses.The efficacy and universality of the model are demonstrated using the classical Nguyen-Dupius network as a numerical example and a real-world highway network in Guangdong Province,China.Finally,a sensitivity analysis is conducted to corroborate the stability of the model.The results show that the operating speed of EVs on DWCLs has the largest impact on total cost,while battery capacity has the smallest.This comprehensive study offers vital insights into the strategic deployment of DWCLs,promoting the sustainable and efficient use of EVs in highway networks.

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.

PSO-based Optimization for Constant-current Charging Pattern for Li-ion Battery

A particle swarm optimization algorithm to search for an optimal five-stage constant-current charge pattern is proposed.The goal is to maximize the objective function for the proposed charge pattern based on the charging capacity,time,and energy efficiency,which all share the same weight.Firstly,an equivalent circuit model is built and battery parameters are identified.Then the optimal five-stage constant-current charge pattern is searched using a particle swarm optimization algorithm.At last,comparative experiments using the constant current-constant voltage(CC-CV)method are performed.Although the charging SOC of the proposed charging pattern was 2.5%lower than that of the CC-CV strategy,the charging time and charging energy efficiency are improved by 15.6%and 0.47%respectively.In particular,the maximum temperature increase of the battery is approximately 0.8℃lower than that of the CC-CV method,which indicates that the proposed charging pattern is more secure.

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Microgrid as an important part of smart grid comprises distributed generators(DGs),adjustable loads,energy storage systems(ESSs)and control units.It can be operated either connected with the external system or islanded with the support of ESSs.While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar,unregulated electric vehicle(EV)charging demand will deteriorate the unbalance between the daily load curve and generation curve.In this paper,a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge(SOC)of EV batteries.The optimization problem is proposed to obtain the economic operation for the microgrid based on this model.In dayahead scheduling,with the estimated power generation and load demand,the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming.With the optimal charging/discharging scheduling of EVs,the daily load curve can better track the generation curve.The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.

Comparative Study on Traction Battery Charging Strategies from the Perspective of Material Structure

The service life of an electric vehicle is,to some extent,determined by the life of the traction battery.A good charging strat-egy has an important impact on improving the cycle life of the lithium-ion battery.Here,this paper presents a comparative study on the cycle life and material structure stability of lithium-ion batteries,based on typical charging strategies currently applied in the market,such as constant current charging,constant current and constant voltage charging,multi-stage constant current charging,variable current intermittent charging,and pulse charging.Compared with the reference charging strategy,the charging capacity of multi-stage constant current charging reaches 88%.Moreover,the charging time is reduced by 69%,and the capacity retention rate after 500 cycles is 93.3%.Through CT,XRD,SEM,and Raman spectroscopy analysis,it is confirmed that the smaller the damage caused by this charging strategy to the overall structure of the battery and the layered structure and particle size of the positive electrode material,the higher the capacity retention rate is.This work facilitates the development of a better charging strategy for a lithium-ion battery from the perspective of material structure.

Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy markets.This study presents an assessment of various strategies for EV aggregators.In this analysis,the smart charging methodology proposed in a previous study is considered.The smart charging technique employs charging power rate modulation and considers user preferences.To adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,Ecuador.Different actions are simulated,and the EV aggregator costs and technical conditions are evaluated.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.

Energy Management Strategy for Hybrid Electric Vehicle Based on System Efficiency and Battery Life Optimization

A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.