Synergistic interactions between the charge‐transport and mechanical properties of the ionic‐liquid‐based solid polymer electrolytes for solid‐state lithium batteries

The performance sensitivity of the solid‐state lithium cells to the synergistic interactions of the charge‐transport and mechanical properties of the electrolyte is well acknowledged in the literature,but the quantitative insights therein are very limited.Here,the charge‐transport and mechanical properties of a polymerized ionic‐liquid‐based solid electrolyte are reported.The transference number and diffusion coefficient of lithium in the concentrated solid electrolyte are measured as a function of concentration and stack pressure.The elastoplastic behavior of the electrolyte is quantified under compression,within a home‐made setup,to substantiate the impact of stack pressure on the stability of the Li/electrolyte interface in the symmetric lithium cells.The results spotlight the interaction between the concentration and thickness of the solid electrolyte and the stack pressure in determining the polarization and stability of the solid‐state lithium batteries during extended cycling.

Morphological peculiarities of the lithium electrode from the perspective of the Marcus-Hush-Chidsey model

This study employs the kinetics framework of Marcus-Hush-Chidsey(MHC)to investigate the charge transfer at the interface of lithium electrode and electrolyte in lithium(ion)-batteries.The chargetransfer rate constant is evaluated for different facets of lithium,namely(100),(110),(101),and(111)as a function of surface charge density with the aid of density functional theory(DFT)calculations.The results highlight and quantify the sensitivity of the rate of lithium plating and stripping to the surface orientation,surface charge density,and charge-transfer over-potential.An intrinsic kinetics competition among the different surface orientations is identified together with an asymmetry between the lithium plating and stripping and showcased to influence the deposit morphology and surface protrusions and indentations.

Bridging the microstructural evolutions from slurry to porous electrode of a lithium-ion battery

1. Introduction The porous electrodes are the crucial components of the modern electrochemical devices including lithium-ion batteries. The detailed configuration of the lithium-insertion, carbon black, and PVDF binder particles in a typical electrode of a lithium-ion cell has a significant impact on its energy and power density. A thinner electrode is desired to decrease the battery volume particularly for portable electronics and mobility applications.

A Hybrid Backward Euler Control Volume Method to Solve the Concentration-Dependent Solid-State Diffusion Problem in Battery Modeling

Several efficient analytical methods have been developed to solve the solid-state diffusion problem, for constant diffusion coefficient problems. However, these methods cannot be applied for concentration-dependent diffusion coefficient problems and numerical methods are used instead. Herein, grid-based numerical methods derived from the control volume discretization are presented to resolve the characteristic nonlinear system of partial differential equations. A novel hybrid backward Euler control volume (HBECV) method is presented which requires only one iteration to reach an implicit solution. The HBECV results are shown to be stable and accurate for a moderate number of grid points. The computational speed and accuracy of the HBECV, justify its use in battery simulations, in which the solid-state diffusion coefficient is a strong function of the concentration.

汽车电池管理市场是否已准备好标准化?

不断变化的BMS要求正在推动CVM IC供应商进行大量的开发工作。同时,缺乏标准接口使OEM和电池系统制造商很难更换CVM IC供应商。不同的OEM厂商总是会有不同的需求,因此基于模块和基于单元的BMS方案将并存,有线或无线以及有无微控制器。如果将第二次使用的要求考虑在内,并选择一些标准的BMS内部接口,则投资效率将会提高。为了增加潜在方案的灵活性并实现新兴标准,提出了分两步走的方法,第一步是在独立的CVM和通信功能之间创建一个行业范围内的类似于SPI的标准接口。

In search of optimal building behavior models for model predictive control in the context of flexibility

Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation forflexibility activation.This studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.

Frequency domain based DC fault analysis for bipolar HVDC grids

This paper proposes a frequency domain based methodology to analyse the influence of High Voltage Direct Current(HVDC) configurations and system parameters on the travelling wave behaviour during a DC fault. The method allows us to gain deeper understanding of these influencing parameters. In the literature, the majority of DC protection algorithms essentially use thefirst travelling waves initiated by a DC fault for fault discrimination due to the stringent time constraint in DC grid protection. However, most protection algorithms up to now have been designed based on extensive time domain simulations using one specific test system. Therefore, general applicability or adaptability to different configurations and system changes is not by default ensured, and it is difficult to gain in-depth understanding of the influencing parameters through time domain simulations. In order to analyse the first travelling wave for meshed HVDC grids, voltage and current wave transfer functions with respect to the incident voltage wave are derived adopting Laplace domain based component models. The step responses obtained from the voltage transfer functions are validated by comparison against simulations using a detailed model implemented in PSCADTM. Then, the influences of system parameters such as the number of parallel branches, HVDC grid configurations and groundings on the first travelling wave are investigated by analysing the voltage and current transfer functions.

Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning

This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.Extracting a relevant set of features from these observations is a challenging task and may require substantial domain knowledge.One way to tackle this problem is to store sequences of past observations and actions in the state vector,making it high dimensional,and apply techniques from deep learning.This paper investigates the capabilities of different deep learning techniques,such as convolutional neural networks and recurrent neural networks,to extract relevant features for finding near-optimal policies for a residential heating system and electric water heater that are hindered by sparse observations.Our simulation results indicate that in this specific scenario,feeding sequences of time-series to an Long Short-Term Memory(LSTM)network,which is a specific type of recurrent neural network,achieved a higher performance than stacking these time-series in the input of a convolutional neural network or deep neural network.

A hybrid circuit breaker with fault current limiter circuit in a VSC-HVDC application

A conventional hybrid circuit breaker(HCB)is used to protect a voltage source converter-based high voltage direct current transmission system(VSC-HVDC)from a short circuit fault.With the increased converter capacity,the DC protection equipment also requires a regular upgrade.This paper adopts a novel type of HCB with a fault current limiter circuit(FCLC),and focuses on the responses of voltage and current during DC faults,which are associated with parameter selection.PSCAD/EMTDC based simulation of a three-terminal VSC-HVDC system confirms the effectiveness and value of HCB with FCLC,by using an equivalent circuit modelling approach.Laboratory experimental tests validate the simulation results.The peak fault current is reduced according to the current limiting inductor(CLI)increase,and can be isolated more quickly.By adopting parallel metal oxide arrester(MOA)with the main branch of HCB,voltage stresses across the breaker components decrease during transient and continuous operation,and less energy needs to be dissipated by the MOA.The remnant current for all cases is transmitted to power dissipating resis-tor(PDR)in the final stage,and the fault current is reduced to the lowest possible value.When the current from the main branch is transferred to the FCLC branch,transient voltage spikes occur,while smaller PDR is required to absorb current in the final stage.

Transfer learning in demand response: A review of algorithms for data-efficient modelling and control

A number of decarbonization scenarios for the energy sector are built on simultaneous electrification of energy demand,and decarbonization of electricity generation through renewable energy sources.However,increased electricity demand due to heat and transport electrification and the variability associated with renewables have the potential to disrupt stable electric grid operation.To address these issues using demand response,researchers and practitioners have increasingly turned towards automated decision support tools which utilize machine learning and optimization algorithms.However,when applied naively,these algorithms suffer from high sample complexity,which means that it is often impractical to fit sufficiently complex models because of a lack of observed data.Recent advances have shown that techniques such as transfer learning can address this problem and improve their performance considerably—both in supervised and reinforcement learning contexts.Such formulations allow models to leverage existing domain knowledge and human expertise in addition to sparse observational data.More formally,transfer learning embodies all techniques where one aims to increase(learning)performance in a target domain or task,by using knowledge gained in a source domain or task.This paper provides a detailed overview of state-of-the-art techniques on applying transfer learning in demand response,showing improvements that can exceed 30%in a variety of tasks.We observe that most research to date has focused on transfer learning in the context of electricity demand prediction,although reinforcement learning based controllers have also seen increasing attention.However,a number of limitations remain in these studies,including a lack of benchmarks,systematic performance improvement tracking,and consensus on techniques that can help avoid negative transfer.

Signal Processing for Fast Fault Detection in HVDC Grids

For multiterminal or meshed Voltage Source Converter(VSC)High-voltage Direct Current(HVDC)systems,high speed protection against DC faults is essential,as power electronic components cannot withstand the rapidly increasing fault currents which would otherwise result.Recently proposed DC fault detection methods were developed based on time domain simulations in EMT-type software,which requires considerable modeling and computational efforts and results in methods specifically designed for the HVDC grid under study.To simplify the initial design of DC fault detection methods,this paper proposes general guidelines based on fundamental theory and offers a reduced modeling approach.Furthermore,the impact of non-ideal measurements is investigated and a method to choose the filters that optimally discriminate these fault signals from noise,is proposed.The approach was evaluated in a case study on fault detection in a realistically dimensioned HVDC grid.The paper shows that the initial design of fast fault detection methods can be based on the relatively simple proposed guidelines and reduced models.The paper furthermore shows that a sufficiently high sampling frequency and a filter matched to the fault signal enable fault detection within hundreds of microseconds and discrimination of DC faults from transients not related to DC faults.

Physics-based electrical modelling of CIGS thin-film photovoltaic modules for system-level energy yield simulations

Copper indium gallium selenide(CIGS)is a commercialized,high-efficiency thin-film photovoltaic(PV)technology.The state-of-theart energy yield models for this technology have a significant normalized root mean square error(nRMSE)on power estimation:De Soto model—26.7%;PVsyst model—12%.In this work,we propose a physics-based electrical model for CIGS technology which can be used for system-level energy yield simulations by people across the PV value chain.The model was developed by considering models of significant electrical current pathways from literature and adapting it for the system-level simulation.We improved it further by incorporating temperature and irradiance dependence of parameters through characterisation at various operating conditions.We also devised a module level,non-destructive characterization strategy based on readily available measurement equipment to obtain the model parameters.The model was validated using the measurements from multiple commercial modules and has a significantly lower power estimation nRMSE of 1.2%.

A novel demand-responsive control strategy for district heating systems,featuring return temperature reduction

The paper presents a novel demand-responsive control strategy to be equipped centrally at the district level for district heating systems.The demand-responsive feature was maintained as to both the direct and the indirect substation configurations(by basing on their rating measures)in order to achieve lowest possible return temper-ature degrees from the end-user substations.Different than the traditional weather-compensation based supply temperature resetting,the new control strategy was formulated to adjust the supply temperature at the district level as to the cooling performance at the end-user substations.Two different simulations were carried out in order to quantify the benefits of the novel control strategy as compared to the traditional weather-compensation,equipped both at the substation level and the district level.The results obtained showed that the new control strategy,when considering the electricity loss at the heat production plant,shows superiority when compared to other control strategies.

A hybrid policy gradient and rule-based control framework for electric vehicle charging

Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.

Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and Long Short-Term Memory

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.