The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
为探究废食用油(waste cooking oil,WCO)两段式工艺(先生成脂肪酸甲酯后生成烷烃)在中国落地建厂的可行性,该研究基于中国本土数据天工数据库v0.2.0,通过生命周期评价软件openLCA 2.4.0进行工艺的环境评估研究。通过WCO收集与运输、预酯...为探究废食用油(waste cooking oil,WCO)两段式工艺(先生成脂肪酸甲酯后生成烷烃)在中国落地建厂的可行性,该研究基于中国本土数据天工数据库v0.2.0,通过生命周期评价软件openLCA 2.4.0进行工艺的环境评估研究。通过WCO收集与运输、预酯化/酯交换、加氢脱氧/临氢异构3个阶段模拟WCO基第二代生物柴油生产的整个生命周期。研究结果表明:WCO基第二代生物柴油生产工艺的全球变暖(CO_(2)排放量)、细颗粒物形成(PM2.5排放量)和陆地酸化(SO_(2)排放量)3个中间点指标分别为506.78 kg/t(10.93 kg/GJ)、0.04 kg/t(8.63×10^(-4) kg/GJ)和0.14 kg/t(3.02×10^(-3) kg/GJ),加氢脱氧/临氢异构阶段分别占整个工艺相应总值的55.73%、56.69%和58.00%。1 t WCO基第二代生物柴油生产工艺对人类健康(伤残调整生命年(disability-adjusted life year,DALY))、生态系统(物种年损失值(loss of species in a year,species.yr))和资源(CNY)的终点指标数值分别为4.97×10^(-4)、-1.75×10^(-5)和-12.39。与化石柴油相比,该工艺生产的第二代生物柴油具有很好的环境优势。后续工艺的改良措施需要关注降低整个工艺中涉及到的含氯和溴的气体排放,以及温室气体的排放。展开更多
Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage.However,an often-overlooked issue is the sometimes-unknown cell che...Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage.However,an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life.In this work,a machine learning based approach for the identi-fication of lithium-ion battery cathode chemistries is presented.First,an initial measurement boundary deter-mination is introduced.Using the Python Battery Mathematical Modelling(PyBaMM)framework,synthetical partial open circuit voltage(OCV)charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied.The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves.The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number.While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies,capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3%for 0.5 Ah and 15 OCV steps.Additionally,the approach was validated by classifying experimental data.The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phos-phate(LFP)and lithium nickel manganese cobalt(NMC)cells.展开更多
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
文摘为探究废食用油(waste cooking oil,WCO)两段式工艺(先生成脂肪酸甲酯后生成烷烃)在中国落地建厂的可行性,该研究基于中国本土数据天工数据库v0.2.0,通过生命周期评价软件openLCA 2.4.0进行工艺的环境评估研究。通过WCO收集与运输、预酯化/酯交换、加氢脱氧/临氢异构3个阶段模拟WCO基第二代生物柴油生产的整个生命周期。研究结果表明:WCO基第二代生物柴油生产工艺的全球变暖(CO_(2)排放量)、细颗粒物形成(PM2.5排放量)和陆地酸化(SO_(2)排放量)3个中间点指标分别为506.78 kg/t(10.93 kg/GJ)、0.04 kg/t(8.63×10^(-4) kg/GJ)和0.14 kg/t(3.02×10^(-3) kg/GJ),加氢脱氧/临氢异构阶段分别占整个工艺相应总值的55.73%、56.69%和58.00%。1 t WCO基第二代生物柴油生产工艺对人类健康(伤残调整生命年(disability-adjusted life year,DALY))、生态系统(物种年损失值(loss of species in a year,species.yr))和资源(CNY)的终点指标数值分别为4.97×10^(-4)、-1.75×10^(-5)和-12.39。与化石柴油相比,该工艺生产的第二代生物柴油具有很好的环境优势。后续工艺的改良措施需要关注降低整个工艺中涉及到的含氯和溴的气体排放,以及温室气体的排放。
基金funded by the German Federal Ministry for Economic Affairs and Climate Action(SUSTAIN,16BZF320B).
文摘Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage.However,an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life.In this work,a machine learning based approach for the identi-fication of lithium-ion battery cathode chemistries is presented.First,an initial measurement boundary deter-mination is introduced.Using the Python Battery Mathematical Modelling(PyBaMM)framework,synthetical partial open circuit voltage(OCV)charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied.The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves.The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number.While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies,capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3%for 0.5 Ah and 15 OCV steps.Additionally,the approach was validated by classifying experimental data.The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phos-phate(LFP)and lithium nickel manganese cobalt(NMC)cells.
