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Rapid determination of lithium-ion battery degradation: High C-rate LAM and calculated limiting LLI

Journal of Energy Chemistry 2022年第4期31卷 663-671页

作者： Gyuwon Seo Jaeyun Ha Moonsu Kim Jihyeon Park Jaewon Lee Eunoak Park Sungyool Bong Kiyoung Lee Soon Jong Kwon Seung-pil Moon Jinsub Choi Jaeyoung Lee Electrochemical Reaction and Technology Laboratory School of Earth Sciences and Environmental EngineeringGwangju Institute of Science and Technology(GIST)Gwangju 61005South Korea Department of Chemistry and Chemical Engineering Inha UniversityIncheon 22212South Korea Ertl Center for Electrochemistry and Catalysis GISTGwangju 61005South Korea Korea Electric Power Corporation Research Institute Daejeon 34056South Korea International Future Research Center of Chemical Energy Storage and Conversion Processes(ifRC-CHESS) GISTGwangju 61005South Korea

Herein,incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries,which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge *** battery degradation modes are complex,the simple output of capacity fading does not yield any useful data in that *** IC and DV curves obtained under restricted conditions (degradation symptoms,a facile,rapid diagnostic approach without intricate,complex calculations is critical in on-board ***,Li Ni_(0.5)Mn_(0.3)Co_(0.2)O_(2)(NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics,i.e.,loss of active materials (LAM) and loss of lithium inventory (LLI),were parameterized using the IC-DV *** the incremental current cycling,the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low-and high-current measurements.A quantitative comparison of LAM at high and low C-rate showed that a IC;revealed the relative amount of available reaction region limited by cell ***,this did not provide a direct relationship for estimating the LAM at a low ***,the limiting LLI,which is calculated at a C-rate approaching 0,was obtained by extrapolating the LLI through more than two points measured at high C-rate,and therefore,the LLI at 0.1C was accurately determined using rapid cycling.

关键词： battery degradation Rapid degradation diagnosis Loss of active materials(LAM) Loss of lithium inventory(LLI) Incremental capacity(IC) Differential voltage(DV)

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Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

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Journal of Modern Power Systems and Clean Energy 2016年第4期4卷 581-592页

作者： Ying WANG Zhi ZHOU Audun BOTTERUD Kaifeng ZHANG Qia DING Key Laboratory of Measurement and Control of CSE School of AutomationSoutheast UniversityNanjing 210096China Argonne National Laboratory LemontIL 60439USA NARI Technology Co.Ltd. Nanjing 211106

Grid-scale battery energy storage systems(BESSs)are promising to solve multiple problems for future power *** to the limited lifespan and high cost of BESS,there is a cost-benefit trade-off between battery effort and operational ***,we develop a battery degradation model to accurately represent the battery degradation and related cost during battery operation and cycling.A linearization method is proposed to transform the developed battery degradation model into the mixed integer linear programming(MILP)optimization *** battery degradation model is incorporated with a hybrid deterministic/stochastic look-ahead rolling optimization model of windBESS bidding and operation in the real-time electricity *** results show that the developed battery degradation model is able to effectively help to extend the battery cycle life and make more profits for ***,the proposed rolling look-ahead operational optimization strategy can utilize the updated wind power forecast,thereby also increase the wind-BESS profit.

关键词： Wind power battery energy storage system(BESS) battery degradation Stochastic programming Rolling optimization

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Consensus-based Optimal Control Strategy for Multi-microgrid Systems with battery degradation Consideration

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 1911-1924页

作者： Tonghe Wang Hong Liang Bo He Haochen Hua Yuchao Qin Junwei Cao Institute of Energy Conversion Chinese Academy of SciencesGuangzhou 510640China Meihua Holdings Group Co. Ltd.Langfang 065001China. Department of Astronautical Science and Technology Space Engineering UniversityBeijing 101416China College of Energy and Electrical Engineering Hohai UniversityNanjing211100China Department of Applied Mathematics and Theoretical Physics University of CambridgeCambridgeCB30WAUK. Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common ***,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)*** this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation *** consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual *** order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between *** our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control *** simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery *** addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.

关键词： battery degradation compressive sensing consensus distributed control multi-microgrid

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Optimum Operation on Electric Vehicles Considering battery degradation in V2G System 36

Optimum Operation on Electric Vehicles Considering Battery D...

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第36届中国控制会议

作者： Qi Li Jianxiao Zou Liying Li School of Automation Engineering University of Electronic Science and Technology of China

ISBN: (纸本)9781538629185

In vehicle-to-grid system（V2G）, a great number of electric vehicles（EVs） can form a massive energy storage to perform the operation of charging, discharging and providing frequency regulation for the grid. This process may lead to battery degradation of the EVs. In this paper, we propose to optimize the operation of EVs in each hour to maximize the user’s total revenue, where Lithium battery is used for the EVs and the battery degradation is considered. Here, battery degradation includes two parts, one is the degradation caused by battery temperature during EVs’ charging and discharging;another is the degradation caused by the depth of discharge（DoD）. Considering the real-time electricity price, the optimum operation of each hour, i.e.,charging, discharging or frequency regulation, can be obtained by V2G control center. Simulation results are provided to demonstrate that an optimum operation in each hour can be obtained to maximize the users profit. It also shows that large charging or discharging rate, and DoD reduces the battery’s capacity and life cycle, which finally leads to the loss of user’s benefit.

关键词： battery degradation electric vehicles(EVs) frequency regulation Lithium battery optimum operation vehicle-to-grid system(V2G)

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A Model‑Based battery Charging Optimization Framework for Proper Trade‑offs Between Time and degradation

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Automotive Innovation 2023年第2期6卷 204-219页

作者： Sean Appleton Abbas Fotouhi Advanced Vehicle Engineering Centre Cranfield UniversityCollege RoadCranfield MK430ALUK

This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging *** the first time,the application of practical limitations on charging and cooling power is considered along with more detailed health *** iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework.A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge,charging rate,temperature and *** optimization results demonstrate an improvement over the benchmark constant current–constant voltage(CCCV)charging protocol when considering both the charging time and battery health.A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging *** a case study,for example,the‘optimal time’charging is found to take 12 min while the‘optimal health’charging profile suggests around 100 min for charging the battery from 25 to 75%*** other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.

关键词： Charging optimization battery degradation Electro-thermal equivalent circuit model Electric vehicles Lithium iron phosphate

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic University Photonics Research Institute The Hong Kong Polytechnic University School of Electrical Computer and Telecommunications Engineering University of Wollongong

Accurate prediction of the remaining useful life(RUL) is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries battery degradation Remaining useful life Semi-supervised learning

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Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 120-131页

作者： Ruoxuan Leng Zhengmao Li Yan Xu School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore

This paper proposes a stochastic programming(SP)method for coordinated operation of distributed energy resources(DERs)in the unbalanced active distribution network(ADN)with diverse correlated ***,the threephase branch flow is modeled to characterize the unbalanced nature of the ADN,schedule DER for three phases,and derive a realistic DER ***,both active and reactive power resources are co-optimized for voltage regulation and power loss ***,the battery degradation is considered to model the aging cost for each charging or discharging event,leading to a more realistic cost ***,copulabased uncertainty modeling is applied to capture the correlations between renewable generation and power loads,and the twostage SP method is then used to get final ***,numerical case studies are conducted on an IEEE 34-bus three-phase ADN,verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

关键词： Active distribution network(ADN) two-stage stochastic programming(SP) uncertainties voltage/var control(VVC) battery degradation

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Industrial battery State-of-Health Estimation with Incomplete Limited Data Toward Second-Life Applications

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Journal of Dynamics, Monitoring and Diagnostics 2024年第4期3卷 246-257页

作者： Shaojie Yang Long Ling Xiang Li Jintong Han Shijie Tong Department of Mechanical and Aerospace Engineering University of California San DiegoLa JollaCA 92093USA Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China Smartville Inc. CarlsbadCA 92011USA

battery state-of-health(SOH)estimation is vital across applications ranging from portable electronics to electric vehicles,particularly in second-life applications where accurate prediction becomes complex due to varying degradation *** paper introduces a novel SOH estimation model to address the lack of labeled data,employing domain-adversarial neural networks(DANNs)combined with one-dimensional convolutional neural networks(CNNs).The proposed method allows for effective transfer of knowledge between diverse battery conditions,enhancing adaptability and efficiency by utilizing both source and target *** results demonstrate that the proposed model achieves a mean absolute error(MAE)of 1.68%and a root mean squared error(RMSE)of 2.50%,with minimal ***,the model requires only one cell of unlabeled data from the second-life target domain,utilizing only the dQ/dV curve for *** model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training,and this approach offers a robust solution for accurate SOH estimation,particularly in scenarios with limited access to labeled data.

关键词： battery degradation deep learning lithium-ion battery(LIB) second life SOH estimation

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