Article
Energy & Fuels
Guan Wang, Bei Jin, Mingzhu Wang, Yuedong Sun, Yuejiu Zheng, Teng Su
Summary: A novel state estimation method for hybrid battery packs is proposed in this study. The method utilizes the easy-to-estimate NMC battery to estimate the SOC of the LFP battery, and achieves continuous feedback correction through the design of a difference state observer. The method shows excellent estimation accuracy and robustness.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Electrochemistry
Shuoyuan Mao, Meilin Han, Xuebing Han, Languang Lu, Xuning Feng, Anyu Su, Depeng Wang, Zixuan Chen, Yao Lu, Minggao Ouyang
Summary: This paper introduces an artificial intelligence-based electrical-thermal coupling battery model and an application-oriented procedure for estimating SOC and RAE in a reliable and effective battery management system. The paper also proposes a model-based strategy for controlling the battery's thermal state in low temperature. Simulation results show that the proposed method can accurately estimate SOC and RAE, and the preheating strategy significantly improves energy output.
Article
Energy & Fuels
Orhan Kalkan, Ali Celen, Kadir Bakirci
Summary: The thermal performance of a LiFePO4 pouch type battery under different discharge rates was experimentally and numerically investigated. It was found that natural convection cooling is not sufficient for the battery at high discharge rates.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Md Ohirul Qays, Yonis Buswig, Md Liton Hossain, Md Momtazur Rahman, Ahmed Abu-Siada
Summary: This paper presents a new balancing approach for parallel LiFePO4 battery cells using a Backpropagation Neural Network (BPNN) based technique to develop a Battery Management System (BMS), which can assess the charging status of all cells and control its operations through a DC/DC Buck-Boost converter. Simulation results show that the proposed approach effectively balances the energy stored in parallel-connected battery cells with a state of charge (SoC) estimation error of only 1.15%.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Article
Energy & Fuels
Guangwei Wan, Qiang Zhang, Menghan Li, Siyuan Li, Zehao Fu, Junjie Liu, Gang Li
Summary: To solve the problem of battery cell disparity in lithium-ion battery systems, battery balancing techniques are necessary. This paper proposes an improved battery balancing strategy using a reconfigurable converter system. The strategy is based on the state of charge (SOC) of batteries and utilizes the converter system to transfer energy from batteries with high SOC to those with lower SOC. The simulation results demonstrate that the proposed strategy achieves more efficient and accurate battery module balancing compared to previous modes.
Article
Thermodynamics
Chunsheng Hu, Liang Ma, Shanshan Guo, Gangsheng Guo, Zhiqiang Han
Summary: This paper proposes a method for estimating the state of charge (SoC) of LiFePO4 batteries during the charging process using a deep neural network (DNN). Battery data collected from different charging protocols are used to train the DNN model. The developed DNN can accurately estimate the battery's SoC during charging and can be used to calculate the SoC during discharging. Experimental results show that the maximum error and root mean square error of the SoC estimation using DNN are within an acceptable range.
Article
Energy & Fuels
Rui Xiong, Yanzhou Duan, Kaixuan Zhang, Da Lin, Jinpeng Tian, Cheng Chen
Summary: Accurate estimation of state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail for LiFePO4 batteries due to their flat voltage-SOC relationship. To address this, an adaptive algorithm is used to identify open-circuit voltage (OCV) and update parameters for the extended Kalman filter based on different OCV ranges. Additional filtering methods improve the stability of estimation. Experimental validation shows high accuracy and stability with a maximum absolute error of <2%. Real battery data further confirms the viability of the proposed method, laying a foundation for reliable LiFePO4 battery management in electric vehicles.
Article
Chemistry, Multidisciplinary
Mostafa Al-Gabalawy, Karar Mahmoud, Mohamed M. F. Darwish, James A. Dawson, Matti Lehtonen, Nesreen S. Hosny
Summary: This paper proposes a reliable and robust observer for simultaneous estimation of SOC and SOH of LiFePO4 batteries, utilizing an equivalent-circuit model and a comprehensive model consisting of thermal, electrical, and aging models. The dual extend Kalman filter is used for high accuracy estimation, and simulations confirm the observer's reliability and robustness under various test conditions.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Ruijie Ye, Niloofar Hamzelui, Martin Ihrig, Martin Finsterbusch, Egbert Figgemeier
Summary: Solid-state lithium batteries (SSLBs) are highly anticipated due to their high capacity and intrinsic safety. This study presents a sustainable, water-based processing route for garnet-supported SSLBs and successfully fabricates high capacity and cycling stability. Further optimization is needed for higher requirements.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2022)
Article
Thermodynamics
Mengmeng Liu, Jun Xu, Yihui Jiang, Xuesong Mei
Summary: This paper proposes a data-driven SOC estimation method based on multi-dimensional features, especially incorporating force signals, to solve the challenge of the flat OCV curve in LiFePO4 (LFP) batteries. A LSTM neural network model is established to estimate SOC, with battery voltage, current, temperature, and force data as input. The proposed method shows high accuracy in the middle SOC region, with less than 0.5% root mean square errors and less than 2.5% maximum errors, and maintains robustness and generalization performance at different temperatures.
Article
Chemistry, Physical
Yizhao Gao, Gregory L. Plett, Guodong Fan, Xi Zhang
Summary: This paper proposes an efficient SOC estimation scheme for LiFePO4 cells, which separates the hysteresis phenomenon from the cell voltage and models it using a single-state hysteresis formulation. The scheme achieves fast convergence and high accuracy of the predicted states, as demonstrated through experiments with different SOC points and the use of the unscented Kalman filter for estimation.
JOURNAL OF POWER SOURCES
(2022)
Article
Thermodynamics
Emanuele Buchicchio, Alessio De Angelis, Francesco Santoni, Paolo Carbone, Francesco Bianconi, Fabrizio Smeraldi
Summary: Estimating the state of charge (SOC) of batteries is crucial for the proper functioning and safety of various systems. This study proposes a SOC estimation approach based on electrochemical impedance spectroscopy (EIS) and an equivalent circuit model. Through experimental validation, the approach achieves over 93% accuracy and has the advantage of efficient model training. The resulting low-dimensional classification model can be embedded into battery-powered systems for online SOC estimation.
Article
Chemistry, Physical
Jonas A. Braun, Rene Behmann, David Schmider, Wolfgang G. Bessler
Summary: Accurately diagnosing the SOC and SOH of batteries is crucial for battery users and manufacturers. This study presents a new algorithm that uses battery voltage as input for a voltage-controlled model to accurately estimate SOC and SOH. The algorithm is self-calibrating, robust against cell aging, allows SOH estimation from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.
JOURNAL OF POWER SOURCES
(2022)
Article
Energy & Fuels
Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu
Summary: A method based on deep neural network is proposed for fast and accurate estimation of SOC for LiFePO4 batteries, with an error of less than 2.03% over the entire battery SOC range. By integrating the DNN with a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved.
Article
Energy & Fuels
Timur Sayfutdinov, Petr Vorobev
Summary: The paper proposes a comprehensive battery storage modeling approach and applies it to realistic scenarios of peak-shaving, demonstrating the importance of considering the developed models. The study finds that larger battery capacity becomes economically feasible when the battery is used more extensively. Additionally, adapting the operation strategy during the battery's lifetime reduces degradation and extends its lifespan, resulting in potential cost savings of up to 12.1% in the battery storage system project.
