Journal articles: 'Battery state-of-health'

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Yang, Qingxia, Ke Ma, Liyou Xu, Lintao Song, Xiuqing Li, and Yefei Li. "A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health." Coatings 12, no. 8 (July 24, 2022): 1047. http://dx.doi.org/10.3390/coatings12081047.

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In a battery management system, the accurate estimation of the battery’s state of health (SOH) and state of capacity (SOC) are vital functions. The traditional estimation methods have limitations. To accurately estimate the SOC and SOH of power battery and improve the performance of the long-term estimation of a battery’s SOC, a joint estimation method based on a Kalman filter is proposed in this work. First, a second-order RC equivalent circuit model of a ternary lithium battery was built, whose parameters were identified online, and the model’s accuracy was verified. Then, the battery data under actual working conditions were collected. The SOC and SOH were estimated based on the Kalman filter algorithm, and the simulation was implemented using MATLAB. Finally, according to a time scale transformation, the battery state was jointly estimated, the SOC was estimated at a short-time scale, the SOH was estimated at a long-time scale, and the SOH estimation results were updated to the model parameters for SOC estimation. The results show that the accuracy of the method is very good, and it can effectively improve estimation accuracy and ensure batteries’ long-term estimation performance.

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Wei, Yupeng. "Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model." Sensors 23, no. 5 (February 26, 2023): 2587. http://dx.doi.org/10.3390/s23052587.

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State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery’s health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery’s degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.

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Huang, Kai, Yong-Fang Guo, Ming-Lang Tseng, Kuo-Jui Wu, and Zhi-Gang Li. "A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach." Applied Sciences 8, no. 10 (October 2, 2018): 1803. http://dx.doi.org/10.3390/app8101803.

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The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.

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O. Hadi, Pradita, and Goro Fujita. "Battery Charge Control by State of Health Estimation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 508. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp508-514.

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Battery lifetime is one of importance consideration in smart system with energy storage system, because it is shorter than others. Extended of battery lifetime can give benefit to entire system, especially to reduce cost. The lifetime is commonly estimated by State of Health (SOH). Decreasing of SOH indicates degradation of battery. It can be influenced by the battery operation, so that operational management is needed. This study proposes control block for charging battery by using decreasing value of SOH as reference. The control block is implemented in battery system that connected to DC bus by bidirectional chopper. Numerical simulation study is performed by using PSIM software version 10.0. The result shows that the proposed block control is successfully used. Moreover, the relative error is less than 2% for delta SOH and less than 1% for battery power.<em> </em>

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Patel, Nisarg. "A Review of State of Health and State of Charge Estimation Methods." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 259–64. http://dx.doi.org/10.22214/ijraset.2021.38693.

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Abstract: The explosive growth of Electric Vehicles has made developing a robust system for managing batteries that are one of the crucial components of an EV, the need of the hour. Accuracy of the estimation models for determining the State of Charge and State of Health levels of the battery packs plays a key role. There are many sophisticated systems to determine these parameters we have tried to review a few of these systems in this paper. The complete electrification of the automotive industry heavily depends on the energy density and longevity that the battery packs provide and maintaining these packs in a safe operating condition can help achieve these goals. Keywords: BMS (Battery management system), SoC(state of charge), SoH (State of Health), Battery Thermal Management Systems (BTMS)

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Brunelli Lazzarin, Telles, and Ivo Barbi. "A system for state-of-health diagnosis of lead-acid batteries integrated with a battery charger." Eletrônica de Potência 17, no. 1 (February 1, 2012): 401–8. http://dx.doi.org/10.18618/rep.2012.1.401408.

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Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, Jie Fan, Naifeng Yang, Junjie Song, and Yuan Zou. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles." Sustainability 13, no. 9 (May 5, 2021): 5166. http://dx.doi.org/10.3390/su13095166.

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Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal.

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Nuroldayeva, Gulzat, Yerkin Serik, Desmond Adair, Berik Uzakbaiuly, and Zhumabay Bakenov. "State of Health Estimation Methods for Lithium-Ion Batteries." International Journal of Energy Research 2023 (March 3, 2023): 1–21. http://dx.doi.org/10.1155/2023/4297545.

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Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

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Yu, Zhilong, Na Liu, Yekai Zhang, Lihua Qi, and Ran Li. "Battery SOH Prediction Based on Multi-Dimensional Health Indicators." Batteries 9, no. 2 (January 24, 2023): 80. http://dx.doi.org/10.3390/batteries9020080.

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Battery capacity is an important metric for evaluating and predicting the health status of lithium-ion batteries. In order to determine the answer, the battery’s capacity must be, with some difficulty, directly measured online with existing methods. This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant current discharge characteristic curve. The values of polarization resistance, polarization capacitance, and initial discharge resistance are identified as the health indicators reflective of the battery’s state of health. Moreover, the retention strategy genetic algorithm (e-GA) selects the optimal voltage drop segment, and the corresponding equal voltage drop discharge time is also used as a health indicator. Based on the above health indicator selection strategy, a battery SOH prediction model based on particle swarm optimization (PSO) and LSTM neural network is constructed, and its accuracy is validated. The experimental results demonstrate that the suggested strategy is accurate and generalizable. Compared with the prediction model with single health indicator input, the accuracy is increased by 0.79%.

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Mei, Peng, Hamid Reza Karimi, Fei Chen, Shichun Yang, Cong Huang, and Song Qiu. "A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health." Sensors 22, no. 23 (December 4, 2022): 9474. http://dx.doi.org/10.3390/s22239474.

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The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles’ (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

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Zhu, Chunxiang, Bowen Zheng, Zhiwei He, Mingyu Gao, Changcheng Sun, and Zhengyi Bao. "State of Health Estimation of Lithium-Ion Battery Using Time Convolution Memory Neural Network." Mobile Information Systems 2021 (December 14, 2021): 1–16. http://dx.doi.org/10.1155/2021/4826409.

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The accurate state of health (SOH) estimation of lithium-ion batteries enables users to make wise replacement decision and reduce economic losses. SOH estimation accuracy is related to many factors, such as usage time, ambient temperature, charge and discharge rate, etc. Thus, proper extraction of features from the above factors becomes a great challenge. In order to extract battery’s features effectively and improve SOH estimation accuracy, this article proposes a time convolution memory neural network (TCMNN), combining convolutional neural networks (CNN) and long short-term memory (LSTM) by dropout regularization-based fully connected layer. In experiment, the terminal voltage and charging current of the battery during charging process are collected, and input and output data sets are sorted out from the experimental battery data. Due to the limited equipment in the laboratory, only one battery can be charged and discharged at a time; the amount of battery data collected is relatively small, which will affect the extraction of features during the training process. Data augmentation algorithms are applied to solve the problem. Furthermore, in order to improve the accuracy of estimation, exponential smoothing algorithm is used to optimize output data. The results show that the proposed method can well extract and learn the feature relationship of battery cycle charge and discharge process in a long time span. In addition, it has higher accuracy than that of CNN, LSTM, Backpropagation (BP) algorithm, and Grey model-based neural network. The maximum error is limited to 3.79%, and the average error is limited to 0.143%, while the input data dimension is 514.

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Al-Gabalawy, Mostafa, Karar Mahmoud, Mohamed M. F. Darwish, James A. Dawson, Matti Lehtonen, and Nesreen S. Hosny. "Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries." Applied Sciences 11, no. 8 (April 16, 2021): 3609. http://dx.doi.org/10.3390/app11083609.

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Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.

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Han, Xuebing, Xuning Feng, Minggao Ouyang, Languang Lu, Jianqiu Li, Yuejiu Zheng, and Zhe Li. "A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle." Automotive Innovation 2, no. 4 (December 2019): 263–75. http://dx.doi.org/10.1007/s42154-019-00080-2.

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AbstractLithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

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Noura, Nassim, Loïc Boulon, and Samir Jemeï. "A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges." World Electric Vehicle Journal 11, no. 4 (October 16, 2020): 66. http://dx.doi.org/10.3390/wevj11040066.

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To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

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Cüneyt;ÖZKAZANÇ, BARLAK. "DETERMINATION OF BATTERY STATE-OF-HEALTH VIA STATISTICAL CLASSIFICATION." Communications Faculty Of Science University of Ankara 52, no. 2 (2010): 1–9. http://dx.doi.org/10.1501/commua1-2_0000000080.

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Pradhan, Sunil K., and Basab Chakraborty. "Battery management strategies: An essential review for battery state of health monitoring techniques." Journal of Energy Storage 51 (July 2022): 104427. http://dx.doi.org/10.1016/j.est.2022.104427.

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Zhang, Yueying, and Yi Guo. "Estimation of State of Charge and State of Health of Marine Lithium Battery based on BP Neural Network." Journal of Physics: Conference Series 2450, no. 1 (March 1, 2023): 012091. http://dx.doi.org/10.1088/1742-6596/2450/1/012091.

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Abstract For the sake of better ensure the safety of a marine system reliability, economy, and power, on the basis of marine lithium battery discharge data, BP neural network is able to grasp the state of charge and health in this battery module. Owing to some stochastic factors of BP neural network weights and limens, the error fluctuates greatly, so a genetic algorithm is lead into improve its results. The improved GA-BP algorithm reduces the error of SOC estimation from 4.2% to 2.3% and of SOH from 0.52% to 0.24%. It indicates that the GA-BP neural network method for forecasting SOC and SOH of the marine battery management system has minor errors and high stability, which is feasible.

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Zhao, Kelun, Zhimao Ming, Jiajie Wu, and Wei Sun. "Health state estimation of lithium ion batteries based on electrochemical impedance spectroscopy." Journal of Physics: Conference Series 2366, no. 1 (November 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2366/1/012030.

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Abstract The estimation method based on the electrochemical impedance spectrum can comprehensively describe the reaction process of Li-ion battery without causing damage to the battery, and is suitable for estimating the state of health of the battery. In this paper, the equivalent circuit model of Li-ion battery is established by analyzing the ac impedance spectrum of Li-ion battery and the internal reaction process of the battery, and corresponding each component of the model and the composite link with the curve segments in the impedance spectrum, and the model parameters are identified offline with the help of The Nyquist diagram of impedance spectrum. It is found that the charge transfer internal resistance and low-frequency impedance amplitude in the equivalent circuit increase monotonically with the aging of the battery. Then, the temperature compensation coefficient was introduced to solve the influence of temperature factors on the SOH estimation of lithium batteries, And the root mean squared error of SOH estimation before and after temperature compensation was reduced from 31.7 to 3.0, and the sum of squares for Error was reduced from 13054.7 to 19.8, which significantly improved the SOH estimation of batteries with different temperatures; and a model based on BP neural network was constructed, And the correlation coefficient of model prediction was 0.994. Solving the influence of production process differences on SOH estimation of lithium batteries, and the advantages and disadvantages of the two estimation methods are summarized.

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Wang, Cheng, Chengyang Yu, Weiwei Guo, Zhenpo Wang, and Jiyuan Tan. "Identification of Typical Sub-Health State of Traction Battery Based on a Data-Driven Approach." Batteries 8, no. 7 (July 4, 2022): 65. http://dx.doi.org/10.3390/batteries8070065.

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As the core component of an electric vehicle, the health of the traction battery closely affects the safety performance of the electric vehicle. If the sub-health state cannot be identified and dealt with in time, it may cause traction battery failure, pose a safety hazard, and cause property damage to the driver and passengers. This study used data-driven methods to identify the two typical types of sub-health state. For the first type of sub-health state, the interclass correlation coefficient (ICC) method was used to determine whether there was an inconsistency between the voltage of a single battery and the overall voltage of the battery pack. In order to determine the threshold, the ICC value of each vehicle under different working conditions was analyzed using box plots, and a statistical ICC threshold of 0.805 was used as the standard to determine the first sub-health type. For the second type of sub-health state, the Z-score and the differential area method were combined to determine whether the single cell voltage deviated from the overall battery pack voltage. A battery whose voltage differential area exceeds the range of u ± 3σ is regarded as having a sub-health state. The results show that both methods can accurately judge the sub-health state type of a single battery. Furthermore, combined with the one-month operation data of the vehicle, we could calculate the sub-health state frequency of each single battery and take single batteries with a high frequency as the key object of attention in future vehicle operations.

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Vilsen, Søren B., and Daniel-Ioan Stroe. "Battery state-of-health modelling by multiple linear regression." Journal of Cleaner Production 290 (March 2021): 125700. http://dx.doi.org/10.1016/j.jclepro.2020.125700.

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Roman, Darius, Saurabh Saxena, Valentin Robu, Michael Pecht, and David Flynn. "Machine learning pipeline for battery state-of-health estimation." Nature Machine Intelligence 3, no. 5 (April 5, 2021): 447–56. http://dx.doi.org/10.1038/s42256-021-00312-3.

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Fang, Liu, Liu Xinyi, Su Weixing, Chen Hanning, He Maowei, and Liang Xiaodan. "State-of-Health Online Estimation for Li-Ion Battery." SAE International Journal of Electrified Vehicles 9, no. 2 (December 31, 2020): 185–96. http://dx.doi.org/10.4271/14-09-02-0012.

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To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

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Micea, M. V., L. Ungurean, Gabriel N. Cârstoiu, and V. Groza. "Online State-of-Health Assessment for Battery Management Systems." IEEE Transactions on Instrumentation and Measurement 60, no. 6 (June 2011): 1997–2006. http://dx.doi.org/10.1109/tim.2011.2115630.

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Richardson, Robert R., Michael A. Osborne, and David A. Howey. "Gaussian process regression for forecasting battery state of health." Journal of Power Sources 357 (July 2017): 209–19. http://dx.doi.org/10.1016/j.jpowsour.2017.05.004.

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Lai, Xin, Ming Yuan, Xiaopeng Tang, Yi Yao, Jiahui Weng, Furong Gao, Weiguo Ma, and Yuejiu Zheng. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing." Energies 15, no. 19 (October 9, 2022): 7416. http://dx.doi.org/10.3390/en15197416.

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State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications.

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Choi, Woongchul. "A Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging." Sustainability 12, no. 24 (December 14, 2020): 10451. http://dx.doi.org/10.3390/su122410451.

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Due to rapid development of industries around the world, more and more consumption of fossil fuels was unavoidable, resulting in serious environmental problems. The many pollutant emissions—a major contributor to global warming and weather pattern change—have been at the center of concern. In order to solve this issue, research and development of electric vehicles and energy storage systems made great progress and successfully introduced products in the market. Nevertheless, accurate measurement of the state of charge (SOC) and state of health (SOH) of the Li-ion battery, the most popular electric energy storage device, has not yet been fully understood due to the nature of battery aging. In this study, ideas to estimate the capacity and ultimately SOC and SOH of Li-ion batteries are discussed. With these ideas, we expect not only to accommodate the issues with battery aging but also to implement an algorithm for an on-board battery management system. The key idea is to chase and monitor internal resistance continuously in a fast and reliable manner in real time. With further investigation of the key idea, we also fully expect to come up with a reliable SOC and SOH measurement scheme in the near future.

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Li, Xiaoyu, Tengyuan Wang, Chuxin Wu, Jindong Tian, and Yong Tian. "Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data." World Electric Vehicle Journal 12, no. 4 (October 20, 2021): 204. http://dx.doi.org/10.3390/wevj12040204.

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In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. Particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform.

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Bao, Zhengyi, Jiahao Jiang, Chunxiang Zhu, and Mingyu Gao. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery." Energies 15, no. 12 (June 16, 2022): 4399. http://dx.doi.org/10.3390/en15124399.

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Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.

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Hemdani, Jamila, Laid Degaa, Moez Soltani, Nassim Rizoug, Achraf Jabeur Telmoudi, and Abdelkader Chaari. "Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health." Energies 15, no. 22 (November 16, 2022): 8558. http://dx.doi.org/10.3390/en15228558.

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The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium–iron–phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental results showed that the proposed methodology can well learn the characteristic relationship of battery discharge capacities as well as its state of health (SOH), where the battery life cycle changes as the battery ages with time and cycles.

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Jiang, Jianfeng, Shaishai Zhao, and Chaolong Zhang. "State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM." World Electric Vehicle Journal 12, no. 4 (November 10, 2021): 228. http://dx.doi.org/10.3390/wevj12040228.

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The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

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Fu, Pengyu, Liang Chu, Zhuoran Hou, Zhiqi Guo, Yang Lin, and Jincheng Hu. "State-of-Health Prediction Using Transfer Learning and a Multi-Feature Fusion Model." Sensors 22, no. 21 (November 5, 2022): 8530. http://dx.doi.org/10.3390/s22218530.

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Existing data-driven technology for prediction of state of health (SOH) has insufficient feature extraction capability and limited application scope. To deal with this challenge, this paper proposes a battery SOH prediction model based on multi-feature fusion. The model is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). The CNN can learn the cycle features in the battery data, the LSTM can learn the aging features of the battery over time, and regression prediction can be made through the full-connection layer (FC). In addition, for the aging differences caused by different battery operating conditions, this paper introduces transfer learning (TL) to improve the prediction effect. Across cycle data of the same battery under 12 different charging conditions, the fusion model in this paper shows higher prediction accuracy than with either LSTM and CNN in isolation, reducing RMSPE by 0.21% and 0.19%, respectively.

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Selvabharathi, D., and N. Muruganantham. "Battery health and performance monitoring system: a closer look at state of health (SoH) assessment methods of a Lead-Acid battery." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 261. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp261-267.

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Batteries are one of the most compact and reliable sources of sustainable energy. Lead-Acid batteries are the battery-powered sort of batteries concocted during the 1980s. The significant utilization of lead-acid battery is in beginning, lighting and start frameworks of vehicles.To guarantee the health and to dodge potential disappointments of a battery it is important to examine its Territory of health precisely. This examination expects to give efficiently evaluating the accessible writing on the condition of health estimation techniques. This study focuses on many factors and provides a suggestion for the defended battery manufacturing process. This study provides increasing efforts toward the advancement of battery interms of specific power, energy density, durability, invulnerability, economics, and performance in various applications.

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Surya, Sumukh, Vidya Rao, and Sheldon S. Williamson. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application." Energies 14, no. 15 (July 30, 2021): 4617. http://dx.doi.org/10.3390/en14154617.

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Electric Vehicles (EV) and Hybrid EV (HEV) use Lithium (Li) ion battery packs to drive them. These battery packs possess high specific density and low discharge rates. However, some of the limitations of such Li ion batteries are sensitivity to high temperature and health degradation over long usage. The Battery Management System (BMS) protects the battery against overvoltage, overcurrent etc., and monitors the State of Charge (SOC) and the State of Health (SOH). SOH is a complex phenomenon dealing with the effects related to aging of the battery such as the increase in the internal resistance and decrease in the capacity due to unwanted side reactions. The battery life can be extended by estimating the SOH accurately. In this paper, an extensive review on the effects of aging of the battery on the electrodes, effects of Solid Electrolyte Interface (SEI) deposition layer on the battery and the various techniques used for estimation of SOH are presented. This would enable prospective researchers to address the estimation of SOH with greater accuracy and reliability.

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Wang, Jiwei, Zhongwei Deng, Jinwen Li, Kaile Peng, Lijun Xu, Guoqing Guan, and Abuliti Abudula. "State of Health Trajectory Prediction Based on Multi-Output Gaussian Process Regression for Lithium-Ion Battery." Batteries 8, no. 10 (September 21, 2022): 134. http://dx.doi.org/10.3390/batteries8100134.

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Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction of battery SOH have caused widespread concern in academic and industrial communities. In this paper, a method is proposed to build an accurate SOH prediction model for battery packs based on multi-output Gaussian process regression (MOGPR) by employing the initial cycle data of the battery pack and the entire life cycling data of battery cells. Firstly, a battery aging experimental platform is constructed to collect battery aging data, and health indicators (HIs) that characterize battery aging are extracted. Then, the correlation between the HIs and the battery capacity is evaluated by the Pearson correlation analysis method, and the HIs that own a strong correlation to the battery capacity are screened. Finally, two MOGPR models are constructed to predict the HIs and SOH of the battery pack. Based on the first MOGPR model and the early HIs of the battery pack, the future cycle HIs can be predicted. In addition, the predicted HIs and the second MOGPR model are used to predict the SOH of the battery pack. The experimental results verify that the approach has a competitive performance; the mean and maximum values of the mean absolute error (MAE) and root mean square error (RMSE) are 1.07% and 1.42%, and 1.77% and 2.45%, respectively.

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Salek, Farhad, Aydin Azizi, Shahaboddin Resalati, Paul Henshall, and Denise Morrey. "Mathematical Modelling and Simulation of Second Life Battery Pack with Heterogeneous State of Health." Mathematics 10, no. 20 (October 17, 2022): 3843. http://dx.doi.org/10.3390/math10203843.

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The service life of Lithium-ion batteries disposed from electric vehicles, with an approximate remaining capacity of 75–80%, can be prolonged with their adoption in less demanding second life applications such as buildings. A photovoltaic energy generation system integrated with a second life battery energy storage device is modelled mathematically to assess the design’s technical characteristics. The reviewed studies in the literature assume, during the modelling process, that the second life battery packs are homogeneous in terms of their initial state of health and do not consider the module-to-module variations associated with the state of health differences. This study, therefore, conducts mathematical modelling of second life battery packs with homogenous and heterogeneous state of health in module level using second-order equivalent circuit model (ECM). The developed second-order ECM is validated against experimental data performed in the lab on SONY VTC6 batteries. The degradation parameters are also investigated using the battery cell’s first life degradation data and exponential triple smoothing (ETS) algorithm. The second-order ECM is integrated with the energy generation system to evaluate and compare the performance of the homogenous and heterogeneous battery packs during the year. Results of this study revealed that in heterogeneous packs, a lower electrical current and higher SOC is observed in modules with lower state of health due to their higher ohmic resistance and lower capacity, compared to the other modules for the specific battery pack configuration used in this study. The methodology presented in this study can be used for mathematical modelling of second life battery packs with heterogenous state of health of cells and modules, the simulation results of which can be employed for obtaining the optimum energy management strategy in battery management systems.

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Wang, Zuolu, Guojin Feng, Xiuquan Sun, Dong Zhen, Fengshou Gu, and Andrew D. Ball. "Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2184/1/012024.

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Abstract Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R 2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.

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Akbar, Khalid, Yuan Zou, Qasim Awais, Mirza Jabbar Aziz Baig, and Mohsin Jamil. "A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries." Electronics 11, no. 8 (April 12, 2022): 1216. http://dx.doi.org/10.3390/electronics11081216.

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The car industry is entering a new age due to electric energy as a fuel in the contemporary era. Electric batteries are being more widely used in the automobile sector these days. As a result, the inner workings of these battery systems must be fully comprehended. There is currently no accurate model for predicting an electric car battery’s state of health (SOH). This study aims to use machine learning to develop a reliable SOH prediction model for batteries. A correct optimal method was also constructed to drive the modeling process in the right direction. Extensive simulations were performed to verify the accuracy of the suggested methodology. A state of health method for data processing was developed. The method involves a complex data-driven model combining Big Data, Artificial Intelligence (A.I.), and the Internet of Things (IoT) technologies. To establish the most effective technique for certifying the actual condition of real-life battery health, researchers compared the accuracy and performance of several states of health models. For improved understanding and prediction of the condition of health behavior, data-driven modeling has certain significant advantages over older methodologies. The methods used in this study can be seen as a revolutionary low-cost, high-accuracy, and dependable approach to understanding and analyzing the state of health of batteries. At first, an intelligent model was created using a data-driven modeling strategy. Secondly, the concurrent battery data are qualified using the data-driven model. The machine learning (ML) method creates a very accurate and dependable model for forecasting battery health in real-world scenarios. Third, the previously established ML model was used to develop a knowledge-based online service for battery health. This web service can be used to test battery health, monitor battery behavior, and perform a variety of other tasks. A variety of similar solutions for diverse systems can be derived using the same technique. The default efficiency of the ML algorithmic module, R-Squared (R2), and Mean Square Error (MSE) were also utilized as performance measures. The R2 as a standard is used to examine the effectiveness of a fit. The result is a value between 0 and 1, with 1 indicating a better model fit. MSE stands for mean squared error. A lower MSE number implies superior model performance, since it reflects how close the parameter estimates are to the actual values. The training set of the battery model had a score of 0.9999, whereas the testing set had a score of 0.9995. The R2 score was one, with an M.S.E. of 0.03. As a result of these three indicators, the data-driven ML model used in this study proved to be accurate.

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Fu, Pengyu, Liang Chu, Jihao Li, Zhiqi Guo, Jincheng Hu, and Zhuoran Hou. "State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution." Sensors 22, no. 23 (December 2, 2022): 9435. http://dx.doi.org/10.3390/s22239435.

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A battery’s charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model’s effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model’s prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging–discharging behaviour in actual operation.

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Yuan, Hongyuan, Youjun Han, Yu Zhou, Zongke Chen, Juan Du, and Hailong Pei. "State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm." Energies 15, no. 4 (February 18, 2022): 1529. http://dx.doi.org/10.3390/en15041529.

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Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

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Bhagavatula, Sai Vasudeva, Venkata Rupesh Bharadwaj Yellamraju, Karthik Chandra Eltem, Phaneendra Babu Bobba, and Naveenkumar Marati. "ANN based Battery Health Monitoring - A Comprehensive Review." E3S Web of Conferences 184 (2020): 01068. http://dx.doi.org/10.1051/e3sconf/202018401068.

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The development of electric vehicles has bought a great revolution in the field of battery management as it deals with the health of the battery and also the protection of the battery. State of Charge (SoC) and State of Health (SoH) are the important parameters in determining the battery’s health. Advancements in Artificial Neural Networks and Machine Learning, a growing field in recent years has bought many changes in estimating these parameters. Access to huge battery data has become very advantageous to these methods. This manuscript presents an overview of different Artificial Neural Network techniques like Feedforward Neural Network (FNN), Extreme Learning Machine (ELM), and the Long Short Term Memory (LSTM). These techniques are trained with already existing data samples consisting of different values of voltages, currents at different temperatures with different charging cycles and epochs. The errors in each technique are different from the other as the constraints in one method are rectified using the other method to get the least error percentage and get the nearest estimate of the SoC and SOH. Each method needs to be trained for several epochs. This manuscript also presents a comparison of different methods with input parameters and error percentages.

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Lee, Jong-Hyun, and In-Soo Lee. "Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries." Sensors 22, no. 15 (July 25, 2022): 5536. http://dx.doi.org/10.3390/s22155536.

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Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.

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Bi, Jikai, Jae-Cheon Lee, and Hao Liu. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics." Energies 15, no. 7 (March 26, 2022): 2448. http://dx.doi.org/10.3390/en15072448.

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The market for eco-friendly batteries is increasing owing to population growth, environmental pollution, and energy crises. The widespread application of lithium-ion batteries necessitates their state of health (SOH) estimation, which is a popular and difficult area of research. In general, the capacity of a battery is selected as a direct health factor to characterize the degradation state of the battery’s SOH. However, it is difficult to directly measure the actual capacity of a battery. Therefore, this study extracted three features from the current, voltage, and internal resistance of a lithium-ion battery during its charging–discharging process to estimate its SOH. A battery-accelerated aging test system was designed to obtain time series battery degradation data. A performance comparison of lithium-ion battery SOH fitting results was conducted for two different deep learning architectures, a long short-term memory (LSTM) network and temporal convolution network (TCN), which are time series deep learning networks based on a recurrent neural network (RNN) and convolutional neural network (CNN), respectively. The results showed that the proposed method has high prediction accuracy, while the performance of the TCN was 3% better than that of the LSTM regarding the average maximum relative error in SOH estimation of a lithium-ion battery.

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Li, Weihan, Monika Rentemeister, Julia Badeda, Dominik Jöst, Dominik Schulte, and Dirk Uwe Sauer. "Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation." Journal of Energy Storage 30 (August 2020): 101557. http://dx.doi.org/10.1016/j.est.2020.101557.

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de la Peña Llerandi, Jaime, Carlos Sancho de Mingo, and José Carpio Ibáñez. "Continuous Battery Health Diagnosis by On-Line Internal Resistance Measuring." Energies 12, no. 14 (July 23, 2019): 2836. http://dx.doi.org/10.3390/en12142836.

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Energy storage in an uninterruptible power supply (UPS) is one of the most frequent applications of batteries. This can be found in hospitals, communication centers, public centers, ships, trains, etc. Most frequent industrial methods for battery state-of health estimation require a technician to move to the battery’s location and, in some cases, require the use of heavy equipment and disconnection of the battery from the UPS. For example, in railway applications, trains must stop at the maintenance depot producing significant total costs. This article proposes a new method to assess a battery’s health by measuring the battery’s internal resistance, based on the measurement of its voltage ripple in response to the current ripple imposed by the charger which in most UPS applications is permanently connected to the battery. Unlike most traditional methods, this system makes it possible a continuous on-line and on-board monitoring, and, therefore, it eases condition-based maintenance (CBM). To verify its viability, a low cost measuring prototype has been built and measurements in a railway battery with its charger have been carried out.

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Lin, Ho-Ta, Tsorng-Juu Liang, and Shih-Ming Chen. "Estimation of Battery State of Health Using Probabilistic Neural Network." IEEE Transactions on Industrial Informatics 9, no. 2 (May 2013): 679–85. http://dx.doi.org/10.1109/tii.2012.2222650.

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Yan, Xiang Wu, Qi Guo, and Heng Bo Xu. "A Novel Method to Estimate the State of Health of each Cell in Battery Pack." Advanced Materials Research 1044-1045 (October 2014): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.545.

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In order to estimate the state of health (SOH) of each cell in the battery pack, polarization resistance and ohmic resistance were analyzed in the aging process of the battery pack. Accelerating aging test for the battery was done, quantitative relationship between the ohmic resistance and the capacity aging was obtained, a method of relative state of health (RSOH) evaluation was proposed accordingly, Experiments on the LiFePO4 battery pack which is connected in series by 100 cells have been taken, the experimental results show that the evaluation method of RSOH can evaluate the cells SOH accurately and is not limited by the operating conditions.

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Bustos, Richard, S. Andrew Gadsden, Mohammad Al-Shabi, and Shohel Mahmud. "Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles." Applied Sciences 13, no. 2 (January 14, 2023): 1132. http://dx.doi.org/10.3390/app13021132.

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To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery’s available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model’s parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery’s SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles.

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Barragán-Moreno, Alberto, Erik Schaltz, Alejandro Gismero, and Daniel-Ioan Stroe. "Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements." Electronics 11, no. 9 (April 28, 2022): 1414. http://dx.doi.org/10.3390/electronics11091414.

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With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.

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Jiang, Shida, and Zhengxiang Song. "Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance." Energies 14, no. 16 (August 8, 2021): 4833. http://dx.doi.org/10.3390/en14164833.

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Lithium-ion batteries are an attractive power source in many scenarios. In some particular cases, including providing backup power for drones, frequency modulation, and powering electric tools, lithium-ion batteries are required to discharge at a high rate (2~20 C). In this work, we present a method to estimate the state of health (SOH) of lithium-ion batteries with a high discharge rate using the battery’s impedance at three characteristic frequencies. Firstly, a battery model is used to fit the impedance spectrum of twelve LiFePO4 batteries. Secondly, a basic estimation model is built to estimate the SOH of the batteries via the parameters of the battery model. The model is trained using the data of six batteries and is tested on another six. The RMS of relative error of the model is lower than 4.2% at 10 C and lower than 2.8% at 15 C, even when the low-frequency feature of the impedance spectrum is ignored. Thirdly, we adapt the basic model so that the SOH estimation can be performed only using the battery’s impedance at three characteristic frequencies without having to measure the entire impedance spectrum. The RMS of relative error of this adapted model at 10 C and 15 C is 3.11% and 4.25%, respectively.

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Ning, Jing, Bing Xiao, Wenhui Zhong, and Bin Xiao. "A rapid detection method for the battery state of health." Measurement 189 (February 2022): 110502. http://dx.doi.org/10.1016/j.measurement.2021.110502.

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Journal articles on the topic 'Battery state-of-health'

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