41 results on '"state of health estimation"'
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2. The [formula omitted]-method: State of health and degradation mode estimation for lithium-ion batteries using a mechanistic model with relaxed voltage points
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Hofmann, Tobias, Li, Jiahao, Hamar, Jacob, Erhard, Simon, and Schmidt, Jan Philipp
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- 2024
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3. Machine learning enables rapid state of health estimation of each cell within battery pack.
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Yu, Quanqing, Nie, Yuwei, Guo, Shanshan, Li, Junfu, and Zhang, Chengming
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FEATURE extraction , *PHYSICAL constants , *GENERALIZATION - Abstract
The health and safety of the battery pack are directly influenced by the state of health of its cells. However, due to the aging inconsistency among cells and the limited measurability of physical quantities for cells within the battery pack, traditional approaches to state of health estimation of cell have significant limitations. This study introduces a machine learning approach for evaluating the state of health of cells within the battery pack. Firstly, a branch charging capacity estimator utilizing BiGRU is formulated, facilitating precise estimation of battery pack branch charging capacity across diverse charging conditions. Then, three categories of features, including aging features, inconsistency features, and operating condition features, are extracted based on aging experimental data at the battery pack level and battery pack branch charging capacity. These features are input into the support vector regression-based generic model, facilitating precise state of health estimation for all cells within the battery pack. The generalization of the model is validated under both five-stage constant current charging conditions and two-stage constant current charging conditions. Additionally, the discussion includes how the choice of model parameters affects the precision of cell state of health estimation. The method proposed enables precise monitoring of cell state of health within the battery pack, offering valuable potential for ensuring overall battery pack safety and issuing safety alerts for cells. • A high-precision branch charging capacity estimator has been developed. • The aging, inconsistency, and operating condition features are extracted. • High-precision estimation of SOH of all cells within battery pack can be achieved. • The model's generalization is validated under different operating conditions. [ABSTRACT FROM AUTHOR] more...
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- 2024
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4. Refined lithium-ion battery state of health estimation with charging segment adjustment.
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Zheng, Kun, Meng, Jinhao, Yang, Zhipeng, Zhou, Feifan, Yang, Kun, and Song, Zhengxiang
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STANDARD deviations , *BATTERY management systems , *CYTOCHEMISTRY , *LITHIUM-ion batteries , *VOLTAGE - Abstract
Accurately monitoring the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for battery management systems (BMS), yet there lack of the possibility to fully use the random charging segments with any length. To this end, a residual convolution and transformer network (R-TNet) is proposed to enable an accurate LIB SOH estimation with the sparse dimension of feature in random segments, where the start and end voltage, the Ampere-hour (Ah) throughput, temperature, and current rate of a charging segment are required for the estimation task. Through the cross-attention mechanism of R-TNet , the operation condition and the position of the partial voltage can be integrated to enable the LIBs SOH estimation within a charging segment. To extend the flexibility with arbitrary charging behaviors, an ElasticNet -based feature transfer strategy is designed to use any charging length. 121 cells with different chemistries and cycling conditions are used to validate the performance of the proposed method. The results of the proposed method show that the root mean square error (RMSE) of SOH estimation can reach 1.6% even for a 50 mV voltage segment. • A residual convolution and transformer network is proposed to ensure SOH estimation of random voltage segments. • An ElasticNet -based feature transfer strategy is designed to use any length of voltage segments. • 121 cells with two chemistries and multiple operating conditions are used to validate the performance of the proposed method. [ABSTRACT FROM AUTHOR] more...
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- 2024
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5. State of health estimation for lithium-ion batteries based on incremental capacity analysis and Transformer modeling.
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Xu, Zhaofan, Chen, Zewang, Yang, Lin, and Zhang, Songyuan
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BATTERY management systems ,LITHIUM-ion batteries ,MACHINE learning ,FEATURE extraction ,MOVING average process - Abstract
As an important performance indicator of battery management systems, lithium-ion battery state of health (SOH) information is crucial to ensure battery safety and extend battery lifetime. Aiming at the problems of feature extraction difficulty, low accuracy of long-term prediction, and poor parallel computing capability of general data-driven methods, this paper proposes a SOH estimation method for lithium-ion batteries based on incremental capacity analysis (ICA) and Transformer. First, the original incremental capacity (IC) curve of the battery is extracted based on the ICA method, and the original IC curve is processed using the dual filtering method of moving average smoothing filter plus Gaussian smoothing filter, which in turn extracts the peak features of the curve. Then, the Transformer network model based on the multi-head attention mechanism is built. Finally, the extracted peak features of the IC curve are used as model inputs, and the Transformer model is utilized to realize the SOH estimation of lithium-ion batteries. In this paper, experiments based on different input features, prediction starting points, and ambient temperatures are conducted using experimental data of lithium-ion batteries from three sources and analyzed in comparison with commonly used machine learning methods. The experimental results show that the SOH estimation method proposed in this paper has higher long-term prediction accuracy and better temperature adaptability than commonly used machine learning methods. • The dual filtering method of MASF plus GSF is used to smooth the IC curve. • Transformer model is proposed to estimate the SOH of lithium-ion batteries. • The peak features of the IC curves are used as model inputs. • The advantages of the proposed method were verified by several experiments. [ABSTRACT FROM AUTHOR] more...
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- 2024
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6. State of health estimation for lithium-ion batteries using impedance-based simplified timescale information.
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Qian, Guangjun, Zheng, Yuejiu, Li, Xinyu, Sun, Yuedong, Han, Xuebing, and Ouyang, Minggao
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Accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for enhancing battery safety and operational reliability. The distribution of relaxation times (DRT) provides information of battery electrochemical impedance spectroscopy (EIS) on the timescales, reflecting internal kinetic processes and showing strong correlations with SOH. However, the extraction and application of this timescale information within battery management systems (BMS) are impeded by the need for broadband EIS data and intricate mathematical processes for DRT method. To address these challenges, a simplified timescale information (STI) method based on impedance is proposed, which delineates different battery states without requiring complex calculations. A data-driven SOH estimation model is developed using a gradient boosting decision tree algorithm with STI. Results from the test set indicate that the model, using selected STI (SSTI) features, achieves an average error of only 1.36 %, outperforming existing impedance feature extraction methods. Even excluding battery usage history (such as degradation temperature and state of charge), the model employing SSTI maintains an average error of 2.4 %. Moreover, the proposed SSTI method imposes minimal computational demands and does not require broadband EIS data. As SSTI features can be rapidly obtained through EIS chip, this method shows promise for online, real-time applications, paving a new path for data-driven BMS. • A simplified timescale information is proposed to delineate the different battery states. • Selected simplified timescale information can be acquired within 10 s using existing laboratory equipment. • Among the comparisons of various feature selection methods, the proposed method achieved the highest model accuracy on the test set. • The impact of battery degradation temperature and state of charge on SOH estimation is quantified. [ABSTRACT FROM AUTHOR] more...
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- 2025
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7. State of health estimation of lithium-ion batteries based on feature optimization and data-driven models.
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Mu, Guixiang, Wei, Qingguo, Xu, Yonghong, Li, Jian, Zhang, Hongguang, Yang, Fubin, Zhang, Jian, and Li, Qi
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With the widespread application of lithium-ion batteries in electric vehicles, accurately estimating their state of health (SOH) has become a key focus of research. This paper explores various feature optimization methods and data-driven models with different structures, and constructs various SOH estimation models suitable for lithium-ion batteries. Based on battery testing data, multiple features are extracted from voltage and temperature to characterize the battery aging process. To reduce information redundancy among features, filtering methods, Principal Component Analysis (PCA), and Multi-dimensional Scaling (MDS) are applied for optimization, aiming to maximize feature information utilization. This paper compares four common and structurally different data-driven models: linear regression (LR), Gaussian process regression (GPR), support vector regression (SVR), and long short-term memory (LSTM) networks. The effectiveness of each model is validated using multi-feature inputs, and a multi-dimensional assessment of feature selection and data-driven model performance in SOH estimation is conducted, the average absolute error of all models under 60 % training set conditions is 0.8 %. The average absolute error of estimating the four batteries using the fused PCA features as input and the GPR model is less than 1.2 %. At the same time, using the optimized features as input reduces the average training time by 46.63 % compared to using multiple features as input. In summary, the combination of PCA features and GPR models has good performance in both estimation accuracy and computational efficiency for different batteries. • Extracting health factors from battery data to describe the aging process of battery. • Choose multiple different types of methods to optimize the extracted features. • Use four data-driven models and all features as inputs to estimate SOH. • Compare the accuracy and efficiency of optimized features with all features. [ABSTRACT FROM AUTHOR] more...
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- 2025
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8. Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability.
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Mo, Daijiang, Wang, Shunli, Fan, Yongcun, Takyi-Aninakwa, Paul, Zhang, Mengyun, Wang, Yangtao, and Fernandez, Carlos
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OPTIMIZATION algorithms , *MACHINE learning , *BATTERY management systems , *DUNG beetles , *KERNEL functions - Abstract
Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications. • Innovating an Adaptive learning kernel function for union Poly to construct a joint kernel function. • Using joint kernel function for extreme learning machine. • Optimizing the dung beetle optimization algorithm under multiple constraints. • Constructed LOWDBO-PAKELM model for SOH estimation with a high adaptation range. [ABSTRACT FROM AUTHOR] more...
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- 2024
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9. An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries.
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Jiang, Fusheng, Ren, Yi, Tang, Ting, Wu, Zeyu, Xia, Quan, Sun, Bo, and Yang, Dezhen
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AUTODIDACTICISM , *MACHINE learning , *LITHIUM-ion batteries , *SUPERVISED learning , *PEARSON correlation (Statistics) , *HEALTH status indicators - Abstract
Accurate and precise online estimation of the state of health (SOH) is crucial when managing lithium-ion batteries. Most existing SOH estimation methods rely on supervised learning algorithms utilizing large amounts of labeled data. However, lithium-ion batteries are typically operated under dynamic conditions, including significant amounts of unlabeled charging or discharging data in online application scenarios. To fully utilize these data, we propose an adaptive semi-supervised self-learning teacher-student model (AS3LTSM) method for online SOH estimation. First, four physically interpretable health indicators (PIHIs) are extracted from the voltage and current data. The Pearson correlation coefficient (PCC) is used to assess significant associations between PIHIs and the SOH. Regressive and autoregressive long short-term memory (LSTM) models are selected as the teacher and student networks. Knowledge is transferred from the teacher to the student through pseudolabels, which guide the updating and evolution of the student network. Furthermore, a self-learning strategy and a retraining process for improving the long-term estimation accuracy are proposed. Two public datasets are used for comparison and ablation experiments. Experimental analysis validates the improved effectiveness and performance of the proposed method, with the RMSE and MAPE of the three experimental groups all within 1.3 % and 1.29 %, respectively. • An adaptive semi-supervised self-learning method is proposed for online SOH estimation. • An amount of online unlabeled data is utilized to improve SOH estimation accuracy. • The self-learning and re-training process are utilized to reduce long-term cumulative error. • An ablation and experimental results validate the performance and robustness. [ABSTRACT FROM AUTHOR] more...
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- 2024
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10. Comparison of techniques based on frequency response analysis for state of health estimation in lithium-ion batteries.
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Wang, Shaojin, Tang, Jinrui, Xiong, Binyu, Fan, Junqiu, Li, Yang, Chen, Qihong, Xie, Changjun, and Wei, Zhongbao
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LITHIUM-ion batteries , *ELECTRIC vehicle batteries , *ELECTRIC vehicles , *CURVE fitting , *MACHINE learning - Abstract
Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy. • Four FRA techniques, Impedance, MLECM, DRT and NFRA-based method are developed. • Two frameworks, MLF and CFF, are applied to each of these four techniques. • The estimation accuracy and estimation range of the eight models are compared. • Application scenarios are identified by using three classification methods. [ABSTRACT FROM AUTHOR] more...
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- 2024
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11. State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges.
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S, Vignesh, Che, Hang Seng, Selvaraj, Jeyraj, Tey, Kok Soon, Lee, Jia Woon, Shareef, Hussain, and Errouissi, Rachid
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ELECTRIC vehicle batteries , *LITHIUM-ion batteries , *ELECTRIC automobiles , *PROBABILITY density function , *ARTIFICIAL intelligence , *HEALTH status indicators , *COMPUTATIONAL complexity - Abstract
Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR] more...
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- 2024
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12. A charging-feature-based estimation model for state of health of lithium-ion batteries.
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Cai, Li and Lin, Jingdong
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LITHIUM-ion batteries , *KRIGING , *FEATURE extraction , *BATTERY management systems - Abstract
Within battery management systems, the state of health of lithium-ion batteries is a key and vital enabler to ensure battery safety and efficiency. However, the accurate state of health estimation is still a critical but challenging task, and the complex electrochemical attributes underlying the degradation processes of lithium-ion batteries are not directly available. In response to this challenge, this study proposes a charging-feature-based model to realize state of health estimation by Gaussian process regression. In this approach, two features are extracted only from the monitoring parameters obtained from charging current and voltage curves. These extracted features have been demonstrated to be correlated with the state of health. Subsequently, a regression model with a 2-dimensional linear mean function and a new double-covariance function is developed to improve estimation performance. Consequently, the proposed model effectively tracks both global and local degradation trends synchronously. Finally, the reliability and accuracy of the proposed model are verified using two different batteries datasets. The results illustrate that the proposed model is capable of realizing accurate batteries' state of health estimation, thereby outperforming other counterparts in uncertainty representation and estimation errors, whether under static profiles or dynamic profiles. [ABSTRACT FROM AUTHOR] more...
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- 2024
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13. State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural network.
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Zhang, Yue, Wang, Yeqin, Zhang, Chu, Qiao, Xiujie, Ge, Yida, Li, Xi, Peng, Tian, and Nazir, Muhammad Shahzad
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LITHIUM-ion batteries , *LITHIUM cells , *MACHINE learning , *LEARNING strategies , *LEARNING - Abstract
Accurate estimation of State of Health (SOH) is crucial to ensure optimal performance and safe operation of lithium-ion battery. This paper proposes a Stacking ensemble learning paradigm for SOH estimation. The Stacking ensemble learning increases adaptability to different features by using base learners with different structures, reducing the risk of overfitting. The model utilizes random vector functional link (RVFL) and active state tracking long-short-term memory network (AST-LSTM) as base learners, where AST-LSTM actively tracks long-term information of lithium-ion battery, and RVFL acts as the meta-learner for stacking. The random vector functional link network helps to avoid the problem of gradient vanishing that is commonly encountered in neural networks due to the gradient descent principle. To further improve estimation accuracy, Singer initialization method and dimension learning method are employed to enhance the Heap-based optimization (HBO) algorithm. In this study, the IHBO algorithm is used to optimize the hyperparameters of the model. Comparing with other methods, the hybrid model proposed in this paper demonstrates superior estimation performance under different operating conditions: at a temperature of 24 °C with a discharge current of 1 A, at a temperature of 4 °C with a discharge current of 1 A, and at a temperature of 4 °C with a discharge current of 2 A. The highest RMSE of the proposed method for the three working conditions are 0.006, 0.01, and 0.017, respectively. Therefore, the proposed Stacking ensemble learning is feasible for SOH estimation of lithium-ion battery and can better adapt to lithium-ion battery data under different operating conditions. • A Stacking ensmeble learning strategy is proposed for SOH estimation. • The improved HBO algorithm is introduced to optimize the Stacking model. • SOH for lithium batteries under three different operating conditions is estimated. • Six benchmark models are used to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR] more...
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- 2024
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14. Critical summary and perspectives on state-of-health of lithium-ion battery.
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Yang, Bo, Qian, Yucun, Li, Qiang, Chen, Qian, Wu, Jiyang, Luo, Enbo, Xie, Rui, Zheng, Ruyi, Yan, Yunfeng, Su, Shi, and Wang, Jingbo
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ELECTRONIC equipment , *CRITICAL analysis , *LITHIUM-ion batteries , *RESEARCH personnel , *EVALUATION methodology - Abstract
The rapid development of lithium-ion battery (LIB) technology promotes its wide application in electric vehicle (EV), aerospace, and mobile electronic equipment. During application, state of health (SOH) of LIB is crucial to enhance stable and reliable operation of the battery system. However, accurate estimation of SOH is a tough task, especially in its large-scale application. Thus far, a variety of works on the estimation of SOH of LIB have been proposed, along with several review studies that aim to summarize the current research status. However, there are some deficiencies in prior reviews, such as unclear classification, incomplete summary, and insufficient evaluation of estimation methods. Thus, to resolve the shortcomings, the enumeration method is used to fully screen published works related to SOH estimation, and a total of one hundred and ninety relevant studies are investigated for a thorough review and discussion. Besides, the definition of SOH from different perspectives and three representative battery models are summarized, respectively. Meanwhile, twenty commonly used evaluation criteria and two explicit SOH estimation schemes are comprehensively introduced, which all are tabulated in detail for systematic evaluation and fair comparison. Finally, the main problems and challenges in SOH estimation are fully discussed, meanwhile, three promising future development trends are proposed and some essential SOH public datasets are summarized. In general, this review is envisioned to offer insightful guidance to researchers or engineers working on SOH estimation and related research, thus further promoting the development of SOH estimation technology and exploration of potential research direction. [Display omitted] • Three typical battery modeling methods in SOH estimation are summarized in detail. • Summarize a variety of evaluation criteria applied to SOH estimation. • Various SOH estimation methods are divided into two general groups. • The characteristics of SOH estimation methods are illustrated and compared. • Suggestions and prospects for the further development of SOH estimation of LIB are put forward. [ABSTRACT FROM AUTHOR] more...
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- 2024
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15. Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles.
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Meng, Jinhao, Cai, Lei, Stroe, Daniel-Ioan, Luo, Guangzhao, Sui, Xin, and Teodorescu, Remus
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LITHIUM-ion batteries , *ELECTRIC vehicle batteries , *BATTERY management systems , *ELECTRIC potential , *GENETIC algorithms - Abstract
Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method. • The SOH estimation accuracy is improved in an optimization manner. • The optimal multiple voltage ranges are automatically selected by NSGA-II and grid search. • Various solutions at different battery charging stages are provided for SOH estimation. • Three NMC-based batteries are aged for 360 days to validate the proposed method. [ABSTRACT FROM AUTHOR] more...
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- 2019
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16. A novel data-model fusion state-of-health estimation approach for lithium-ion batteries.
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Ma, Zeyu, Yang, Ruixin, and Wang, Zhenpo
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LITHIUM-ion batteries , *OPEN-circuit voltage , *ESTIMATION theory , *PARAMETRIC modeling , *STORAGE batteries - Abstract
Highlights • A novel data-model fusion state-of-health estimation approach is developed. • The correlation between capacity fade and open-circuit-voltage changes is applied. • An open-circuit-voltage parametric model is built to characterize voltage plateaus. • The robustness of the approach is evaluated against different cell aging paths. Abstract In order to ensure the efficient, reliable, and safe operation of the lithium-ion battery system, an accurate battery state-of-health estimation is essential and remaining challenges. Here we propose a novel data-model fusion battery state-of-health estimation approach based on open-circuit-voltage parametric modeling considering the correlation between capacity degradation and the open-circuit-voltage changes. An open-circuit-voltage model is built to capture the aging behavior associated with the reactions progress in the cell. Then the battery state-of-health estimation approach is developed based on the correlation between capacity fade and the changes of the open-circuit-voltage model parameters. In addition, a data-driven based method is applied to identify the parameters of the proposed battery model to obtain the open-circuit-voltage online. The proposed state-of-health estimation approach has been verified by the cells experienced different aging paths. The results show that the average relative errors of the state-of-health estimation for all cells are less than 3% against different aging paths and levels. [ABSTRACT FROM AUTHOR] more...
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- 2019
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17. Predicting the discharge capacity of a lithium-ion battery after nail puncture using a Gaussian process regression with incremental capacity analysis.
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Jones, Casey, Sudarshan, Meghana, and Tomar, Vikas
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KRIGING , *LITHIUM-ion batteries , *STANDARD deviations , *MEDIAN (Mathematics) - Abstract
This work uses a Gaussian process regression to predict the discharge capacity of small Lithium-ion pouch cells after a nail puncture. Previous studies have shown that cells can operate at a reduced capacity after experiencing abuse similar to what can be seen during extreme field operation, where the ability to predict cell functionality can be critical to safety. Other studies have shown that different features of cell incremental capacity curves can be used to determine the extent of cell degradation during normal operation, which can be used to predict future operation. For this work, 15 cells are punctured with a nail and allowed to continue operating for 100 total cycles to collect data. The incremental capacity curves are calculated, then the magnitude and corresponding voltage of the highest peak are determined. A Gaussian process regression is used to predict the discharge capacity during operation after the nail punctures. The results show a mean coefficient of determination of 0.923 with a median value of 0.95, a mean root mean square error of 0.013 and median value of 0.09, and a mean absolute error of 0.011 with a median value of 0.08, indicating the regression can be useful in predicting discharge capacity. • Observed effect of partial nail puncture on cell incremental capacity behavior. • Examined relationship between incremental capacity and discharge capacity. • Predicted discharge capacity fade using a Gaussian process regression. • Investigated error associated with prediction of discharge capacity. [ABSTRACT FROM AUTHOR] more...
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- 2023
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18. State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network.
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Zhang, Hao, Gao, Jingyi, Kang, Le, Zhang, Yi, Wang, Licheng, and Wang, Kai
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LITHIUM-ion batteries , *POLLINATION , *DEEP learning , *BATTERY management systems , *FEATURE extraction , *OHMIC resistance , *FLOWERS - Abstract
Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries. [Display omitted] • The MFPA algorithm is introduced to optimize several key hyper parameters in the TCN structure • Extracting external morphological features from the raw voltage and current curves • Ohmic resistances trajectories in the ECM with aging mechanisms to improve the SOH estimation accuracy • High estimation accuracy, great robustness to varied training set and satisfied universality to different battery types [ABSTRACT FROM AUTHOR] more...
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- 2023
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19. A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging.
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Chen, Junxiong, Hu, Yuanjiang, Zhu, Qiao, Rashid, Haroon, and Li, Hongkun
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HEALTH status indicators , *BATTERY management systems , *PARTICLE swarm optimization , *ELECTRIC batteries , *SAMPLING (Process) , *ELECTRIC vehicle batteries , *LEAST squares - Abstract
Efficient battery health indicator (HI) extraction and accurate estimation method are two important issues in the study of battery state of health (SOH) estimation. Although machine learning-based methods have been widely applied to the battery SOH estimation in recent years, the battery HI extraction in most studies is too tedious, the estimation method lacks pertinence, and the aging pattern of the battery aging dataset is simple. To solve the above problems, this paper proposes a novel battery HI based on the charging duration of the equal voltage intervals in the constant current charging process, which can effectively characterize the battery aging characteristics by only 10 continuous charging duration counts directly from the battery management system. Considering the difficulty of collecting battery aging data and the high dimensionality of the extracted HI, the least squares support vector regression (LSSVR), which is suitable for small samples and high dimensional data, is used to build the SOH mapping model and the optimal hyperparameters are found with the help of particle swarm optimization (PSO). The satisfactory SOH estimation accuracy of the proposed method is validated on a public LiFePO 4 battery aging dataset containing different temperatures, discharge rates, discharge depths and cycle intervals. [Display omitted] • A novel battery health indicator with low sampling and processing cost is proposed. • The voltage resolution and range of health indicator extraction are discussed. • SOH estimation method suitable for small sample and high dimensionality is adopted. • The experiments are performed on a public LFP dataset with various aging patterns. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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20. State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining.
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Huang, Kai, Yao, Kaixin, Guo, Yongfang, and Lv, Ziteng
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LEARNING strategies , *FEATURE extraction , *MINERAL industry equipment , *LITHIUM-ion batteries , *MINE safety , *RELIABILITY in engineering , *DEIONIZATION of water - Abstract
Accurate state of health (SOH) estimation of lithium-ion batteries is essential to ensure the reliability of power equipment. However, the degradation trajectory of different cells and different types of batteries is not repeatable. At present, there is no unified model or method to effectively predict SOH for all batteries. Therefore, a new SOH estimation method is proposed in the paper. Firstly, two types of new features are proposed in this paper. One is the voltage features extracted from the constant-current charging stage, and the other is the capacity recovery feature. They are used to reflect the nonlinear degradation process of the battery. Secondly, the relationship between features and SOH is established by using the LSTM model, which can prevent the problem of gradient vanishing and gradient explosion during model learning. Finally, for the inconsistencies between the same type or different types of batteries, two different transfer learning strategies (fine-tuning and rebuilding) are proposed in this paper, and the effectiveness of the proposed features and transfer learning strategies is verified on three open-source battery data sets (NASA, Oxford, and CALCE). Experimental results show that the SOH estimation method proposed in the paper has good universality, robustness, and accuracy. [Display omitted] • New voltage features are extracted from the constant-current charging voltage curve. • Capacity recovery feature is proposed and combined with voltage features to estimate SOH. • A transfer learning strategy (fine-tuned and rebuilding) is proposed to deal with battery inconsistency. • Three types of open-source data are used to verify the performance of the proposed SOH estimation method. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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21. A fast estimation algorithm for lithium-ion battery state of health.
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Tang, Xiaopeng, Zou, Changfu, Yao, Ke, Chen, Guohua, Liu, Boyang, He, Zhenwei, and Gao, Furong
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LITHIUM-ion batteries , *STORAGE batteries , *ELECTRIC potential , *ELECTROCHEMICAL analysis , *POTENTIAL energy , *NEURAL circuitry , *ELECTRODES - Abstract
This paper proposes a novel and computationally efficient estimation algorithm for lithium-ion battery state of health (SoH) under the hood of incremental capacity analysis. Concepts of regional capacity and regional voltage are introduced to develop an SoH model against experimental cycling data from four types of batteries. In the obtained models, SoH is a simple linear function of the regional capacity, and the R-square of linear fitting is up to 0.948 for all the considered batteries with properly selected regional voltage. The proposed method without using characteristic parameters directly from incremental capacity curves is insensitive to noise and filtering algorithms, and is effective for common current rates, where rates of up to 1C have been demonstrated. Then, a model-based SoH estimator is designed and shown to be capable of closely matching battery's aging data from NASA, with the error less than 2.5%. Furthermore, such a small scale of error is achieved in the absent of state of charge and impedance which are often used for SOH estimation in available methods. [ABSTRACT FROM AUTHOR] more...
- Published
- 2018
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22. Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online.
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Ning, Bo, Cao, Binggang, Wang, Bin, and Zou, Zhongyue
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SLIDING mode control , *LITHIUM-ion batteries , *PARAMETER estimation , *PROBLEM solving , *ERROR analysis in mathematics - Abstract
Simplicity and accuracy are both important factors in real-time battery states estimation applications. However, a battery model initialized with static parameters which are identified in ideal laboratory conditions will not be able to get an accurate estimation in various actual applications. Besides, it is time-consuming and complex in implement. To solve the above problem, a new battery states estimation method is proposed. Firstly, an adaptive battery model is proposed according to a new online parameter estimation algorithm. Based on it, the parameter adaptive sliding mode observer for state of charge is proposed. Thus, the state of charge systematic error led from various work environments could be effectively reduced. The parameter adaptive sliding mode observer for state of health is proposed by tracing the derivative of open circuit voltage estimated online. As the reference open circuit voltage is estimated based on measurable inputs and outputs, rather than conventional observer with an assumed constant capacity. The estimated battery capacity could converge to the actual value while the error of battery open circuit voltage converges to zero. The proposed method is verified through the urban dynamometer driving schedule driving cycle. The results indicate that:1) parameters estimated online are accurate, 2) the absolute error of state of charge is less than 2%, 3) the estimated lithium-ion battery capacity could converge to the actual value with small capacity error. [ABSTRACT FROM AUTHOR] more...
- Published
- 2018
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23. State of health estimation for lithium-ion batteries on few-shot learning.
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Zhang, Shuxin, Liu, Zhitao, and Su, Hongye
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LITHIUM-ion batteries , *FEATURE extraction , *DATA distribution - Abstract
State of health (SOH) is a critical indicator for implementing detection, diagnostics and prognostics on lithium-ion batteries. However, considering the difficulty of data collection and additional cost for gathering comprehensive field data in practical application, only limited data can be available for model establishment. In order to handle this insufficient data scenario, a novel Bayesian deep neural network has been established and validated on few-shot learning. Moreover, from the perspective of feature extraction, degradation patterns extracted from temporal cyclic discharge profiles are utilized for reflecting degradation mode and operation state, while the Gramian angular field is proposed for data distribution learning and information enhancement. Different percentages of data are conducted on model training to compare the comprehensive performance on various features and state-of-art methods with the proposed method on few-shot learning. Ultimately, experimental results prove better adaptability, generalization and effectiveness of the proposed method on lithium-ion battery SOH estimation regardless of data size. • Degradation pattern and Gramian Angular Field constitute input vectors. • A Bayesian deep neural network is established for SOH estimation. • Comparative experiments are implemented on three battery datasets. • The proposed method can work well on few-shot learning. [ABSTRACT FROM AUTHOR] more...
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- 2023
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24. State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism.
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Jiang, Yiyue, Chen, Yuan, Yang, Fangfang, and Peng, Weiwen
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LITHIUM-ion batteries , *FEATURE extraction , *VIDEO coding , *PRIOR learning - Abstract
Accurate state of health (SOH) estimation is significantly important to ensure the safe and reliable operation of lithium-ion battery. Most existing data-driven estimation methods are based on feature engineering and rely heavily on expert experience and manual operation. However, manually extracting qualified health features requires rich prior knowledge, and these highly-designed features for one specific application may not generalize well to other situations. In this work, an automatic feature extraction method combining convolutional autoencoder and self-attention mechanism is proposed for battery SOH estimation. With preprocessed data fed into the convolutional autoencoder, efficient features characterizing battery health are automatically extracted without human intervention. A self-mechanism module is then further employed to map these high-dimensional abstract health features into battery SOH. Finally, experimental study of battery aging is implemented to demonstrate the proposed method, and comparisons of the proposed method with existing data-driven approaches and the manual feature-based methods have also been presented. With the help of the convolutional autoencoder and self-attention module, the proposed method replaces the conventional manual feature engineering with automatic feature extraction, and reaches 0.0048 average test root-mean-squared error (RMSE) and 0.46% mean-absolute-percentage error (MAPE) on our dataset and 3.69% on the NASA public dataset. • An automatic health feature extraction method for LIBs without prior knowledge is proposed. • Convolutional autoencoder model is used to extract features automatically. • Self-attention mechanism is incorporated to obtain accurate SOH estimation results. • The performance is compared with the manual feature-based methods and other data-driven methods. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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25. A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System.
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Pastor-Fernández, C., Bruen, T., Widanage, W.D., Gama-Valdez, M.A., and Marco, J.
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BATTERY management systems , *ELECTRIC vehicle batteries , *LITHIUM-ion batteries , *ELECTRIC capacity , *ELECTRIC resistance - Abstract
Vehicle battery systems are usually designed with a high number of cells connected in parallel to meet the stringent requirements of power and energy. The self-balancing characteristic of parallel cells allows a battery management system (BMS) to approximate the cells as one equivalent cell with a single state of health (SoH) value, estimated either as capacity fade (SoH E ) or resistance increase (SoH P ). A single SoH value is however not applicable if the initial SoH of each cell is different, which can occur when cell properties change due to inconsistent manufacturing processes or in-homogeneous operating environments. As such this work quantifies the convergence of SoH E and SoH P due to initial differences in cell SoH and examines the convergence factors. Four 3 Ah 18650 cells connected in parallel at 25 °C are aged by charging and discharging for 500 cycles. For an initial SoH E difference of 40% and SoH P difference of 45%, SoH E converge to 10% and SoH P to 30% by the end of the experiment. From this, a strong linear correlation between ΔSoH E and ΔSoH P is also observed. The results therefore imply that a BMS should consider a calibration strategy to accurately estimate the SoH of parallel cells until convergence is reached. [ABSTRACT FROM AUTHOR] more...
- Published
- 2016
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26. Integration of sampling based battery state of health estimation method in electric vehicles.
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Ozkurt, Celil, Camci, Fatih, Atamuradov, Vepa, and Odorry, Christopher
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ELECTRIC vehicles , *ESTIMATION theory , *SAMPLING (Process) , *ELECTRIC batteries , *MICROCONTROLLERS , *COST control - Abstract
Battery cost is one of the crucial parameters affecting high deployment of Electric Vehicles (EVs) negatively. Accurate State of Health (SoH) estimation plays an important role in reducing the total ownership cost, availability, and safety of the battery avoiding early disposal of the batteries and decreasing unexpected failures. A circuit design for SoH estimation in a battery system that bases on selected battery cells and its integration to EVs are presented in this paper. A prototype microcontroller has been developed and used for accelerated aging tests for a battery system. The data collected in the lab tests have been utilized to simulate a real EV battery system. Results of accelerated aging tests and simulation have been presented in the paper. The paper also discusses identification of the best number of battery cells to be selected for SoH estimation test. In addition, different application options of the presented approach for EV batteries have been discussed in the paper. [ABSTRACT FROM AUTHOR] more...
- Published
- 2016
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27. Online state of health estimation on NMC cells based on predictive analytics.
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Berecibar, Maitane, Devriendt, Floris, Dubarry, Matthieu, Villarreal, Igor, Omar, Noshin, Verbeke, Wouter, and Van Mierlo, Joeri
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LITHIUM-ion batteries , *BATTERY management systems , *SUPERVISED learning , *MULTILAYER perceptrons , *SUPPORT vector machines - Abstract
Accurate on board state of health estimation is a key battery management system function to provide optimal management of the battery system under control. In this regard, this paper presents an extensive study and comparison of three of commonly used supervised learning methods for state of health estimation in Graphite/Nickel Manganese Cobalt oxide cells. The three methods were based from the study of both incremental capacity and differential voltage curves. According to the ageing evolution of both curves, features were extracted and used as inputs for the estimation techniques. Ordinary Least Squares, Multilayer Perceptron and Support Vector Machine were used as the estimation techniques and accurate results were obtained while requiring a low computational effort. Moreover, this work allows a deep comparison of the different estimation techniques in terms of accuracy, online estimation and BMS applicability. In addition, estimation can be developed by partial charging and/or partial discharging, reducing the required maintenance time. [ABSTRACT FROM AUTHOR] more...
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- 2016
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28. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application.
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Berecibar, Maitane, Garmendia, Maitane, Gandiaga, Iñigo, Crego, Jon, and Villarreal, Igor
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LITHIUM compounds , *BATTERY management systems , *GRAPHITE , *ROBUST control , *PARAMETER estimation - Abstract
This paper discusses a novel differential voltage curve capacity estimation to determine the state of health of LiFePO 4 cells. Differential voltage curves are used because of their ability to detect and quantify degradation mechanisms. The estimation is carried out through partial charging or discharging tests, and is specifically designed for battery management systems, due to the trade off between accuracy and low computational effort. This means the method can be effectively executed online, in a real application. The technique is also able to accurately detect the end of life of the cells. Aging datasets of 18 cells with identical chemistry were used for both parametrization and validation. The cells were subjected to a wide range of cycling and storage conditions, including temperature, state of charge, charging and discharging rate, depth of discharge and state of health. The performance and robustness of the estimation are validated by means of the degradation datasets from more than 25 different scenarios at the cell and battery pack level. The related results indicate that the proposed health management strategy has an average relative error of 1.5% at the battery pack level. [ABSTRACT FROM AUTHOR] more...
- Published
- 2016
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29. Critical review of state of health estimation methods of Li-ion batteries for real applications.
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Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., and Van den Bossche, P.
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LITHIUM-ion batteries , *BATTERY management systems , *ENERGY storage , *ADAPTIVE control systems , *POTENTIAL energy - Abstract
Lithium-ion battery packs in hybrid and electric vehicles, as well as in other traction applications, are always equipped with a Battery Management System (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The accurate and reliable State of Health (SOH) estimation is a challenging issue and it is a core factor of a battery energy storage system. In this paper, battery SOH monitoring methods are reviewed. To this end, different scientific and technical literature is studied and the respective approaches are classified in specific groups. The groups are organized in terms of the way the method is carried out: Experimental Techniques or Adaptive Models. Not only strengths and weaknesses for the use in online BMS applications are reviewed but also their accuracy and precision is studied. At the end of the document a potential, new and promising via in order to develop a methodology to estimate the SOH in real applications is detailed. [ABSTRACT FROM AUTHOR] more...
- Published
- 2016
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30. State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles.
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Bockrath, Steffen, Lorentz, Vincent, and Pruckner, Marco
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CONVOLUTIONAL neural networks , *LITHIUM-ion batteries , *STANDARD deviations , *PARTIAL discharges , *CELLULAR aging - Abstract
An accurate aging forecasting and state of health estimation is essential for a safe and economically valuable usage of lithium-ion batteries. However, the non-linear aging of lithium-ion batteries is dependent on various operating and environmental conditions wherefore the degradation estimation is a complex challenge. Moreover, for on-board estimations where only limited memory and computing power are available, a state of health estimation algorithm is needed that is able to process raw sensor data without complex preprocessing. This paper presents a data-driven state of health estimation algorithm for lithium-ion batteries using different segments of partial discharge profiles. Raw sensor data is directly input to a temporal convolutional neural network without the need of executing feature engineering steps. The neural network is able to process raw sensor data and estimate the state of health of battery cells for different aging and degradation scenarios. After executing Bayesian hyperparameter tuning together with a stratified cross validation approach for splitting the training and test data, the achieved generalized aging model estimates the state of health with an overall root mean squared error of 1.0%. • Investigation of the influence of using partial load profiles on SOH Estimation. • SOH model that is able to process raw sensor data without preprocessing steps. • Generalized SOH model that accurately estimates the SOH for different aging histories. • Optimal model selection using Bayesian hyperparameter tuning. • Temporal convolutional neural network model with an overall SOH Estimation RMSE of 1%. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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31. State of health estimation of second-life lithium-ion batteries under real profile operation.
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Braco, Elisa, San Martín, Idoia, Sanchis, Pablo, Ursúa, Alfredo, and Stroe, Daniel-Ioan
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- *
LITHIUM-ion batteries , *HEALTH products , *STORAGE batteries - Published
- 2022
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32. State of health estimation in composite electrode lithium-ion cells.
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Bartlett, Alexander, Marcicki, James, Rhodes, Kevin, and Rizzoni, Giorgio
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LITHIUM-ion batteries , *ELECTROCHEMISTRY , *ELECTRODES , *KALMAN filtering , *ALGORITHMS - Abstract
Electrochemical models of lithium-ion batteries have been increasingly considered for online state of health estimation. These models can more accurately predict cell performance than traditional circuit models and can better relate physical degradation mechanisms to changes in model parameters. However, examples of state of health estimation algorithms that are validated with experimental data are scarce in the literature, particularly for cells with a composite electrode. The individual electrode active materials in a composite electrode may degrade at different rates and according to different physical mechanisms, and online estimation of this degradation facilitates more robust knowledge of how battery performance changes over its life. In this paper we use a reduced-order electrochemical model for a composite LiMn 2 O 4 -LiNi 1/3 Mn 1/3 Co 1/3 O 2 (LMO-NMC) electrode cell for online estimation of active material loss. Experimental data collected from composite electrode half cells that were aged under constant current cycling are used in an extended Kalman filter to estimate model parameters associated with loss of each active material. The capacity loss predicted by the online estimates agrees well with the measured capacity loss. Additionally, a differential capacity analysis demonstrates that active materials lose capacity at a similar rate, the same conclusion obtained from the online estimation algorithm. [ABSTRACT FROM AUTHOR] more...
- Published
- 2015
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33. Sampling based State of Health estimation methodology for Li-ion batteries.
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Camci, Fatih, Ozkurt, Celil, Toker, Onur, and Atamuradov, Vepa
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LITHIUM-ion batteries , *ENERGY management , *HYBRID electric vehicles , *INDUSTRIAL applications , *ESTIMATION theory - Abstract
Storage and management of energy is becoming a more and more important problem every day, especially for electric and hybrid vehicle applications. Li-ion battery is one of the most important technological alternatives for high capacity energy storage and related industrial applications. State of Health (SoH) of Li-ion batteries plays a critical role in their deployment from economic, safety, and availability aspects. Most, if not all, of the studies related to SoH estimation focus on the measurement of a new parameter/physical phenomena related to SoH, or development of new statistical/computational methods using several parameters. This paper presents a new approach for SoH estimation for Li-ion battery systems with multiple battery cells: The main idea is a new circuit topology which enables separation of battery cells into two groups, main and test batteries, whenever a SoH related measurement is to be conducted. All battery cells will be connected to the main battery during the normal mode of operation. When a measurement is needed for SoH estimation, some of the cells will be separated from the main battery, and SoH estimation related measurements will be performed on these units. Compared to classical SoH measurement methods which deal with whole battery system, the proposed method estimates the SoH of the system by separating a small but representative set of cells. While SoH measurements are conducted on these isolated cells, remaining cells in the main battery continue to function in normal mode, albeit in slightly reduced performance levels. Preliminary experimental results are quite promising, and validate the feasibility of the proposed approach. Technical details of the proposed circuit architecture are also summarized in the paper. [ABSTRACT FROM AUTHOR] more...
- Published
- 2015
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34. A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve.
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Huang, Huanyang, Meng, Jinhao, Wang, Yuhong, Feng, Fei, Cai, Lei, Peng, Jichang, and Liu, Tianqi
- Subjects
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LITHIUM-ion batteries , *STANDARD deviations , *FEATURE extraction , *KRIGING , *FEATURE selection - Abstract
• Low complexity LCCC is proposed as features to be selected. • Unify the accuracy and feature acquisition difficulty into one objective function. • Feature selection and model training are simultaneously processed by GA. • Different variables in the gene are separately operated to avoid interference. Accurate State-of-Health (SOH) is critical to ensure the safe operation of Lithium-ion (Li-ion) batteries in electrified transportation and energy storage applications. The data-driven method is expected to greatly improve the SOH estimation in many aspects, thanks to the internet of things technology nowadays. Considering it is difficult to obtain valid information in real applications, efficient features and reasonable training procedures are two main points for establishing a superior data-driven SOH estimator. Thus, this paper proposes a comprehensive optimization framework for Li-ion battery SOH estimation with the Local Coulomb Counting Curve (LCCC), enabling both efficient feature extraction and good accuracy. Without the necessity of any complex calculations and smooth techniques, the LCCC in this work can be conveniently obtained by counting the coulomb amount of a specified voltage segment. After unifying the estimation accuracy and feature collection difficulty into one objective function, the Genetic Algorithm (GA) is utilized to optimize the LCCC selection and training procedure of the Gaussian Regression Process (GPR) further. Eight LiFePO4 batteries cycled under four different current rates aging conditions are selected for validation. The proposed estimator achieves root mean squared errors of 0.7745%, 1.0837%, 0.7208%, and 1.5795%, respectively, and optimized features can be collected within 300mV. Such results prove that the proposed method can achieve a good SOH estimation accuracy with fewer LCCC features and higher computing efficiency. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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35. Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries.
- Author
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Kim, Sung Wook, Oh, Ki-Yong, and Lee, Seungchul
- Subjects
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DEEP learning , *ELECTRIC vehicle batteries , *RECURRENT neural networks , *ARTIFICIAL neural networks , *LITHIUM-ion batteries , *ENERGY consumption - Abstract
• Informed deep learning-based framework is proposed for on-board battery health monitoring. • Impedance-related features model battery degradation efficiently. • Layer-wise relevance propagation reveals impedance features' contribution to output. • Knowledge infusion to a recurrent neural network improves estimation accuracy. • Monte Carlo dropout secures the model reliability by providing uncertainty measures. This paper proposes a novel, informed deep-learning-based prognostics framework for on-board state of health and remaining useful life estimations of lithium-ion batteries, which are critical components for strategizing energy and power used in electric vehicles. The framework comprises three phases. First, reliable and online accessible impedance-related features are collected from discharge curves. Second, these features are inputted into the proposed knowledge-infused recurrent neural network, a hybrid model that combines an empirical model with a deep neural network. Third, Monte Carlo dropout, a deep learning method for obtaining a probabilistic prediction of a neural network, is addressed to secure robustness in estimating the state of health and remaining useful life. Layer-wise relevance propagation, a deep learning technique for tracking the evolution of feature importance and offering scientific reasoning of the output, confirms that impedance-related features significantly contribute to the estimation accuracy compared to other features investigated in previous studies. Moreover, the hybrid model improves the estimation accuracy and robustness, whereas Monte Carlo dropout ensures robustness and reliability. Specifically, the estimation results for the public degradation data reveal that the proposed model can output significantly more accurate state of health and remaining useful life estimations than the baseline deep neural networks. The findings of this study provide insight into the explicable and uncertainty-based pipeline of deep neural networks with respect to battery health monitoring, which are highly recommendable features for decision-making and corrective planning of power and energy used in lithium-ion battery cells and packs. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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36. Model based identification of aging parameters in lithium ion batteries
- Author
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Prasad, Githin K. and Rahn, Christopher D.
- Subjects
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LITHIUM-ion batteries , *DETERIORATION of materials , *ELECTRIC impedance , *MATHEMATICAL models , *PARAMETER estimation , *SUPERIONIC conductors - Abstract
Abstract: As lithium ion cells age, they experience power and energy fade associated with impedance rise and capacity loss, respectively. Identification of key aging parameters in lithium ion battery models can validate degradation hypotheses and provide a foundation for State of Health (SOH) estimation. This paper develops and simplifies an electrochemical model that depends on two key aging parameters, cell resistance and the solid phase diffusion time of Li+ species in the positive electrode. Off-line linear least squares and on-line adaptive gradient update processing of voltage and current data from fresh and aged lithium ion cells produce estimates of these aging parameters. These estimated parameters vary monotonically with age, consistent with accepted degradation mechanisms such as solid electrolyte interface (SEI) layer growth and contact loss. [Copyright &y& Elsevier] more...
- Published
- 2013
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37. A review on the state of health estimation methods of lead-acid batteries.
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Jiang, Shida and Song, Zhengxiang
- Subjects
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LEAD-acid batteries , *MODERN society , *DETERIORATION of materials - Abstract
Batteries play an important role in modern society. Among the different types of batteries, lead-acid batteries account for over 70% of all the sales of rechargeable markets and are widely employed in people's daily lives. To avoid unexpected incidents and subsequent losses, it is considerably important to estimate the state of health (SOH) of lead-acid batteries. In this work, we review different types of SOH estimation methods for lead-acid batteries. First, we introduce the concept of the SOH and the mechanism of battery aging. Next, different SOH estimation methods are categorized into four classes: direct measurement-based, model-based, data-driven, and other methods. Then, we provide a detailed analysis of the characteristics of each method and discuss the corresponding advantages and limitations in practical applications. Subsequently, based on these characteristics, we systematically evaluate and compare different types of methods. Considering different scenarios, we indicate the recommended methods for each situation and provide the reasons for the choices. We also indicate the unsolved problems of each method to inspire further research. This review can serve as a reference for future works in this field, as no other review has been conducted on the same topic. • Different state of health estimation methods are classified into four categories. • A detailed investigation of the pros and cons of each method is presented. • The recommended methods for different industrial conditions are provided. • Suggestions for future development of state of health estimation are discussed. [ABSTRACT FROM AUTHOR] more...
- Published
- 2022
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38. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries.
- Author
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Ge, Ming-Feng, Liu, Yiben, Jiang, Xingxing, and Liu, Jie
- Subjects
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LITHIUM-ion batteries , *BATTERY management systems , *SCIENTIFIC literature , *TECHNICAL literature , *ELECTRIC vehicle batteries , *INTERNET of things - Abstract
Lithium-ion batteries have been generally used in industrial applications. In order to ensure the safety of the power system and reduce the operation cost, it is particularly important to accurately and timely estimate the state of health (SOH) and predict the remaining useful life (RUL) of lithium-ion batteries. With the development of intelligent tools such as artificial intelligence, big data analysis and the Internet of Things, the methods of battery health assessment have been gradually diversified. Here, we have compiled four publicly available battery datasets. The SOH estimations and RUL prognostics of lithium-ion batteries are reviewed by analyzing the research status. To this end, after studying different scientific and technical literatures, the respective methods are divided into specific groups, and the advantages and limitations of the battery management system application are discussed. At the end, the future development trend and research challenges are analyzed. All key insights in this review will hopefully drive the development of battery health estimation and life prediction techniques. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
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39. A hybrid prognostic strategy with unscented particle filter and optimized multiple kernel relevance vector machine for lithium-ion battery.
- Author
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Sun, Xiaofei, Zhong, Kai, and Han, Min
- Subjects
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HILBERT-Huang transform , *FILTERS & filtration , *PARTICLES , *PREDICTION models , *LITHIUM-ion batteries - Abstract
• The initial estimation is obtained by unscented particle filter model. • Multiple kernel relevance vector machine is adopted to predict error trend. • Grid search is used to select the optimal parameters for the prediction model. • The initial estimation is corrected by predicted error trend. To make up the deficiencies of single methods in lithium-ion battery state of health (SOH) and remaining useful life (RUL) estimation, this paper presents a novel hybrid method using unscented particle filter (UPF) with optimized multiple kernel relevance vector machine (OMKRVM). Firstly, the errors between the initial estimation by UPF and the actual capacity are obtained. After that, the residuals are reconstructed by complementary ensemble empirical mode decomposition (CEEMD) to reduce interference. In addition, OMKRVM is adopted to provide multiple predictive abilities, and kernel parameters and weights of OMKRVM are yielded by the grid search. Finally, the initial estimation is corrected by the predicted residuals using OMKRVM to further improve prediction performance. The new method (UPF-OMKRVM) is compared with existing methods in predicting the degradation process of lithium-ion battery. The experimental results show that the UPF-OMKRVM has high prediction accuracy in lithium-ion battery SOH and RUL estimation. [ABSTRACT FROM AUTHOR] more...
- Published
- 2021
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40. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis.
- Author
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Li, Yuanyuan, Sheng, Hanmin, Cheng, Yuhua, Stroe, Daniel-Ioan, and Teodorescu, Remus
- Subjects
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STANDARD deviations , *ELECTRIC vehicle batteries , *DATA distribution , *LITHIUM-ion batteries , *KNOWLEDGE transfer - Abstract
Describes how the semi-supervised transfer component analysis method estimates the battery SOH based on the KRR model. Shows the process of the t semi-supervised transfer component analysis method. • The idea of knowledge transfer is proposed for estimating battery state of health. • A small amount of samples are used to achieve accurate state of health estimation. • Multi-Features indication of battery state of health are extracted. • The correlation between features and targets is measured by mutual information. • The state of health is estimated effectively with less than 2.5% by various errors. Accurate state-of-health estimation can ensure the safe and reliable operation of Lithium-ion batteries in any given application. Nevertheless, most of the state-of-health estimation methods require a large amount of laboratory aging data to offer precise results. As obtaining battery aging data under laboratory conditions requires a considerable amount of time and incurs high economic costs, in this paper, a method based on transfer learning is proposed to monitor state-of-health of batteries. A novel data processing method based on maximum mean discrepancy is considered to eliminate redundant information and minimize the difference between different data distributions. Then, mutual information is used to prove that the correlation between processed data is not decreased. To validate the developed transfer learning method, the data sets of four batteries in different working conditions are considered. Different error-detection methods, maximum average error, mean squared error and root mean squared error, which are utilized to evaluate the proposed model. The state of health is estimated effectively with less than 2.5 % error considering the aforementioned errors after processed by using semi-supervised transfer component analysis algorithm, although the training set only accounts for about 35% of the entire set. The results indicate that transfer learning plays an important role in improving the estimation accuracy of a battery state-of-health. [ABSTRACT FROM AUTHOR] more...
- Published
- 2020
- Full Text
- View/download PDF
41. An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system.
- Author
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Meng, Jinhao, Cai, Lei, Stroe, Daniel-Ioan, Ma, Junpeng, Luo, Guangzhao, and Teodorescu, Remus
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ENERGY storage , *BATTERY storage plants , *LITHIUM-ion batteries , *ELECTRIC vehicle batteries , *DIFFERENTIAL evolution , *ALGORITHMS - Abstract
Battery State of Health (SOH) is critical for the reliable operation of the grid-connected battery energy storage systems. During the long-term Lithium-ion (Li-ion) battery degradation, large amounts of data can be recorded. Unfortunately, massive raw data are naturally with different qualities, which makes it difficult to guarantee the superior performance of one unified and powerful data driven estimator. Thus, this paper proposes a novel ensemble learning framework to estimate the battery SOH, which can boost the performance of the data driven SOH estimation through a well-designed integration of the weak learners. Moreover, the short-term current pulses, which are convenient to be obtained from real applications, act as the deterioration feature for SOH estimation. To establish the weak learners with good diversity and accuracy, support vector regression is chosen to utilize the measurement from a specific condition. A Self-adaptive Differential Evolution (SaDE) algorithm is used to effectively integrate the weak learners, which can avoid the trial and error procedure on choosing the trial vector generation strategy and the related parameters in the traditional differential evolution. For the validation of the proposed method, two LiFePO 4 /C batteries are cycling under a mission profile providing the primary frequency regulation service to the grid. • A novel optimized ensemble learning method is proposed for Li-ion battery SOH estimation. • Short term features from current pulse tests are utilized. • The integration of each weak learner is optimized by the self-adaptive differential evolution algorithm. • LiFePO 4/ C batteries are aged with the mission profile providing the primary frequency regulation service to the grid. [ABSTRACT FROM AUTHOR] more...
- Published
- 2020
- Full Text
- View/download PDF
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