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21 pages, 3837 KiB

Open AccessArticle

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Cubic Polynomial Degradation Model and Envelope Extraction

by Kangze Su, Biao Deng, Shengjin Tang, Xiaoyan Sun, Pengya Fang, Xiaosheng Si and Xuebing Han

Batteries 2023, 9(9), 441; https://doi.org/10.3390/batteries9090441 - 29 Aug 2023

Viewed by 1150

Abstract

Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of [...] Read more.

Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of lithium-ion batteries, and there is a problem with low precision in parameter estimation. Therefore, this paper proposes a method for predicting the RUL of lithium-ion batteries based on a cubic polynomial degradation model and envelope extraction. Firstly, based on the degradation characteristics of lithium-ion batteries, a cubic polynomial function is used to fit the degradation trajectory and compared with other nonlinear degradation models for verification. Secondly, a subjective parameter estimation method based on envelope extraction is proposed that estimates the actual degradation trajectory by using the average of the upper and lower envelope curves of the degradation data of lithium-ion batteries and uses the maximum likelihood estimation (MLE) method to estimate the unknown model parameters in two steps. Finally, for comparison with several typical nonlinear models, experiments are carried out based on the practical degradation data of lithium-ion batteries. The effectiveness of the proposed method to improve the accuracy of RUL prediction for lithium-ion batteries was demonstrated in terms of the mean square error (MSE) of the model and MSE of RUL prediction. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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16 pages, 8164 KiB

Open AccessArticle

AdaBoost.Rt-LSTM Based Joint SOC and SOH Estimation Method for Retired Batteries

by Ran Li, Pengdong Liu, Kexin Li and Xiaoyu Zhang

Batteries 2023, 9(8), 425; https://doi.org/10.3390/batteries9080425 - 15 Aug 2023

Viewed by 1315

Abstract

Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely [...] Read more.

Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the huge number of retired batteries and obvious consistency differences bring bottleneck problems such as long learning time and low model updating efficiency to the traditional data-driven algorithm. In view of this, this paper proposes an integrated learning algorithm based on AdaBoost. Rt-LSTM to realize the joint estimation of SOC and SOH of retired lithium batteries, which relies on the LSTM neural network model and completes the correlation adaption in the spatio-temporal dimension of the whole life cycle sample data. The LSTM model is used as the base learner to construct the AdaBoost. Rt-LSTM strong learning model. The LSTM weak predictor is combined with weights to form a strong predictor, which greatly solves the problem of low accuracy of state estimation due to the large number and variability of retired batteries. Simulation and experimental comparison show that the integrated algorithm proposed in this paper is suitable for improving the SOC and SOH prediction accuracy and the generalization performance of the model. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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19 pages, 7765 KiB

Open AccessEditor’s ChoiceArticle

High-Precision and Robust SOC Estimation of LiFePO₄ Blade Batteries Based on the BPNN-EKF Algorithm

by Zhihang Zhang, Siliang Chen, Languang Lu, Xuebing Han, Yalun Li, Siqi Chen, Hewu Wang, Yubo Lian and Minggao Ouyang

Batteries 2023, 9(6), 333; https://doi.org/10.3390/batteries9060333 - 20 Jun 2023

Cited by 2 | Viewed by 1910

Abstract

The lithium iron phosphate (LiFePO₄) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit [...] Read more.

The lithium iron phosphate (LiFePO₄) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO₄ blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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24 pages, 7220 KiB

Open AccessArticle

Degradation Evaluation of Lithium-Ion Batteries in Plug-In Hybrid Electric Vehicles: An Empirical Calibration

by Hongchang Cai, Xu Hao, Yong Jiang, Yanan Wang, Xuebing Han, Yuebo Yuan, Yuejiu Zheng, Hewu Wang and Minggao Ouyang

Batteries 2023, 9(6), 321; https://doi.org/10.3390/batteries9060321 - 10 Jun 2023

Cited by 3 | Viewed by 2741

Abstract

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation [...] Read more.

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (

R^{2}

) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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13 pages, 5133 KiB

Open AccessArticle

Model Predictive Control for Residential Battery Storage System: Profitability Analysis

by Patrick Kobou Ngani and Jean-Régis Hadji-Minaglou

Batteries 2023, 9(6), 316; https://doi.org/10.3390/batteries9060316 - 06 Jun 2023

Cited by 1 | Viewed by 1383

Abstract

For increased penetration of energy production from renewable energy sources at a utility scale, battery storage systems (BSSs) are a must. Their levelized cost of electricity (LCOE) has drastically decreased over the last decade. Residential battery storage, mostly combined with photovoltaic (PV) panels, [...] Read more.

For increased penetration of energy production from renewable energy sources at a utility scale, battery storage systems (BSSs) are a must. Their levelized cost of electricity (LCOE) has drastically decreased over the last decade. Residential battery storage, mostly combined with photovoltaic (PV) panels, also follow this falling prices trend. The combined effect of the COVID-19 pandemic and the war in Ukraine has caused such a dramatic increase in electricity prices that many consumers have adjusted their strategies to become prosumers and self-sufficient as feed-in subsidies continue to drop. In this study, an investigation is conducted to determine how profitable it is to install BSSs in homes with regards to battery health and the levelized cost of total managed energy. This is performed using mixed-integer linear programming (MILP) in MATLAB, along with its embedded solver Intlinprog. The results show that a reasonable optimized yearly cycling rate of the BSS can be reached by simply considering a non-zero cost for energy cycling through the batteries. This cost is simply added to the electricity cost equation of standard optimization problems and ensures a very good usage rate of the batteries. The proposed control does not overreact to small electricity price variations until it is financially worth it. The trio composed of feed-in tariffs (FITs), electricity costs, and the LCOE of BSSs represents the most significant factors. Ancillary grid service provision can represent a substantial source of revenue for BSSs, besides FITs and avoided costs. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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11 pages, 916 KiB

Open AccessArticle

Battery Test Profile Generation Framework for Electric Vehicles

by Dongxu Guo, Hailong Ren, Xuning Feng, Xuebing Han, Languang Lu and Minggao Ouyang

Batteries 2023, 9(5), 256; https://doi.org/10.3390/batteries9050256 - 29 Apr 2023

Cited by 1 | Viewed by 1925

Abstract

This paper proposes a framework for generating a battery test profile that accounts for the complex operating conditions of electric vehicles, which is essential for ensuring the durability and safety of the battery system used in these vehicles. Additionally, such a test profile [...] Read more.

This paper proposes a framework for generating a battery test profile that accounts for the complex operating conditions of electric vehicles, which is essential for ensuring the durability and safety of the battery system used in these vehicles. Additionally, such a test profile could potentially accelerate the development of electric vehicles. To achieve this objective, the study utilizes a simplified longitudinal dynamics model that incorporates various factors such as the drivetrain efficiency, battery system energy conversion efficiency, and regenerative braking efficiency. The battery test profile is based on the China light-duty vehicle test cycle-passenger car (CLTC-P) and is validated through testing on an electric vehicle with a chassis dynamometer. The results indicate a high degree of consistency between the generated and measured profiles, confirming the efficacy of the simplified longitudinal dynamics model. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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21 pages, 2675 KiB

Open AccessArticle

Fractional-Order Sliding-Mode Observers for the Estimation of State-of-Charge and State-of-Health of Lithium Batteries

by Minghao Zhou, Kemeng Wei, Xiaogang Wu, Ling Weng, Hongyu Su, Dong Wang, Yuanke Zhang and Jialin Li

Batteries 2023, 9(4), 213; https://doi.org/10.3390/batteries9040213 - 01 Apr 2023

Cited by 4 | Viewed by 1261

Abstract

Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to [...] Read more.

Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to improve battery life, reliability, and utilization efficiency. In this paper, three cascaded fractional-order sliding-mode observers (FOSMOs) are designed for the estimation of SoC by observing the terminal voltage, the polarization voltage, and the open-circuit voltage of a lithium cell, respectively. Furthermore, to calculate the value of the SoH, two FOSMOs are developed to estimate the capacity and internal resistance of the lithium cell. The control signals of the observers are continuous by utilizing fractional-order sliding manifolds without low-pass filters. Compared with the existing sliding-mode observers for SoC and SoH, weaker chattering, faster response, and higher estimation accuracy are obtained in the proposed method. Finally, the experiment tests demonstrate the validity and feasibility of the proposed observer design method. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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15 pages, 7480 KiB

Open AccessArticle

Experimental Investigation for the Phase Change Material Barrier Area Effect on the Thermal Runaway Propagation Prevention of Cell-to-Pack Batteries

by Kai Shen, Jieyu Sun, Chengshan Xu, Shaw Kang WONG, Yuejiu Zheng, Changyong Jin, Huaibin Wang, Siqi Chen and Xuning Feng

Batteries 2023, 9(4), 206; https://doi.org/10.3390/batteries9040206 - 30 Mar 2023

Cited by 2 | Viewed by 2489

Abstract

Thermal runaway propagation (TRP) is a primary safety issue in lithium-ion battery (LIB) applications, and the use of a thermal barrier is considered to be a promising solution for TRP prevention. However, the operating conditions of the battery are extremely complicated, such as [...] Read more.

Thermal runaway propagation (TRP) is a primary safety issue in lithium-ion battery (LIB) applications, and the use of a thermal barrier is considered to be a promising solution for TRP prevention. However, the operating conditions of the battery are extremely complicated, such as fast charging, low-temperature heating and thermal runaway. To date, there is no consistent answer as to how to choose the appropriate thermal barrier for such a complicated working environment. In this study, the characteristics of hydrogel based on sodium polyacrylate are explored, and the impact of thermal barrier area on TRP is investigated through experiments. Due to the prismatic battery structure, thermal barriers placed between cells are designed with different areas (148 × 98 mm, 128 × 88 mm, and 108 × 78 mm). The results indicate that test 1 without a placed thermal barrier quickly completes the TRP process, and the thermal runaway (TR) behavior is more violent. With a thermal barrier that does not have full area coverage placed between cells (test 2 and test 3), the propagation time is prolonged, but TRP still occurs. Compared with test 1, the triggered temperature of T2 F (the front surface of cell 2) is reduced by 207.6 °C and 295.2 °C, respectively. The complete area coverage thermal barrier successfully prevents TRP, and the T2 F of cell 2 only reaches 145.4 °C under the phase change by the hydrogel. This study may suggest a safety design for battery modules and prevent propagation among batteries. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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25 pages, 11761 KiB

Open AccessArticle

Research on Multi-Time Scale SOP Estimation of Lithium–Ion Battery Based on H∞ Filter

by Ran Li, Kexin Li, Pengdong Liu and Xiaoyu Zhang

Batteries 2023, 9(4), 191; https://doi.org/10.3390/batteries9040191 - 23 Mar 2023

Cited by 3 | Viewed by 1964

Abstract

Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium–ion batteries consider only a single constraint and rarely pay [...] Read more.

Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium–ion batteries consider only a single constraint and rarely pay attention to the estimation of battery state on different time scales, which can reduce the accuracy of SOP estimation and even cause safety problems. In view of this, this paper proposes a multi-time scale and multi-constraint SOP estimation method for lithium–ion batteries based on H∞ filtering. Firstly, a second-order RC equivalent circuit model is established with a ternary lithium–ion monolithic battery as the research object, and parameter identification is performed by using the recursive least squares method with a forgetting factor. Secondly, the H∞ filtering algorithm is applied to estimate the state of charge (SOC), and then the joint multi-time scale multi-constrained SOC-SOP estimation is performed. Finally, the joint estimation algorithm is validated under UDDS conditions. The mean absolute value relative error (MARE) of SOC estimation is 1.17%, and the MARE of SOP estimation at different time scales is less than 1.6%. The results indicate the high accuracy and strong robustness of the joint estimation method. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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23 pages, 10317 KiB

Open AccessArticle

A Data-Driven LiFePO₄ Battery Capacity Estimation Method Based on Cloud Charging Data from Electric Vehicles

by Xingyu Zhou, Xuebing Han, Yanan Wang, Languang Lu and Minggao Ouyang

Batteries 2023, 9(3), 181; https://doi.org/10.3390/batteries9030181 - 20 Mar 2023

Cited by 6 | Viewed by 2772

Abstract

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are [...] Read more.

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO₄ batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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20 pages, 5255 KiB

Open AccessArticle

Battery SOH Prediction Based on Multi-Dimensional Health Indicators

by Zhilong Yu, Na Liu, Yekai Zhang, Lihua Qi and Ran Li

Batteries 2023, 9(2), 80; https://doi.org/10.3390/batteries9020080 - 24 Jan 2023

Cited by 1 | Viewed by 2554

Abstract

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 [...] Read more.

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%. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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18 pages, 7783 KiB

Open AccessEditor’s ChoiceArticle

Fast Identification of Micro-Health Parameters for Retired Batteries Based on a Simplified P2D Model by Using Padé Approximation

by Jianing Xu, Chuanyu Sun, Yulong Ni, Chao Lyu, Chao Wu, He Zhang, Qingjun Yang and Fei Feng

Batteries 2023, 9(1), 64; https://doi.org/10.3390/batteries9010064 - 16 Jan 2023

Cited by 18 | Viewed by 2988

Abstract

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in [...] Read more.

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO₄ batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (ε_n, D_s,n) and electrolyte (D_e, C_e) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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17 pages, 3752 KiB

Open AccessArticle

State of Charge Estimation of LiFePO₄ in Various Temperature Scenarios

by Mingzhu Wang, Guan Wang, Zhanlong Xiao, Yuedong Sun and Yuejiu Zheng

Batteries 2023, 9(1), 43; https://doi.org/10.3390/batteries9010043 - 06 Jan 2023

Cited by 4 | Viewed by 3818

Abstract

The state estimation of a battery is a significant component of a BMS. Due to the poor temperature performance and voltage plateau phase in LiFePO₄ batteries, the difficulty of state estimation is greatly increased. At the same time, the ambient temperature in [...] Read more.

The state estimation of a battery is a significant component of a BMS. Due to the poor temperature performance and voltage plateau phase in LiFePO₄ batteries, the difficulty of state estimation is greatly increased. At the same time, the ambient temperature in which the battery operates is changeable, and its parameters will vary with the temperature. Therefore, it is extremely challenging to estimate the state of LiFePO₄ batteries under variable temperatures. In an effort to accurately estimate the SOC of LiFePO₄ batteries at different and variable temperatures, as well as its capacity at low temperature, the characteristics of LiFePO₄ batteries at different temperatures are first tested. In addition, a variable temperature OCV experiment is designed to obtain the OCV of the full SOC range. Then, the ECM considering temperature is established and all parameters are identified by PSO. Finally, an improved EKF algorithm is presented to accurately estimate the SOC of LiFePO₄ batteries at different and variable temperatures. Meanwhile, the battery capacity at low temperature is further estimated based on the estimated SOC result. The results show that SOC estimation errors at variable temperature are all within 3%, and the capacity estimation errors at low temperature are all within 1%. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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18 pages, 4024 KiB

Open AccessArticle

A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network

by Wenkang Bao, Haidong Liu, Yuedong Sun and Yuejiu Zheng

Batteries 2022, 8(12), 289; https://doi.org/10.3390/batteries8120289 - 16 Dec 2022

Cited by 7 | Viewed by 2819

Abstract

The battery is an important part of pure electric vehicles and hybrid electric vehicles, and its state and parameter estimation has always been a big problem. To determine the available energy stored in a battery, it is necessary to know the current state-of-charge [...] Read more.

The battery is an important part of pure electric vehicles and hybrid electric vehicles, and its state and parameter estimation has always been a big problem. To determine the available energy stored in a battery, it is necessary to know the current state-of-charge (SOC) and the capacity of the battery. For the determination of the battery SOC and capacity, it is generally estimated according to the Electromotive Force (EMF) of the battery, which is the open-circuit-voltage (OCV) of the battery in a stable state. An off-line battery SOC and capacity estimation method for lithium-ion batteries is proposed in this paper. The BP neural network with a high accuracy is trained in the case of sufficient data with the new neural network intelligent algorithm, and the OCV can be accurately predicted in a short time. The model training requires a large amount of data, so different experiments were designed and carried out. Based on the experimental data, the feasibility of this method is verified. The results show that the neural network model can accurately predict the OCV, and the error of capacity estimation is controlled within 3%. The mentioned method was also carried out in a real vehicle by using its cloud data, and the capacity estimation can be easily realized while limiting inaccuracy to less than 5%. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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16 pages, 4997 KiB

Open AccessFeature PaperArticle

A Car-Following Model with the Acceleration Generalized Force Coupled with External Resistance and the Temporal-Spatial Distribution of Battery Decline

by Yanfei Gao, Hai Lin, Fengyan Yi, Xuesheng Zhou, Long Qi and Yalun Li

Batteries 2022, 8(12), 257; https://doi.org/10.3390/batteries8120257 - 26 Nov 2022

Viewed by 1715

Abstract

A novel energy storage mode based on the vehicle-to-grid (V2G) and vehicle-to-vehicle (V2V) concept will be greatly researched and applied as a new green solution to energy and environmental problems. However, the existing research on battery capacity decline in V2G applications has mainly [...] Read more.

A novel energy storage mode based on the vehicle-to-grid (V2G) and vehicle-to-vehicle (V2V) concept will be greatly researched and applied as a new green solution to energy and environmental problems. However, the existing research on battery capacity decline in V2G applications has mainly focused on modeling the battery capacity to investigate its decline during vehicle charging and discharging, in order to reduce the battery capacity decline and evaluate its economics. A car-following model with the acceleration generalized force coupled with external resistance is proposed in the paper. A linear stability analysis was used to analyze the stability of the model. The stability of the traffic flow was improved when the value of the resistance coefficient increases. Then, the currents of different vehicles were also calculated according to the velocities. Moreover, the effect of different physical characteristics of driving on the decline of distributed energy storage batteries in the Internet of Vehicles (IoV) was investigated. The results suggest that in different road types and road slopes, vehicles which are at the end of the platoon position have less battery capacity degradation and better battery condition. It provides a reference for subsequent research related to V2G energy storage in the context of vehicle networking. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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22 pages, 6303 KiB

Open AccessFeature PaperArticle

A Comprehensive Study of Degradation Characteristics and Mechanisms of Commercial Li(NiMnCo)O₂ EV Batteries under Vehicle-To-Grid (V2G) Services

by Yifan Wei, Yuan Yao, Kang Pang, Chaojie Xu, Xuebing Han, Languang Lu, Yalun Li, Yudi Qin, Yuejiu Zheng, Hewu Wang and Minggao Ouyang

Batteries 2022, 8(10), 188; https://doi.org/10.3390/batteries8100188 - 17 Oct 2022

Cited by 9 | Viewed by 5292

Abstract

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 [...] Read more.

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O₂ (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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Review

Jump to: Research

23 pages, 3941 KiB

Open AccessReview

State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends

by Long Zhou, Xin Lai, Bin Li, Yi Yao, Ming Yuan, Jiahui Weng and Yuejiu Zheng

Batteries 2023, 9(2), 131; https://doi.org/10.3390/batteries9020131 - 13 Feb 2023

Cited by 23 | Viewed by 9433

Abstract

The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the [...] Read more.

The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries. First, the key issues and technical challenges of battery state estimation are summarized from three aspects of characteristics, models, and algorithms, and the technical challenges in state estimation are deeply analyzed. Second, four typical battery states (state of health, state of charge, state of energy, and state of power) and their joint estimation methods are reviewed, and feasible estimation frameworks are proposed, respectively. Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions. The research results provide a valuable reference for battery state estimation in the next-generation battery management system. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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Open AccessReview

Recent Advances in Hybrid Energy Storage System Integrated Renewable Power Generation: Configuration, Control, Applications, and Future Directions

by Ibrahem E. Atawi, Ali Q. Al-Shetwi, Amer M. Magableh and Omar H. Albalawi

Batteries 2023, 9(1), 29; https://doi.org/10.3390/batteries9010029 - 30 Dec 2022

Cited by 28 | Viewed by 11627

Abstract

The increased usage of renewable energy sources (RESs) and the intermittent nature of the power they provide lead to several issues related to stability, reliability, and power quality. In such instances, energy storage systems (ESSs) offer a promising solution to such related RES [...] Read more.

The increased usage of renewable energy sources (RESs) and the intermittent nature of the power they provide lead to several issues related to stability, reliability, and power quality. In such instances, energy storage systems (ESSs) offer a promising solution to such related RES issues. Hence, several ESS techniques were proposed in the literature to solve these issues; however, a single ESS does not fulfill all the requirements for certain operations and has different tradeoffs for overall system performance. This is mainly due to the limited capability of a single ESS and the potency concerning cost, lifespan, power and energy density, and dynamic response. In order to overcome the tradeoff issue resulting from using a single ESS system, a hybrid energy storage system (HESS) consisting of two or more ESSs appears as an effective solution. Many studies have been considered lately to develop and propose different HESSs for different applications showing the great advantages of using multiple ESSs in one combined system. Although these individual methods have been well documented, a comprehensive review of HESS-integrated RE has not been fully investigated in the literature before. Thus, as a novel contribution to the literature, this study aims to review and analyze the importance and impact of HESSs in the presence of renewable energy towards sustainable development that will facilitate this newly emerging topic to researchers in this field. In this regard, the present scenario and recent trend of HESSs in RESs at the global level, including a comparison with main ESS features, are discussed and analyzed along with the concept, design, classifications, and a detailed comparison of HESSs. The emerging role of HESSs in terms of their benefits and applications has been analyzed. Recent control and optimization methods of HESSs associated with RESs and their advantages and disadvantages have been reviewed. Finally, open issues and new challenges toward more efficient, sustainable, and green energy have also been highlighted herein. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of an advanced HESS for future renewable energy optimal operation. Full article

(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)

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