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Prof. Rui Xiong
Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, No.5 Zhongguancun Street, Beijing 100081, China

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0 Batteries
0 Energy Storage
0 Machine Learning
0 control
0 AI

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Optimization and management.

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Review
Published: 13 July 2021 in Applied Energy
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Lithium plating on anodes, which can happen during fast charging and low-temperature charging, and/or after long-term cycling, plays a crucial role in the aging of lithium-ion batteries (LIBs) and leads to irreversible capacity fade and severe safety hazards. This study systematically reviews the recent progress in developing methods for in-situ detecting lithium plating in order to provide guidelines regarding selecting proper methods for on-board applications. In general, lithium plating can be divided into three stages according to the damage level. There are two categories of methods, electrochemical methods and physical methods, which can be used to detect lithium plating. Their principles, features, and limitations have been thoroughly analyzed. Trends for the prospective development of novel technologies are also discussed.

ACS Style

Yu Tian; Cheng Lin; Hailong Li; Jiuyu Du; Rui Xiong. Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods. Applied Energy 2021, 300, 117386 .

AMA Style

Yu Tian, Cheng Lin, Hailong Li, Jiuyu Du, Rui Xiong. Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods. Applied Energy. 2021; 300 ():117386.

Chicago/Turabian Style

Yu Tian; Cheng Lin; Hailong Li; Jiuyu Du; Rui Xiong. 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods." Applied Energy 300, no. : 117386.

Review
Published: 09 June 2021 in Chinese Journal of Mechanical Engineering
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Lithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging and safety have not been fully understood. In view of the research and preliminary application of the digital twin in complex systems such as aerospace, we will have the opportunity to use the digital twin to solve the bottleneck of current battery research. Firstly, this paper arranges the development history, basic concepts and key technologies of the digital twin, and summarizes current research methods and challenges in battery modeling, state estimation, remaining useful life prediction, battery safety and control. Furthermore, based on digital twin we describe the solutions for battery digital modeling, real-time state estimation, dynamic charging control, dynamic thermal management, and dynamic equalization control in the intelligent battery management system. We also give development opportunities for digital twin in the battery field. Finally we summarize the development trends and challenges of smart battery management.

ACS Style

Wenwen Wang; Jun Wang; Jinpeng Tian; Jiahuan Lu; Rui Xiong. Application of Digital Twin in Smart Battery Management Systems. Chinese Journal of Mechanical Engineering 2021, 34, 1 -19.

AMA Style

Wenwen Wang, Jun Wang, Jinpeng Tian, Jiahuan Lu, Rui Xiong. Application of Digital Twin in Smart Battery Management Systems. Chinese Journal of Mechanical Engineering. 2021; 34 (1):1-19.

Chicago/Turabian Style

Wenwen Wang; Jun Wang; Jinpeng Tian; Jiahuan Lu; Rui Xiong. 2021. "Application of Digital Twin in Smart Battery Management Systems." Chinese Journal of Mechanical Engineering 34, no. 1: 1-19.

Journal article
Published: 04 June 2021 in Energy Storage Materials
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Electrochemical impedance spectroscopy (EIS) is an effective means for monitoring and diagnosing lithium ion batteries. However, its stringent test requirements hinder its wide adoption. In this paper, we propose a deep learning-based method to predict impedance spectra at the fully charged and fully discharged states over battery life using a convolutional neural network (CNN). The CNN only requires input data collected under constant-current charging, which is prevalent in battery applications. A battery degradation dataset that contains over 1500 impedance spectra collected from eight batteries over a wide lifespan is established to validate the proposed method. The results show that the impedance spectra can be accurately predicted with a root mean square error (RMSE)<1.5 mΩ. The effectiveness of the proposed method is also demonstrated by the distribution of relaxation times and the extracted ohmic resistance. Besides, the proposed method can give reliable predictions in the case of incomplete charging data. We demonstrate that using data collected in a 500 mV voltage window, our method can still give reliable predictions with most RMSEs less than 3mΩ. Our method makes EIS a more accessible tool and opens a new way to comprehensively monitor battery performances.

ACS Style

Yanzhou Duan; Jinpeng Tian; Jiahuan Lu; Chenxu Wang; Weixiang Shen; Rui Xiong. Deep neural network battery impedance spectra prediction by only using constant-current curve. Energy Storage Materials 2021, 41, 24 -31.

AMA Style

Yanzhou Duan, Jinpeng Tian, Jiahuan Lu, Chenxu Wang, Weixiang Shen, Rui Xiong. Deep neural network battery impedance spectra prediction by only using constant-current curve. Energy Storage Materials. 2021; 41 ():24-31.

Chicago/Turabian Style

Yanzhou Duan; Jinpeng Tian; Jiahuan Lu; Chenxu Wang; Weixiang Shen; Rui Xiong. 2021. "Deep neural network battery impedance spectra prediction by only using constant-current curve." Energy Storage Materials 41, no. : 24-31.

Journal article
Published: 01 June 2021 in Joule
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Summary Accurate degradation monitoring over battery life is indispensable for the safe and durable operation of battery-powered applications. In this work, we extend conventional capacity degradation estimation to the estimation of entire constant-current charging curves. A deep neural network (DNN) is developed to estimate complete charging curves by featuring small portions of the charging curves to form the input. We demonstrate that the charging curves can be accurately captured with an error of less than 16.9 mAh for 0.74 Ah batteries with 30 points collected in less than 10 min. Validation based on batteries working at different current rates and temperatures further demonstrates the effectiveness of the proposed method. This method also enjoys the advantage of transfer learning; that is, a DNN trained on one battery dataset can be used to improve the curve estimation of other batteries operating under different scenarios by using few training data.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Jiahuan Lu; Xiao-Guang Yang. Deep neural network battery charging curve prediction using 30 points collected in 10 min. Joule 2021, 5, 1521 -1534.

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu, Xiao-Guang Yang. Deep neural network battery charging curve prediction using 30 points collected in 10 min. Joule. 2021; 5 (6):1521-1534.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Jiahuan Lu; Xiao-Guang Yang. 2021. "Deep neural network battery charging curve prediction using 30 points collected in 10 min." Joule 5, no. 6: 1521-1534.

Journal article
Published: 26 April 2021 in Applied Energy
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External short circuit (ESC) fault, which can cause large current and high temperature, is one of the main reasons for battery failure. Its analysis and diagnosis remains a challenging task due to complex electro-thermal characteristics of batteries under ESCs. In this paper, ESC experiments at various temperatures are conducted to investigate the impact of temperature on battery electro-thermal behaviors. Based on the analysis of the experimental data, heat generation inside a battery caused by ESC-induced high current and side reactions is modeled. The heat distribution and diffusion are also modeled by considering battery's internal jellyroll structure. The combination of the heat generation, distribution and diffusion models forms a novel electro-thermal coupling model, which is used to predict the complex thermal and electrical properties of a battery under ESCs. The presented model is simulated and verified by the test data. The maximum root mean square error of ESC current prediction is less than 1.73A and the maximum errors of the internal temperatures and the surface temperatures are only 1.771% and 3.915%, respectively. These results verify the effectivceness of the presented model. It is expected that the presented model is useful for safety analysis, temperature prediction and fault diagnosis applications of the lithium-ion batteries under ESC.

ACS Style

Zeyu Chen; Bo Zhang; Rui Xiong; Weixiang Shen; Quanqing Yu. Electro-thermal coupling model of lithium-ion batteries under external short circuit. Applied Energy 2021, 293, 116910 .

AMA Style

Zeyu Chen, Bo Zhang, Rui Xiong, Weixiang Shen, Quanqing Yu. Electro-thermal coupling model of lithium-ion batteries under external short circuit. Applied Energy. 2021; 293 ():116910.

Chicago/Turabian Style

Zeyu Chen; Bo Zhang; Rui Xiong; Weixiang Shen; Quanqing Yu. 2021. "Electro-thermal coupling model of lithium-ion batteries under external short circuit." Applied Energy 293, no. : 116910.

Journal article
Published: 25 March 2021 in Nature Energy
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By increasing the charging voltage, a cell specific energy of >400 W h kg−1 is achievable with LiNi0.8Mn0.1Co0.1O2 in Li metal batteries. However, stable cycling of high-nickel cathodes at ultra-high voltages is extremely challenging. Here we report that a rationally designed sulfonamide-based electrolyte enables stable cycling of commercial LiNi0.8Co0.1Mn0.1O2 with a cut-off voltage up to 4.7 V in Li metal batteries. In contrast to commercial carbonate electrolytes, the electrolyte not only suppresses side reactions, stress-corrosion cracking, transition-metal dissolution and impedance growth on the cathode side, but also enables highly reversible Li metal stripping and plating leading to a compact morphology and low pulverization. Our lithium-metal battery delivers a specific capacity >230 mA h g−1 and an average Coulombic efficiency >99.65% over 100 cycles. Even under harsh testing conditions, the 4.7 V lithium-metal battery can retain >88% capacity for 90 cycles, advancing practical lithium-metal batteries. Charging at high voltages in principle makes batteries energy dense, but this is often achieved at the cost of the cycling stability. Here the authors design a sulfonamide-based electrolyte to enable a Li metal battery with a state-of-the-art cathode at an ultra-high voltage of 4.7 V while maintaining cyclability.

ACS Style

Weijiang Xue; Mingjun Huang; Yutao Li; Yun Guang Zhu; Rui Gao; Xianghui Xiao; Wenxu Zhang; Sipei Li; Guiyin Xu; Yang Yu; Peng Li; Jeffrey Lopez; Daiwei Yu; Yanhao Dong; Weiwei Fan; Zhe Shi; Rui Xiong; Cheng-Jun Sun; Inhui Hwang; Wah-Keat Lee; Yang Shao-Horn; Jeremiah A. Johnson; Ju Li. Ultra-high-voltage Ni-rich layered cathodes in practical Li metal batteries enabled by a sulfonamide-based electrolyte. Nature Energy 2021, 6, 495 -505.

AMA Style

Weijiang Xue, Mingjun Huang, Yutao Li, Yun Guang Zhu, Rui Gao, Xianghui Xiao, Wenxu Zhang, Sipei Li, Guiyin Xu, Yang Yu, Peng Li, Jeffrey Lopez, Daiwei Yu, Yanhao Dong, Weiwei Fan, Zhe Shi, Rui Xiong, Cheng-Jun Sun, Inhui Hwang, Wah-Keat Lee, Yang Shao-Horn, Jeremiah A. Johnson, Ju Li. Ultra-high-voltage Ni-rich layered cathodes in practical Li metal batteries enabled by a sulfonamide-based electrolyte. Nature Energy. 2021; 6 (5):495-505.

Chicago/Turabian Style

Weijiang Xue; Mingjun Huang; Yutao Li; Yun Guang Zhu; Rui Gao; Xianghui Xiao; Wenxu Zhang; Sipei Li; Guiyin Xu; Yang Yu; Peng Li; Jeffrey Lopez; Daiwei Yu; Yanhao Dong; Weiwei Fan; Zhe Shi; Rui Xiong; Cheng-Jun Sun; Inhui Hwang; Wah-Keat Lee; Yang Shao-Horn; Jeremiah A. Johnson; Ju Li. 2021. "Ultra-high-voltage Ni-rich layered cathodes in practical Li metal batteries enabled by a sulfonamide-based electrolyte." Nature Energy 6, no. 5: 495-505.

Journal article
Published: 23 March 2021 in Applied Energy
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State of charge (SOC) estimation constitutes a critical task of battery management systems. Conventional SOC estimation methods designed for dynamic profiles have difficulties in estimating SOC for LiFePO4 batteries due to their flat open circuit voltage characteristics in the middle range of SOC. In this study, a deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input. This method enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range. Thus, it can be used to calibrate the SOC estimation for the Ampere-hour counting method. We also demonstrate that by incorporating the DNN into a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved. In the case of significant disturbances, the method still maintains a root mean square error of 0.385%. Moreover, the trained DNN can quickly adapt to various scenarios, including different ageing states and battery types charged at different rates, thanks to the transfer learning nature. Compared with developing a new DNN, transfer learning can provide more accurate estimation results at less training costs. By only fine-tuning one layer of the pre-trained DNN, the root mean square error can be less than 3.146% and 2.315% for aged batteries and different battery types, respectively. When more layers are fine-tuned, superior performance can be achieved.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Jiahuan Lu. State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach. Applied Energy 2021, 291, 116812 .

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu. State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach. Applied Energy. 2021; 291 ():116812.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Jiahuan Lu. 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach." Applied Energy 291, no. : 116812.

Journal article
Published: 10 March 2021 in Applied Thermal Engineering
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Preheating batteries is crucial to improve the performance and lifetime when using lithium-ion batteries in cold weather conditions. Even though the immersing preheating system (IPS) has demonstrated attracting advantages, there is still lack of systematical evaluation about its performance and factors affecting the performance. To bridge the knowledge gap, this work considered the following key performance indicators: the rate of temperature rise, the temperature uniformity of the cell and the pack and the energy storage density; and the influences of the inlet flow rate and inlet temperature of heat transfer fluid (HTF), the gap between the batteries, the number of the batteries and the location of the HTF inlet and outlet on the preheating performance were investigated. A 3D CFD model was developed, which has been validated against experiments. Based on simulations, it was found that the IPS can achieve a high rate of temperature rise, which is up to 4.18 °C/min, and a small temperature difference in the battery pack, which is less than 4 °C. The number of batteries has been identified to have the biggest impact on the rate of temperature rise and the uniformity of the battery pack. Allocating the inlet on the left/right faces of IPS can effectively reduce both maximum temperature difference of the cell and the pack.

ACS Style

Yabo Wang; Zhao Rao; Shengchun Liu; Xueqiang Li; Hailong Li; Rui Xiong. Evaluating the performance of liquid immersing preheating system for Lithium-ion battery pack. Applied Thermal Engineering 2021, 190, 116811 .

AMA Style

Yabo Wang, Zhao Rao, Shengchun Liu, Xueqiang Li, Hailong Li, Rui Xiong. Evaluating the performance of liquid immersing preheating system for Lithium-ion battery pack. Applied Thermal Engineering. 2021; 190 ():116811.

Chicago/Turabian Style

Yabo Wang; Zhao Rao; Shengchun Liu; Xueqiang Li; Hailong Li; Rui Xiong. 2021. "Evaluating the performance of liquid immersing preheating system for Lithium-ion battery pack." Applied Thermal Engineering 190, no. : 116811.

Journal article
Published: 03 March 2021 in IEEE Transactions on Industrial Electronics
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Lithium-ion battery has advantages such as high energy density, and long calendar life, but it suffers from the risk of thermal runaway. Overcharge induced thermal runaway accidents hold a considerable percentage. It is discovered in this paper that the slope of the dynamic impedance in the frequency band of 30 - 90 Hz turns positive from negative when the cell just starts to overcharge, and proposes the theoretical explanation. Taking 70 Hz impedance as an example, the thermal runaway accident can be successfully avoided by cutting off the charging when the slope turns position from negative during charging. The warning time is 580 seconds ahead of the thermal runaway. This feature is easy-to-identify, and requires no complex mathematical models and parameters. Besides, the prediction method based on this feature can be conducted by using an online dynamic impedance measurement device designed by us, which is suitable for largescale applications. Thus, the overcharge induced thermal runaway accidents can be avoided.

ACS Style

Nawei Lyu; Yang Jin; Rui Xiong; Shan Miao; Jinfeng Gao. Real-time overcharge warning and early thermal runaway prediction of Li-ion battery by online impedance measurement. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Nawei Lyu, Yang Jin, Rui Xiong, Shan Miao, Jinfeng Gao. Real-time overcharge warning and early thermal runaway prediction of Li-ion battery by online impedance measurement. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Nawei Lyu; Yang Jin; Rui Xiong; Shan Miao; Jinfeng Gao. 2021. "Real-time overcharge warning and early thermal runaway prediction of Li-ion battery by online impedance measurement." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Journal article
Published: 16 February 2021 in Journal of Energy Storage
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Energy management strategy (EMS) plays an important role in improving energy economy of plug-in hybrid electric vehicles (PHEVs). The frequently fluctuations of electricity and oil prices have significant impacts on EMS optimization. In this study, the influence law of the varying prices on the optimal control policy is revealed. The statistical analysis of electricity and fuel prices for typical cities in China is proposed as a case study, and then a varying prices-conscious EMS for PHEVs is developed. The presented control strategy is optimized based on the simulated annealing particle swarm optimization (SA-PSO) algorithm. Taking into account the above efforts, the price influence surfaces (PIS) are proposed and a PIS-based adaptive EMS is finally established. The presented control strategy can achieve better energy economy under real-world driving condition than traditional EMS that doesn't concern the impacts of price variations. The results indicate that the energy cost of PHEV can be further reduced by up to 9.88% under certain driving situations.

ACS Style

Zeyu Chen; Hao Zhang; Rui Xiong; Weixiang Shen; Bo Liu. Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China. Journal of Energy Storage 2021, 36, 102347 .

AMA Style

Zeyu Chen, Hao Zhang, Rui Xiong, Weixiang Shen, Bo Liu. Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China. Journal of Energy Storage. 2021; 36 ():102347.

Chicago/Turabian Style

Zeyu Chen; Hao Zhang; Rui Xiong; Weixiang Shen; Bo Liu. 2021. "Energy management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: A case study of ten typical cities in China." Journal of Energy Storage 36, no. : 102347.

Journal article
Published: 10 February 2021 in Energy Storage Materials
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Open circuit voltage (OCV) test is an effective way of ageing diagnosis for lithium ion batteries and it constitutes a basis for state of charge (SOC) estimation. However, onboard OCV tests are rarely feasible due to the time-consuming nature. In this paper, we propose a method to estimate the results of offline OCV based ageing diagnosis, including electrode capacities and initial SOCs, termed electrode ageing parameters (EAPs). In this method, parts of daily charging profiles are sampled and directly fed into a convolutional neural network to estimate EAPs without feature extraction. Validation results on eight cells show that the estimated EAPs are very close to those obtained by using offline OCV tests. Therefore, this method enables a fast ageing diagnosis at an electrode level. Furthermore, we can use the estimated EAPs to reconstruct OCV-Q (charge amount) curves of batteries at different ageing levels over the entire battery life. The error for the OCV-Q reconstruction is within 15 mV compared with actual OCV-Q curves. Based on the OCV-Q curves, we show that battery capacity can be accurately obtained with an error of less than 1% although it is not explicitly considered as a target. The influence of voltage ranges on estimation results is also discussed.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Fengchun Sun. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries. Energy Storage Materials 2021, 37, 283 -295.

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Fengchun Sun. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries. Energy Storage Materials. 2021; 37 ():283-295.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Fengchun Sun. 2021. "Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries." Energy Storage Materials 37, no. : 283-295.

Research article
Published: 09 February 2021 in Engineering
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Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This paper proposes a multi-stage model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer (PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multi-stage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.

ACS Style

Rui Xiong; Ju Wang; Weixiang Shen; Jinpeng Tian; Hao Mu. Co-Estimation of State of Charge and Capacity for Lithium-ion Batteries with Multi-Stage Model Fusion Method. Engineering 2021, 1 .

AMA Style

Rui Xiong, Ju Wang, Weixiang Shen, Jinpeng Tian, Hao Mu. Co-Estimation of State of Charge and Capacity for Lithium-ion Batteries with Multi-Stage Model Fusion Method. Engineering. 2021; ():1.

Chicago/Turabian Style

Rui Xiong; Ju Wang; Weixiang Shen; Jinpeng Tian; Hao Mu. 2021. "Co-Estimation of State of Charge and Capacity for Lithium-ion Batteries with Multi-Stage Model Fusion Method." Engineering , no. : 1.

Journal article
Published: 05 February 2021 in Energies
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The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

ACS Style

Quanqing Yu; Changjiang Wan; Junfu Li; Rui Xiong; Zeyu Chen. A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles. Energies 2021, 14, 829 .

AMA Style

Quanqing Yu, Changjiang Wan, Junfu Li, Rui Xiong, Zeyu Chen. A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles. Energies. 2021; 14 (4):829.

Chicago/Turabian Style

Quanqing Yu; Changjiang Wan; Junfu Li; Rui Xiong; Zeyu Chen. 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles." Energies 14, no. 4: 829.

Journal article
Published: 11 December 2020 in IEEE Access
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The depletion of fossil fuels, the increase of energy demands, and the concerns over climate change are the major driving forces for the development of renewable energy such as solar and wind. However, the intermittency of renewable energy has hindered the deployment of large-scale intermittent renewable energy, which therefore has necessitated the development of advanced large-scale energy storage technologies [item 1) in the Appendix]. The use of large-scale energy storage can effectively improve the efficiency of energy resource utilization and increase the use of variable renewable resources, energy access, and end-use sector electrification [items 2) and 3) in the Appendix]. Over the years, considerable research has been conducted on many types of energy, such as thermal energy, mechanical energy, electrical energy, and chemical energy, using different types of systems such as phase change materials, batteries, supercapacitors, fuel cells, and compressed air, which are applicable to various types of applications, such as heat and power generation and electrical/hybrid transportation [items 4) and 5) in the Appendix].

ACS Style

Rui Xiong; Suleiman M. Sharkh; Hailong Li; Hua Bai; Weixiang Shen; Peng Bai; Xuan Zhou. IEEE Access Special Section Editorial: Advanced Energy Storage Technologies and Their Applications. IEEE Access 2020, 8, 218685 -218693.

AMA Style

Rui Xiong, Suleiman M. Sharkh, Hailong Li, Hua Bai, Weixiang Shen, Peng Bai, Xuan Zhou. IEEE Access Special Section Editorial: Advanced Energy Storage Technologies and Their Applications. IEEE Access. 2020; 8 ():218685-218693.

Chicago/Turabian Style

Rui Xiong; Suleiman M. Sharkh; Hailong Li; Hua Bai; Weixiang Shen; Peng Bai; Xuan Zhou. 2020. "IEEE Access Special Section Editorial: Advanced Energy Storage Technologies and Their Applications." IEEE Access 8, no. : 218685-218693.

Journal article
Published: 14 November 2020 in Engineering
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External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.

ACS Style

Ruixin Yang; Rui Xiong; Weixiang Shen; Xinfan Lin. Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit. Engineering 2020, 7, 395 -405.

AMA Style

Ruixin Yang, Rui Xiong, Weixiang Shen, Xinfan Lin. Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit. Engineering. 2020; 7 (3):395-405.

Chicago/Turabian Style

Ruixin Yang; Rui Xiong; Weixiang Shen; Xinfan Lin. 2020. "Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit." Engineering 7, no. 3: 395-405.

Journal article
Published: 11 August 2020 in IEEE Transactions on Industry Applications
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A novel non-dissipative two-stage equalization circuit topology based on the traditional Buck-Boost circuit is proposed to achieve balancing of series-connected lithium-ion battery packs with higher efficiency and lower cost. Against the backgrounds on global energy issues and advances in battery technologies, the proposed topology achieves high efficiency balancing of lithium-ion battery packs without additional devices. Detailed illustrations of the topology, the operation principles and control approaches are described with visualized figures. Then, by using experimental data, a high fidelity model of the lithium-ion battery is developed within the MATLAB platform. Furthermore, the prototype of the proposed topology has been designed and manufactured. The effectiveness of the proposed topology is verified by the agreements in the results from simulation and experiment.

ACS Style

Xiaofeng Ding; Donghuai Zhang; Jiawei Cheng; Binbin Wang; Yameng Chai; Zhihui Zhao; Rui Xiong; Partrick Chi Kwong Luk. A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs. IEEE Transactions on Industry Applications 2020, 56, 6892 -6903.

AMA Style

Xiaofeng Ding, Donghuai Zhang, Jiawei Cheng, Binbin Wang, Yameng Chai, Zhihui Zhao, Rui Xiong, Partrick Chi Kwong Luk. A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs. IEEE Transactions on Industry Applications. 2020; 56 (6):6892-6903.

Chicago/Turabian Style

Xiaofeng Ding; Donghuai Zhang; Jiawei Cheng; Binbin Wang; Yameng Chai; Zhihui Zhao; Rui Xiong; Partrick Chi Kwong Luk. 2020. "A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs." IEEE Transactions on Industry Applications 56, no. 6: 6892-6903.

Review
Published: 06 August 2020 in Science China Technological Sciences
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Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles. Recent studies have revealed the fractional order nature of lithium ion batteries, leading to fractional order modelling techniques. In this paper, a comprehensive review of the fractional order battery models and their applications in battery management of electric vehicles is provided from the perspectives of frequency and time domains. In the frequency domain, the fractional order models to fit electrochemical impedance spectroscopy data are investigated, followed by their applications in health diagnosis, battery heating and charging strategies In the time domain, the fractional order models adopted for voltage simulation are discussed, followed by their applications in battery state estimation and fault diagnosis. Finally, from the perspectives of time domain and frequency domain applications, critical challenges and research trends for future work in terms of fractional order modelling are highlighted to advance the development of next-generation battery management.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Fengchun Sun. Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives. Science China Technological Sciences 2020, 63, 2211 -2230.

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Fengchun Sun. Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives. Science China Technological Sciences. 2020; 63 (11):2211-2230.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Fengchun Sun. 2020. "Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives." Science China Technological Sciences 63, no. 11: 2211-2230.

Review article
Published: 15 July 2020 in Renewable and Sustainable Energy Reviews
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Lithium-ion batteries decay every time as it is used. Aging-induced degradation is unlikely to be eliminated. The aging mechanisms of lithium-ion batteries are manifold and complicated which are strongly linked to many interactive factors, such as battery types, electrochemical reaction stages, and operating conditions. In this paper, we systematically summarize mechanisms and diagnosis of lithium-ion battery aging. Regarding the aging mechanism, effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures. The influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors. As for aging diagnosis, three widely-used methods are discussed: disassembly-based post-mortem analysis, curve-based analysis, and model-based analysis. Generally, the post-mortem analysis is employed for cross-validation while the curve-based analysis and the model-based analysis provide quantitative analysis. The challenges in the use of quantitative diagnosis and on-board diagnosis on battery aging are also discussed, based on which insights are provided for developing online battery aging diagnosis and battery health management in the next generation of intelligent battery management systems (BMSs).

ACS Style

Rui Xiong; Yue Pan; Weixiang Shen; Hailong Li; Fengchun Sun. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renewable and Sustainable Energy Reviews 2020, 131, 110048 .

AMA Style

Rui Xiong, Yue Pan, Weixiang Shen, Hailong Li, Fengchun Sun. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renewable and Sustainable Energy Reviews. 2020; 131 ():110048.

Chicago/Turabian Style

Rui Xiong; Yue Pan; Weixiang Shen; Hailong Li; Fengchun Sun. 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives." Renewable and Sustainable Energy Reviews 131, no. : 110048.

Journal article
Published: 13 July 2020 in Chinese Journal of Mechanical Engineering
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State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Ju Wang. A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries. Chinese Journal of Mechanical Engineering 2020, 33, 1 .

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Ju Wang. A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries. Chinese Journal of Mechanical Engineering. 2020; 33 (1):1.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Ju Wang. 2020. "A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries." Chinese Journal of Mechanical Engineering 33, no. 1: 1.

Preprint content
Published: 25 June 2020
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State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

ACS Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Ju Wang. A comparative study of fractional order models on state of charge estimation for lithium ion batteries. 2020, 1 .

AMA Style

Jinpeng Tian, Rui Xiong, Weixiang Shen, Ju Wang. A comparative study of fractional order models on state of charge estimation for lithium ion batteries. . 2020; ():1.

Chicago/Turabian Style

Jinpeng Tian; Rui Xiong; Weixiang Shen; Ju Wang. 2020. "A comparative study of fractional order models on state of charge estimation for lithium ion batteries." , no. : 1.