Article
Energy & Fuels
Alejandro Gismero, Kjeld Norregaard, Bjarne Johnsen, Lasse Stenhoj, Daniel-Ioan Stroe, Erik Schaltz
Summary: The state of health (SOH) is crucial for the proper and safe operation of electric vehicle (EV) batteries, as well as for the second-hand market. This study aims to develop and verify a non-invasive method using incremental capacity analysis (ICA) to estimate the SOH of real EVs. The proposed method has been proven effective in both battery cells and commercial vehicles, allowing the estimation of SOH during charging without interference.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Xiong Shu, Wenxian Yang, Kexiang Wei, Bo Qin, Ronghua Du, Bowen Yang, Akhil Garg
Summary: This study experimentally investigates the degradation of electric vehicle lithium-ion batteries (LIBs) at different temperatures and discharge rates, and proposes a new degradation model to more accurately predict the capacity degradation of EV LIBs. The research reveals that the available capacity of LIBs transiently increases with the increase of charge-discharge cycles during the initial stage of battery use. Temperature has a significant impact on the available capacity of LIBs, and alternating changes in ambient temperature can accelerate the degradation of LIBs. The proposed degradation model exhibits higher prediction accuracy compared to the traditional model.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Yan Jiang, Xin Meng
Summary: This study proposes a battery capacity estimation method based on the battery equivalent circuit model and quantile regression. The joint Kalman filter is used to estimate the state of charge, and quantile regression is applied to fit the discharge data. The method is robust to poor data quality, reducing estimation errors.
Article
Energy & Fuels
Hongao Liu, Zhongwei Deng, Yalian Yang, Chen Lu, Bin Li, Chuan Liu, Duanqian Cheng
Summary: Accurately calculating the capacity of battery packs is essential for various aspects in electric vehicles. This paper proposes a specialized method for EVs, using an OCV correction strategy to ensure the credibility of battery SOC. The method is validated with a mean absolute error of 2.6 Ah and applied to 707 on-road electric vehicles, resulting in degradation models with mean absolute errors of 3.138 Ah and 3.137 Ah. The analysis also reveals the correlation between capacity degradation and user behaviors, suggesting that starting the charging at a SOC between 30% and 40% can effectively alleviate degradation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Zhicheng Xu, Chuang Zhang, Bo Sun, SuZhen Liu
Summary: The widespread deployment of electric vehicles not only relates to the electrification reform of the transport sector, but also plays a crucial role in the low-carbon transformation for urban energy systems. The performance, cost, and safety of electric vehicles are heavily influenced by the lithium-ion battery, which serves as the main energy supply. Strengthening the energy management of the lithium-ion battery system is essential to ensure its efficiency and safety. This study utilizes a prototype simulation system to estimate and predict the electrical and thermal properties of multi-level batteries and accurately assess the battery state, showing its capability in guaranteeing the safe and stable operation of the lithium-ion battery system.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Fan Yang, Dongliang Shi, Kwok-ho Lam
Summary: With the popularization of electric vehicles, accurate estimation of voltage and state-of-charge (SOC) for rechargeable batteries becomes crucial. Traditional extended Kalman Filtering algorithms suffer from limitations in SOC and voltage estimations. This study proposes a modified extended Kalman filtering (MEKF) algorithm to improve the estimation accuracy of voltage and SOC through real-time parameter adjustment and error reduction.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Shuaishuai Lv, Xingxing Wang, Wenfan Lu, Jiaqiao Zhang, Hongjun Ni
Summary: This study investigates the impact of temperature on the capacity of lithium ion batteries with different anodes, finding the optimal operating temperature range to be 20-50 degrees Celsius. The research suggests that the discharge capacity of a lithium ion battery can be approximated by a cubic polynomial of temperature, and the internal resistance of the battery is largest at 0% and 100% state of charge.
Review
Energy & Fuels
Yuefeng Liu, Yingjie He, Haodong Bian, Wei Guo, Xiaoyan Zhang
Summary: With the rapid growth in productivity, the demand for fossil fuels has increased, leading to research and development of new energy sources. Electric vehicles powered by lithium-ion batteries have become the mainstream in the automotive industry. Battery management systems are important for ensuring the safety and reliability of electric vehicle operation. Deep neural networks have been widely used in the field of battery state estimation, and this review classifies recent estimation methods based on deep learning and discusses future directions.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Prashant Shrivastava, Tey Kok Soon, Mohd Yamani Idna Bin Idris, Saad Mekhilef, Syed Bahari Ramadzan Syed Adnan
Summary: This study proposes a comprehensive co-estimation method for battery states, maximum available capacity, and maximum available energy, utilizing the correlation between different battery states to achieve high accuracy and reduce computational burden. The method is validated on two different chemistry battery cells under dynamic load profiles at different operating temperatures, and shows superior accuracy compared to existing methods.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
El Marghichi Mouncef, Bouzi Mostafa
Summary: This paper proposes a battery capacity estimation framework based on the sunflower optimization algorithm, which takes into account capacity error sources and uses a reduction strategy of the search space to enhance accuracy. Experimental results demonstrate that this approach has high accuracy and predictive performance.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Prashant Shrivastava, Tey Kok Soon, Mohd Yamani Idna Bin Idris, Saad Mekhilef, Syed Bahari Ramadzan Syed Adnan
Summary: In this work, a highly accurate and computationally efficient model-based battery states estimation method is proposed. It can concurrently estimate different battery states and has been validated with experimental results for accuracy and computational efficiency.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu
Summary: A method based on deep neural network is proposed for fast and accurate estimation of SOC for LiFePO4 batteries, with an error of less than 2.03% over the entire battery SOC range. By integrating the DNN with a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved.
Article
Energy & Fuels
Friedrich von Buelow, Joshua Mentz, Tobias Meisen
Summary: The study presents a machine learning method for predicting the state of health of batteries in electric vehicles, which can be applied in real-world applications. It was found that combining different cycle window widths into one training dataset improves the generalization of the model, and the granularity of the operational ranges of the signals does not limit the model's performance.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Engineering, Multidisciplinary
Erik Schaltz, Daniel-Ioan Stroe, Kjeld Norregaard, Lasse Stenhoj Ingvardsen, Andreas Christensen
Summary: This article examines the feasibility of using incremental capacity analysis (ICA) method for estimating the state of health (SoH) of electric vehicle (EV) batteries, showing consistent results on both individual cell and car level testing. The root-mean-square errors for NMC and LMO type batteries were found to be 1.33% and 2.92% respectively, indicating the applicability of ICA method for battery SoH estimation at the car level.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Zeeshan Ahmad Khan, Prashant Shrivastava, Syed Muhammad Amrr, Saad Mekhilef, Abdullah A. Algethami, Mehdi Seyedmahmoudian, Alex Stojcevski
Summary: This study evaluates the performance of seven different online SOC estimation algorithms using experimental data. The extended Kalman filter and sliding mode observer performed the best in terms of estimation accuracy and computation time.
Article
Thermodynamics
Fengqi Zhang, Lehua Xiao, Serdar Coskun, Hui Pang, Shaobo Xie, Kailong Liu, Yahui Cui
Summary: This article presents a comprehensive comparative study of energy management strategies (EMSs) for a parallel hybrid electric vehicle (HEV) considering battery ageing. The principles of dynamic programming (DP), Pontryagin's minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) with battery ageing are elaborated. A gearshift map is obtained from DP optimization results to optimize drivability and fuel economy, and it is applied in the PMP and ECMS. Fuel economy, battery state-of-charge charge-sustainability, and computational efficiency are compared for different EMSs. Battery ageing is also included in the optimization solution using a control-oriented model. DP achieves the best fuel economy compared to other methods, with about a 2% difference in fuel economy compared to PMP. The analysis results provide valuable insights into the advantages and disadvantages of each approach.
Article
Automation & Control Systems
Tianyu Hu, Huimin Ma, Kailong Liu, Hongbin Sun
Summary: In this article, a knowledge-data-driven attention model (CFKDA) is proposed for Li-ion battery calendar health prognostics. By combining domain knowledge and data, CFKDA has demonstrated improved theoretical strength and prognostic performance. Experimental results show the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over state-of-the-art models.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Wei Li, Yi Xie, Kailong Liu, Rui Yang, Bin Chen, Yangjun Zhang
Summary: This article proposes an enhanced thermal model for pouch batteries, which can be used for battery management systems in low temperature and high current conditions. The model integrates a virtual resistance model (VRM) and a post resistance model for low temperatures. The VRM is extrapolated from the resistance model under room temperature conditions based on the Arrhenius equation. The complete thermal model is validated at low temperatures and high discharge rates, with maximum mean errors of 0.72°C for cyclic pulse current and 0.79°C for constant current working conditions.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Chong Zhu, Jingbo Han, Hua Zhang, Fei Lu, Kailong Liu, Xi Zhang
Summary: To solve the degradation of lithium-ion batteries in cold climates, researchers have developed an integrated battery self-heater based on traction motor drive topology reconfiguration. This eliminates the need for additional hardware and effectively preheats automotive batteries.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Tianyu Hu, Huimin Ma, Hongbin Sun, Kailong Liu
Summary: In this article, a generative adversarial network-based (GAN-based) model called Capacit Forecast GAN (CFGAN) is proposed for the forecast of battery calendar aging. CFGAN utilizes electrochemical knowledge to design its crucial part, the conditioner, to maintain consistency between knowledge and data, improving its theoretical strength and forecast performance significantly. Results from practical case studies demonstrate the superiority of CFGAN in forecasting and generalization to unseen conditions, indicating its ability to capture the complex multimodality of the condition-varying calendar aging process.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Letter
Automation & Control Systems
Kailong Liu, Qiao Peng, Remus Teodorescu, Aoife M. Foley
Summary: This letter presents an effective battery calendar ageing trajectory prediction model based on support vector regression (SVR) technology, which combines the mechanism and empirical knowledge elements of battery storage temperature, state-of-charge (SoC), and time. The model achieves highly accurate predictions for witnessed conditions and also demonstrates good generalization ability for unwitnessed conditions, reducing the required experimental time and cost.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Energy & Fuels
Xiaopeng Tang, Yuanqiang Zhou, Furong Gao, Xin Lai
Summary: This article proposes a low-computational leader-follower framework for estimating the state-of-charge (SoC) and state-of-health (SoH) of battery packs. The framework uses an enhanced algorithm to handle a selected battery (leader) and updates the states of the other batteries (followers) with lightweight calibrators. Battery-in-the-loop experiments show that the proposed method can achieve accurate estimations with significantly reduced computational time.
Editorial Material
Automation & Control Systems
Kailong Liu, Yujie Wang, Weixiang Shen, Zhongbao Wei, Chunhui Zhao, Huazhen Fang
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Yi Xie, Wei Li, Xiaosong Hu, Manh-Kien Tran, Satyam Panchal, Michael Fowler, Yangjun Zhang, Kailong Liu
Summary: This article proposes a distributed spatial-temporal online correction algorithm for the coestimation of the state of charge (SOC) and state of temperature (SOT) of batteries, which is crucial for a battery management system in achieving a green industrial economy. The algorithm identifies the internal resistance and estimates SOC using an adaptive Kalman filter. It then couples SOC estimation with an online restoration algorithm for distributed temperature, using an improved fractal growth process. The proposed coestimation algorithm improves SOC estimation fidelity by up to 1.5% and maintains the mean relative error of SOT estimation within 8%.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Energy & Fuels
Kailong Liu, Qiao Peng, Yunhong Chec, Yusheng Zheng, Kang Lid, Remus Teodorescuc, Dhammika Widanage, Anup Barai
Summary: This paper presents a systematic review on the applications of transfer learning in the field of battery management, with particular focuses on battery state estimation and ageing prognostics. Transfer learning can offer potential solutions to the issues faced by conventional battery management by transferring existing knowledge from different but related domains. The state of the art in terms of principles, algorithm frameworks, advantages and disadvantages are discussed, followed by a discussion on future trends of data-driven battery management with transfer learning.
ADVANCES IN APPLIED ENERGY
(2023)
Article
Thermodynamics
Xin Lai, Yi Yao, Xiaopeng Tang, Yuejiu Zheng, Yuanqiang Zhou, Yuedong Sun, Furong Gao
Summary: This study proposes a new method for estimating the state of health (SOH) of batteries, which reduces the dependency on specific working conditions and shows strong potential for practical applications.
Article
Engineering, Electrical & Electronic
Guang Wang, Gaofeng Zhao, Jiale Xie, Kailong Liu
Summary: This article proposes an improved correlation coefficient method for multifault diagnosis of battery packs in electric vehicles. It utilizes multivariate statistical analysis and Bayesian probability theory under the framework of ensemble learning. The method creates local submodels based on cross-cell voltage correlation signals and implements fault diagnosis using independent component analysis. Results are integrated using Bayesian probabilistic ensemble interface, and accurate fault type identification and localization are achieved using ensemble fault probability and ensemble contribution rate.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Mechanical
Xinghao Du, Jinhao Meng, Kailong Liu, Yingmin Zhang, Shunli Wang, Jichang Peng, Tianqi Liu
Summary: This paper proposes a co-estimation framework utilizing the advantages of both recursive least squares (RLS) and recursive total least squares (RTLS) for a higher parameter identification performance of the battery equivalent circuit model (ECM). RLS quickly converges by updating the parameters along the gradient of the cost function, while RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results show that the proposed method has good accuracy, a fast convergence rate, and robustness against noise corruption.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Xiaopeng Tang, Xin Lai, Changfu Zou, Yuanqiang Zhou, Jiajun Zhu, Yuejiu Zheng, Furong Gao
Summary: This study proposes a few-shot learning-based method for detecting battery lifetime abnormalities, which can identify all abnormal batteries using only the first-cycle aging data with a low false alarm rate.
Article
Automation & Control Systems
Xin Gu, Jinglun Li, Kailong Liu, Yuhao Zhu, Xuewen Tao, Yunlong Shang
Summary: This study presents a minor fault diagnosis approach for lithium-ion batteries based on phase plane sample entropy, which accurately detects minor faults and predicts the time of occurrence. Experimental results demonstrate the effectiveness, robustness, and generalizability of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.