Yongzhi Zhang

Bio: Yongzhi Zhang is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Battery (electricity) & Anode. The author has an hindex of 21, co-authored 50 publications receiving 1986 citations. Previous affiliations of Yongzhi Zhang include Chalmers University of Technology & Sichuan University.

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Abstract: Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures is deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.

Nitrogen-rich porous carbon derived from biomass as a high performance anode material for lithium ion batteries

Abstract: Nitrogen-rich porous carbon derived from ox horns has been successfully synthesized through an economically viable and an environmentally benign approach Such an ox horn derived carbon (OHC) possesses a large surface area (BET surface area is 1300 m2 g−1), a unique 3D porous nanostructure and a high inherent nitrogen content (55%) The OHC, as an anode material for lithium ion batteries (LIBs), exhibits superior electrochemical performances, such as a high reversible capacity (1181 mA h g−1 at a current density of 100 mA g−1) and a superior rate capability (304 mA h g−1 at 5 A g−1) Furthermore, this study demonstrates the exploitation of a universal material in nature, viz, ox horn, as a potential anode for the most sought after energy storage application

Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

Abstract: This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 °C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.

Hierarchically porous nitrogen-rich carbon derived from wheat straw as an ultra-high-rate anode for lithium ion batteries

Abstract: A facile, economical and effective method to produce hierarchically porous nitrogen-rich carbon (HPNC) from wheat straw has been reported. Acid pretreatment is introduced before KOH activation, and plays the role of promoting the formation of thinner pore walls. Without any N-doping, the N content is as high as 5.13%. The HPNC when used as an anode for Li-ion batteries exhibits a superior specific capacity of 1470 mA h g−1 at 0.037 A g−1, and possesses an ultrahigh rate capability of 344 mA h g−1 at 18.5 A g−1. Even at an extremely high current density of 37 A g−1, the reversible capacity is still as high as 198 mA h g−1.

A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries

Abstract: In order for the battery management system (BMS) in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This robustness requires that the energy state can be estimated accurately even when the working conditions of batteries change dramatically. This paper implements battery remaining available energy prediction and state-of-charge (SOC) estimation against testing temperature uncertainties, as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion (AIC) is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2%, even when the wrong temperature is indicated. In this case, the developed double-scale particle filtering method is expected to be robust in practice.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Applications of hierarchically structured porous materials from energy storage and conversion, catalysis, photocatalysis, adsorption, separation, and sensing to biomedicine

Abstract: Over the last decade, significant effort has been devoted to the applications of hierarchically structured porous materials owing to their outstanding properties such as high surface area, excellent accessibility to active sites, and enhanced mass transport and diffusion. The hierarchy of porosity, structural, morphological and component levels in these materials is key for their high performance in all kinds of applications. The introduction of hierarchical porosity into materials has led to a significant improvement in the performance of materials. Herein, recent progress in the applications of hierarchically structured porous materials from energy conversion and storage, catalysis, photocatalysis, adsorption, separation, and sensing to biomedicine is reviewed. Their potential future applications are also highlighted. We particularly dwell on the relationship between hierarchically porous structures and properties, with examples of each type of hierarchically structured porous material according to its chemical composition and physical characteristics. The present review aims to open up a new avenue to guide the readers to quickly obtain in-depth knowledge of applications of hierarchically porous materials and to have a good idea about selecting and designing suitable hierarchically porous materials for a specific application. In addition to focusing on the applications of hierarchically porous materials, this comprehensive review could stimulate researchers to synthesize new advanced hierarchically porous solids.

Data-driven prediction of battery cycle life before capacity degradation

Abstract: Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

High-Power Lithium Batteries from Functionalized Carbon Nanotube Electrodes

Abstract: Energy storage devices that can deliver high powers have many applications, including hybrid vehicles and renewable energy. Much research has focused on increasing the power output of lithium batteries by reducing lithium-ion diffusion distances, but outputs remain far below those of electrochemical capacitors and below the levels required for many applications. Here, we report an alternative approach based on the redox reactions of functional groups on the surfaces of carbon nanotubes. Layer-by-layer techniques are used to assemble an electrode that consists of additive-free, densely packed and functionalized multiwalled carbon nanotubes. The electrode, which is several micrometres thick, can store lithium up to a reversible gravimetric capacity of approximately 200 mA h g(-1)(electrode) while also delivering 100 kW kg(electrode)(-1) of power and providing lifetimes in excess of thousands of cycles, both of which are comparable to electrochemical capacitor electrodes. A device using the nanotube electrode as the positive electrode and lithium titanium oxide as a negative electrode had a gravimetric energy approximately 5 times higher than conventional electrochemical capacitors and power delivery approximately 10 times higher than conventional lithium-ion batteries.