Lithium Titanate Battery Research Articles

A Data-Driven Online Prediction Model for Battery Charging Efficiency Accounting for Entropic Heat

This study proposes a charging efficiency calculation model based on an equivalent internal resistance framework. A data-driven neural network model is developed to predict the charging efficiency of lithium titanate (LTO) batteries for 5% state of charge (SOC) segments under various charging conditions. By considering the impact of entropy change on the open-circuit voltage (OCV) during the charging process, the accuracy of energy efficiency calculations is improved. Incorporating battery data under various charging conditions, and comparing the predictive accuracy and computational complexity of different hyperparameter configurations, we establish a backpropagation neural network model designed for implementation in embedded systems. The model predicts the energy efficiency of subsequent 5% SOC segments based on the current SOC and operating conditions. The results indicate that the model achieves a prediction error of only 0.29% under unknown charging conditions while also facilitating the deployment of the neural network model in embedded systems. In future applications, the relevant predictive data can be transmitted in real time to the cooling system for thermal generation forecasting and predictive control of battery systems, thereby enhancing temperature control precision and improving cooling system efficiency.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Modeling and SOC estimation of on-board energy storage device for trains under emergency traction

The sudden interruption of train power supply in an extreme environment will seriously threaten the safety of passengers and affect the operational efficiency of the railway system. In this case, the focus of attention becomes a method of running the train to the nearest rescue point based on the limited capacity of the on-board emergency energy storage device. Therefore, this paper reports research on the state of charge (SOC) estimation of train energy storage equipment to optimize the emergency traction strategy and energy utilization rate of trains to the maximum extent. First, with the emergency power supply of on-board lithium titanate batteries, the energy flow model of train emergency power supply is constructed for the first time. Second, based on the second-order equivalent circuit model and considering the utilization of regenerative braking energy, the relationship between battery capacity and open circuit voltage (OCV), temperature, current and SOC are constructed through experimental data, which significantly improves the accuracy and robustness of SOC estimation in complex and variable environments. Then, to effectively eliminate the impact of environmental on the estimation especially in the case of extremely low temperature (−20 °C), online model parameter discrimination and SOC estimation of lithium batteries are realized by combining recursive least squares with an optimal forgetting factor (FRLS) algorithm and a new temperature forgetting factor based adaptive extended Kalman filter (TFFAEKF) algorithm. The experimental results show that the proposed method achieves accurate SOC estimation, and the maximum error is less than 1% at different temperatures.

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

Research on coordinated control technology of hybrid energy storage under grid-connected/off-grid dual mode

Abstract Aiming at the multi-time scale intermittency of renewable energy in microgrids and frequent power fluctuations will lead to a series of power quality issues. So, it is necessary to propose appropriate strategies for controlling the energy storage systems of microgrids to ensure energy balance. In microgrids, two types of battery energy storage are used to form a hybrid energy storage system, namely, zinc-bromine flow battery for energy storage and lithium titanate battery for energy storage. By analyzing the functionality of various energy storage systems in the grid-connected/off-grid dual style, the operation objectives of the hybrid energy storage system in different modes are proposed. In the meantime, the control strategy is formulated by combining the smoothing fluctuation characteristics of power-type energy storage and the peak load shifting features of energy-type energy storage. Finally, the efficiency of the hybrid energy system control strategy is checked by the simulation software in the connected/off-the-grid mode.

State-of-charge estimator design and experimental verification for a lithium-titanate battery cell

State-of-charge estimator design and experimental verification for a lithium-titanate battery cell

Data-physics-driven estimation of battery state of charge and capacity

High-power density lithium-ion batteries have been utilized in both energy storage and high rate charging and discharging applications. Accurate state estimation is fundamental to enhancing battery life and safety. Therefore, a data-physics-driven estimation of the state of charge and capacity for lithium-titanate batteries was conducted using Gaussian distribution fusion. Firstly, a fractional order model was selected as the physical analytical model for lithium-titanate batteries. Secondly, a data-driven model that combines convolutional neural networks and long short-term memory networks was employed to predict the battery state of charge. Thirdly, the physical analytical model and data-driven model were fused to estimate the state of charge by employing the principle of Gaussian distribution fusion. Finally, both the state of charge and actual capacity of the battery were jointly estimated. The results showcase the capability of the proposed method to accurately estimate the state of charge and capacity, ensuring an accuracy level within 1%. Furthermore, this approach exhibits a 20% improvement in accuracy compared to traditional methods.

Adaptive Models for Improved Battery Charging Systems / Adaptivni modeli za poboljšane sisteme punjenja baterija

During its operation, sometimes it is a needed to swiftly replenish the battery from a partially depleted state, while strictly adhering to its technological limitations such as the battery terminal voltage and rated continuous charging current. To achieve this goal, this contribution outlines the dynamic battery recharging system, utilizing feedback provided by the nonlinear estimator of the battery state-of-charge (SoC) or SoC-related open-circuit-voltage (OCV). In the former case, the estimator is realized as an extended Kalman filter (EKF), while in the latter case it is implemented using the methodology of a System Reference Adaptive Model (SRAM), whose design is based on the Lyapunov stability theory. Thus-obtained innovative adaptive battery chargers are compared against the conventional constant-current/constant-voltage (CCCV) charging system, which relies solely on battery voltage feedback. A comprehensive comparative analysis is conducted through extensive simulations utilizing the nonlinear equivalent circuit model of the lithium titanate battery (LTO) cell.

A Simplified Fractional Order Model Adapted to Temperature and Aging for Fast Estimation of State of Power of Lithium Titanate Batteries

A Simplified Fractional Order Model Adapted to Temperature and Aging for Fast Estimation of State of Power of Lithium Titanate Batteries

Performance Analysis Based on Desert Rescue UAV Power Battery in High Temperature Environment

The main purpose of this paper is to analyze and discuss the performance of the lithium iron phosphate battery and the new lithium titanate battery commonly used in UAV that affect the endurance in high temperature environment. This paper first introduces the functions of desert rescue UAV and the development status of UAV power battery technology. This paper first points out the main factors that affect their endurance at high temperature, expounds the reasons for the failure of lithium-ion batteries and high-temperature polymer batteries at high temperature, and introduces the materials and general performance characteristics of the two types of batteries, and then reviews the methods and results of various studies on the performance of lithium-ion batteries and lithium polymer batteries at high temperature. Finally, this paper points out the shortcomings of the existing research and points out the direction for the future development. By comparing the working performance of the two batteries at high temperature and combining with the practical problems that need to be considered in production and use, this paper puts forward selection suggestions according to the existing conditions, so that later workers can choose the type of UAV power battery according to different working environments.

Research of electric vehicle battery packs and charging stations based on an active three-phase rectifier

Problem. The proliferation of electric vehicles has underscored the need for the development of energy-efficient charging stations and systems. These systems must deliver high efficiency, maintain a power factor close to unity, adhere to electromagnetic compatibility standards, emit low levels of higher harmonics, and enable fast charging capabilities. Current electric vehicle charging devices encounter issues such as significant power losses, high harmonic emissions to the power grid, and challenges in implementing fast charging modes. Consequently, enhancing the energy efficiency of charging stations – by reducing power losses, minimizing harmonic emissions, ensuring a high power factor, and supporting modes for regulated current and voltage sources for fast charging – is a pressing concern. Goal. The aim of this work is to perform a comparative analysis of different types of batteries for electric vehicles, exemplified by TESLA cars, presenting comparative characteristics of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. Methodology. The study showcases an evaluation of battery units for electric vehicles and outlines a comparative assessment of lithium-ion, lithium-iron-phosphate, and lithium-titanate batteries. The research focuses on enhancing the energy efficiency of electric vehicle charging station systems through the use of active rectifiers, with modes for active power factor correction and optimal configuration. Results. An examination of the quality of converters for electric vehicle charging stations was conducted, proposing schemes for a three-phase active voltage source rectifier and an active current source rectifier. Power circuitry and space-vector switching states for power transistors are introduced. Analysis concludes that an active current rectifier may be a more promising topology. Originality. A model of a charging station based on an active current source rectifier was developed using Matlab/Simulink. Transient processes of the converter's operation and the charging of a 60 kWh battery pack were examined. It was found that the active rectifier facilitates regulation of the battery charging current, achieves a power factor close to unity, and maintains a low level of higher harmonic emissions with a harmonic distortion factor of 2.52%. Practical value. The advancement of electric vehicles necessitates the ongoing development and enhancement of the energy indicators for electric vehicle batteries and the converters of charging stations, especially those that enable fast charging. Active progress is being made in each of these areas.

Equivalent circuit modeling and state-of-charge estimation of lithium titanate battery under low ambient pressure

The low ambient pressure during the flight of aircraft has a significant impact on the performance and safety of the onboard power battery. In order to ensure the safe operation of the battery system, it is very important to accurately estimate and manage the state-of-charge (SOC) of the battery. In this work, the equivalent circuit modeling (ECM) of lithium titanate battery (LTB) is studied in detail, and the influence of analog circuit model parameters in low ambient pressures is discussed for the first time. The forgetting factor recursive least square algorithm is introduced to accurately identify the ECM parameters of the LTB under different pressures online. The particle swarm optimization algorithm is innovatively proposed to optimize the covariance matrix of the Kalman filter algorithm. The verification shows that the root mean square error of the ECM of LTB under different ambient pressures is less than 0.025. In the SOC estimation process, the noise covariance matrixes of the extended Kalman filter and the unscented Kalman filter are optimized by the particle swarm algorithm. The optimized SOC estimation absolute error is less than 3 %, especially at 96 kPa and 30 kPa, where the absolute error is less than 2 %.

State of charge estimation of lithium-titanate battery based on multi-model extended Kalman filter considering temperature and current rate

To tackle the issue of accurately estimating the state of charge (SOC) of lithium-titanate (Li-Ti) batteries in complex vehicle applications, a multi-model extended Kalman filter (MM-EKF) algorithm considering the effects of temperature and current rate is proposed. Based on the operational characteristics of Li-Ti batteries in the context of electric vehicle applications, second-order RC equivalent circuit models (ECMs) are established to account for the temperature and current rate influences. Model parameters are identified using an adaptive recursive least squares method with a forgetting factor based on experimental data. Subsequently, a SOC estimation method based on the MM-EKF algorithm for Li-Ti batteries is proposed and its effectiveness is validated under different ambient temperatures. Experimental results demonstrate that the MM-EKF algorithm, which considers the effects of temperature and current rate, can accurately estimate the SOC of Li-Ti batteries. The maximum estimation error is within 5 % at different ambient temperatures, and the algorithm can quickly eliminate initial SOC errors. Consequently, it fulfills the requirements for SOC estimation of hybrid tracked vehicles in intricate operating conditions.

Application of nanotechnology in lithium titanate batteries

With the introduction of dual carbon policy and the development of new energy industry, new energy batteries play a pillar role in the whole industry. However, the new energy batteries made in the context of traditional cathode materials often have many defects and cannot meet the demand. This study introduces nanotechnology, lithium titanate batteries and their integrated applications. Among them, nanofabrication technology, as an emerging technology, can be used to dope new particles to modify the conventional lithium titanate to improve its own shortcomings of insufficient conductivity of a single material. The significance of this thesis is to compare each technique and each material, and to propose new experimental solutions based on the existing research results.

A data-driven equivalent circuit model’s parameter estimation method applied to Lithium-Titanate battery

Lithium battery systems are becoming essential for a variety of applications, such as maximisation of self-produced renewable energy in buildings, grid balances, and electric vehicles. Accurate and equivalent models are required to study and analyse the behaviour of batteries, both static and dynamic. This paper presents a novel data-driven parameter estimation procedure for high-current battery modelling, which has been applied to experimental measurement data conducted on Lithium Titanate batteries. The proposed estimation methodology has been applied to three different equivalent circuit models characterised by increasing modelling complexity. Genetic algorithm optimisation techniques solve the optimisation problem, estimating the model’s parameters and a look-up table of battery parameters estimated in discretised State of Charge. Enhance the battery operation modelling at high depth of discharge, the model is applied to the battery operated at high currents, covering its entire domain of state of charge. The fully depleted and charged state is given increased focus to maintain good modelling performance. Unlike previous works, the proposed methodology repeats the parameters estimation procedure for more than thirty-five state of charge ranges using non-equal width ranges to increase model density in critical regions. Based on the estimation results for one C-rate, it was found that the parameter estimation error ranges from 0.17% for the Rint model to 0.03% for the Dual-Polarisation model. The validation results confirmed that the proposed approach had a normalised root mean square error lower than 2%. The proposed methodology encompasses the entire process of parameter estimation, starting from raw experimental data. As such, it can be generalised and applied to other battery technologies.

Lithium titanate battery system enables hybrid electric heavy-duty vehicles

Electrification plays an important role in the transformation of the global vehicle industry. Targeting the rapidly growing heavy-duty off-highway vehicles, we developed a battery system for hybrid-electric heavy-duty trucks based on lithium titanium oxide (LTO) batteries. With LTO as the anode and nickel manganese cobalt (NCM) as the cathode, comprehensive measurements of the battery cells and modules indicate that LTO batteries are uniquely suitable for applications requiring high-rate fast charging and discharging. An LTO battery system was constructed and implemented to realize the first advanced lithium-ion battery-based hybrid-electric heavy-duty vehicle, a hybrid-electric mining truck with vehicle mass 34 ton and maximum load 60 ton. Field operation tests of the hybrid-electric vehicle suggest that the performance of the LTO battery system meet the design expectations. After analyzing operation parameters of the hybrid-electric heavy-duty vehicle, we found 54.9 % fuel savings compared to conventional diesel-powered truck of the same mass and load.

The Incremental Capacity Curves and Frequency Response Characteristic Evolution of Lithium Titanate Battery during Ultra-High-Rate Discharging Cycles

The high-rate discharging performance of lithium titanate batteries is a crucial aspect of their functionality. Under high-power demands, the discharge rate, which is defined as the ratio of discharge current to the maximum capacity, can exceed 50 C or higher. This study investigates the evolution of incremental capacity (IC) curves and frequency response characteristic of 2 Ah lithium titanate batteries subjected to aging cycles at 50 C. The results provide a new indicator to assess the fading of the state of health (SOH) of lithium titanate batteries during ultra-high-rate discharge cycles.

Enhance the Market Competitiveness of Electric Vehicles by Improving the Design of Lithium-ion Batteries

Electric vehicles (EVs) are increasingly popular because they are energy efficient and capable of providing a good driving experience. However, the performance and market competitiveness of electric vehicles has been limited by a series of shortcomings, which are mainly caused by the limitations of the power source, lithium-ion batteries. To address these problems from the origin, this research analysed the causes of these problems and proposed some possible solutions based on the characteristics and working principles of lithium-ion batteries. The limitations of electric vehicles such as safety issues, low range, high cost, and limited life span are all proved to be related to the EV batteries. Lithium-ion batteries used by electric vehicles have the risk of fire and explosion when broken, overcharged, or exposed to high temperatures. The capacity and life span of them can easily be affected by different factors, which leads to the dissatisfaction of customers with the range and EV batteries’ life span. Furthermore, the production of lithium-ion batteries is very costly. Different ways to improve the performance of lithium-ion batteries are collected and evaluated. Finally, it is concluded that Li-S batteries and lithium titanate batteries are likely to become the solutions to what limited the development of electric vehicles. Since Li-S batteries and lithium titanate batteries still have some disadvantages, researchers should manage to tackle them in the future before they can be widely applied. This paper provides directions for the future development of EV batteries to improve the market competitiveness of electric vehicles.

Advantages and Application of Nano LTO battery

In order to promote the development of new energy vehicles, it is very necessary to develop battery packs with high capacity and high safety performance. For achieving this goal, developing new negative electrode materials has proved to be an effective strategy. Among the many candidate materials, lithium titanate has received extensive attention due to its zero strain property. However, the size control of lithium titanate has become a very important factor to improve battery performance. By reducing the particle size, the growth of lithium dendrite can be suppressed and the ion utilization efficiency can be improved. Nanotechnology has been proved to be an effective way to create nano-scale Li4Ti5O12. By optimizing the preparation process parameters, the size of lithium titanate can be controlled within the desired range In recent years, there are many reports about nano LTO technology. In order to have a comprehensive understanding of this field. This paper summarizes and analyzes the development status and future development direction of nanotechnology and lithium titanate battery, the application of nanotechnology in LTO such as sol-gel, molten salt, microwave, hydrothermal and solvothermal synthesis, including the latest synthesis method in recent years, and illustrated the structure-property relationship between Li4Ti5O12 and LTO performance, which provides theoretical guidance for the development of the battery field.

Technical and economic efficiency of the application of lithium-titanate batteries in quarry railway transport

Technical and economic efficiency of the application of lithium-titanate batteries in quarry railway transport