Lithium-ion Battery Energy Storage Research Articles

Grid-connected lithium-ion battery energy storage system towards sustainable energy: A patent landscape analysis and technology updates

Presently, as the world advances rapidly towards achieving net-zero emissions, lithium-ion battery (LIB) energy storage systems (ESS) have emerged as a critical component in the transition away from fossil fuel-based energy generation, offering immense potential in achieving a sustainable environment. This study conducts an in-depth analysis of grid-connected LIB ESS patents published from 1998 to 2022, aiming to comprehend essential developments and trends in the technology landscape while identifying key players and inventors. Employing extensive keyword searches within the Lens database, 95 patent documents were extracted along with the bibliometric data. The research highlights two prominent factors in the field of grid-connected LIB ESS patents. Firstly, a detailed patent bibliometric analysis including patent growth trends, keyword analysis, patent distribution over jurisdiction, and subject categories was presented. Moreover, the research identifies key owners, inventors and hierarchical jurisdiction in patent document publishing and evaluates the possibilities for future research sustainability. Secondly, detailed updates regarding technological advancements that are driving sustainable grid-connected LIB ESS development in various sectors such as; gird connected ESS, power conversion system (PCS), energy management system (EMS) and transportation sector are provided. The study concluded that the patents related to grid-connected ESS, minimizing voltage and frequency regulation to achieve grid stability and EMS of LIB are the key trending topics in the field of grid-connected LIB ESS. The comprehensive analysis not only illuminates crucial trends but also provides valuable guidance to future researchers and industry stakeholders, facilitating the path towards sustainable grid-connected LIB ESS and paving the way for a more environmentally conscious energy landscape.

Techno-economic assessment of small-size residential solar PV + battery systems under different tariff structures in Brazil

This paper proposes a methodology to assess the energy and economic impact of adopting small-scale residential photovoltaic (PV) systems paired with lithium-ion battery energy storage (BESS) systems in single-family homes, under the current energy feed-in legislation. The suggested methodology was applied to a case study of a prosumer unit (PU) in Brazil. Data from energy bills, measured PV generation, BESS behaviour, and energy consumption were analysed to compare different viewpoints and estimate key economic indicators. As a result, the PV + BESS combination increases residential users' energy self-consumption and self-sufficiency, resulting in respective increases of 15 % and 2 %. Under the conventional tariff, adopting the PV + BESS system led to an average 29 % reduction in electricity costs compared to scenarios without PV + BESS. Furthermore, by transitioning to the white tariff mode in conjunction with PV + BESS, the PU achieved a 49 % reduction. This result emphasizes the advantages of time-of-use adoption for PV + BESS consumers. A greater discrepancy between tariff periods enhances the feasibility of storing off-peak energy for use during peak periods. Currently, BESS implementation costs impede competitiveness, with attractive LCOS metrics achieved only at costs below $600.00/kWh and MRAs below 9 %. BESS + PV lowers generation costs compared to off-peak rates, increasing the viability of shared usage. Integrating PV + BESS systems with rate switching significantly diminishes electricity expenses. The substantial BESS implementation cost necessitates a noteworthy rate differential for viability, which could potentially impact non-PV and non-BESS consumers.

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets

The penetration of renewable energy in modern power systems is still increasing. Battery energy storage can rapidly respond to a dispatch order and is expected to provide multiple auxiliary services. However, deep charging cycles have negative impacts on battery health. This paper presents a health-aware long-term operation strategy for lithium-ion battery energy storage participating in the energy and frequency regulation markets. The strategy determines the capacity bounds that can be bid in the energy and frequency regulation markets and updates these bounds every three months, aiming to preserve battery health and increase market revenue. A long-term operational modeling framework is proposed to address the multi-timescale nature, integrating frequency control, energy arbitrage, and the evolution of battery degradation in a holistic model. A nonlinear degradation model is developed to approximate the health impact of main stress factors, which captures the nonlinearity of three-stage capacity degradation process caused by the formation of solid electrolyte interphase film and lithium plating. For intraday operation, a two-scale stochastic programming model is proposed, in which the market bidding and automatic generation control response are simulated in the timescales of one hour and two seconds, respectively. This simulation based method is closer to industrial practice, as it accounts for various factors in BESS operation. For the seasonal update of the capacity allocation strategy, a dynamic programming problem is established and solved based on the nonlinear degradation model and the daily revenue obtained from simulation of intraday operation; the capacity allocation strategy is determined from the renowned Principle of Optimality by Bellman. This two timescale modeling framework captures the interaction between short-term operation strategy and long-term degradation process. Numerical simulations validate that the proposed method can slow down battery degradation and increase lifetime revenue.

Simultaneous anodic de-lithiation/cathodic lithium-embedded regeneration method for recycling of spent LiFePO4 battery

The widespread use of lithium-ion batteries for energy storage will result in millions of tons of scrapped LiFePO4 (LFP) batteries. Current recycling technologies for LFP cathode materials require harsh acid treatments and are expensive. Hence, in this work, an ingenious electrochemical method is developed to recycle scrapped LFP. Specifically, through the charging/discharging process of an electrolytic system in which both the cathode and anode are scrapped LFP, the anode is de-lithiated to obtain FePO4 (FP), meanwhile, the Li+ from the anode is embedded into the cathode scrapped LFP to achieve regeneration. The experimental results show that the anode de-lithiation rate is 92 % and the cathode regeneration performance is closely related to the driving voltage. A voltage of 1 V converts the FP phase to LFP and effectively eliminates the LFP defect lattice. However, an excessive voltage (1.2 V) leads to an extremely fragile LFP crystal structure, resulting in the ineffective embedment of Li+ into the defective lattice and ineffective capacity restoration. The regenerated LFP exhibits a high restored capacity (136.5 mAh g−1 at 1 C) and excellent stability (95.32 % capacity retention after 300 cycles at 5 C). This novel method does not require an additional Li source, consumes low electrical energy (31.9 kWh t−1) and has a high current efficiency (>98 %), and it yields economic and environmental benefits that will contribute to the sustainable development of the LFP industry.

A Comprehensive Evaluation of Battery Technologies for High-Energy Aqueous Batteries.

Aqueous batteries have garnered significant attention in recent years as a viable alternative to lithium-ion batteries for energy storage, owing to their inherent safety, cost-effectiveness, and environmental sustainability. This study offers a comprehensive review of recent advancements, persistent challenges, and the prospects of aqueous batteries, with a primary focus on energy density compensation of various battery engineering technologies. Additionally, cutting-edge high-energy aqueous battery designs are emphasized as a reference for future endeavors in the pursuit of high-energy storage solutions. Finally, a dual-compatibility battery configuration perspective aimed at concurrently optimizing cycle stability, redox potential, capacity utilization for both anode and cathode materials, as well as the selection of potential electrode candidates, is proposed with the ultimate goal of achieving cell-level energy densities exceeding 400 Wh kg-1.

Alternative Energy Options for a Thai Durian Farm: Feasibility Study and Experiments for the Combination of Solar Photovoltaics and Repurposed Lithium-Ion Batteries

This research examines the viability of incorporating a second-life lithium-ion battery energy storage system with a solar photovoltaic power source to facilitate energy acquisition in two irrigation scenarios at a durian farm located in Thailand. The first option entails selecting the battery with suitable dimensions to effectively harness solar energy following morning irrigation, while the second option involves sizing the battery specifically for storing solar energy solely for the purpose of powering the motor pump. This study conducts an analysis on the degradation of the battery, focusing on the influence of specific environmental conditions and its specification. The degradation analysis employs polynomial functions as an alternative to exponential semi-empirical stress models for fitting stress models. This approach demonstrates a robust fit, as evidenced by a high R-squared value of approximately 1. The economic analysis considers various factors, including solar irradiance derating factors, battery deterioration rate, and photovoltaic power ratings for all scenarios. The sensitivity analysis evaluates the variations in irradiance and costs associated with the battery. When solar radiation levels surpass 500 W/m2 and the cost of the battery remains below $100/kWh, the estimated payback period is approximately 5 years over the duration of the 10-year project lifespan.

Assessing the value of hydrogen thermal energy storage and electric thermal energy storage in NEOM city

A large community such as NEOM City which is supposed to be powered only by wind and solar renewable energy requires a significant amount of energy storage. Given the limitations of the current energy storage technologies, from the total storable energy to the time this energy is stored, from the cost to the technology readiness level, it is important to provide assessments of continuing evolving alternative technologies. The cases of electric (external) thermal energy storage (eTES), and hydrogen thermal energy storage (hTES), are here considered. eTES is based on warming up a molten salt by using an electric resistance. The molten salt is then used to warm up a power cycle fluid for dispatchable energy production running a thermal power cycle. eTES suffers from round trip efficiency below 50 % but may handle a larger amount of energy at a lower cost compared to lithium-ion battery energy storage. eTES may benefit from integration with concentrated solar power with (internal) thermal energy storage. hTES is then based on green hydrogen production by electrolyzers, storage of the hydrogen in tanks, and combustion of the hydrogen to warm up a power cycle fluid for dispatchable energy production running a thermal power cycle. Of cost more difficult to estimate, also depending on the deployment of electrolyzers and round trip efficiencies below 50 %, hTES has the benefit to permit the storage of any amount of energy even over long time scales such as those needed to compensate for seasonal variability. Before electrolyzers provide the due amount of green hydrogen, the combustion power plant can be run with natural gas, delivering if not zero CO2 emission, certainly the lowest life cycle CO2 emission for dispatchable electricity at present. Finally, eTES and hTES can be coupled with each other, and also integrated with waste-to-energy (WTE) systems, for a more comprehensive and sustainable approach based on common thermal power cycles.

A fast-response preheating system coupled with supercapacitor and electric conductive phase change materials for lithium-ion battery energy storage system at low temperatures

The electrochemical performance of lithium batteries deteriorates seriously at low temperatures, resulting in a slower response speed of the energy storage system (ESS). In the ESS, supercapacitor (SC) can operate at −40 °C and reserve time for battery preheating. However, the current battery preheating strategy has a slow heating rate and cannot preheat batteries to a comfortable temperature range during the time reserved by SC. This study proposes a preheating system that combines the instantaneous high-power discharge of SC with the efficient electro-to-thermal conversion of ECPCM, which has a super-fast preheating rate and contributes to the fast response of ESS at low temperatures. Under extreme conditions of −40 °C, the preheating system can recover >85 % discharge capacity and voltage of the battery system in 2 min. By adjusting SC capacitance and ECPCM resistance, the preheating rate of the battery system can reach 69.5 °C/min, and the temperature difference is less than 5 °C. Moreover, the pulse current of SC can reduce the temperature difference inside the battery within 5 °C. The preheating system can also enable the battery system quickly enter super-charging mode, greatly reducing the charging time. The total charging time was reduced by 72 % when the battery pack was preheated to 20 °C. Nevertheless, based on the outcome of this study, this preheating strategy can effectively improve the low-temperature response speed of the ESS and meet the requirements of the microgrid.

Research on frequency modulation capacity configuration and control strategy of multiple energy storage auxiliary thermal power unit

The rapid development of new energy sources has had an enormous impact on the existing power grid structure to support the “dual carbon” goal and the construction of a new type of power system, make thermal power units better cope with the impact on the original grid structure under the background of the rapid development of new energy sources, promote the consumption of new energy sources such as solar and wind energy, and further expand the channels for upgrading and flexibility transformation of thermal power energy storage, improve the stability, safety and economy of thermal power unit operation. Therefore, based on the research status at home and abroad, a hybrid energy storage system is added to solve the above problems. The lithium battery-flywheel control strategy and the regional dynamic primary frequency modulation model of thermal power units are proposed, and study the capacity configuration scheme of flywheel‑lithium battery hybrid energy storage system under a certain energy storage capacity, the frequency modulation performance is evaluated by the system frequency fluctuation degree, fluctuation peak range and other indicators. Study under a certain energy storage capacity thermal power unit coupling hybrid energy storage system to participate in a frequency modulation of the optimal capacity configuration scheme, and perform simulation verification using MATLAB/Simulink. When the thermal power unit is coupled with a 10.8612 MW/2.7151 MWh flywheel energy storage system and a 4.1378 MW/16.5491 MWh lithium battery energy storage system, while adaptive variable coefficient droop control is adopted, the system frequency range is 0.00328 p.u.Hz, and the fluctuation degree of the output power of the thermal power units is 0.00498 p.u.MW, compared to the individual frequency regulation of thermal power units, it reduces 52.07 % and. The results show that when the thermal power unit is disturbed by external load, hybrid energy storage assisted thermal power unit frequency modulation reduces the mechanical loss of thermal power unit to a certain extent and extends the service life of the unit, effectively improve the operation stability and economy of thermal power units, further reduce the phenomenon of abandoning wind and light, and better promote the utilization of new energy.

Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation

The lithium battery energy storage system (LBESS) has been rapidly developed and applied in engineering in recent years. Maritime transportation has the advantages of large volume, low cost, and less energy consumption, which is the main transportation mode for importing and exporting LBESS; nevertheless, a fire accident is the leading accident type in the transportation process of LBESS. This paper applied fault tree analysis and Bayesian network methods to evaluate the fire accident risk of LBESS in the process of maritime transportation. The Bayesian network was constructed via GeNIe 2.3 software, and the probability of LBESS fire accidents during maritime transportation was calculated based on the probability of basic events occurring. The results showed that an unsuitable firefighting system for putting out lithium battery fires, high humidity, and monitoring equipment without a real-time alarm function have the most significant influence on the occurrence of LBESS fire accidents during maritime transportation. The research work of this paper provides a theoretical basis for the risk assessment of LBESS during maritime transportation.

Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.

Use of Physics-Based Models for Different Applications of Electrochemical Energy Storage and Conversion Devices

This talk will present the application of physics-based models applied to two different electrochemical storage and conversion systems, viz. Li-ion batteries and Proton Exchange Membrane Fuel Cells (PEMFCs). Battery energy storage system (BESS) is becoming a crucial part of standalone renewable hybrid power systems due to the intermittent nature of power generation and for aiding in various operations like frequency regulation, voltage support, and peak shaving. The lithium-ion battery is a promising choice for BESS due to its high energy density, power density, operating voltage, negligible self-discharge, suitable operating temperature range, modularity, and reliability. A standalone renewable hybrid power system framework with lithium-ion battery energy storage is developed to investigate its performance using physics-based battery models. Power management and control strategies are developed for standalone renewable hybrid power systems. These control strategies can track the MPP of the solar cells and wind turbines while avoiding overcharging the battery and guaranteeing 0% dumping power under different ambient and working conditions. The battery is exposed to harsh ambient conditions in the real world, resulting in the dynamic and continuous capacity fade. A thermal management and control strategy is developed to analyze the effect of temperature on lithium-ion batteries' performance and degradation without using external cooling systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of renewable hybrid power systems. The impact of BESS size variation on degradation and the cost of energy generation is analyzed. The developed renewable hybrid power system framework is independent of location and system size and can be extended to incorporate other renewable energy generation sources and energy storage systems.The second part of this talk will focus on using physics-based models to understand the water dynamics and degradation mechanisms inside PEMFC, which is necessary for water management, long-life operation, and cost reduction. PEMFCs have emerged as an alternative green energy technology in various applications such as automobiles, portable and stationary applications, and auxiliary power supplies for space exploration missions. The cost and durability targets remain unmet largely, which restrains the commercialization of the PEMFCs. The major difficulty in modeling lies in the wide scale of dimensions from the millimeter level (flow channel) to the micron level (catalyst layer (CL) thickness). The multi-scale physics-based model enables the life cycle analysis of PEMFC under various operating conditions. The mass transport limitation is the major hurdle in performance enhancement for higher current density operations. It arises from the liquid water generated during the electrochemical reactions, which hinders the reaction sites of CL and oxygen transport reaction sites in the gas diffusion layer, resulting in performance deterioration. Many experimental studies reported the two-phase flow visualization techniques in PEMFCs, viz. neutron imaging, optical visualization, and X-ray radiography. However, the difficulties in conducting experiments for flow visualization and related parameters such as liquid volume fraction have encouraged researchers to adopt numerical simulations that are less expensive and computationally efficient. Understanding liquid water movement inside the cell at the micro-scale help to improve the PEMFC performance at the stack level. Here, we develop a comprehensive 3-D, multiphase, non-isothermal, steady-state physics-based model of the PEMFC that can closely approximate the liquid water flooding and membrane drying. The findings from this work can be used to design an efficient flow configuration and to study membrane deformation due to the shrinking-swelling under humidification and thermal cycling. The ultimate goal of the multi-scale model is to enhance the PEMFC stack output with less degradation.

Machine-learning and water energy harvesting based wireless water consumption sensing system in buildings

In recent years, smart wireless remote water meters have become popular, but this kind of smart water meter needs an external power supply, and every 100 to 200 installed this type of water meter, needs to set up an independent collector. This kind of water meter is expensive, and the external power supply will increase the energy consumption of the building, which is not conducive to the circuit safety of the old community. In order to reduce the burden of manual meter reading and reduce the building's "carbon emissions". A wireless water consumption sensing system for buildings based on machine learning and water energy collection is designed. Images are collected and recognized by visual sensors to realize the visual reading of water meter data. The system is powered by a 12V micro water generator and transferred to lithium battery energy storage to realize the self-energy supply of the whole system. The lightweight YOLOv5 was used to implement the model training process. Then, on the premise of the small sample data set, the data set was expanded by a data enhancement method which randomly changed the image brightness and hue. Finally, the training model is deployed on MCU through the TensorFlow Lite framework to realize edge AI detection. Experiments show that the size of the lightweight YOLOv5 is reduced by 87.1% and the mAP reaches 92.3%. The system can complete the water meter wireless meter reading and realize the system self-power stably. In addition, the system can be expanded for other meter reading tasks based on visual features.

State of health estimation of lithium-ion battery considering sensor uncertainty

The state of health (SOH) is closely related to the stable operation of lithium-ion (Li-ion) battery energy storage systems. Existing methods for SOH estimation assume a reliable multi-source sensor in the battery management system, yet the sensors are prone to failures leading to abnormal measurement data in practical applications. To alleviate the above issue, this paper proposes a detection before fusion multi-model framework (DFMF) considering the uncertainties of multi-source sensor data. Firstly, a density-based spatial clustering of applications with noise is adopted to identify the working states of sensors via detecting the faults from their raw data. Then, the working states of sensors are fed to a state machine to realize the fault isolation. Finally, a data-driven SOH estimation based on multi-source sensors is developed, which includes both the single-sensor mode and the multi-sensor joint mode. The proposed DFMF is validated on two degradation datasets from different aging conditions and chemistries. The experimental results demonstrate the proposed DFMF has a high accuracy and strong robustness in Li-ion SOH estimation. It is noted that the DFMF can provide SOH estimation results with the root mean squared error within 1.68 % and 1.17 % on the two datasets, respectively.

Research on Safety Operation and Maintenance Management and Health Status Assessment for Lithium Battery Energy Storage System

The safe operation and maintenance of lithium batteries not only needs to monitor the working status of lithium batteries timely and accurately, but also needs to evaluate its health status and automatically switch the backup battery when safety hazards are found. However, research on the safe operation and maintenance of lithium batteries is still lacking. In light of this, this paper constructs a safe operation and maintenance mechanism by monitoring the voltage and surface temperature of the lithium battery. In addition, a novel online health assessment model based on GRU-CNN is proposed to find out the potential safety hazards of lithium batteries timely. The working voltage, current and ambient temperature are used as the input features of the model to predict the degradation of lithium batteries in advance. In addition, a security operation and maintenance controller is designed to quickly switch the backup battery pack when a lithium battery pack appears to have performance degradation or security risks. The system realizes the real-time monitoring of the safe operation of lithium batteries and the rapid assessment of health status, providing technical guarantees for the reliable, stable, and safe operation of the lithium battery system. The experimental comparison verified that the proposed assessment model of the status of health (SOH for short) has less prediction error.

Multi-objective electricity cost and indirect CO2 emissions minimization in commercial and industrial buildings utilizing stand-alone battery energy storage systems

A large portion of global carbon emissions are attributable to electricity generation. Several previous studies indicate that both electricity cost and carbon emission reductions are not attainable with stand-alone battery energy storage systems for residential buildings. However, in this study, lithium-ion battery energy storage dispatch (charging and discharging) is optimized as a multi-objective decarbonization and cost-saving strategy in ten commercial and industrial facilities. The analysis tests 100 energy storage capacities, 5 discharge times, and 2 control strategies with and without enrollment in event-based demand response. Unlike smaller energy consumers, the results show significant indirect CO2 emissions reductions (>31%) paired with significant electricity cost reductions (>10%) are possible from stand-alone battery energy storage systems in a large commercial facility. Additionally, the results indicate that enrollment in the event-based demand response program and dispatch under the load shifting control strategy are always optimal to minimize both the discounted payback period and the indirect CO2 emissions.

Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries

Deep learning (DL) methods is applied extensively in the field of state of charge (SOC) estimation, which require training data and test data to have similar distribution. Discrepancies in data distribution arising from the complexity and diversity of lithium-ion batteries under operational conditions in practice, as well as the difficulty in obtaining data labels, make it enormously challenging to access sufficient battery data to train a specific deep estimator. Aiming to improve the performance of cross-domain SOC estimation for lithium-ion batteries, a model for SOC estimation which combines transfer learning with singular value decomposition (SVD) is proposed. To begin with, a gated recurrent unit (GRU) network is employed to avail the nonlinear dynamic characteristics of the battery from the source and target domains. Then, the features are decoupled by using SVD method to extract task-relevant, important and minor information in the network. Further, the amount of transferred information over the source network to the target network is automatically tuned by the maximum mean discrepancy (MMD) to determine the different degrees of similarity in domain, and the cosine discrepancy to measure the discrepancy on the same domain, which achieves the optimized performance of the target network.

Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%.

Energy and Economic Analysis of Renewable Energy-Based Isolated Microgrids with AGM and Lithium Battery Energy Storage: Case Study Bigene, Guinea-Bissau

By the year 2020, 90% of the population with access to electricity worldwide was surpassed. However, the reality is very different for many countries, especially for those on the African continent that had more than 572 million people without electricity service at the end of 2019. This work studies the implementation of an isolated microgrid activated with photovoltaic energy and energy storage in batteries under the case study of the community of Bigene, located in the African country of Guinea-Bissau. This type of project is a potential solution to the problem of access to energy, but as the cost of the energy storage system is typically very high, this work technically and economically addresses the effect of using absorbed glass material (AGM) and lithium batteries. A simulator was developed using TRNSYS software to analyze the operation of the microgrid under a defined annual demand profile for different types of users, and economic analysis was conducted considering a project lifetime of 25 years. The results showed no significant differences in the solar fraction of both types of batteries when the photovoltaic power was less than 600 kW, regardless of the capacity of the storage bank. The analysis of auxiliary power requirements showed that lithium technology leads to a lower consumption from 800 kW of PV capacity, and utilizing less than this capacity did not have a significant difference with AGM batteries. In this microgrid with a photovoltaic capacity of less than 700 kW and an energy storage of less than 2580 kWh, the type of storage technology, AGM or lithium, did not represent a considerable difference in the levelized cost of energy, indicating that AGM technology could be selected considering its low initial investment cost compared to lithium batteries.