Extend Battery Life Research Articles

Hybrid energy storage system for electric motorcycles: Technical and economic analysis

This paper presents the multiple energy storage system usability for electric motorcycle focused on hybrid topology. This study focuses on evaluating the cost-effectiveness of a hybrid energy storage system (HESS) for e-motorcycles over a 10-year period by considering the number of battery replacements instead of solely the initial system price. The motorcycle studied in this manuscript, is a recreational electric motorcycle utilized as an experimental test bench in EV-SCHS laboratory at the OJEN company. The 10-year equivalent price of the energy storage system includes both the initial purchase cost and the cost of battery replacements over the system's lifespan. A battery life model is essential for estimating the frequency of replacements over the 10-year period. Incorporating ultracapacitors (UC) in the HESS enhances power specifications, leading to improved acceleration and regenerative braking capabilities. This reduces battery current and extends battery life, resulting in fewer required replacements and lower overall system costs over the 10-year period. The study findings demonstrate that optimally-sized HESS configurations offer favorable power specifications, with the 10-year equivalent price of HESS being $3466 lower than that of conventional energy storage systems, despite the initial price of HESS being $833 higher.

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

A new energy-efficient design for quantum-based multiplier for nano-scale devices in internet of things

An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems.

World experience of legislative regulation for Lithium-ion electric vehicle batteries considering their second-life application in power sector

Understanding and incorporating global regulatory experiences and standards related to battery management is of greatest importance, particularly when considering the rapid evolution of the electric vehicle (EV) market and its implications for energy storage and sustainability. This is especially relevant for Ukraine, where the burgeoning secondary market for EVs and a keen interest in renewable energy sources underscore the need for proactive policy-making and standardization to address the challenges of battery second life and recycling. This article delves into the role of Electric Vehicle Lithium-Ion batteries within the ambit of the circular economy, underscoring the significance of legislative frameworks across the globe with a particular focus on European initiatives in light of Ukraine's EU integration ambitions. This encompasses extending battery life through recycling and repurposing, thereby ensuring both economic viability and minimal environmental footprint. The narrative outlines the varied legislative landscapes internationally, noting the differences in strategies from Asia's technological and safety emphasis to Europe's robust regulatory directives aimed at battery lifecycle management. In Europe, the drive towards sustainable battery utilization is marked by comprehensive policies like the EU Battery Directive and the emerging Regulation on Batteries and Waste Batteries, which set forth ambitious recycling targets and introduce innovative concepts like the battery passport. Drawing from this global overview, the article posits a set of recommendations for Ukraine, suggesting the development of extensive battery management legislation, adoption of European standards to smooth the path towards EU membership, investment in recycling infrastructures, fostering of public-private partnerships, and public awareness initiatives. These recommendations are designed to elevate Ukraine's position in the sustainability, promoting environmental stewardship and economic competitiveness. The growing importance of secondary lithium-ion batteries for electric vehicles in supporting and harmonizing renewable energy sources is emphasized, and accordingly, the need for adequate legislation and standardization to support a closed-loop economy. Keywords: Lithium-Ion Batteries, Second-Life Application, EV Battery Life Cycle, Circular Economy, Repurpose, Reuse, Recycling, Standards, Regulation, Legislation.

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

AbstractThe issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

Boehmite-PVDF-CTFE/F-PI composite separator irradiated by electron beam for high-rate lithium-ion batteries

As an important part of the fields such as renewable energy and electric vehicles, electrochemical energy storage technology faces significant challenges in improving energy density, extending battery life and improving safety. We successfully prepared a composite battery separator of polyvinylidene fluoride-chlorotrifluorinylene (PVDF-CTFE) and fluorinated polyimide (F-PI) -based materials, mixed it with different contents of boehmite nanoparticles, and then modified it by different doses of electron beam irradiation to improve the performance, especially to overcome the problem of maintaining capacity decline in high-speed charging environment. The results showed that the prepared battery separators exhibited outstanding thermal stability, electrochemical properties and ionic conductivity with 12 % boehmite nanoparticles. After 1000 high-speed charge and discharge cycles in current density of 10C, the battery based on the modified separator still maintained a capacity retention rate of 84.4 %, with the initial discharge specific capacity of 111.9 mAhg−1. This means that irradiated separators not only maintain a high energy density in a high-speed charging environment, but also overcome the problem of maintaining capacity decline. Electron beam irradiation further improved the comprehensive performance of the separator. After 1000 and 1500 cycles at the current density of 10C and 15C, the specific discharge capacity of the battery can still retain more than 90 % and 80 % of the initial specific discharge capacity, which provides a longer life in practical applications.

A Comparative Study on Electric Vehicle Battery Management Systems Using Machine Learning for Enhanced Safety and Longevity

This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.

Effective Strategies for EV Battery Monitoring and Maintenance

Abstract: The growing popularity of Electric Vehicles (EVs) has led to an increased demand for sophisticated battery management systems that guarantee safety and efficiency. In order to track and control the operation of electric vehicle batteries in real time, this project presents a novel pairing of an Internet of Things gadget with an Android application. This integrated solution gives customers greater safety and extends battery life by delivering critical insights and notifications regarding battery health, percentage, temperature, and essential events including full charge, low battery, overheating, and fire detection. The technology prioritizes user safety and battery longevity while optimizing the EV experience through user-friendly interfaces and timely notifications.

Design Of Electric Motorcycle Variable with Battery Management System

This study focuses on conceptualization and development of a battery management system (BMS) with two main functions, battery monitoring and management, in the context of brushless direct current motors (BLDCs). The main challenge in variable estimation is to protect the battery from potential risks during the charge and discharge cycle. The new proposed resolution combines a comprehensive BMS with monitoring capabilities for charge (SoC), health (SoH), voltage, current and battery temperature. In addition, a protective mechanism is incorporated to prevent variables from overshooting safety parameters. This research uses two different methodologies for estimating SOC, coulomb counting and open circuit voltage. In experimental tests, resistance potentiometers of 1,650, 3,300 and 0 were used, with SoC estimates of 37%, 19% and 65%, while coulomb counting method has a marginal error of 1.13%. On the contrary, the open-circuit voltage method generated a SoC estimate of 0% for all potentiometer resistance, with an error rate of 0.64 %. As a result, the open circuit voltage method is chosen because of its superior accuracy compared to the coulomb counting method. The state assessment of the battery showed a value of 100% after seven cycles. In addition, a protective system has been implemented to ensure that battery variables remain within the safe thresholds throughout the charge and discharge process. Consequently, the implementation of this BMS is expected to significantly improve overall performance and extend battery life.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

Review On Evolution and Development of Electric Road Roller

The evolution and development of electric road rollers mark a significant advancement in the construction machinery industry, driven by the imperative of sustainable infrastructure development. This paper provides a comprehensive overview of the historical progression, technological innovations, and current trends shaping electric road rollers. Beginning with the early developments of steam-powered and diesel-driven road rollers, the transition towards electric propulsion systems is traced, highlighting the environmental benefits and operational efficiencies offered by electric models. An electric-applied road roller vehicle represents a sustainable and innovative solution for the construction industry. This eco-friendly equipment utilizes electric power to propel the roller, reducing carbon emissions and noise pollution compared to traditional fossil fuel-powered counterparts. The vehicle's design incorporates advanced technology, such as gear chain mechanism to maximize energy efficiency and extend battery life. With its quiet operation and reduced environmental impact, the electric road roller vehicle demonstrates a promising step towards greener construction practices, contributing to a cleaner and more sustainable future. Top of Form Keywords— Electric Road Roller, Battery, High torque dc motor, propulsion system, gear assembly, etc.

Enhancing battery cell safety and lifespan through strategic application of insulating and phase change materials utilizing critical thickness for enhanced heat transfer

Efficient and secure operation of electric vehicles relies significantly on the cooling system for lithium iron phosphate (LiFePO4) batteries, a key component in electric vehicle technology. One of the critical challenges faced by electric vehicle is battery cooling to ensure optimal performance, extended battery life, and enhanced safety. The excessive heat generated during battery operation can lead to decrease in performance and potential safety hazards. Conventional cooling systems, such as air or liquid cooling, have limitations in terms of their cooling capacity, weight, and complexity. Therefore, there is a need to explore innovative cooling techniques that can effectively reduce the thermal issues associated with Electric Vehicles LiFePO4 batteries. Employing a thermal insulating coating and phase change material at the critical thickness emerges as an innovative approach to mitigate the surface temperature of battery cells. This is evident during the charging phase, where the bare cell, Teflon-insulated, and paraffin wax-coated cells reached respective peak temperatures of 69 °C, 57 °C, and 53.3 °C. Notably, the Teflon-coated cell exhibited a 17.39% reduction in peak temperature compared to the bare cell, while the paraffin wax-coated cell displayed a more substantial 23.18% reduction. A similar temperature reduction trend is observed during the discharging phase of the battery cell. Utilizing insulating materials or phase change materials with a critical thickness significantly lowers surface temperatures, enhancing the safety of the battery cell and ensuring prolonged life.

Design of Battery Charging System with CC-CV Method Using Interleaved Buck-Boost Converter

The transition of renewable energy has become an interesting issue and a worldwide concern that is frequently discussed. Solar panels are regarded to have the most potential in tropical areas like Indonesia. However, weather and sunlight intensity have a substantial impact on the amounts of electricity generated by solar panels. Therefore, a battery which functions as a backup supply is required. Lead acid batteries are used in numerous applications. The performance of lead-acid battery is commonly influenced by temperature and charging time, which makes it vulnerable to overcharging. Multistage charge methods, namely Constant Current-Constant Voltage (CC-CV), are used to extend battery life, reduce charging time, and avoid the risk of overcharging. Fuzzy Logic Controller (FLC) is used to adjust charging current and voltage on the battery based on the setpoint. Based on the CC-CV charging system simulation results, a constant current value can be obtained when the CC condition is 4.5 A, and a transition to the CV condition occurs when the voltage value on the battery reaches 14.4 V. When the battery reaches its maximum capacity, the current is reduced to 3% of the battery’s capacity. The rate of fully charged current triggers the relay to turn on, to ensure that the charging process has been completed.

Towards to Battery Digital Passport: Reviewing Regulations and Standards for Second-Life Batteries

Greenhouse gas emissions from transportation harm the environment. In response to these environmental concerns, numerous countries encourage the adoption of electric vehicles (EVs) as a more environmentally friendly option than traditional gasoline-powered vehicles. Advances in battery technology have made batteries an alternative solution for energy storage in stationary applications and for electric mobility. Reduced lithium-ion batteries (LIBs) production costs due to economies of scale, electrode material and cell design developments, and manufacturing process improvements have driven this success. This trend is expected to increase the number of LIBs on the market that may be discarded in the environment at the end of their useful life if more sustainable alternatives are not technologically mature. This coming environmental concern can be mitigated by collecting wasted EV batteries, reconfiguring them, and reusing them for applications with less stringent weight, performance, and size requirements. This method would extend battery life and reduce environmental effects. The present work investigates the main regulatory structures of the second-life battery industry that require rules, technical standards, and laws. To achieve this objective, a systematic review was carried out following a strict protocol that includes identifying relevant studies, extracting data and information, evaluating, and summarizing information. This paper explains the primary rules and technical standards governing the second-life battery business. The findings highlight the need for universities, research institutions, and government agencies to evaluate the second-life battery industry objectively. This would enable the creation of new technological regulations and laws for this burgeoning industry.

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

AbstractThe Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things (NB‐IoT) technology. The core objective is establishing a seamless data transmission pipeline that bridges these devices with the nearest base station. The associated data is routed directly from the edge computing infrastructure to the Cloud storage. The proposed architecture increases data security and optimises system performance by avoiding dependencies on central data centres. The critical components of this system allow the edge node to transmit data to the cloud, where primary data processing and analysis occur. The selection of NB‐IoT as the underlying protocol facilitates real‐time data collection and monitoring in healthcare with boosted capacity and coverage, extended battery life, and cost efficiency.

Comparative analysis of equivalent circuit battery models for electric vehicle battery management systems

Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. BMS controls parameters, such as current, voltage, temperature, state of charge (SoC),state of health (SoH), state of power (SoP) and etc. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. Therefore, the battery model is crucial to the BMS. This model is used to optimize the performance, capacity, lifetime and safety of the battery. Using the accurate battery model for BMS and electric vehicles can improve energy efficiency, extend battery life and reduce safety risks. Therefore, it is important that the model can accurately reflect the battery behavior under different load conditions. In this study, the performance of Rint, Partnership for a New Generation of Vehicles (PNGV), Thevenin, and Dual Polarization (DP) battery models, which are widely known in the literature, to simulate static and dynamic voltage behavior is compared. A 18650 NMC battery was used for this purpose, and Hybrid Pulse Power Characterization (HPPC), Dynamic Stress Test (DST), Worldwide Harmonised Light Vehicle Test Procedure (WLTP), and Constant Current (CC) discharge tests were performed. The performance of the models for the four tests is compared. The maximum error values for WLTP are 2.98 % in Rint, 1.32 % in PNGV, 2.80 % in Thevenin, and 1.09 % in DP. Comparing the performances of models for all tests, it is found that the DP model is the most accurate model under both constant and dynamic current conditions.

Improved rule-based power distribution algorithm for hybrid battery storage systems and real-world validation

Large-scale battery energy storage systems (BESS) can serve many applications and are already widely used for grid services. The rapidly growing BESS market and the recent interest in their deployment accentuate the need for safe, reliable, and highly available energy management systems (EMS) for automated control. However, the EMS and their integrated power distribution algorithms (PDA) can still be optimized to adapt various characteristics of the BESS. This study investigates a new version of a PDA with a particular focus on battery aging and system efficiency. The rule-based PDA has been validated on a 6 MW/7.5 MWh BESS system with five battery technologies providing frequency containment reserve to the German power grid. The results underline the PDA's capability to exploit the individual strengths of each battery technology. The PDA sets objectives for the state of charge, energy throughput, and power of the batteries to extend battery life. The distribution of energy throughputs among batteries can be selected in advance through the new implementation of the PDA. At the same time, the inverters are significantly less often activated and used in the optimal efficiency range, increasing the overall system efficiency to approximately 82 %. The optimized switching behavior leads to less frequent power switching between individual battery units and longer phases with more constant power. In addition, the operational efficiency of BESS can be improved by the choice of battery technology and the overall system layout on the hardware side. The improvements on the software side are only possible by increasing the overall power requests through multi-use operation by about 6 % compared to our benchmark test. The results can be used by BESS operators to increase operational profits due to longer battery life and fewer efficiency losses.

Exploring challenges and potential for a commercially viable piezoelectric energy harvesting system—Can Energy-as-Data concept thrive?

Over the past two decades, piezoelectric energy harvesting systems have undergone extensive research, spanning from materials to devices and electronics. Recently, there has been a notable introduction of the term “self-powered sensors,” which essentially refers to conventional, older passive sensors, associated with piezoelectric (or triboelectric) nanogenerators. Unfortunately, neither approach has yet yielded a successful market example. The primary challenge seems to lie in the intermittent nature of ambient kinetic energy input to these harvesters, despite the high output power density of piezoelectric energy harvesters. This article emphasizes the concept of Energy-as-Data, holding promise for a potentially brighter future for commercializable piezoelectric energy harvesting systems. The key advantage offered by this concept is the substantial extension of battery life through the optimization of data acquisition protocols. This involves transitioning from the traditional passive sensing mode with a high sampling rate to an extremely low duty cycle. This transition allows the harvested energy, accumulated in an energy storage unit over a specific interval, to be translated into critical information regarding kinetic environmental changes during that period. The article also discusses the broader context that necessitates the Energy-as-Data concept, establishing it as one of the few viable pathways to cultivate specialized markets for piezoelectric energy harvesting.

State-of-Health Estimation of Li-Ion Batteries Using Semiparametric Adaptive Transfer Learning

For lithium-ion batteries (LIBs), precise state of health (SoH) estimation can provide direction for reasonable use, minimize battery failure rates, and extend battery life. Data-driven methods are promising for SoH estimation since they work effectively without requiring human interaction and have great nonlinear prediction capabilities. The majority of studies also found that the training data was sufficient. However, in real-world contexts, data collection is frequently time-consuming and expensive. This paper suggests using transfer learning to make the model less dependent on data for tracking battery state of health (SoH). Many approaches assume training and testing data have the same distribution. Due to distribution mismatch, a model that works for one data set may not for another. We propose an adaptive transfer learning technique relying on Gaussian Processes regression (AT-GPR) in this research. Automatically evaluating source-to-target task similarity can construct learning systems. Bayesian semi-parametric transfer kernel proposed to learn target task model. Battery cyclic aging data from two publicly available data sets in various working environments are considered to validate the suggested method. The experiments show that AT-GPR generates reliable prediction results; however, only 20% of the overall data set is made up of training data.