Battery-powered Systems Research Articles

Short-Circuit Detection in Lithium-Ion Batteries Using Machine Learning: Analysis and Comparison with Physics-Based Method

Early detection of short circuits in battery-powered systems is critical in preventing potential catastrophic failures. However, nascent short-circuit signatures are extremely weak and challenging to detect using existing algorithms without compromising on prediction accuracy. Traditional physics-based approaches rely on hand-crafted models to establish relationships between battery operating parameters and short resistance, which limits their ability to capture all relevant details, resulting in sub-optimal accuracies. In this study, we present a machine learning-based approach that leverages rest period voltage data to detect short circuits. Our method employs a 1D convolutional neural network (CNN) classifier/estimator that extracts temporal dynamic features relevant to the short circuit prediction problem from both the long and short tails of the rest period voltage profile. The approach is validated using commercial battery data, generated at different conditions including temperatures, and short circuits of varying severities; with prediction accuracies greater than 90% even for soft shorts of 500Ω. The key performance parameters of the 1D CNN model are compared against a physics-based short detection approach, demonstrating its superior performance and cost-effectiveness. Overall, our work represents a significant advancement in the field of short circuit detection in battery-powered systems, offering improved accuracy, efficiency, and cost-effectiveness.

TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages for feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable capacity estimation approach utilising short-duration random charging voltages during the constant-current charging stage and leveraging convolutional neural networks (CNNs). Based on the user-friendly “Vstart−tend” strategy, two health features including charging voltage and its increment are firstly extracted from random charging segments. Secondly, a feature evolution pattern analysis over the battery's lifespan is proposed to divide the charging voltage range for robust model development. An optimal combination of both the sampling interval and data length is determined for the feature extraction. Then, a two-dimensional CNN model is developed to effectively learn ageing-related knowledge from various random charging segments in a specific charging voltage range. The effectiveness of the proposed approach is ultimately verified using two distinct types of batteries across three operational temperatures. The results demonstrate that the proposed approach show much potential as a promising capacity estimation technique utilising a 600 s random charging segment sampled at a 20 s interval.

Battery Monitoring and Protective System

Abstract: The growing demand for battery-powered devices and systems has led to an increased need for battery monitoring and protective systems. These systems play a critical role in ensuring the safe and reliable operation of battery-powered systems, such as electric vehicles, renewable energy systems, and mobile devices. This work emphasizes to monitor the state of charge (SOC) of the battery to ensure optimal performance and prevent premature failure. The battery is protected from damage due to overcharging, over-discharging, overheating, and short-circuiting. It also provides early warning of potential battery issues, such as low SOC or high temperature, so that corrective action can be taken to prevent damage. The charging and discharging of the battery is optimized to maximize its lifespan and efficiency. The proposed model facilitates remote monitoring and control of the battery using Arduino and BLYNK App accompanied by predictive maintenance and reduced downtime.

Artificial intelligence-powered visual internet of things in smart cities: A comprehensive review

The field of smart cities has seen significant advancements in recent years to improve citizens' quality of life. Technologies such as the Internet of Things (IoT) and Edge Computing (EC), along with Artificial Intelligence (AI), are being utilized to achieve this goal. This study focuses on a specific branch of IoT known as Visual IoT, which uses digital cameras as sensors and relies on visual data. Advances in AI have enabled researchers to integrate AI models into camera-based edge devices, increasing the use of AI-powered Visual IoT systems in smart cities. However, since the energy consumption in battery-powered systems is naturally a concern, being deployed outdoors for visual data gathering with the integration of AI-based processing raises a significant challenge. This paper examines AI-powered Visual IoT systems in smart cities with a special emphasis on energy efficiency. Our goal is not only to evaluate how AI is used in Visual IoT systems in the context of smart cities but also to evaluate the level of consideration given to the energy efficiency aspect in the reviewed studies. Furthermore, we explore all of the methods used to address it. Through our work, readers will gain insights into the current landscape of Visual IoT in smart cities and an understanding of how much importance is placed on energy consumption in AI-integrated solutions.

A proactive energy management strategy for battery-powered autonomous systems

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies.

A self-attention knowledge domain adaptation network for commercial lithium-ion batteries state-of-health estimation under shallow cycles

Accurate state-of-health (SOH) estimation is crucial for the safety, efficiency, and reliability of battery-powered systems. Conventional SOH estimation methods are designed for the full state-of-charge (SOC) range of 0%–100%, assuming uniform distributions. Yet, batteries in real-world applications often operate under partial SOC ranges and experience shallow-cycle conditions, which result in distinct and unlabeled degradation profiles that make SOH estimation more complex. To tackle this issue, we propose a novel method—self-attention knowledge domain adaptation network (SKDAN)—which utilizes a self-attention distillation module combined with a multi-kernel maximum mean discrepancy framework to effectively navigate between these disparate domains. This strategy successfully extracts domain-specific features from charging curves, facilitating the transfer of knowledge from well-labeled full cycles to unlabeled shallow cycles. The effectiveness of the SKDAN method is validated through extensive testing on the CALCE and SNL battery datasets, demonstrating its robust capability to estimate SOH accurately across different SOC ranges, temperatures, and discharge rates. With a root-mean-square error (RMSE) of less than 2%, the SKDAN method outperforms existing transfer learning techniques for various SOC ranges. Additionally, it exhibits exceptional SOH estimation performance when applied to batteries from different manufacturers and operating under varied conditions. This study represents the first instance of accurately estimating battery SOH under shallow-cycle conditions without relying on full-cycle characteristic tests.

The future of tire energy: a novel one-end cap structure for sustainable energy harvesting

Piezoelectric energy harvesting is gaining popularity as an eco-friendly solution to harvest energy from tire deformation for tire condition monitoring systems in vehicles. Traditional piezoelectric harvesters, such as cymbal and bridge structures, cannot be used inside tires due to their design limitations. The wider adoption of renewable energy sources into the energy system is increasing rapidly, reflecting a global attraction toward the utilization of sustainable power sources (Aljendy et al. in Int J Power Energy Convers 12(4): 314–337, 2021; Yesner et al. in Evaluation of a novel piezoelectric bridge transducer. In: 2017 Joint IEEE International Symposium on the Applications of Ferroelectric (ISAF)/International Workshop on Acoustic Transduction Materials and Devices (IWATMD)/Piezoresponse Force Microscopy (PFM). IEEE, 2017). The growing interest in capturing energy from tire deformation for Tire Pressure Monitoring Systems (TPMS) aligns with this trend, providing a promising and self-sustaining alternative to traditional battery-powered systems. This study presents a novel one-end cap tire strain piezoelectric energy harvester (TSPEH) that can be used efficiently and reliably inside a tire. The interaction between the tire and energy harvester was analyzed using a decoupled modeling approach, which showed that stress concentration occurred along the edge of the end cap. The TSPEH generated a maximum voltage of 768 V under 2 MPa of load, resulting in an energy output of 32.645 J/rev under 1 MPa. The computational findings of this study were consistent with previous experimental investigations, confirming the reliability of the numerical simulations. The results suggest that the one-end cap structure can be an effective energy harvester inside vehicle tires, providing a valuable solution for utilizing one-end cap structures in high-deformation environments such as vehicle tires.

Review on li-ion battery model used in electrical vehicle

In recent years, battery models have become increasingly crucial, particularly with the surge in electric vehicle usage within the transportation sector. Accurate models are essential for assessing battery performance and devising effective battery management systems. Various modeling approaches exist in literature, each carrying its own set of advantages and drawbacks. Typically, more intricate models offer precise results but demand greater computational resources and entail time-consuming, costly laboratory tests for parameter determination. Hence, for first stage assessments and battery managing system designs, models employing simpler parameter documentation procedures are the most viable and suitable options. The presence of a precise battery model within a simulation platform holds immense significance in crafting efficient battery-powered systems. Absolutely! The lithium-ion battery model is a standout option owing to its potential as an energy storage system, particularly for renewable technology applications. This is largely due to its remarkable power compactness and energy storage capabilities.

Flight-Validated Electric Powertrain Efficiency Models for Small UASs

Minimizing electric losses is critical to the success of battery-powered small unmanned aerial systems (SUASs) that weigh less than 25 kgf (55 lb). Losses increase energy and battery weight requirements which hinder the vehicle’s range and endurance. However, engineers do not have appropriate models to estimate the losses of a motor, motor controller, or battery. The aerospace literature often assumes an ideal electrical efficiency or describes modeling approaches that are more suitable for controls engineers. The electrical literature describes detailed design tools that target the motor designer. We developed SUAS powertrain models targeted for vehicle designers and systems engineers. The analytical models predict each component’s losses using high-level specifications readily published in SUAS component datasheets. We validated the models against parametric experimental studies involving novel powertrain flight data from a specially instrumented quadcopter. Given propeller torque and speed, our integrated models predicted a quadcopter’s battery voltage within 5% of experimental data for a 5+ min mission despite motor and controller efficiency errors up to 10%. The models can reduce development costs and timelines for different stakeholders. Users can evaluate notional or existing powertrain configurations over entire missions without testing any physical hardware.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management, contributing to the reliability and longevity of battery-powered systems. However, complex operating conditions, coupling cell-to-cell inconsistency, and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation. To address these issues, this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data. Unlike traditional data-driven methods that lack interpretability, the hierarchical data-driven framework unveils the “mechanism” of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets. A generalized feature matrix is devised without requiring all cell voltages, significantly reducing the computational cost and memory resources. The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models, effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation. Using only 10% of degradation data, the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods, achieving mean absolute percentage errors of 0.608%, 0.601%, and 1.128% for three battery packs whose degradation load profiles represent real-world operating conditions. Its high accuracy, adaptability, and robustness indicate the potential in different application scenarios, which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Feasible Power Control Method with a Low Sampling Frequency for Bidirectional Wireless Power Transfer in Battery-Powered Railway Vehicle Systems

Feasible Power Control Method with a Low Sampling Frequency for Bidirectional Wireless Power Transfer in Battery-Powered Railway Vehicle Systems

Estimating Electrical Energy and Capacity Demand for Regional Electric Flight Operations at Two Mid-Size Airports in Washington, U.S

Advances in battery-powered electric motor systems, lightweight materials, and aircraft design have resulted in the development of new battery-electric aircraft that could gradually replace conventional fuel-powered aircraft for certain use cases in the coming years. In the face of tight climate action goals and large airport hubs facing capacity constraints, electric aircraft at regional airports could help respond to increased regional travel demands. Charging these aircraft may be a significant new load on the electric grid serving airports. In this paper, to understand these load impacts, we develop a framework for estimating future energy (annual MWh) and power (average and peak MW) demand for charging battery-electric aircraft at regional airports. We apply our modeling framework to two mid-size case study airports in Washington, U.S: Paine Field/Snohomish County Airport and Grant County International Airport. Our method has three parts: assumptions on flight operations growth, technical feasibility to serve these flights with electric aircraft, and actual adoption of electric aircraft to serve feasible trips. The results reveal that, while electricity demand could rise substantially over time, during the first decade of adoption utility companies are expected to be able to serve the energy and power needs of electric aviation with available capacity at existing substations close to the airports in our case studies.

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

The use of Lithium-Ion Batteries (LIBs) have increased in recent years in many applications such as hybrid electrical vehicles (HEV), consumer electronic equipment, and electricity grid. The batteries undergo degradation during usage due to material aging and electrochemical processes, leading to efficiency reduction of battery-powered systems as well as catastrophic events. Several stress factors such as battery temperature, ambient temperature, and C-rate in the loading profiles influence the degradation. Therefore, predicting the health of the battery has gained attention. The service life can be extended or a system failure can be avoided by maintenance measures precisely matched to the function loss or by changing usage strategies. The State-of Health (SoH) condition of the battery can be determined by the application of lifetime models. Various health indicators such as remaining useful lifetime (RuL) and capacity fade are determined by the models based on the stress factors (utilization variables). For optimal use of the battery, it is helpful to develop an accurate lifetime model to represent the dynamic properties. However, models developed are less computationally efficient and unable to represent the non-linear degradation behavior well. The development of a precise model with correct parameterization is also costly. This is particularly true for models developed based on physical and chemical properties of the battery. In this contribution, an artificial neural network (ANN)-based state machine approach is introduced for capacity fade estimation. The degradation process is represented using three states modeling three different levels and the progression from the first state to the last. Capacity associated with each state is described using the non-linear auto regressive neural network with external input (NARX). The NARX is selected due to its ability to accurately model non linear behavior and time series data. Unlike known models, which are developed using analytical mathematical equations related to the battery properties, a combined machine learning approach is used here instead to learn the capacity behavior from historical data. Battery data sets from NASA are used for experimental verification. Based on the results, the estimated capacity fade show close proximity to actual capacity fade, with a low mean square error for different data sets. In addition, the estimated state progression follows the actual state progression.

Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method

Accurate capacity estimation is essential in the management of lithium-ion batteries, as it guarantees the safety and dependability of battery-powered systems. However, direct measurement of battery capacity is challenging due to the unpredictable working conditions and intricate electrochemical characteristics, which complicates the identification of battery degradation. In this work, through in-depth analysis of battery aging data, an incremental slope (IS) aided feature extraction method is proposed to obtain universal multidimensional features that adapt to different working conditions. With the extracted features, a simple multilayer perceptron (MLP) is used to achieve high-precision capacity estimation. Furthermore, a feature matching based transfer learning (FM-TL) method is proposed to automatically adapt the capacity estimation across different types of batteries that are cycled under various working conditions. 158 batteries covering five material types and 15 working conditions are used to validate the proposed method. Results suggest that the MLP model can provide an accurate capacity estimation, where the overall mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are limited to 1.22% and 1.61%, respectively. Furthermore, compared with the traditional fine-tuning method, the overall MAPE and RMSPE under various transfer learning application scenarios respectively decrease by up to 78.23% and 75.31%, indicating that the FM-TL method is promising to construct a reliable transfer learning path, which improves the accuracy and reliability of capacity estimation when applied to various target domains.

A wireless, solar-powered, optoelectronic system for spatial restriction-free long-term optogenetic neuromodulations.

Numerous wireless optogenetic systems have been reported for practical tether-free optogenetics in freely moving animals. However, most devices rely on battery-powered or coil-powered systems requiring periodic battery replacement or bulky, high-cost charging equipment with delicate antenna design. This leads to spatiotemporal constraints, such as limited experimental duration due to battery life or animals' restricted movement within specific areas to maintain wireless power transmission. In this study, we present a wireless, solar-powered, flexible optoelectronic device for neuromodulation of the complete freely behaving subject. This device provides chronic operation without battery replacement or other external settings including impedance matching technique and radio frequency generators. Our device uses high-efficiency, thin InGaP/GaAs tandem flexible photovoltaics to harvest energy from various light sources, which powers Bluetooth system to facilitate long-term, on-demand use. Observation of sustained locomotion behaviors for a month in mice via secondary motor cortex area stimulation demonstrates the notable capabilities of our device, highlighting its potential for space-free neuromodulating applications.

Technical and Ecological Limits of 2.45-GHz Wireless Power Transfer for Battery-Less Sensors

Wireless power transfer (WPT) is a possible alternative to batteries for supplying smart sensors. It is often described as “greener" because it avoids the use of batteries. However, this claim usually lacks the proper assessment of the environmental impacts of WPT and is particularly questionable given the poor efficiency of wirelessly transmitting power over a few meters of distance. In this paper, we design and thoroughly optimize a complete state-of-the-art WPT system and compare it to an equivalent battery-powered solution to assess whether it effectively represents an eco-friendly alternative. The proposed WPT system is a 2.45-GHz simultaneous wireless information and power transfer (SWIPT) system with beamforming and a high peak-to-average power ratio waveform for supplying a battery-less passive infrared sensor for room occupancy tracking. The final prototype of the smart sensor consumes 4and can operate in steady state at 5from the remote power head with an overall power transfer efficiency of 17/and can cold-start at 3.5. We carry out a life-cycle assessment (LCA) of the environmental impacts through four indicators, i.e., primary energy demand, global warming potential, terrestrial ecotoxicity, and freshwater consumption. Results show that for a 10-year lifetime, the WPT alternative has at least 5.5 to 10.3× higher environmental impacts than its battery-powered equivalent. This demonstrates the importance of LCA during the design of IoT smart sensors and shows that the use of meter-range 2.45¯/GHz SWIPT should be limited to applications where conventional battery-powered systems cannot be used.

Estimation of State of Charge of Battery Used In Electric Vehicles With Wireless Battery Management System

This research paper presents a comprehensive investigation into the development and analysis of a wireless battery management system (BMS) using MATLAB Simulink. The primary objective of this study is to create an efficient, reliable, and scalable BMS that caters to the demands of various applications, such as electric vehicles, grid energy storage, and portable electronics. Our methodology involves designing and simulating key BMS components, including state estimation algorithms, fault detection mechanisms, and communication protocols, within the MATLAB Simulink environment. The paper first elucidates the motivation for adopting wireless technology in BMS, emphasizing its advantages over traditional wired systems. Subsequently, we explore the intricacies of the proposed wireless BMS architecture, detailing the implementation of essential features such as state-of-charge estimation, fault diagnosis, and thermal management. We also address the challenges associated with wireless communication, including latency, security, and energy efficiency, by incorporating robust communication protocols and power management strategies. Through rigorous simulations, we demonstrate the efficacy of the proposed wireless BMS, showcasing its ability to ensure optimal performance, safety, and longevity of battery packs. The outcomes of this research not only contribute to the advancement of BMS technology but also pave the way for further improvements in battery-powered systems. In conclusion, this paper offers a holistic perspective on wireless BMS design, emphasizing its potential to revolutionize energy management and extend the applications of battery technology in various domains.

Battery SOC estimation from EIS data based on machine learning and equivalent circuit model

Estimating the state of charge (SOC) of batteries is fundamental for the proper management and safe operation of numerous systems, including electric vehicles, smart energy grids, and portable electronics. While there is no practical method for direct measurement of SOC, several estimation approaches have been developed, including a growing number of machine-learning-based techniques. Machine learning methods are intrinsically data-driven but can also benefit from a-priori knowledge embedded in a model. In this work, we first demonstrate, through exploratory data analysis, that it is possible to discriminate between different SOC from electrochemical impedance spectroscopy (EIS) measurements. Then we propose a SOC estimation approach based on EIS and an equivalent circuit model to provide a compact way to describe the frequency domain and time-domain behavior of the impedance of a battery. We experimentally validated this approach by applying it to a dataset consisting of EIS measurements performed on four lithium-ion cylindrical cells at different SOC values. The proposed approach allows for very efficient model training and produces a low-dimensional SOC classification model that achieves above 93% accuracy. The resulting low-dimensional classification model is suitable for embedding into battery-powered systems and for online SOC estimation.

Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping

The widespread penetration of electric bicycles (E-bicycles) raises numerous charging safety concerns. However, online diagnosis of charging safety for E-bicycles remains challenging due to the limited data and involvement of multiple factors, such as battery, charger, charging mode, and user behavior. To overcome this difficulty and promote charging safety, this article proposes a nonintrusive charging safety intelligent diagnosis scheme on the inputted power grid side. First, more than 150 000 charging records are collected from the grid side, and various charging current patterns are formally identified according to the working principles of different batteries, charging modes, and user behaviors. Then, on the basis of longest similar substring (LSS), an improved dynamic time warping (DTW) model, referred to as LSS-DTW, is established to efficiently identify the charging current profile similarities and meanwhile restrict the overregularization of DTW. By this manner, the abnormal charging processes can be accurately identified. Experimental results reveal that the built LSS-DTW model can distinguish the unsafe charging processes online, and achieve the average identification precision, recall, and F1-score of 94%. Furthermore, the proposed algorithm can be extended to similar charging safety identifications in electric vehicles and other battery-powered systems and provides early warnings to avoid catastrophic consequences.