Battery Life Research Articles

IQL‐OCDA: An intelligent Q‐learning‐based for optimal clustering and data‐aggregation for wireless sensor networks

AbstractWireless sensor networks (WSNs) can suffer from low battery life due to the energy consumption of the routing protocol. Small sensor nodes are often difficult to recharge after deployment. In a WSN, data aggregation is generally used to reduce or eliminate data redundancy between nodes in order to save energy. In the proposed algorithm, sensor nodes are deployed in appropriate clusters and cluster heads are elected using Q‐learning techniques. Nodes are clustered based on the mean values computed during the clustering phase. Lastly, a performance evaluation and comparison of existing clustering algorithms are performed based on Intelligent Q‐learning. The proposed IQL‐OCDA model reduces end‐to‐end delay by 10.11%, increases throughput by 4.15%, and increases network lifetime by 5.1%.

Comparison of 6 handheld ultrasound devices by point-of-care ultrasound experts: a cross-sectional study

BackgroundPoint-of-care ultrasound (POCUS) has emerged as an essential bedside tool for clinicians, but lack of access to ultrasound equipment has been a top barrier to POCUS use. Recently, several handheld ultrasound devices (“handhelds”) have become available, and clinicians are seeking data to guide purchasing decisions. Few comparative studies of different handhelds have been done. We conducted a cross-sectional study comparing 6 handhelds readily available in the United States (Butterfly iQ + ™ by Butterfly Network Inc.; Clarius™ by Clarius Mobile Health; Kosmos™ by EchoNous; TE Air™ by Mindray; Vscan Air™ SL and CL by General Electric; and Lumify™ by Philips Healthcare). A multi-specialty group of physician POCUS experts (n = 35) acquired three standard ultrasound views (abdominal right upper quadrant, cardiac apical 4-chamber, and superficial neck and lung views) in random order on the same standardized patients and rated the image quality. Afterward, a final survey of the overall ease of use, image quality, and satisfaction of each handheld was completed.ResultsThirty-five POCUS experts specializing in internal medicine/hospital medicine, critical care, emergency medicine, and nephrology acquired and rated right upper quadrant, apical 4-chamber, and superficial neck and lung views with 6 different handhelds. For image quality, the highest-rated handhelds were Vscan Air™ for the right upper quadrant view, Mindray TE Air™ for the cardiac apical 4-chamber view, and Lumify™ for superficial views of the neck and lung. Overall satisfaction with image quality was highest with Vscan Air™, Lumify™, and Mindray, while overall satisfaction with ease of use was highest with Vscan Air™. The 5 most desirable characteristics of handhelds were image quality, ease of use, portability, probe size, and battery life. Ultimately, all 6 handhelds had notable advantages and disadvantages, with no single device having all desired qualities or features.ConclusionsThe overall satisfaction with image quality was rated highest with Vscan Air™, Lumify™, and Mindray TE Air™when acquiring right upper quadrant, apical 4-chamber, and superficial neck and lung views. No single handheld was perceived to be superior in image quality for all views. Vscan Air™ was rated highest for overall ease of use and was the most preferred handheld for purchase by POCUS experts.

Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

Patient Experience and Feasibility of a Remote Monitoring System in Parkinson's Disease.

Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long-term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking. The aim was to gain insight into user experience and long-term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data. We analyzed the data of the "Bringing Parkinson Care Back Home" study, a 1-year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1-year compliance. We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1-year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less-severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1-year compliance. The 1-year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.

Enhancing electric car lithium-ion batteries by using adaptive materials to improve heat transfer and reduce fatigue

The widespread adoption of electric vehicles (EVs) hinges on the efficiency and reliability of their battery systems. However, EV batteries still have many issues, mostly in terms of quantity, storage efficacy, charging speed, lifespan, and security, particularly, temperature variations, which can cause accidents that can lead to losses of products and persons. Hence, any advancement in the field of EV batteries would improve their performance and thus, boost daily usage of EVs that would reduce the pollution caused by traditional cars. Therefore, the main objective of this study focuses on improving the thermal and mechanical stability, energy storage efficiency, and safety of electric vehicle batteries by incorporating multifunctional materials, particularly phase-change materials (PCM) and shape memory alloys (SMA). By incorporating PCM, the thermal regulation of batteries is enhanced, resulting in better charge-discharge cycles, extended battery life, and reduced overheating risks, all of which contribute to increased safety. SMA integration, on the other hand, offers mechanical protection, flexibility in heat management, and resilience against thermomechanical fatigue, further improving battery longevity. Moreover, the incorporation of these adaptive materials not only boosts battery performance but also ensures that the materials can be seamlessly integrated without affecting the overall design or significantly increasing the weight of the battery. Thus, a systematic numerical study was conducted in order to adapt the multifunctional materials with each other and with EV battery systems. The study demonstrated a significant improvement in battery thermomechanical stability, leading to better storage performance, quality, and charging speed. Particularly, the findings showed a remarkable increase around 23: % in thermal stability duration, preventing excessive temperature variations, and an important reduction in mechanical vibrations around 79 %, which minimizes fatigue and enhances durability. Finally, the results revealed that the proposed adaptable materials are the most stable for car batteries even during fast charging while driving. These materials can also support alternative ways of charging, such as using solar sources.

Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

Accurate prediction of the remaining useful life (RUL) is crucial for the design and management of lithium-ion batteries. Although various machine learning models offer promising predictions, one critical but often overlooked challenge is their demand for considerable run-to-failure data for training. Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years. Here, we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels. Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels. The approach is validated using three datasets collected from 34 batteries operating under various conditions, encompassing over 19,900 charge and discharge cycles. Our method achieves a root mean squared error (RMSE) within 25 cycles, even when only 1/50 of the training dataset is labelled, representing a reduction of 48% compared to the conventional approach. We also demonstrate the method’s robustness with varying numbers of labelled data and different weights assigned to the three decoder heads. The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data. Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

Доклинические испытания отечественных устройств, применяемых при внегоспитальной остановке кровообращения

Summary. The aim of the study was to evaluate the effectiveness of domestic devices for supporting blood circulation in case of out-of-hospital circulatory arrest during preclinical trials. Research materials and methods. The study assessed the effectiveness of the Russian cardiac massager “CardioRobot” – an automated device for performing indirect cardiac massage of domestic design. This device has the following technical characteristics: piston type design; compression depth – (50-60±2) mm; compression frequency – (102±2) compressions/min; 2 operating modes – continuous and 30:2; weight – 6.5 kg; continuous battery life – 45 min; trapezoidal compression curve. To evaluate the device, a comparative experiment was conducted on large mammals (pigs), during which the work of the domestic prototype and the foreign device “LUCAS-2” was compared by comparing the indicators of central hemodynamics and gas exchange. During the clinical trials of LifeStream ECMO, postmortem extracorporeal membrane oxygenation (ECMO) of two donors was performed. During the experiment, the following were assessed: pO2, pH, pCO2, arterial blood lactate and creatinine concentrations. Research results and their analysis. During preclinical trials, domestically produced devices demonstrated experimental effectiveness comparable to similar foreign-made products. Currently, the LIFESTREAM ECMO perfusion emergency circulatory restoration complex for human resuscitation has received a certificate and is actively used in the Russian Federation, and the CardioRobot automated chest compression device is undergoing state registration.

Design and implementation of a multifunctional desktop robot for computer peripherals

Abstract As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.

Research Progress of Battery Thermal Management Systems with Minichannels

With the developing technology, energy storage systems, especially lithium-ion batteries (LiBs), play a critical role in electric vehicles and renewable energy applications. However, the performance and life of batteries are significantly affected by their operating temperatures. In this context, battery thermal management systems (BTMS) are of vital importance to ensure temperature control of batteries and to create a safe operating environment. BTMSs are divided into main two groups active and passive which require and does not require extra energy consumption, respectively. In this review, the basic principles, design criteria and application areas of battery thermal management systems are examined. First of all, the components of BTMS, passive and active cooling methods, heat dissipation and temperature monitoring techniques are detailed. In addition, the effects of different BTMS approaches on efficiency and performance are compared. The analysis of existing studies in the literature reveals the positive contributions of BTMS on battery life, charge-discharge efficiency and safety. In addition, future research areas and development opportunities are also highlighted. In conclusion, an effective thermal management system is a critical factor in the development of battery technology and has great potential in terms of sustainability of energy systems.

OPTIMIZATION OF AN INTELLIGENT CONTROLLED BRIDGELESS POSITIVE LUO CONVERTER FOR LOW-CAPACITY ELECTRIC VEHICLES

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

The Embedded System of Battery Charging with Constant Current System using Microcontroller and IC LM338

The battery has the function of an electrical energy storage system. However, charging the battery is prone to damage. Damage to the battery is often caused by overcharging. The time used during the battery charging process is an important factor that must be considered when charging. thus, efficient battery charging is important and very necessary to avoid damage. An another important aspect of a battery charging method is the current and voltage values supplied during its charging process. The presence of inappropriate values damages and reduce battery life, while the maintenance of a constant current is an important charging process step that extends service life. This research is expected to create a safe and optimal battery charging system with a simple low-cost circuit based on constant current by utilizing the LM388 Integrated Circuit (IC) regulator. This study discusses a 12 V battery charging system with monitored and controlled constant current using an R-Shunt sensor. Voltage monitoring while charging was carried out with voltage sensors using a constant current of 0.1 to 1.3 Ampere. Furthermore, voltages and currents were displayed through a Liquid Crystal Display (LCD) screen controlled with Arduino Uno during battery charging. The measurement results showed that the circuit reached voltages and currents of 14.07 V and 0.19 A respectively for a charge duration of 8 hours.

Development of a Non-Linear Battery Management System Model for Lithium-Ion Batteries Using MATLAB Simulation

Abstract: This research focuses on developing a comprehensive battery model tailored for Battery Management Systems (BMS) using lithium-ion batteries. Given the increasing reliance on lithium-ion batteries in electric vehicles and portable electronic devices, this study aims to construct a non-linear battery model that accurately reflects the charging and discharging characteristics under various load conditions. The research identifies suitable characteristics of lithium-ion batteries essential for effective battery model construction. A MATLAB-Simulink approach is employed to build the model, which integrates both charging and discharging processes into a single simulation framework. This merged model enhances the existing non-linear models by offering improved accuracy in simulating battery behavior under fixed parameters. The outcome is expected to provide a reference model for future studies and applications in power management systems. Additionally, this research underscores the critical role of BMS in extending battery life and ensuring safety, making it a significant contribution to the field of energy management. The study's findings highlight the potential of the proposed model to serve as a foundation for further research and development in battery technologies.

Self-consuming solar-powered campus monitoring drone

Drones are utilised for a variety of purposes and are a regular sight these days. Selfies, insecticide application, and military monitoring. The issue with surveillance and monitoring is that a lot of applications call for continuous surveillance. Although they have a major disadvantage, drones do offer a good view for surveillance and monitoring. This is the battery life of the drone. The main worry a drone operator has when operating a surveillance drone is that the battery might die and the drone might crash into a building, tree, or other unreachable place from which it cannot be recovered and hence cannot be charged. This is also true for drones used in military surveillance; the risk of a drone's battery dying and becoming unusable places restrictions on drone operators while they are conducting surveillance or monitoring. Here, we've developed a drone that solves these issues by using solar power to continuously charge the device, extending its flying time. It can also land anywhere and remotely recharge its battery so it can take off later. The development and deployment of a solar-powered, self-charging campus surveillance drone are suggested in this research. By offering continual monitoring capabilities without requiring manual charge, the drone seeks to answer the demand for increased security measures on campus. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. This maximizes security coverage while minimizing downtime and guarantees continuous operation. Drone capabilities are further enhanced by the incorporation of cutting-edge surveillance technology, which enable detection of threats and constant surveillance. With possible applications in educational institutions, corporate campuses, and large-scale facilities, the suggested method provides an effective and long-lasting answer to campus security concerns.

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

DFT investigation on effectiveness of Beryllium-doped and defective graphene for anode material in lithium-ion batteries

Lithium-ion batteries employing graphite as the anode material are now widely used in powering electronic gadgets like, mobile phones, laptops, electronic watches and calculators and also to provide required power to run engine of electric vehicles. However, storage capacity, open circuit voltage, energy density and battery life are the major considerations on which researchers are trying different alternatives to achieve better performance of the battery. In this research, Beryllium-doped and defective graphenes have been investigated by employing the ab initio DFT method. It has been found that graphene with a defect and Be–doped graphene with defects can serve as an efficient materials for anode in Li-ion batteries.

Influence of straight and inclined baffles on enhancement of battery thermal management system performance

Battery thermal management systems (BTMSs) are used in electric vehicles (EVs) to regulate the heat generated by batteries while in use. Existing research on the improvement of BTMSs revealed that more research can be done by exploring different strategies to extend the service life and capabilities of EV batteries beyond the current limitations. In this study, effects of orientation of baffles; straight (90°), inclined (60° and 30°), height of baffles, thickness of baffles, positioning of baffles and number of baffles on the performance of conventional Z – Type BTMS were studied using Computational Fluid Dynamics (CFD) approach. The CFD approach was validated with existing experimental result from literature. Findings from the study showed that for straight baffle, the maximum temperature (Tmax) reduces as the height of baffles decreases. Furthermore, increasing the baffle thickness from 1 mm to 2 mm, produced reduction in Tmax by 0.26 K. For inclined baffles, by comparing the BTMS without baffles and BTMS with 2 baffles, Tmax and maximum temperature difference (ΔTmax) reduced by 0.82 K and 0.68 K, respectively at angle 60°, and reduced by 1.65 K and 1.43 K, respectively at angle 30°. The BTMS with 2 baffles, inclined at angle 60°, with height of 6 mm and thickness of 1 mm, yielded the optimum Tmax (Lowest) value of 333.15 K, a reduction by 2.65 K when compared to the conventional Z – Type BTMS. This was also accompanied with a slight increase in ΔP by 1.12 Pa. In conclusion, it can be said that findings from this study will be beneficial in enhancing the design of BTMSs through adequate selection and utilization of baffles orientation.

Energy management strategy of integrated adaptive fuzzy power system in fuel cell vehicles

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle’s ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles.

Estimation of state of charge for polymer solid-state batteries: Ensemble learning models and temperature impact study

In addition to the research and development of solid electrolytes to improve battery performance, an efficient battery management system (BMS) is a must to ensure safe use and extend battery life, and state of charge (SOC) estimation is critical in the BMS. This paper presents a novel temperature-influenced method for estimating the SOC of polymer-based solid-state batteries in real-time, using machine learning to capture the relationship between SOC and battery characteristics at different temperatures. The results show that accurate SOC estimation results can be achieved at different temperatures, with an average root mean square error of 1.42 %. Furthermore, the method uses Shapley Additive explanation (SHAP) to reduce the degree of black box nature of the model and analyzes the electrochemical processes inside the battery at different temperatures through relaxation time distribution. Accurate estimates of SOC and guidelines for appropriate operating temperatures can serve as a basis for BMS decision-making and improve the lifetime of polymer-based solid-state batteries.