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Considering the integration of the power battery pack with the vehicle body/chassis and the limited ground clearance, the importance of bottom safety protection becomes more pronounced. Therefore, exploring how to utilize the mechanical advantages of new structures and approaches to ensure both the bottom impact resistance and the lightweight nature of the power battery pack is a critical issue. To address the problem, this paper first designs a novel power battery pack featuring a three-dimensional (3D) star-shaped auxetic structure. Additionally, an improved hybrid surrogate method combining the adaptive t-distribution sparrow search algorithm (TSSA) and the generalized regression neural network (GRNN) is proposed. Finally, considering deviations and uncertainties of the design process of power battery pack, an uncertainty bottom impact optimization integrating the TSSA-GRNN surrogate model and NSGA-II algorithm is carried out to further enhance the comprehensive crashworthiness and robustness. The results demonstrate superior performance of the novel battery pack compared to conventional ones, with TSSA-GRNN achieving the highest accuracy and efficiency in constructing surrogate models. Moreover, the F p-b and the maximum von Mises stress values obtained by the uncertainty optimization are 17.99 kN and 93.75 MPa, respectively, representing reductions of 23.90% and 25.45% compared with the initial design. Most importantly, the robustness of the optimization is significantly improved, with the standard deviation (σ) of the constraint condition reaching 8. It can provide theoretical support and engineering references in electric vehicle safety design. • A novel power battery pack considering crashworthiness and lightweight is established. • TSSA-GRNN surrogate model with higher accuracy and efficiency is proposed. • An uncertainty bottom impact optimization is executed by integrating the TSSA-GRNN surrogate model and the NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault diagnosis method based on relative entropy and state of charge (SOC) estimation is proposed to detect fault in lithium-ion batteries. First, the relative entropies of the voltage, temperature and SOC of battery cells are calculated by using a sliding window, and the cumulative sum (CUSUM) test is adopted to achieve fault diagnosis and isolation. Second, the SOC estimation of the short-circuit cell is obtained, and the short-circuit resistance is estimated for a quantitative analysis of the short-circuit fault. Furthermore, the effectiveness of our method is validated by multiple fault tests in a thermally coupled electrochemical battery model. The results show that the proposed method can accurately detect different types of faults and evaluate the short-circuit fault degree by resistance estimation. The voltage/temperature sensor fault is detected at 71 s/58 s after faults have occurred, and a short-circuit fault is diagnosed at 111 s after the fault. In addition, the standard error deviation of short-circuit resistance estimation is less than 0.12 Ω/0.33 Ω for a 5 Ω/10 Ω short-circuit resistor. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

Summary: Developing high‐power electric‐driven system is the key to realize green exploration of vibroseis. To improve the safety and extend the cycle life of the lithium‐ion batteries for electric‐driven vibroseis, two types of liquid‐cooling structure for the battery pack were developed and evaluated. The thermal performance of the liquid‐cooling structures was evaluated by three indexes of the maximum temperature in the whole battery pack, the maximum temperature difference between the cells, and the standard deviation (SD) coefficient of the cells' temperature. Results suggest that the maximum temperature difference and the SD coefficient of the single‐inlet‐single‐outlet liquid‐cooling structure is 7.43°C and 6.49%, respectively, and both fail to meet the indexes. In the double‐inlet‐double‐outlet liquid‐cooling structure, the maximum temperature increases linearly with the growth of the coolant inlet temperature. With higher coolant flow rate, the maximum temperature is lower, while the temperature difference between the two sides of the cell is greater. When the inlet temperature of the coolant is below 33.9°C and the coolant flow rate is greater than or equal to 350 g/s, the thermal performance evaluation indexes of the battery pack could be achieved. Field test of the electric‐driven vibroseis shows that the double‐inlet‐double‐outlet structure can fully achieve the cooling requirements. This research provides significant support for the development of green‐exploration vibroseis. [ABSTRACT FROM AUTHOR]

Parallel battery pack (PBP) is an important unit for its application in electric vehicles and energy storage, and precise state of charge (SOC) is the basic parameter for battery efficient operation. However, the SOC is an internal hidden immeasurable variable, and the measurable battery parameters of the PBP are limited, which makes it difficult to precisely estimate SOC for the PBP. The main efforts are as follows: An improved equivalent circuit model of the PBP is first established on the basis of the fuzzy-proportional integral model regulator, which can accurately describe the influence of battery cell inconsistency on the PBP discharging characteristics. Under constant current and UDDS operating conditions, the battery model voltage can accurately capture the measured voltage during the discharging process, especially at the final stage of discharge with the maximum voltage absolute error below 0.12 V (about 3.2%). A model-based SOC prediction algorithm using an adaptive unscented Kalman filter (AUKF) with a sliding window noise estimator is developed for the PBP. It can adaptively achieve accurate process and measurement noise statistics of the PBP for the AUKF. The SOC of the PBP can be precisely estimated using the developed method with the absolute errors below 2% even if the noise statistics are randomly given respectively. Moreover, compared to the unimproved AUKF and the Sage-Husa method, the presented algorithm illustrates the highest SOC prediction precision with the lowest root mean square error of 1.12% and the minimum mean absolute error of 1.08%. • Battery pack model is built based on a fuzzy-proportional integral model regulator. • Sliding window noise estimator is used to attain the correct noise statistics. • Adaptive unscented Kalman filter is developed to estimate the SOC of the PBP. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a most significant bits (MSBs) prediction strategy for the design of successive approximation register (SAR) analog-to-digital converter (ADC) in battery pack voltage detection is proposed to reduce conversion time without losing accuracy. The strategy takes advantage of the battery pack voltages characteristics where there is a strong correlation among neighboring cell voltage values. By generating the MSBs with the quantization results of the battery pack in the previous loop, the ADC just needs to convert the remaining least significant bits (LSBs). This paper analyzes the relationship between saved time and five parameters: reading frequency (f), cell number of battery pack (N), cell number (CN) for MSB-prediction, standard deviation (σ), change rate of digital number (K), which can give a feasible prediction on saved working time in any conditions. With this strategy, in a f = 50 kHz, N = 10, CN = 8, σ = 10 system, up to 27.83% A/D conversion time can be saved in charging state, and 27.89% A/D conversion time can be saved in discharging state. [ABSTRACT FROM AUTHOR]

Accurate estimation of battery pack remaining discharge energy is a crucial challenge to the battery energy storage systems. In this paper, a new method of battery pack remaining discharge energy estimation is proposed using the recursive least square-unscented Kalman filter. To predict the remaining discharge energy precisely, the inconsistency of the battery pack caused by different working temperatures is taken into consideration and the degree of battery inconsistency is quantified based on mathematical methods of statistics. In addition, the recursive least square is applied to identify the parameters of the battery pack model on-line and the unscented Kalman filter is employed in battery pack remaining discharge energy and energy utilization ratio estimation. The experimental results in terms of battery states estimation under the new European driving cycle and real driven profiles, with the root mean square error less than 0.01, further verify that the proposed method can estimate the battery pack remaining discharge energy with high accuracy. What's more, the relationship between the pack energy utilization ratio and the degree of battery inconsistency is summarized in the paper. [ABSTRACT FROM AUTHOR]

Summary: This work establishes an improved electrothermal‐coupled model for the estimation of the temperature evolution in an air‐cooled pack with three parallel branches and four serial cells in each branch. This model includes the influences of the cells' state of charge (SOC) and temperature on the ohmic and polarization resistances and polarization capacitance. The current distribution in the pack is considered in the model and applied to predicting the inconsistent effect of cell temperature. Moreover, the pipe network theory is used to model the airflow in the pack and the heat convection between the air and the batteries. An experiment is implemented to verify prediction precision in the electrical and thermal parameters of the pack. The results show that the electrothermal model accurately estimates the electrical and thermal performance of the air‐cooled pack. The relative error of the pack terminal voltage between the prediction and the experiment is 3.22% under the conditions of a discharging rate is 1.5 C (C denotes the ratio of charging/discharging current to battery capacity), environment temperature of 37°C, and air inlet velocity of 6 m/s. Regarding the prediction error in the temperature, the root mean square errors of most batteries are no more than 0.6°C under the conditions of discharge rates of 1 C and 1.5 C and ambient temperatures of 17°C, 27°C, and 37°C. [ABSTRACT FROM AUTHOR]

Aiming at the problem of on-line real-time estimation of the state of charge (SOC) and capacity of the battery pack, a new mean-difference model (MDM) for series battery pack is proposed. The model realizes the fusion of data-driven Autoregression (AR) model and equivalent circuit model (ECM) to improve the dynamic characteristics of the battery pack model under complex operating conditions. For the model parameters of the new MDM, the differentiated updating strategy is proposed. The differentiated updating strategy proposed can timely update the new MDM parameters online according to their changing characteristics and changing rate. Based on the new MDM, new mean-difference space equations expressing the coupling relationship between battery pack SOC and capacity, as well as a multi-timescale estimation framework based on mean state / difference state changing speed, are proposed. Finally, under this multi-timescale estimation framework, the multi-timescale H infinity filter (Mts-HIF) is used to realize the joint estimation of SOC and capacity of series battery packs. Based on 3 operating conditions, we have verified the proposed new MDM under noiseless and colored noise environment and compared it with the MDM based on n-order RC-ECM (nRC-ECM). The Akaike information criterion (AIC) indicates that the 3-order AR mean model (AR-MM) has the optimal tradeoff between accuracy and complexity under noiseless environment, while the 4-order AR-MM has the optimal tradeoff under colored noise environment and its root mean square error (RMSE) is less than 14.5 mV. Compared with the multi-timescale extended Kalman filter (Mts-EKF) joint estimation algorithm, the Mts-HIF proposed has better robustness under colored noise environment. For the capacity estimation of each cell of battery pack, the RMSE of the Mts-HIF does not exceed 2%, while the RMSE of the Mts-EKF exceeds 4%. For the SOC estimation, the RMSE of the Mts-HIF does not exceed 1.2%, while the RMSE of the Mts-EKF is close to 3%. [ABSTRACT FROM AUTHOR]

Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs. [ABSTRACT FROM AUTHOR]

Practical and accurate state of charge estimation for battery pack is a challenging task due to the inconsistency among in-pack cells. In this paper, we propose a low-complexity state of charge estimation method for series-connected battery pack. Firstly, to reduce computation cost, the capacity and state of charge calculations of battery pack are effectively simplified based on the probability theory. Secondly, we propose a selection method for the representative cells, which is validated by the simulation under Dynamic Stress Test. Subsequently, the state of charge of each representative cell is estimated by the recursive least squares-adaptive extend Kalman filter algorithm successively. Finally, the aging experiments for LiNCM and LiFePO 4 battery packs are carried out to validate the feasibility of the simplified method, and the complexity of the three estimation methods is compared. Experimental results indicate that the capacity of battery pack depends only on the representative cells at different cycles, and the proposed "representative cell" method can estimate the state of charge of battery pack with high accuracy and low complexity. The mean absolute errors and root mean square errors for state of charge estimation are less than 3% under Urban Dynamometer Driving Schedule test at different cycles. • Proposed a low-complexity SoC estimation method for series-connected battery pack. • Simplified the pack capacity and SoC calculations based on the probability theory. • A selection method for the representative cells is proposed. • Verified the feasibility of simplified method by two aging experiments. [ABSTRACT FROM AUTHOR]

There is not much research carried out on factors such as temperature differences in different regions of battery pack, which is highly important and poses a great challenge. Temperature changes across batteries affect the battery pack performance and its life span largely. Thus, the present work proposes the Integrated Finite Element‐Meta‐Optimization Framework for solving this problem. The integrated framework includes the formulation of Finite Element Model of a battery pack and Meta‐Optimization Framework for multi‐objective minimization of three objectives such as the inter‐cell temperature differences, temperature standard deviation and the total volume of the cell. In this framework, firstly Support Vector Regression (SVR) model is formulated based on the data and the SVR parameters are optimized using a genetic algorithm. It can conclude that the resulting model has a coefficient of correlation metric of 99%. The simulated annealing algorithm is then applied to find the optimal values of inter‐cell and cell‐enclosure distances for minimization of inter‐cell temperature differences and the total volume of the cell. Optimization analysis concluded that the volume of the battery pack was decreased by 29%, temperature difference (K) by 42% and SD of the temperature over the entire pack decreased by 55%. [ABSTRACT FROM AUTHOR]

Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction of battery SOH have caused widespread concern in academic and industrial communities. In this paper, a method is proposed to build an accurate SOH prediction model for battery packs based on multi-output Gaussian process regression (MOGPR) by employing the initial cycle data of the battery pack and the entire life cycling data of battery cells. Firstly, a battery aging experimental platform is constructed to collect battery aging data, and health indicators (HIs) that characterize battery aging are extracted. Then, the correlation between the HIs and the battery capacity is evaluated by the Pearson correlation analysis method, and the HIs that own a strong correlation to the battery capacity are screened. Finally, two MOGPR models are constructed to predict the HIs and SOH of the battery pack. Based on the first MOGPR model and the early HIs of the battery pack, the future cycle HIs can be predicted. In addition, the predicted HIs and the second MOGPR model are used to predict the SOH of the battery pack. The experimental results verify that the approach has a competitive performance; the mean and maximum values of the mean absolute error (MAE) and root mean square error (RMSE) are 1.07% and 1.42%, and 1.77% and 2.45%, respectively. [ABSTRACT FROM AUTHOR]

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data. [ABSTRACT FROM AUTHOR]

In this paper, an innovative modeling approach for Li-ion battery packs is proposed by considering intrinsic cell unbalances and packaging elements. The proposed modeling method shows that the accurate battery pack model can be achieved if the overall influences of intrinsic cell unbalances and packaging elements are taken account. Concurrently, the proposed method takes a practical model structure, resulting in the reduction of computational burden in a battery management system. Furthermore, because the proposed method utilizes cell information without a manufactured battery pack, it can be helpful to design optimal battery packs. The proposed method is verified through simulation and experimental results of the Li-ion battery pack along with the battery cycler. In three test profiles, the mean absolute percentage errors and root mean square errors of the proposed pack model do not exceed 0.5% and 0.07 V, respectively. [ABSTRACT FROM AUTHOR]

This manuscript focuses on the modeling of electric vehicle (EV) driver's range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year's charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas. [ABSTRACT FROM AUTHOR]

Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree. [ABSTRACT FROM AUTHOR]

A battery management system (BMS) is an important link between on-board power battery and electric vehicles. The BMS is used to collect, process, and store important information during the operation of a battery pack in real time. Due to the wide application of lithium-ion batteries in electric vehicles, the correct estimation of the state of charge (SOC) of lithium-ion batteries (LIBS) is of great importance in the battery management system. The SOC is used to reflect the remaining capacity of the battery, which is directly related to the efficiency of the power output and management of energy. In this paper, a new long short-term memory network with attention mechanism combined with Kalman filter is proposed to estimate the SOC of the Li-ion battery in the BMS. Several different dynamic driving plans are used for training and testing under different temperatures and initial errors, and the results show that the method is highly reliable for estimating the SOC of the Li-ion battery. The average root mean square error (RMSE) reaches 0.01492 for the US06 condition, 0.01205 for the federal urban driving scheme (FUDS) condition, and 0.00806 for the dynamic stress test (DST) condition. It is demonstrated that the proposed method is more reliable and robust, in terms of SOC estimation accuracy, compared with the traditional long short-term memory (LSTM) neural network, LSTM combined with attention mechanism, or LSTM combined with the Kalman filtering method. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and hybrid vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for their implementation are that they consume less or do not consume fossil fuel (no carbon dioxide pollution) and do not cause sound pollution. However, this technology has some challenges, including complex and troublesome accurate state of health estimation, which is affected by different factors. According to the increase in electric and hybrid vehicles' application, it is crucial to have a more accurate and reliable estimation of state of charge (SOC) and state of health (SOH) in different environmental conditions. This allows improving battery management system operation for optimal utilization of a battery pack in various operating conditions. This article proposes an approach to estimate battery capacity based on two parameters. First, a practical and straightforward method is introduced to assess the battery's internal resistance, which is directly related to the battery's remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, simple, accurate, and reliable technique is proposed to estimate battery capacity appropriately. The root mean square percentage error and the mean absolute percentage error of the proposed methods were calculated and were less than 0.02%. It was concluded the geometry method has all the advantages of a recursive manner, including a fading memory, a close form of a solution, and being applicable in embedded systems. [ABSTRACT FROM AUTHOR]

Accurate state of charge and capacity estimation is crucial for battery packs in electric vehicles. However, the cell inconsistencies, computational complexity, temperature variations, and complex drive cycles all pose great challenges for the state of charge and capacity estimation of battery packs in field operation. This work aims to develop a framework for state of charge and capacity estimation of battery packs in real electric vehicles with few representative cells and physics-informed machine learning. By analyzing the battery data using a quantity-capacity diagram, the representative cells within the pack can be identified based on the relaxation terminal voltage online. Leveraging the physics information of batteries, the state of charge of the representative cells are monitored in a closed-loop manner. Then, state of charge and available capacity of battery pack are achieved based on the state of only two representative cells. We validate our method by evaluating its performance on the datasets of battery pack in real electric vehicles across different temperatures and drive cycles. The improvements of 83.60 % and 85.13 % in average root mean square errors can be achieved at real drive cycles under 30 °C and 15 °C. The results demonstrate that accurate and robust state estimation of battery packs can be achieved in field operations by utilizing the battery data of only two cells in laboratory conditions. [Display omitted] • Co-estimation of state of charge and capacity of battery pack in real EVs. • Only using state information of two cells based on a quantity-capacity analysis. • Physics parameters are incorporated into data-driven model in a closed-loop. • Experimental validation is conducted on a battery pack from commercial EVs. [ABSTRACT FROM AUTHOR]

A compact electronic circuit capable of performing Electrochemical Impedance Spectroscopy (EIS) on either single Lithium-ion cells or modules formed by the series of two cells is presented. The proposed device, named Double Cell Management Unit (DCMU), constitutes an important improvement to a recently proposed cell management unit, which combined EIS acquisition functions with a multichannel sensor interface compatible with thermistors, strain-gauges and moisture detectors. The proposed circuit maintains the versatility of the previous version and significantly extends the EIS frequency range, allowing vector impedance measurements from 0.1 Hz to about 15 kHz. The capability of handling both single Lithium-ion cells or series of two cells is obtained by adding a few external components to the previous version. This also allowed increasing the stimulation current to a maximum amplitude of 200 mA, resulting in improved resolution. Experiments consisting in EIS acquisition performed on batteries of different capacity at different temperatures and states of charge are described. Estimated impedance resolution (standard deviation) is 20 μΩ obtained at 1 kHz with a stimulation current of 100 mA amplitude. [ABSTRACT FROM AUTHOR]

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry. [ABSTRACT FROM AUTHOR]

The global transportation system's need for electrification is driving research efforts to overcome the drawbacks of battery electric vehicles (BEVs). The accurate and reliable estimation of the states of charge (SOC) and health (SOH) of Li-Ion batteries (LIBs) is crucial for the widespread adoption of BEVs. Transformers, cutting-edge deep learning (DL) models, are demonstrating promising capabilities in addressing various sequence-processing problems. This manuscript presents a thorough survey study of previous research papers that introduced modifications in the development of Transformer-based architectures for the SOC and SOH estimation of LIBs. This study also highlights approximately 15 different real-world datasets that have been utilized for training and testing these models. A comparison is made between the architectures, addressing each state using the root mean square error (RMSE) and mean absolute error (MAE) metrics. [ABSTRACT FROM AUTHOR]

Energy storage batteries still have usable capacity after retirement, with excellent secondary utilization value. Estimating the state of health (SOH) of retired batteries is critical to ensure their reuse. As the battery first reaches the end of its useful life, its performance degradation pattern significantly differs from that in service, increasing the difficulty of accurate SOH estimation. This study developed a SOH estimation method for retired batteries based on battery positive, negative, and center temperature data from 80% to 50% of retired battery health. The variational mode decomposition technique divides the temperature signal into multiple trends representing different battery aging mechanisms. The decomposed modes are given a physical meaningfulness, providing a new perspective to monitor battery health. In addition, this study proposes a multi-task learning framework that realizes the parallel processing of two tasks under this framework. On the one hand, the gated recurrent unit is used to estimate the relationship between the battery baseline temperature and SOH, which captures macro-degradation trends of the battery. On the other hand, the transformer network is responsible for analyzing short-term battery health fluctuations caused by subtle temperature changes. This multi-task approach can simultaneously process and analyze both macro-degradation trends and micro-fluctuations in battery degradation, estimating that the root mean square error of battery health is 5.22 × 10−5. Compared to the existing techniques, this study shows potential applications in the retired battery state of health assessment. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH 4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s -1 when the wind magnitude is less than 3.5 m s -1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH 4 and CO 2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature. [ABSTRACT FROM AUTHOR]

To explore an effective analysis model and method for estimating Cinnamomum camphora's (C. camphora's) growth using unmanned aerial vehicle (UAV) multispectral technology, we obtained C. camphora's canopy spectral reflectance using a UAV-mounted multispectral camera and simultaneously measured four single-growth indicators: Soil and Plant Analyzer Development (SPAD)value, aboveground biomass (AGB), plant height (PH), and leaf area index (LAI). The coefficient of variation and equal weighting methods were used to construct the comprehensive growth monitoring indicators (CGMI) for C. camphora. A multispectral inversion model of integrated C. camphora growth was established using the multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), random forest (RF), radial basis function neural network (RBFNN), back propagation neural network (BPNN), and whale optimization algorithm (WOA)-optimized BPNN models. The optimal model was selected based on the coefficient of determination (R2), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE). Our findings indicate that apparent differences in the accuracy with different model, and the WOA–BPNN model is the best model to invert the growth potential of C. camphora, the R2 of the model test set was 0.9020, the RMSE was 0.0722, and the MAPE was 7%. The R2 of the WOA–BPNN model improved by 18%, the NRMSE decreased by 33%, and the MAPE decreased by 9% compared with the BPNN model. This study provides technical support for the modern field management of C. camphora essential oil and other dwarf forestry industries. [ABSTRACT FROM AUTHOR]

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of a cost-efficient and uncomplicated real-time localization system (RTLS) for use in mobile robotics, specifically within indoor and storage environments. By harnessing ultrasonic waves to measure distances from three beacons, the system provides stable and reliable localization. This method utilizes the time-of-flight (TOF) principle, allowing for accurate distance calculations with simple microcontrollers. The system is designed to update the robot's position at a frequency of at least 10 times per second, ensuring smooth navigation. Our trilateration-based approach allows for the precise determination of the robot's position with a notable standard deviation accuracy of up to 15 mm. The aim was to design a simple yet sufficiently accurate system and verify its precision through experimental measurements. The experimental results demonstrate the system's efficacy and lay a solid foundation for advancing this technology. Furthermore, the cost for the components required to build this indoor localization system (ILS) with three beacons and one tag is remarkably low, under EUR 80. [ABSTRACT FROM AUTHOR]

The state of health is a crucial state that suggests the capacity of lithium-ion batteries to store and restitute energy at a certain power level, which should be carefully monitored in the battery management system. However, the state of health of batteries is unmeasurable and, currently, it is usually estimated within a specific area of the whole charging data, which is very limited in practical application because of the incomplete and random charging behaviors of users. In this paper, we intend to estimate the state of health of batteries with flexible partial charging curves and normal multi-layer perceptron based on the degradation data of eight 0.74 Ah batteries. To make the estimation more adaptive and flexible, we extract several features from partial charging curves. Analysis of the relationship between extracted features and the state of health shows that the extracted features are useful in estimation. As the length of the partial charging curve increases, the extracted features still function well, and the root mean square error of the test set is lower than 1.5%. Further validation on the other two types of batteries reveals that the proposed method achieves high accuracy even with different sampling and working conditions. The proposed method offers an easy-to-implement way to achieve an accurate estimation of a battery's state of health. [ABSTRACT FROM AUTHOR]

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles. [ABSTRACT FROM AUTHOR]

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery's aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%. [ABSTRACT FROM AUTHOR]

• The proposed method is based on unsupervised learning, avoiding the problems of lack of samples; • A knowledge-based guidance matrix is established to accelerate training speed and to ensure convergence; • An interpretable Autoencoder is used to ensure extensibility among different vehicles; • Effectiveness has been verified using data from real-world EVs. The abnormality detection of lithium-ion battery pack is crucial to ensure the safety of electric vehicles (EVs). However, the dynamic and complex operating conditions of EVs making it challenging for algorithms designed under laboratory conditions to perform properly. In this study, a novel data-driven framework for abnormality detection is developed through establishment of a neural network with interpretable modules on top of an Autoencoder using data from real EVs to recognize abnormality while charging. The encoding guide matrix proposed in this method greatly accelerates the training speed, which also helps retains the learning ability of the neural network with consideration of the influence from each feature to provide supplementary information. The proposed algorithm is validated with data from real EVs. The results show that, compared with most existing algorithms, evidently higher accuracy can be achieved with shorter training time and lower computational cost, where the accuracy remains above 94% for all tested sample and the average root mean square error (RMSE) is as small as 0.03913. The proposed method can be utilized for both cloud-based and vehicle-based battery fault diagnoses. [ABSTRACT FROM AUTHOR]

Demand for Li-ion batteries is soaring daily in the global Electric Vehicle (EV) market. Therefore, developing an accurate, efficient and low-cost Battery Management System (BMS) plays a pivotal role in the battery's performance. One of the significant roles of the BMS is the accurate State of Charge (SoC) estimation of batteries. This paper uses Thevenin's model-based Extended Kalman Filter (EKF) algorithm for SoC estimation. In order to prove the validity of the estimation model, SoC estimated using the EKF model is verified with the SoC obtained from the vehicle dynamic model developed in the MATLAB Simulink environment considering various drive cycles like LA92, FTP-72, and NEDC. The Root Mean Square Error (RMSE) chosen as the performance index has achieved much smaller values as 0.0277%, 0.00278%, and 0.0145% for one cycle of LA92, FTP-72, and NEDC drive cycles, respectively, and hence this approves its real-time applications. The proposed work provides a reliable and trustable estimated SoC for range estimation of the EV since the estimated SoC value here takes into account not only the drive cycle load current demand but also it considers other external factors like the weight of the vehicle, wind, rolling resistance between road and tire etc. For that purpose, the SoC estimation algorithm in this proposed work incorporates a vehicle dynamic model developed in the Simulink environment. It delivers a reliable estimated SoC on which the EV driver can depend on calculating its remaining range. • An EKF based State of Charge method has been employed for a Turnigy Graphene 5000 mAh battery. • Thevenin's battery model is used for representing the dynamic behaviour of the battery. • Robustness of the model is verified under different temperature like, −10 °C, 10 °C, 25 °C, and 40 °C and along with a 10% initial SoC offset. • Vehicle Glider model is developed in the MATLAB Simulink environment for a battery pack of 50 Ah. • The estimated SoC matches with vehicle dynamic model SoC for various drive cycles with low Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000. [ABSTRACT FROM AUTHOR]

A microchannel heat sink (MCHS) is a potential solution for chip and battery thermal management. The new microchannel structure is beneficial for further improving the thermal-hydraulic performance of MCHSs. Inspired by leaf veins, six new channel structures were designed, and the effects of the channel structures (three parallel structures named PAR I, II, and III and three pinnate structures named PIN I, II, and III), channel depths (0.4, 0.8, and 1.6 mm), and heat fluxes (20, 50, and 80 kW/m2) were investigated via numerical simulation. The cooling medium was water, and the heating area was 40 × 40 mm2. Both PAR II and PIN III exhibit superior overall performance, characterized by the highest Nusselt number and the lowest heating wall temperature. Moreover, PIN III demonstrates the lowest standard deviation in heating wall temperature, while PAR II exhibits the lowest friction factor. The greater the channel depth is, the larger the solid–liquid contact area is, leading to a reduced wall temperature at the interface under identical conditions of inlet Reynolds number and heating wall heat flux. Consequently, an increase in the Nusselt number corresponds to an increase in the friction factor. The maximum value and standard deviation of the heating wall temperature increase with increasing heat flux, while the Nusselt number and friction factor remain unaffected. The overheating near the two right angles of the outlet should be carefully considered for an MCHS with a single inlet–outlet configuration. [ABSTRACT FROM AUTHOR]

The utilization of porous media and its effects on thermal management of internally cooled lithium-ion battery with the aid of porous media has been investigated. Two different configurations of the porous zone have been studied through three-dimensional transient thermal analysis of prismatic lithium-ion battery with liquid electrolyte as a coolant. At first, both configurations of the porous zone were investigated in pursuit of detecting the optimum configuration. Afterward, the effects of various materials, porosities and pores sizes were explored on the standard deviation of the temperature field and maximum temperature inside the battery in the optimum porous zone configuration. In the end, the utilization of the response surface method due to the parametric study and discover the most crucial parameters in battery performance was scrutinized. Compared to the non-inclined porous, the inclined porous zone case decreases the standard deviation of the temperature field significantly. Using the inclined porous zone also indicates that decreasing porosity results in the enhancement of both maximum temperature and standard deviation of the temperature about 20.75%. Hence, the increment of pore size results in decreasing maximum temperature inside the battery, while it could help the standard deviation of the temperature to be improved by about 24.88%. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have considerable potential in promoting energy efficiency and carbon neutrality. State of health (SOH) estimations for battery systems can be effective for avoiding accidents involving EVs. However, existing methods have rarely been developed using real driving data. The complex working environments of EVs and their limited data acquisition capability increase the challenges for estimating SOH. In this study, a novel battery SOH definition for EVs was established by analyzing and extracting six potential SOH indicators from driving data. The definition proposed using the entropy weight method (EWM) described the degradation trend for different EV batteries. Combined with a denoising autoencoder, a novel long short-term memory neural network model was established for SOH prediction. It can learn robust features using noisy input data without being affected by different environments or driver behaviors. The network achieved a maximum mean absolute percentage error (MAPE) of 0.8827% and root mean square error (RMSE) of 0.9802%. The results have shown that the proposed method has a higher level of accuracy and is more robust than existing methods in the field. [ABSTRACT FROM AUTHOR]

Accurate state of health (SOH) estimation of lithium-ion batteries is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). The machine learning-based method with health features (HFs) is encouraging for health prognostics. However, the machine learning method assumes that the training and testing data have the same distribution, which restricts its application for different types of batteries. Thus, in this paper, a deep learning neural network and fine-tuning-based transfer learning strategy are proposed for accurate and robust SOH estimation toward different types of batteries. First, a universal HF extraction strategy is proposed to obtain four highly related HFs. Second, a deep learning neural network consisting of long short-term memory (LSTM) and fully connected layers is established to model the relationship between the HFs and SOH. Third, the fine-tuning-based transfer learning strategy is exploited for SOH estimation of various types of batteries. The proposed methods are comprehensively verified using three open-source datasets. Experimental results show that the proposed deep learning neural network with the HFs can estimate the SOH accurately in a single dataset without using the transfer learning strategy where the mean absolute error (MAE) and root mean square error (RMSE) are constrained to 1.21% and 1.83%. For the transfer learning between different aging datasets, the overall MAE and RMSE are limited to 1.09% and 1.41%, demonstrating the reliability of the fine-tuning strategy. [ABSTRACT FROM AUTHOR]

The impact of different charging currents and surrounding temperatures has always been an important aspect of battery lifetime for various electric vehicles and energy storage equipment. This paper proposes a bidirectional long short-term memory model to quantify these impacts on the aging of gel batteries and calculate their state of health. The training data set of the bidirectional long short-term memory model is collected by charging and discharging the gel battery for 300 cycles in a temperature-controlled box and an automated charge and discharge device under different operating conditions. The testing set is generated by a small energy storage device equipped with small solar panels. Data for 220 cycles at different temperatures and charging currents were collected during the experiment. The results show that the mean absolute error (MAE) and root-mean-square error (RMSE) between the training set and testing set are 0.0133 and 0.0251, respectively. In addition to the proposed model providing high accuracy, the gel battery proved to be stable and long-lasting, which makes the gel battery an ideal energy storage solution for renewable energy. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs. [ABSTRACT FROM AUTHOR]

Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4). [ABSTRACT FROM AUTHOR]

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs' SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study's results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications. [ABSTRACT FROM AUTHOR]

To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25 °C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH. [ABSTRACT FROM AUTHOR]

Summary: The development of a novel method to estimate the state of charge (SOC) with low read‐only memory (ROM) occupancy, high stability, and high anti‐interference capability is very important for the battery management system (BMS) in actual electric vehicles. This paper proposes the square root cubature Kalman filter (SRCKF) with a temperature correction rule, based on the BMS of a common on‐board embedded micro control unit (MCU), to achieve smooth estimation of SOC. The temperature correction rule is able to reduce the testing effort and ROM space used for data table storage (189.3 kilobytes is much smaller than the storage of the MPC5604B, with 1000 kilobytes), while the SRCKF is adopted to achieve highly robust real‐time SOC estimation with high resistance to interference and moderate computing cost (68.3% of the load rate of the MPC5604B). The results of multiple experiments show that the proposed method with less computational complexity converges rapidly (in approximately 2.5 s) and estimates the SOC of the battery accurately under dynamic temperature condition. Moreover, the SRCKF algorithm is not sensitive to the high measuring interference and highly nonlinear working conditions (even with 1% current and voltage measurement disturbances, the root mean square error of the proposed method can be as high as 0.679%). [ABSTRACT FROM AUTHOR]

For lithium-ion batteries, the state of charge (SOC) of batteries plays an important role in the battery management system, and the accuracy of the battery model and parameter identification is the basis of SOC estimation. Considering that the system has inevitable steady-state errors and the influence of random noise on SOC estimation results under dynamic conditions, this paper proposed an improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering (PCFFRLS-MCAEKF) algorithm for high-precision state-of-charge estimation of lithium-ion batteries. The experimental results show that the proportional control forgetting factor recursive least square algorithm has higher parameter identification accuracy under HPPC and BBDST conditions. Under HPPC working conditions, the root mean square error of PCFFRLS-MCAEKF algorithm is reduced by 1.275%, 0.687%, and 0.549% compared with FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithm, and the average absolute error is reduced by 0.71%, 0.537%, and 0.11%. Under BBDST working conditions, the SOC estimation result of PCFFRLS-MCAEKF algorithm is closer to the real SOC, which is consistent with the result obtained under HPPC working conditions. The experimental results show that under HPPC and BBDST working conditions, the PCFFRLS-MCAEKF algorithm can better improve the accuracy and robustness of SOC estimation than FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithms. [ABSTRACT FROM AUTHOR]

Underwater magnetic detection based on unmanned underwater vehicles (UUVs) is an important approach for marine exploration and monitoring. Due to the interference of the carrier magnetic field, the detection accuracy is severely limited. To improve the performance of magnetic detection, various compensation methods have been developed. However, the compensation effectiveness of these methods is greatly diminished when UUVs sail at different velocities. In this paper, we propose a velocity-related interference magnetic compensation method that enhances the conventional Tolles–Lawson model. By introducing a velocity-related term and combining the ridge regression with the linear fitting algorithm, we determine the compensation parameters for various UUV velocities. Field experiments are conducted to verify the effectiveness of the proposed method. The results show that the root mean square error of the total magnetic field after compensation is reduced from 246.90 nT to 2.23 nT. Our study demonstrates that the velocity-related compensation method can significantly improve the accuracy of interference magnetic compensation under different UUV velocities, making it applicable to actual underwater magnetic detection. [ABSTRACT FROM AUTHOR]

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The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model's robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model's feasibility and accuracy. [ABSTRACT FROM AUTHOR]