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Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation. [ABSTRACT FROM AUTHOR]

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications. [ABSTRACT FROM AUTHOR]

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection ( Robust - mv - M - LSTM - CI ), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv - M - LSTM - CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust - mv - M - LSTM - CI demonstrates substantial improvements over mv - M - LSTM - CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R 2 over mv - M - LSTM - CI . In addition, the proposed model outperforms "adv-" models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust - mv - M - LSTM - CI achieves remarkable improvements in the R 2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems. [ABSTRACT FROM AUTHOR]

Amongst the various topologies of multi-input multi-output (MIMO) DC-DC converters, single-inductor MIMO (SI-MIMO) converters have the advantages of a reduced component count, a simpler structure, and low cost. These converters are suitable in electric vehicle (EV) applications involving variable ports, essential for performing different functions. Digital control in SI-MIMO converters is promising for enhancing transient performance due to its numerous benefits. However, delays in digital control, particularly computational and pulse width modulation (PWM) delays, can negatively impact the performance of DC-DC converters. Multi-sampling and double PWM update methods can mitigate these control delays, but they often necessitate complex control schemes, adding computational burden. In this work, an advanced multi-sampling PWM technique, integrating sample shift and multi-sampling, is proposed while employing a simple digital PID control scheme. The proposed method was tested for a shared-switch SI-MIMO converter with battery discharging and charging modes in the MATLAB/Simulink environment and compared with the conventional single- and multi-sampling PWM methods. The results demonstrated that the proposed method significantly improved the converter performance, surpassing the conventional single- and multi-sampling PWM methods. In the battery discharging mode, utilizing the proposed method, the output voltage achieved a settling time of 0.075 s in response to a step change in its reference, significantly outperforming multi-sampling, which yielded a settling time of 0.124 s, and single sampling, which exhibited an even longer settling time of 0.898 s. It also demonstrated a minimal overshoot of 0.06 volts compared to 1.5 volts with multi-sampling during the step change in the input voltage. Similarly, in the battery charging mode, upon a step change in the reference output voltage, the proposed method effectively minimized the overshoot of the output voltage to 0.845 volts compared to 1.175 volts with multi-sampling, and it decreased the inductor current settling time to 0.296 s from 0.330 s recorded under multi-sampling. These findings underscore the potential of the proposed method in enhancing the digital control performance of SI-MIMO DC-DC converters in electric vehicles. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control. [ABSTRACT FROM AUTHOR]

Future transportation will replace the current mechanical combustible engines with chargeable electric vehicles (EVs). The DC-Dc boost converter is an essential part of such systems that not only helps to generate the required level but also helps to regulate the load voltage. In conventional boost converter topologies, their gain, switching losses, and high voltage stress across switches are the main reason for limiting the output power. Coupled inductor-based isolated converters increase the system weight and cost. Therefore, in this paper, a transformerless adjustable gain and non-isolated DC-DC boost converter with multi-port flexible power supply, reduced switch voltage stress, and considerable good range efficiency is proposed. The proposed converter is tested with the 100 W resistive load to test the desired characteristics. [ABSTRACT FROM AUTHOR]