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A lithium-ion battery for mobile devices obtains a desired voltage by connecting a plurality of battery cells in series. However, the variation in the voltage of each battery cell shortens the lifetime of the battery. For this reason, a battery charge equalization circuit called a battery equalizer (BE) is commonly used. Among others, an inductor-less switched-capacitor (SC) BE is one of the most promising circuits, because it enables low cost, easy control, low EMI, and IC integration. However, to the best of our knowledge, there has been no research on the effects of element variation in inductor-less SCBEs. In this paper, the effect of circuit element variation is investigated regarding the inductor-less SCBE. Specifically, we use Monte-Carlo simulations to clarify the effects of circuit element variation in an inductor-less SCBE with a CMOS cross-coupled structure. Furthermore, the feasibility of the inductor-less SCBE is confirmed by experiments on a laboratory prototype circuit.

This paper describes the design of a control unit for efficient battery charge management in battery electric vehicles (BEVs). The system design aims at controlling the performance of the charging process of dual lithium-ion battery blocks in electric vehicles, with a main battery that powers the vehicle and an auxiliary one for servicing the ancillary equipment. In this paper, we design and analyze the protocol of a control unit that operates and regulates the battery charge in electric vehicles to obtain optimum performance. The so-designed system enhances the battery charge process and protects the main battery from capacity reduction, thus enlarging the driving range of the electric vehicle. We design a specific protocol for an electric circuit that reproduces the structure of the battery charge system of an electric vehicle. The control system improves the efficiency of the auxiliary battery charge by 4.5%. The theoretical simulation matches experimental values in a simulation test by 98.4%.

Off-grid and grid-connected photovoltaic (PV) systems with battery storage rely heavily on efficient energy transfer to maximize PV power utilization and battery lifespan. However, existing literature often overlooks the crucial role of energy transfer efficiency in these systems. This paper proposes a novel, fundamental-based PV power flow strategy that addresses this gap by employing a concept of source-load voltage matching. The proposed strategy ensures optimal voltage matching between the PV array and the battery bank. This is achieved by configuring the battery bank voltage range (between nominal and fully charged states) to closely align with the variation in PV voltage at its Maximum Power Point (MPP) across different operating temperatures. This systematic approach requires specifying the DC load voltage, configuring the battery bank, and selecting PV modules with compatible Vmp (voltage at maximum power) parameters. The system utilizes a high-current blocking diode and current limiter for safe connection between the PV array and battery bank. Additionally, the strategy protects against battery overcharge by controlling parallel fans used for PV panel cooling. Experimental results with a 1.62 kW PV system employing lead-acid batteries demonstrate improved efficiency compared to traditional methods. This innovative approach has the potential to significantly enhance energy transfer efficiency, contributing to improved energy sustainability in PV systems.

Electric vehicles (EVs) have gained prominence in the present energy transition scenario. Widespread adoption of EVs necessitates an accurate State of Charge estimation (SoC) algorithm. Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions, marking a crucial milestone in sustainable electric vehicle technology. In this research study, machine learning methods, particularly Artificial Neural Networks (ANN), are employed for SoC estimation of LiFePO4 batteries, resulting in efficient and accurate estimation algorithms. The investigation first focuses on developing a custom-designed battery pack with 12 ​V, 4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup. The voltage, current and open-circuit voltage of the battery are monitored with computerized battery analyzer. The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi. Principal components are derived for the collected battery data set and analyzed for feature engineering. Three principal components were generated as input parameters for the developed ANN. Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN. While considering eleven combinations for ten different optimizers loss function is minimized. Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998. The proposed algorithm was also implemented for two different types of datasets, a UDDS drive cycle and a standard cell-level dataset. The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

The safety of lithium-ion batteries is closely related to the charge transfer process inside the battery. With the rapid increase in battery usage, battery safety issues have become increasingly prominent. Due to technical limitations, the current battery thermal failure management system cannot realize risk early warning. Charge transfer resistance (Rct) is a macroscopic parameter characterizing the battery transport process, which can be detected by electrochemical impedance spectroscopy (EIS). Due to the high cost of EIS test equipment and high signal-to-noise ratio requirements, it is still not applicable to electric vehicles. In addition, Rctis affected by ambient temperature and battery aging, which brings challenges to online safety diagnosis. Here we propose a method to obtain the activation energy of a battery using direct current impedance spectroscopy (DCIS), which enables the stability diagnosis of the charge transport process. DCIS is a time-domain impedance spectroscopy technique. It uses the time constant characteristic of the internal resistor–capacitor network to detect battery parameters. Activation energy is the energy barrier for lithium-ions to cross the electrode/electrolyte interface and can indicate the stability of the charge transport process. Since activation energy is a microscopic parameter, it is not affected by temperature and aging, which is beneficial for battery safety diagnosis. The experimental results show that the charge transfer process of the sample cells with a state of health (SOH) greater than 80% is stable. When the SOH is less than 80%, the activation energy drops rapidly from 0.55 eV to 0.16 eV, indicating an increased risk of thermal runaway. Since DCIS supports online detection, it provides an option for safety diagnosis of electric vehicle batteries.

Abstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance.

One of the most critical systems of any satellite is the Electrical Power System (EPS) and without any available energy, the satellite would simply stop to function. Therefore, the presented research within this paper investigates the areas relating to the satellite EPS with the main focus towards the CubeSat platform. In this paper, an appropriate EPS architecture with the suitable control policy for CubeSat missions is proposed. The suggested control strategy combines two methods, the Maximum Power Point Tracking (MPPT) and the Battery Charge Regulation (BCR), in one power converter circuit, in order to extract the maximum power of the Photovoltaic (PV) system and regulate the battery voltage from overcharging. This proposed combined control technique is using a Fuzzy Logic Control (FLC) strategy serving two main purposes, the MPPT and BCR. Without an additional battery charger circuit and without switching technique between the two controllers, there are no switching losses and the efficiency of the charging characteristic can be increased by selecting this proposed combined FLC. By testing a space-based PV model with the proposed EPS architecture, some simulation results are compared to demonstrate the superiority of the proposed control strategy over the conventional strategies such as Perturb and Observe (P&O) and FLC with a Proportional Integral Derivative (PID) controller.

With technological advancement, the automobile industry is undergoing a design evolution. There has been an increase in the use of electrically actuated systems; electronics-based control systems, along with an increase in average power consumption. But such increased incorporation has been one of the main causes of roadside breakdowns. However, through the help of modelling and simulation manufacturers can evaluate vehicle operation; reduce their cost while achieving a competitive advantage in the automotive market. Here an automotive electrical system has been discussed. Its architecture consists of a battery, alternator, starter solenoid, and starter motor. Detailed modelling and simulation based on Simulink have been developed. Here charge level of automobile battery and its effect on the operational characteristics of an automobile has been considered and simulated in a MATLAB Simulink environment. First, a time-domain analysis has been made of the current response recorded from its maximum, minimum value, and slope characteristics. The response characteristics hence obtained are also analysed in the frequency domain through FFT analysis to obtain data characteristics in the frequency domain. A detailed analysis has been done at different levels of battery charge degradation to obtain a continuous data set for different states. The result shows significant changes both in time and frequency domain displaying discrimination in terms of DC and frequency components along with relative harmonics distortions enabling battery charge monitoring from starting transients. This method of response characteristics analysis helps in easier computation and in designing a system that can help with vehicle diagnosis and quality control. [ABSTRACT FROM AUTHOR]