Search Results (17)

Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency. Full article

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods. Full article

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%. Full article

Lithium-ion batteries are essential for the development of consumer electronics and electric vehicles due to their high energy density, low self-discharge rate, and easy maintenance. To optimize the performance of lithium-ion batteries and meet the battery requirements of devices, it is necessary to charge the batteries at a faster rate. Therefore, this paper proposes a five-stage constant current charging method based on Lamb wave depolarization to enhance the charging efficiency. Specifically, the orthogonal experimental method is first used to determine the near-optimal value of the charging current in each stage of the five-stage constant current charging process. Subsequently, Lamb waves are introduced during the charging process of each constant current charging stage. Compared with the traditional five-stage constant current charging method, the five-stage constant current charging method based on Lamb wave depolarization improves the charging efficiency. The charging efficiency of the five-stage constant current charging method based on Lamb wave depolarization with an excitation voltage peak-to-peak amplitude Vpp of 120 and an excitation duration of 6 min is 20% higher than that of the traditional five-stage constant current charging method. The weakening of the polarization effect is positively correlated with the Lamb wave excitation voltage. In addition, the five-stage constant current charging method based on Lamb wave depolarization is superior to the five-stage constant current shelving depolarization charging method and the five-stage constant current negative pulse depolarization charging method in improving the charging efficiency. Full article

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions. Full article

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively. Full article

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO₄ batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery. Full article

New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system. Full article

Since lithium-ion batteries seem to be the most eligible technology to store energy for e-mobility applications, it is fundamental to focus attention on kilometric ranges and charging times. The optimization of the charging step can provide the appropriate tradeoff between time saving and preserving cell performance over the life cycle. The implementation of new multistage constant current profiles and related performances after 1000 cycles are presented and compared with respect to a reference profile. A physicochemical (SEM, XRD, particle size analysis, etc.) and electrochemical (incremental capacity analysis, internal resistance measurements) characterization of the aged cells is shown and their possible implementation on board is discussed. Full article

Modular multilevel converter battery energy storage systems (MMC-BESSs) have become an important device for the energy storage of grid-connected microgrids. The efficiency of the power transmission of MMC-BESSs has become a new research hotspot. This paper outlines a multi-stage charging method to minimize energy consumption and maximize the capacity of MMC-BESSs. Firstly, based on condition monitoring and data collection, the functional relationship between the internal resistance/capacity and other states of lithium batteries is established. Since the energy consumption of the battery is related to internal resistance, current, and time, the energy consumption calculation expression of the battery pack is established, and the objective function is designed to optimize energy consumption and capacity in order to determine the charging current curve of each stage. Compared with the constant current charging method, the proposed multistage current charging method for an MMC-BESS decreases energy consumption by 4.3% and increases the capacity of 5 SOC intervals by 1.56%. Full article

Despite fast technological advances, the worldwide adoption of electric vehicles (EVs) is still hampered mainly by charging time, efficiency, and lifespan. Lithium-ion batteries have become the primary source for EVs because of their high energy density and long lifetime. Currently, several methods intend to determine the health of lithium-ion batteries fast-charging protocols. Filling a gap in the literature, a clear classification of charging protocols is presented and investigated here. This paper categorizes fast-charging protocols into the power management protocol, which depends on a controllable current, voltage, and cell temperature, and the material aspects charging protocol, which is based on material physical modification and chemical structures of the lithium-ion battery. In addition, each of the charging protocols is further subdivided into more detailed methodologies and aspects. A full evaluation and comparison of the latest studies is proposed according to the underlying parameterization effort, the battery cell used, efficiency, cycle life, charging time, and increase in surface temperature of the battery. The pros and cons of each protocol are scrutinized to reveal possible research tracks concerning EV fast-charging protocols. Full article

The widespread use of electric vehicles is nowadays limited by the “range anxiety” of the customers. The drivers’ main concerns are related to the kilometric range of the vehicle and to the charging time. An optimized fast-charge profile can help to decrease the charging time, without degrading the cell performance and reducing the cycle life. One of the main reasons for battery capacity fade is linked to the Lithium plating phenomenon. This work investigates two methodologies, i.e., three-electrode cell measurement and internal resistance evolution during charging, for detecting the Lithium plating conditions. From this preliminary analysis, it was possible to develop new Multi-Stage Constant-Current profiles, designed to improve the performance in terms of charging time and cells capacity retention with respect to a reference profile. Four new profiles were tested and compared to a reference. The results coming from the new profiles demonstrate a simultaneous improvement in terms of charging time and cycling life, showing the reliability of the implemented methodology in preventing Lithium plating. Full article

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users. Full article

In order to shorten the wireless charging time of electric vehicles (EVs) and achieve stable charging, an adaptive smart control method for EV wireless charging is proposed in the paper. The method dynamically tracks the rechargeable battery state during the whole charging process, realizes multi-stage charging of constant current (CC) or constant voltage (CV) by switching two kinds of compensation networks of bilateral L3C and L3C-C, and regulates the charging voltage and current to make it as close as possible to the battery charging characteristic curve. This method can be implemented because the voltage source connected to the coupler and the compensation networks of bilateral L3C and L3C-C have the CC and CV source characteristics, respectively. On the basis of the established adaptive smart control system of EV wireless charging, the experiments of wireless data transmission and adaptive smart charging were conducted. The results showed that the designed control system had a response time of less than 200 ms and strong anti-interference ability and it shortened the charging time by about 16% compared with the time using traditional charging methods, thereby achieving a fast, stable, safe, and complete wireless charging process. Full article

In order to optimize the charging of lithium-ion batteries, a multi-stage charging method that considers the charging time and energy loss as optimization targets has been proposed in this paper. First, a dynamic model based on a first-order circuit has been established, and the model parameters have been identified. Second, on the basis of the established model, we treat the objective function of the optimization problem as a weighted sum of charging time and energy loss. Finally, a dynamic programming algorithm (DP) has been used to calculate the charging current of the objective function. Simulation and experimental results show that the proposed charging method could effectively reduce the charging time and decrease the energy loss, compared with the constant-current constant-voltage charging method, under the premise of exerting little influence on the attenuation of battery capacity. Full article