scholarly journals Detection of Lithium Plating in Li-Ion Cell Anodes Using Realistic Automotive Fast-Charge Profiles

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Matteo Dotoli ◽  
Emanuele Milo ◽  
Mattia Giuliano ◽  
Riccardo Rocca ◽  
Carlo Nervi ◽  
...  
Keyword(s):  
Internal Resistance ◽  
Constant Current ◽  
Fast Charge ◽  
Resistance Evolution ◽  
Charging Time ◽  
Multi Stage ◽  
Battery Capacity ◽  
Electrode Cell ◽  
Cell Measurement ◽  
Range Anxiety

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.

scholarly journals Adaptive Smart Control Method for Electric Vehicle Wireless Charging System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2685 ◽  
Author(s):  
Lingbing Gong ◽  
Chunyan Xiao ◽  
Bin Cao ◽  
Yuliang Zhou
Keyword(s):  
Control System ◽  
Control Method ◽  
Characteristic Curve ◽  
Constant Current ◽  
Voltage Source ◽  
Wireless Charging ◽  
Charging Time ◽  
Multi Stage ◽  
Smart Charging ◽  
Smart Control

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.

scholarly journals An Energy Storage System’s Operational Management and Control Method Considering a Battery System

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 356 ◽  
Author(s):  
Jeong Lee ◽  
Jun-Mo Kim ◽  
Kyung Ryu ◽  
Chung-Yuen Won
Keyword(s):  
Energy Storage ◽  
Control Method ◽  
Thermal Characteristics ◽  
Internal Resistance ◽  
Constant Current ◽  
Efficient Operation ◽  
Charging Time ◽  
Charging Control ◽  
Battery Systems ◽  
In The Beginning

Losses in energy storage systems (ESSs) result from losses in battery systems and power conversion systems (PCSs). Thus, the power difference between the input and output occurs as a loss, which is considered an operational cost. Additionally, since battery systems consist of modules, there is always a temperature difference. Even if voltage balancing is conducted, deviations between the state of health (SoH) and state of charge (SoC) always exist. Therefore, a battery characteristic should be considered in relation to the efficient operation of an ESS. In this paper, charging control is implemented based on the SoC. When errors occur in the beginning, the coulomb counting method (CCM) continues to produce errors; it also calculates the SoC through an improved equation. Thus, it can calculate the SoC by using high-accuracy initial values. Moreover, battery deterioration occurs during charging and discharging, which increases a battery’s internal resistance. This reduces the switching time to the battery cut-off voltage or constant voltage (CV) mode, so it becomes possible to calculate the SoH. Therefore, in this paper, the algorithms and equations are proposed to perform SoH operations according to the charging time that is able to reach CV after charging. A conventional battery is usually charged by using constant current (CC) charging until the voltage of the battery module reaches the cut-off area. A switch to CV then occurs when the cut-off area is reached and maintained. However, SoC-based selective charging control is carried out to prevent heat problems. In addition, the battery is charged safely and efficiently by conducting SoH prediction considering the battery thermal characteristics, which vary depending on the charging time and other characteristics. In this paper, a 3 kW ESS was produced, and the proposed algorithm’s feasibility was verified.

scholarly journals Research on Optimal Charging of Power Lithium-Ion Batteries in Wide Temperature Range Based on Variable Weighting Factors

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1776
Author(s):  
Boshi Wang ◽  
Haitao Min ◽  
Weiyi Sun ◽  
Yuanbin Yu
Keyword(s):  
Ambient Temperature ◽  
Large Scale ◽  
Lithium Ion ◽  
Constant Current ◽  
Specific Situation ◽  
Target Weight ◽  
Charging Time ◽  
Weight Factors ◽  
Multi Stage ◽  
Charging Strategy

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.

Application and Performance Analysis of Lithium Iron Phosphate Battery in Photoelectric Complementary System

2019 ◽  
Vol 14 (12) ◽  
pp. 1709-1716 ◽  
Author(s):  
Weiping Liu ◽  
Ximing Zhang ◽  
Zhaofeng Wang ◽  
Ruijian Wang ◽  
Chen Chen
Keyword(s):  
Power Supply ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Internal Resistance ◽  
Green Energy ◽  
Supply System ◽  
Constant Current ◽  
Battery Capacity ◽  
Remaining Capacity

In order to make the device better capable of power supply, it is necessary to select a corresponding battery, and the lithium iron phosphate battery is favored for its excellent performance. In this study, the requirements of the lithium iron phosphate battery for optoelectronic complementary power supply system were analyzed at first, then the application of the battery in the supply system was further analyzed. Next, the structure and working principle of the battery were deeply studied, and a large number of experiments were designed to analyze the performance of the battery. It has been confirmed by a large number of experiments that the charging process of lithium iron phosphate battery is slow; the remaining capacity of lithium iron phosphate battery has a correlation with the voltage it carries. During the charging process, the internal resistance of the lithium iron phosphate battery will rapidly drop to a stable value, and the conversion of constant current charging to constant voltage charging will further reduce the internal resistance. Adjusting the battery temperature can adjust the internal capacity of the battery, that is, increasing the temperature can promote the expansion of the battery capacity to a certain extent, and lowering the temperature can impair the overall performance of the battery. In the battery AC (alternating current) impedance spectrum test, the impedance of the battery was affected by factors such as inductance, lithium ion diffusion, and internal polarization of the battery. Through the above research, the power consumption of lithium iron phosphate battery can be better understood to make better use of solar energy and provide people with stable green energy.

scholarly journals Charging time influence on dynamic lead acid battery capacity with H2SO4 electrolyte

2018 ◽  
Author(s):  
Muhammad Ghufron ◽  
Kurriawan Budi Pranata ◽  
Istiroyah Istiroyah ◽  
Muhammad Yusmawanto ◽  
Nur Khairati ◽  
...  
Keyword(s):  
Lead Acid Battery ◽  
Charging Time ◽  
Battery Capacity ◽  
Lead Acid

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

2017 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Diagnostic Technique ◽  
Lithium Ion ◽  
Constant Current ◽  
Time Data ◽  
Capacity Estimation ◽  
Voltage Range ◽  
Battery Capacity

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

Swarm Intelligence-Based Optimization for PHEV Charging Stations

2015 ◽  
pp. 374-405 ◽  
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Hybrid Electric Vehicle ◽  
Performance Indicator ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Computation Time ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Charging Stations

In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly non-linear objective function evaluates the performance of both techniques in terms of global best fitness and computation time.

scholarly journals Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2856 ◽  
Author(s):  
Lijun Zhang ◽  
Zhongqiang Mu ◽  
Xiangyu Gao
Keyword(s):  
Lithium Ion Batteries ◽  
Influencing Factor ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Performance Study ◽  
Vibration Direction ◽  
Vibration Condition ◽  
Coupling Conditions ◽  
Battery Capacity ◽  
Vibration Coupling

At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances under temperature, vibration and temperature-vibration coupling conditions. Under the temperature conditions, the discharge capacity of the same battery at the low temperature was only 85.9% of that at the high temperature. Under the vibration condition, mathematical statistics methods (the Wilcoxon Rank-Sum test and the Kruskal-Wallis test) were used to analyze changes of the battery capacity and the internal resistance. Changes at a confidence level of 95% in the capacity and the internal resistance were considered to be significantly different between the vibration conditions at 5 Hz, 10 Hz, 20 Hz and 30 Hz versus the non-vibration condition. The internal resistance of the battery under the Y-direction vibration was the largest, and the difference was significant. Under the temperature-vibration coupling conditions, the orthogonal table L9 (34) was designed. It was found out that three factors were arranged in order of temperature, vibration frequency and vibration direction. Among them, the temperature factor is the main influencing factor affecting the performance of lithium-ion batteries.

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