Constant Current Voltage Research Articles

An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging

Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates, which can be driven by lithium plating, increased solid electrolyte interphase growth due to elevated temperatures, and particle cracking due to mechanical stress. Lithium plating depends on the overpotential of the negative electrode, and mechanical stress depends on the concentration gradient, both of which cannot be measured directly. Techniques based on physics-based models of the battery and optimal control algorithms have been developed to this end. While these methods show promise in reducing degradation, their optimization algorithms' complexity can limit their implementation. In this paper, we present a method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CV$\eta \sigma$T (VEST). The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. We demonstrate the new CC-CV$\eta \sigma$T charging using an electrochemical model with mechanical and thermal effects included. Furthermore, we discuss how uncertainties can be accounted for by considering safety margins for the plating and stress constraints.

Design and Implementation of a Cost-Effective Wireless Charger for an Electric Bicycle

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control algorithm prove to be crucial to the performance of the wireless transfer. Concerning the placement of the secondary coil, the paper proposes and validates a theoretical model to quantify the effects of the surrounding interfering materials. A CC-CV (Constant Current - Constant Voltage) control algorithm has been implemented to validate the theoretical results in these two charging phases. The control, the coils and the compensation networks have been designed to be simple and robust. The theoretical model, the charger and its control have proven to be effective in a real 84-W e-bike.

Light-Load Efficiency Optimization for an LCC-Parallel Compensated Inductive Power Transfer Battery Charger

Wireless power transfer (WPT) techniques have gained wide acceptance across a range of battery charging applications such as cell phones, cardiac pacemakers, and electric vehicles. In a wireless battery charging system, a constant current/constant voltage (CC/CV) charging strategy, regardless of the variation of the battery load which may roughly range from a few ohms to several hundred ohms, is typically adopted to ensure the safety, durability, and performance of the battery. However, system efficiency drops significantly as the load increases in CV mode, especially at very light-load conditions. This paper proposes an efficiency optimization method for an LCC-parallel compensated inductive power transfer (IPT) battery charging system without the help of any additional power converter and control method. The equivalent circuit and resonant conditions of the LCC-parallel compensation topology are firstly analyzed to achieve the load-independent CV output at a zero phase angle (ZPA) operating frequency. Over the full range of CV charging mode, the efficiency of the LCC-parallel resonant tank circuit is analyzed and optimized. An IPT battery charger prototype with 48 V charging voltage and 1 A charging current is implemented. A measured DC–DC transfer efficiency of greater than 90.48% is achieved during the whole CV charging profile.

Quantification of Heat Loss for Different Charging Profiles in a Li-Ion Battery

The thermal characteristics of a battery are vital in order to understand its health and safety. The thermal characteristics are influenced by charging techniques. This paper presents theoretical explanation, experimental validation, and an analytical method to quantify the heat loss influenced by charging techniques for a Li-Ion battery. Constant current constant voltage (CC-CV), sinusoidal ripple current constant voltage, and pulse current constant voltage charging profiles are used as test cases for validation. The electrical and entropic heat generation for different charging profiles are modeled and estimated based on battery equivalent parameters. The estimated heat generation is compared with the heat flux measurement from a differential scanning calorimeter. In addition to the heat flux measurement, temperature, state of charge, terminal voltage, and current were recorded and analyzed.

The effect of pulse charging on commercial lithium nickel cobalt oxide (NMC) cathode lithium-ion batteries

Fast charging is essential for high energy applications such as electric vehicles. Many methods have been proposed for faster charging and increased energy efficiencies. Pulse charging is considered as one of the promising methods for faster and efficient charging. Researchers have proposed pulse charging methods using different pulse frequencies and duty cycles for better performance. However, the effect of pulse charging frequencies on charging time and cycle life is seldom investigated. In this study, rectangular pulse-CV charging experiments on lithium nickel manganese cobalt oxide (NMC) cathode commercial lithium-ion batteries were performed at 50 Hz, 100 Hz, and 1 kHz and compared with benchmark constant current – constant voltage (CC-CV) charging. From the results, it was determined that pulse-CV charging is significantly faster than CC-CV charging. It was also observed that pulse-CV charging at 1 kHz showed comparable discharge capacities to CC-CV charging throughout cycling with an approximate reduction of 17% in charging time. Battery impedance characteristics were examined using electrochemical impedance spectroscopy (EIS) measurements, and equivalent circuit parameters (ECPs) were obtained for analyzing the behavior of the batteries.

Modeling of analog battery management system for single cell lithium ion battery

AbstractThe Li‐ion battery is an energy storage system in consumer and industrial applications. Because of their cell and pack level protection, Li‐ion battery requires a battery management system (BMS). The important function of the BMS is to monitor and protect the Li‐ion battery cells and packs from fault conditions, in order to maximize their lifecycle and increase safety features. In this research article, an analog BMS is presented for the protection of nickel manganese cobalt oxide‐chemistry‐based single‐cell Li‐ion battery. The Analog BMS is a battery protection circuit module that includes battery protection integrated circuit to protect batteries from overvoltage, undervoltage, overcurrent charging, and overcurrent discharging conditions. In addition, the semiconductor switches are used for charge and discharge protection control. This protection circuit module is designed for 18650 single‐cell Li‐ion battery and test the performance of analog BMS based on protection parameters. A real‐time experiment is carried out on analog BMS interconnected with 18650 single‐cell Li‐ion battery and tested under different charge and discharge rates using standard constant current‐constant voltage (CC‐CV) charging method. The experimental results show that the developed analog BMS protected single‐cell Li‐ion battery from overvoltage, undervoltage, overcurrent charging, and discharging conditions under different C/5, C/2, and 1C rates.

Stress and its influencing factors in positive particles of lithium‐ion battery during charging

In this paper, the stress in positive particles of a Li-ion battery during charging is obtained. The effects of the charging rates, charging modes, and structural parameters of the positive electrode on the stress are investigated. A mesoscopic electrochemical–mechanical coupling model for Li-ion battery is built and verified. The SOC, strain, and stress distributions in positive particles during the constant current (CC)–constant voltage (CV) charging process are calculated by the model. The results show that the stress in positive particles quickly increases at the CC charging stage, especially when the state of charge (SOC) of the battery exceeds 80%. Then it slowly increases at the CV charging stage. Under the CC–CV charging mode, the charging rate has little effect on the stress in positive particles at the end of charging. But the distance between particles, the particle radius, and the electrode thickness can affect the stress at the end of charging. The conclusions obtained could provide references for the design and optimization of the charging strategy and mesoscopic microstructure of LIBs.

Hybrid IPT system with natural CC–CV output characteristics for EVs battery charging applications

To charge the battery of electric vehicles (EVs), inductive power transfer (IPT) system has been considered to be more appropriate than traditional plug-in system especially for its security and convenience. This study proposes a hybrid IPT system for EVs charging applications adopting one transmitter coil with series compensation and two receiver coils with series compensation and LCL compensation. With the help of the two receiver coils and proper parameters design, the proposed system can naturally obtain constant current (CC) and constant voltage (CV) outputs and the maximum output power can be naturally limited which can prevent the system from possible overload. Therefore, the reliability of the proposed system is increased. Since the mode switching process can be automatically realised according to the value of battery equivalent load without extra control strategy and circuit, the fixed-frequency control can be used. Therefore, not only zero phase angle condition, but also the soft switching can be achieved. The theoretical analysis is presented with the design process of the IPT coils and resonance parameters for required battery charging profile. The performance of the system is verified by a 3.3 kW experimental prototype. The experimental results coincide well with the theoretical analysis.

An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery

This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth of the solid electrolyte interface (SEI) layer, which is the primary source of degradation of batteries. This article addresses the challenge of minimising SEI layer growth during charging by incorporating the first-order SEI layer growth rate model into a non-linear model predictive control approach. A single particle model (SPM) is used for optimal charging using orthogonal projection-based model reformulation. Gauss pseudo-spectral method is used for the optimisation of charging trajectories. Results of the optimal algorithm are compared with the traditional constant current constant voltage (CCCV) approach without considering SEI layer growth. It is ensured that overpotential caused by lithium plating remains in a healthy regime which is another feature of the proposed strategy. Simulation results are presented to demonstrate the advantages of the proposed charging method.

An investigation into awareness and usage of lead acid batteries in E-rickshaws: A field survey in New Delhi

Objectives: The purpose of this study is to identify the reasons which contributed to the premature failure of the e-rickshaw battery that puts the e-rickshaw operator (ERO) in financial stress. Methods: A field survey was conducted from 1st October 2019 to 30th October 2019, in the North-West Delhi suburbs to extrapolate the shortcomings. Different EROs, charging station operators (CSOs), and battery vendors were interviewed with an administered questionnaire. The simple random sample consisted of 50 EROs, 20 CSOs, and 40 battery vendors. The quantitative data procured was scrutinized through descriptive as well as inferential analysis. Findings: Constant Current (CC) based chargers were found to be the major reason for the shortened life of the battery. These chargers along with other erroneous practices are preventing both the ERO and the CSO to get the full profitability that they could. It was found during the field survey that the e-rickshaw ecosystem is in a dire need of a fast-charging algorithm. This study investigates the technical concepts which are absent in the e-rickshaw technology like modern fast charging techniques such as Constant Current-Constant Voltage (CC-CV), Pulse-Current, Superimposed Pulse Frequency, Intermittent- Current Charing, and Interrupted Charge-Control Techniques (ICCT) and the Hybrid Energy Systems.Novelty/Benefit: The study succinctly points to the technological dots that remain unconnected in the e-rickshaw and shows how the CC-CV based fast charging algorithm and the hybrid energy systems could fill those technological gaps, increase the efficiency and profitability of the e-rickshaw ecosystem. Keywords: E-rickshaw battery life; battery chargers; charging techniques; pulse current; superimposed pulse frequency; interrupted charge-control

A novel deep learning framework for state of health estimation of lithium-ion battery

Abstract The state-of-health (SOH) estimation is a challenging task for lithium-ion battery, which contribute significantly to maximize the performance of battery-powered systems and guide the battery replacement. The complexity of degeneration mechanism enables data-driven methods to replace mechanism modeling methods to estimate SOH. The insight that motivates this study is that the charging curve of constant current-constant voltage charging mode could reflect the magnitude of SOH from the perspective of capacity. The proposed approach is based on a hybrid neural network called gate recurrent unit-convolutional neural network (GRU-CNN), which can learn the shared information and time dependencies of the charging curve with deep learning technology. Then the SOH could be estimated with the new observed charging curves such as voltage, current and temperature. The approach is demonstrated on the public NASA Randomized Battery Usage dataset and Oxford Battery Degradation dataset, and the maximum estimation error is limited to within 4.3%, thus proving its effectiveness.

An improved resistance-based thermal model for prismatic lithium-ion battery charging

This study proposed a dynamic resistance-based thermal model to predict the temperature evolution of a prismatic lithium-ion battery in fast and regular charging strategies. The effects of battery temperature, state of charge (SOC), and charging current on resistance were investigated to form a dynamic heat generation model. This model included both the heat generation of battery body and the heat generation of electrodes. Then the charging resistance-based thermal model was used to predict the temperature distribution and temperature evolution of a 50 Ah prismatic battery under different charging strategies and ambient temperatures. The charging strategies included a multi-stage constant current-constant voltage (MSCC-CV) for fast charging and a constant current-constant voltage (CC-CV) for regular charging. Experiments verified the prediction accuracy of the proposed thermal model. The results showed that the dynamic thermal model accurately predicted the temperature distribution and its evolution under different charging strategies and ambient temperatures. With regard to temperature distribution prediction, the average root mean square errors (RMSEs) of temperature among the five test points were 0.43 °C for the MSCC-CV charging strategy and 0.3 °C for the CC-CV charging strategy. For the temperature prediction under various ambient temperatures, the average RMSEs at different ambient temperatures were 0.34 °C for the MSCC-CV strategy and 0.25 °C for the CC-CV strategy. Finally, the effect of the charging and discharging resistance on the temperature prediction under the MSCC-CV charging condition was studied. Although the discharging resistance-based thermal model could describe the temperature change with charging time, it showed higher predicted temperatures than the tested values and a larger estimation error than the charging resistance-based thermal model.

Optimization of Variable-Current Charging Strategy Based on SOC Segmentation for Li-ion Battery

This paper presents a variable-current charging strategy of Li-ion batteries. Since the battery characteristics vary with state of charge (SOC), it is more reasonable to divide the charging process based on SOC than on cut off voltage. We find an optimal charging pattern of the proposed strategy by Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The Second-order Thevenin model of battery is established to simulate the charging process. Verification experiments are performed and results show that the obtained charging pattern has a temperature and loss reduction of 2.9 °C and 0.5% compared with constant current-constant voltage (CC-CV) strategy under the same charging time and capacity.

MPC strategies based on the equivalent hydraulic model for the fast charge of commercial Li-ion batteries

In this work, we propose and demonstrate two low-computational MPC-based strategies to charge low-capacity commercial Li-ion batteries in the shortest time possible, while limiting the occurrence of degradation mechanisms. To do so, we use a low-complexity electrochemical model (the Equivalent Hydraulic Model, EHM). To reduce the computational burden of the resulting non-convex optimization problem, two approximations of the admissible region are considered: a time-invariant convexification, and a time-varying convex embedding. These approximations are experimentally tested on commercial LCO batteries (Turnigy 160mAh), under controlled ambient temperatures (10, 20, and 30 ∘C) and a non-controlled scenario. They are compared with the Constant Current-Constant Voltage (CCCV) protocol. Experimental results show that the proposed schemes achieve relevant reductions of the charging time with respect to the CCCV in thermal non-controlled and controlled environments. Results also highlight that the time-varying convex embedding does not significantly impact the computational time while further reducing the charging time.

Effect of the Charge Process and Discharge Rate on the Lithium Stripping Process Visibility in LiFePO<sub>4</sub>-Graphite Li-ion Cells during Charge-Discharge Cycling at 0°C

Commercially available 18650 LiFePO4-Graphite Li-ion cells were exposed to charge-discharge cycling at 0°C following two different charge methods: constant current-constant voltage (CC-CV) and constant current (CC), and two different discharge rates: 1 C and 0.2 C. The effect of the charge method and discharge rate on the cell performance was analyzed. The cell exposed to CC charge and 1 C discharge-rate showed a high voltage plateau at the beginning of the discharge voltage, while a high voltage plateau was not observed in the discharge profiles of the cells exposed to a discharge rate of 0.2 C.

Research on Optimization of Charging Strategy Control for Aged Batteries

Rapid charging of lithium-ion batteries has become a major bottleneck for the large-scale deployment of electric vehicles. In this article, a charging optimization method is proposed for lithium ion batteries based on their aging characteristic, which takes charging time and temperature rise into consideration. An electric-thermal battery model is utilized to estimate the battery temperature during charging. A search for the optimal charging current pattern is performed using a particle swarm optimization (PSO) algorithm combined with fuzzy reasoning. The experiments are conducted on batteries with different aging states to verify the proposed method. Besides, the proposed method is compared with traditional constant current-constant voltage (CC-CV) charging method. Results show that the proposed charging strategy has advantages on maximum temperature rise maintenance and charging speed improvement for aged batteries with unknown aging state. The proposed charging method provides a way for aged batteries charging considering charging time and temperature rise.

Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control

Previous research has shown that a drastic reduction of charging time compared to constant current constant voltage (CC/CV) charging protocol is possible while suppressing degradation causes by combining constant charging and pulse discharging current. However, effects of temperature variation on side reaction and lithium plating and effects of the frequency of pulse current on lithium stripping have not been considered. In fact, charging currents and operating temperatures dominantly affect the cycle life of battery, where both side reaction and lithium plating are competing. In this paper, reduced order electrochemical life models are developed and validated against experimental data collected at different currents and temperatures and then used to find out optimal temperatures at given charging currents with respect to degradation rates. Thereafter, an optimal charging current at different SOCs has been found using nonlinear model predictive control and then the optimal temperature is determined from the relationship obtained by the models, which reduces side reaction rate and lithium plating rate. In addition, pulse discharging current is added to promote the lithium stripping that recovers ions out of the plated lithium, where the amplitude and frequency of the pulse current are optimized using the models. Finally, the proposed charging method and the 1 C CC/CV charging method were tested in Battery-In-the-Loop using a large format pouch type lithium ion battery to compare each other, where 61% and 39% of charging time is reduced up to 80% and 100% SOC, respectively, while the capacity fade is comparable.

Comparison of common DC and AC bus architectures for EV fast charging stations and impact on power quality

Abstract This work covers the comparative analysis of common DC and AC bus architectures for grid-connected Electric Vehicle Fast Charging Stations (EVFCS) and addresses the relevant power quality issues. In this manuscript, Unipolar Common DC Bus and Common AC bus topologies are proposed for FCS. Two-level Voltage Source Converter (VSC) is used to connect the EVFCS to the grid through DC and AC buses in both the methods. Converters are connected to the grid through a Distribution Transformer (DT), and the output side is connected to the EV battery chargers. Grid-connected FCS are examined in terms of harmonic generation, and their impact on the host electric grid is evaluated. Voltage and current harmonics of both the topologies with different transformer configurations are compared, and an optimum transformer configuration is proposed. The Unit Template-based Control (UTC) algorithm is also proposed in this work, and the Constant Current-Constant Voltage (CC-CV) strategy is used for the charging control of the batteries. The work is focused on feasibility and comparison of common DC and AC bus FCS and impacts of harmonics generated due to EV charging on the grid power quality. The proposed architectures are then evaluated as per IEC and IEEE Power Quality standards. Power quality impacts are studied by varying the number of EV charging bays. The charging profile of both the architectures is examined by performing steady-state and dynamic analysis. Furthermore, a comparative analysis is also performed by considering different transformer configurations.

Structural Investigation of Quaternary Layered Oxides upon Na-Ion Deinsertion.

Na-ion batteries are emerging alternatives to Li-ion chemistries for large-scale energy storage applications. Quaternary layered oxide Na0.76Mn0.5Ni0.3Fe0.1Mg0.1O2 offers outstanding electrochemical performance in Na-ion batteries compared to pure-phase layered oxides because of the synergistic effect of the P/O-phase mixing. The material is indeed constituted by a mixture of P3, P2, and O3 phases, and a newly identified Na-free phase, i.e., nickel magnesium oxide phase, which improves heat removal and enhances the electrochemical performance. Herein, we structurally investigate, through synchrotron-radiation X-ray diffraction, the modifications occurring after full desodiation, detailing the material structural rearrangement upon Na removal and revealing the effect of two different charging protocols, i.e., constant current (CC) and constant current-constant voltage (CCCV). While the Na-free phase is electrochemically inactive, likely helping in homogenization of the thermal gradient in the electrode during cycling, O-P intergrown phases appear during the extraction of Na ions from interslab layers, and they are dependent on the desodiation level. The application of a constant voltage step at the end of the galvanostatic charge is responsible for a shortening of the interslab distance and a significant volume contraction (-11.9%).

Data-driven smart charging for heterogeneous electric vehicle fleets

The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging processes follow nonlinear charge profiles such as constant-current, constant-voltage (CCCV). Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power consumption. Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available. In this work we propose a data-driven approach for integrating a machine learning model to predict arbitrary charge profiles into a smart charging algorithm. We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models. Each charging process includes the time series of charging power. After preprocessing, the dataset contains 10.595 charging processes leading to 1.2 million data points in total. We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error (MAE) of 126W and a relative MAE of 0.06. Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21% more energy charged compared to smart charging without considering charge profiles. Furthermore, an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions. However, charging features are required including the number of phases used for charging.