Conventional Constant Current-constant Voltage Research Articles

Experiment-Based Determination of Optimal Parameters in Constant Temperature–Constant Voltage Charging Technique for Lithium-Ion Batteries Using Taguchi Method

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise.

Health-conscious optimal control of Li-ion cell using simplified full homogenized macro-scale model

This article presents the optimal charging of a Li-ion cell based on a simplified full homogenized macro-scale (FHM) model. A solid electrolyte interface (SEI) layer model is included in the simplified FHM model to quantify cell degradation. With these models, a multi-objective optimal control problem subject to constraints from safety concerns is formulated to achieve health-conscious optimal charging. This constrained optimal control problem is converted to a nonlinear programming problem (NLP). A nonlinear model predictive control (NMPC) strategy is adopted by solving the NLP at each sampling time using the pseudo-spectral optimization method. The effect of the input current upper bound on the cell film resistance Rfilm and state of health (SoH) reveals that Rfilm and SoH are more sensitive to input current upper bound at lower values of input current upper bound. Simulation results show that the simplified model and pseudo-spectral method are crucial for reducing the computational load of the model. The proposed algorithm is more efficient in reducing health degradation than the conventional constant current constant voltage (CCCV) charging algorithm since it can explicitly handle the film resistance and capacity as health parameters. Multiple cycle charging simulations revealed that the health-conscious algorithm decreases health degradation and increases battery life.

Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries

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.

Comparison of Two Different Battery Charging Methods

Abstract This paper describes charging of battery stack using model predictive control (MPC) algorithm. Temperature model and hybrid electrical model are used in algorithm. The battery stack consists of four serially connected valve-regulated lead-acid (VRLA) batteries. The objective of the optimization is to charge the proposed battery stack without violating the constraints in order to maintain the battery health. Validation of MPC algorithm for battery charging is performed using comparison between closed-loop MPC simulations and obtained characteristics of actual MPC charging. Previously, the conventional constant current constant voltage (CCCV) algorithm was proved as good, taking into account the degradation effects but not considering the speed of the charging. The comparison between charging of proposed battery stack using MPC algorithm and CCCV algorithm is performed and the comparison in charging proves MPC algorithm as better compared to the CCCV algorithm.

Determination of constant current to constant voltage switch-over point for health-aware fast charging using heuristic algorithm

High charging times for EVs are a hurdle to mainstream adoption since quick charging shortens battery life. The paper proposes a quick charging method that reduces charging time and battery deterioration. The proposed charging method modifies the conventional Constant Current – Constant Voltage (CC-CV) charging approach, where the switching point from CC to CV mode is altered. Sixteen charging techniques with varied CC-CV switchover points have been explored, comprising nine charging strategies with capacity switching and seven with voltage switching. The gathered data is processed using a mathematical relationship to determine the State of Health, capacity deterioration, and Internal Resistance characteristics. In addition, a Fuzzy Logic Controller is employed to determine the two effective switchover points based on the least amount of cyclic capacity deterioration, SoH, and charging time. The optimal CC-CV transition point between these two points is estimated using Genetic Algorithm Optimization. Compared with the traditional CC-CV charging approach, the effectiveness of the resulting switchover point is evaluated. The results indicate that the recommended charging approach costs an additional time of 4.22 %, and it increases the cell's cycle life by 51.77 % compared to CC-CV. Thus, the comparative analysis reveals that the proposed charge technique offers more efficient performance.

The Effects of Temperature and Cell Parameters on Lithium-Ion Battery Fast Charging Protocols: A Model-Driven Investigation

The risk of lithium plating is a key barrier to lithium-ion battery fast charging. Among other strategies, many alternative charging protocols have been proposed to reduce the plating propensity compared to the conventional constant current-constant voltage (CC-CV) protocol. However, conflicting results have been reported on their impacts on battery lifetime. This work investigates the performance of CC-CV and a boost charging protocol using an electrochemical-thermal model which accounts for nonlinear diffusion and reversible lithium plating. The relative performance of the protocols is found to ultimately depend on the solid phase and ion diffusion timescales. Boost charging is beneficial when both these timescales are short, i.e. in power cells in general or in energy cells at sufficiently high temperatures. The high concentration gradients that develop during the boost stage can sufficiently relax in the subsequent lower current stage to reduce the plating propensity in these cases. When the diffusion timescales are long, boost charging leads to increased plating heterogeneity (driven by the ion diffusion limitations) and slightly increased plating propensity (driven by the solid phase diffusion limitations). Our findings highlight the need to study alternative charging protocols at a wide range of conditions and on different cells before practical deployment.

A Mixed Continuous-Discrete Approach to Fast Charging of Li-ion Batteries While Maximizing Lifetime

Fast charging studies for lithium-ion batteries aim to minimize charging time while maximizing battery lifetime. Real-time optimal control problems are typically solved using empirical or simplified physical models with constraint-based model predictive control (MPC). In this article, we derive physics-based operating modes based on degradative governing equations, which are used to ensure safe use and minimal degradation during long-term cycling. The fast-charging protocols are efficiently and deterministically simulated using a mixed continuous-discrete (aka hybrid) approach to fast charging. This simultaneously solves the battery system of equations and the constraint-based control problem. The approach is evaluated using a Porous Electrode Theory-based model that includes solid-electrolyte interface (SEI) capacity fade. Three physics-based charging protocols are compared to a conventional constant current-constant voltage (CC-CV) protocol. Given identical levels of capacity fade after 500 cycles, the physics-based protocols uniformly reach a greater charge capacity compared to CC-CV after charging for 10 and 15 minutes. The computational cost of simulating physics-based charging protocols is only about 30% greater than the CC-CV method. The fast charging framework is easily extendable to other battery models, irrespective of model complexity.

The development of optimal charging strategies for lithium-ion batteries to prevent the onset of lithium plating at low ambient temperatures

Abstract This study develops two novel charging strategies for lithium-ion batteries, designed to prevent the onset of lithium plating when the cells are charged at low ambient temperatures. Commercially available 3.1 Ah 18650-type cells with NCA and graphite electrodes have been selected for this study. Experimental results highlight that for these cells, lithium plating can be detected when the cells are charged with a traditional constant-current constant-voltage (CC-CV) profile at an ambient temperature of 5 °C and a charge rate of 1C. The occurrence of lithium plating is known to lead to a considerable capacity reduction. To avoid the onset of lithium plating with minimum impact on the charge time, two optimal charging strategies are proposed. The first is based on detecting the onset of lithium plating through the online analysis of the voltage relaxation profile (VRP). The second is to manage the cell charging process to achieve a pre-defined rate of battery degradation per charge cycle. Experimental results highlight that the capacity fade of cells using the proposed charging strategies can be significantly reduced compared to when charged using the conventional CC-CV approach by 45% and 70% respectively while minimizing their impact on the charging speed.

Adaptive Charging Strategy With Temperature Rise Mitigation and Cycle Life Extension for Li-ion Batteries

Charging battery with a large C-rate current to shorten the charging time (CT) will induce the drastic electrochemical reaction, and thus bring about the significant temperature rise (TR), energy loss, performance degradation, and safety concern as well. To tackle this problem, an adaptive charging strategy with TR mitigation and cycle life extension is proposed in this study. Based on the relationship between the charging current and remaining state of charge (RSOC) which is extracted experimentally, a baseline charging current is determined first. To control the temperature rise further, a fuzzy TR controller (FTC) is devised to fine-tune the charging current according to the temperature gradient. Thus an adaptive charging current with temperature rise suppression mechanism can be generated dynamically in the entire charging process. The proposed charging strategy is validated by means of experimental studies and compared with the conventional constant current-constant voltage (CC-CV) method. The results show that the average temperature rise, charging efficiency, and estimated cycle life have 31.24%, 2.06%, and 57.3% of improvement respectively as compared with that of the conventional CCCV obtained.

Optimal Charge Pattern for the High-Performance Multistage Constant Current Charge Method for the Li-Ion Batteries

In this paper, a method is introduced to find out the optimal charge pattern (OCP) of the multistage constant current (MSCC) charge method based on the equivalent circuit model of a Li-ion battery. Unlike the conventional methods, the proposed one does not require several kinds of time-consuming and labor-intensive experiments. The OCP can be simply calculated by the developed equations with the results obtained by a simple experiment. The validity of the proposed method has been verified through the experimental results and it shows that the charge time and the charge energy can be reduced by 12% and 1.8%, respectively, and the charge efficiency of a Li-ion battery can be improved by 0.54% as compared to the conventional constant current-constant voltage (CC-CV) method. A lower temperature rise during the charge can be regarded as an additional advantage of the proposed method.

태양광 배터리 충전기를 위한 개선된 충전 알고리즘

In this paper, the proposed charging algorithm is converted from the charging mode to compensate the transient state in the solar battery charging system. The maximum power point tracking (MPPT) control methods and the various charging algorithms for the optimal battery charging are reviewed. The proposed algorithm has excellent transient characteristics compare to the previous algorithm by adding the optimal control method to compensate the transient state when the charging mode switches from the constant current mode to the constant voltage mode based on the conventional constant-current constant-voltage (CC-CV) charging algorithm. The effectiveness of the proposed method has been verified by simulations and experimental results.

Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

Lithium ion (Li-ion) batteries have been widely used in various kinds of applications, including consumer electronics, green energy systems and electrical vehicles. Since the charging method has a significant influence on the performance and lifetime of Li-ion batteries, an intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based) five-stage Li-ion battery charger is proposed. The proposed charger takes the temperature rise and the gradient of temperature rise of battery into account, and adjusts the charging current accordingly. To further improve the performance of the proposed FLC, the fuzzy-based Taguchi method is utilized to determine the optimal output membership functions (MFs). Comparing with the conventional constant current-constant voltage (CC-CV) method, the charging time, charging efficiency, average temperature rise and the obtained cycle life of the Li-ion battery are improved by about 58.3%, 1.65%, 26.7% and 59.3%, respectively.

Intelligent Optimal Energy Management System for Hybrid Power Sources Including Fuel Cell and Battery

In this study, an intelligent optimal energy management system is designed for hybrid power sources including a fuel cell (FC) system and a battery module. In the proposed intelligent optimal energy management system, a simple fan temperature control is introduced to reduce the possible energy waste during the startup of the FC system. Moreover, a fuzzy hydrogen control is designed to manipulate the FC system stably and generate the same unit power with less hydrogen. In addition, a stable adaptive current-voltage fast-charging control is investigated to improve the charge speed in conventional constant current/constant voltage (CC/CV) scheme with proportional-integral control. The objectives of fast charging, energy saving, power source protection, and system stability assurance can be simultaneously achieved. Furthermore, the effectiveness of the proposed intelligent optimal energy management system is verified by experimental results. Its merits are indicated by comparing a conventional management system without the fan temperature control and with a fixed hydrogen pressure and a CC/CV charging framework.

Search for an Optimal Five-Step Charging Pattern for Li-Ion Batteries Using Consecutive Orthogonal Arrays

Lithium-ion (Li-ion) batteries have emerged as the major power source for today's electronic products because they can offer advantages such as high energy density, low maintenance requirement, high open-circuit voltage, low self-discharge rate, and lack of memory effect. The performance and lifespan of Li-ion batteries are closely related to the quality of the charging pattern. Therefore, an optimal charging pattern is essential for Li-ion batteries to achieve shorter charging time and longer cycle life. In this paper, a Taguchi-based algorithm is presented. Consecutive orthogonal array techniques are utilized to determine the optimal charging pattern for the five-step constant current charging method. The problem formulation, implementation procedure, and parameter setting method are described and explained in detail. Experimental results show that the obtained charging pattern is capable of charging the Li-ion batteries to 95% capacity. Comparing to the conventional constant current-constant voltage method, the benefits of the obtained pattern are faster and safer charging and longer battery cycle life.

Search for an Optimal Rapid Charging Pattern for Lithium–Ion Batteries Using Ant Colony System Algorithm

Commercial lithium-ion batteries are playing important roles as supplies for mobile phones, laptop computers, and other electronics. In order to maximize the performance of lithium-ion batteries, an advanced rapid charging pattern is required. In this paper, an Ant-Colony-System (ACS)-based algorithm is presented. The proposed ACS-based algorithm can be integrated into a commercially available battery tester to search for the optimal rapid charging pattern. Experimental results show that the obtained rapid charging pattern is capable of charging the lithium-ion batteries to 70% capacity in 30 min. The obtained pattern also provides 25% more cycle life than the conventional constant current-constant voltage method.