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Charging algorithms of lithium-ion batteries: An overview


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This paper presents the overview of charging algorithms for lithium-ion batteries, which include constant current-constant voltage (CC/CV), variants of the CC/CV, multistage constant current, pulse current and pulse voltage. The CC/CV charging algorithm is well developed and widely adopted in charging lithium-ion batteries. It is used as a benchmark to compare with other charging algorithms in terms of the charging time, the charging efficiency, the influences on battery life and other aspects, which can serve as a convenient reference for future work in developing a charger for lithium-ion battery.
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Charging Algorithms of Lithium-Ion Batteries:
an Overview
Weixiang Shen, Thanh Tu Vo, Ajay Kapoor
Faculty of Engineering and Industrial Sciences
Swinburne University of Technology
Hawthorn, Victoria, 3122, Australia
Abstract—This paper presents the overview of charging
algorithms for lithium-ion batteries, which include constant
current-constant voltage (CC/CV), variants of the CC/CV,
multistage constant current, pulse current and pulse voltage. The
CC/CV charging algorithm is well developed and widely adopted
in charging lithium-ion batteries. It is used as a benchmark to
compare with other charging algorithms in terms of the charging
time, the charging efficiency, the influences on battery life and
other aspects, which can serve as a convenient reference for
future work in developing a charger for lithium-ion battery.
Keywords-Lithium-ion batteries; charging algorithms
The fast growth of portable electronic devices during past
decades, such as mobile phones, laptop and tablet computers,
has created huge demand in compact and light-weight
batteries. Among existing battery technologies, lithium-ion
batteries provide one of the best energy-to-weight/volume
ratios and exhibit the appealing features of long life cycles, no
memory effect and low self-discharge [1], [2]. These beneficial
properties have established lithium-ion batteries as a leading
candidate for these applications. Recently, the increasing
concerns on environment and energy sustainability, as well as
continuously decreasing costs and improving safety and
reliability in lithium-ion batteries, have pushed electric vehicle
(EV) industries to adopt lithium-ion batteries, thereby making
EVs more viable and more competitive to conventional
In most of these applications, a battery system consists of
the battery and battery management system (BMS). Battery
charging is playing an important role in the BMS, where the
charging algorithms, namely charging profiles or charging
currents over time, have a strong influence on the battery
performance and life cycles. As such, many charging
algorithms have been developed and implemented [3]. The
algorithms vary in the charging time, the charging efficiency
and the impact on the battery life cycles as well as
implementation complexity, sensors required, cost and
popularity. They range from the almost obviously simple
charging algorithm, such as constant current-constant voltage
(CC/CV), to being most creatively complicated one, such as
multistage charging algorithm with ant colony, which is not
necessarily the most effective. In fact, so many charging
algorithms have been developed that it has become very
difficult to determine which algorithm is most appropriate for a
given application. However, the review of all the algorithms
would be very beneficial to researchers and practical engineers
who are working on the areas of battery charging systems. We
apologize if one or more important charging algorithms, or
significant improvements of, have not been included.
The rest of this paper is arranged as follows. Section II
discusses and analyzes various charging algorithms. Section III
provides the summary of the major characteristics of each
charging algorithm in terms of their implementation
complexity, charging time, charging efficiency, cycle life and
sensors required. The conclusion is drawn in Section VI.
The lithium-ion batteries involve a reversible insertion
(extraction) of lithium ions into/from two porous electrodes
during the discharging (charging) process, where two
electrodes are separated by a foil that prevents electrical
contact, and both two electrodes and the separator foil are
immersed in a liquid electrolyte containing charged species Li
ions [4]. Note that some lithium-ion batteries have a solid
electrolyte, which serves both as ionic conducting medium and
an electrically insulating separator. These lithium-ion batteries
are sometimes called lithium ion polymer batteries or lithium
polymer batteries. However, no matter what electrolyte (liquid
or solid) is used in the battery, the charged species that
intercalates in the battery are the Li
ions, and hence they are
generally named as lithium-ion batteries.
Fig. 1 Charging of lithium-ion batteries
978-1-4577-2119-9/12/$26.00 c
2011 IEEE
The lithium insertion/extraction process occurring with a flow
of ions through the electrolyte is accompanied by a reduction
(oxidation) reaction of two electrodes assisted with a flow of
electrons through the external circuit. Fig. 1 shows the
schematic representation of lithium-ion batteries during
charging, where Li
ions are extracted from the positive
electrode and inserted into the negative electrode. The energy
stored in the lithium-ion batteries through the charging process
depends on the difference in energy states of the intercalated
ion in the positive and negative electrodes [5].
A. Constant current-constant voltage
Among all charging algorithms, the constant current-
constant voltage (CC/CV) charging algorithm is well
developed and widely adopted in charging lithium-ion batteries
because of its simplicity and easy implementation. Under the
arrangement of the CC/CV charging algorithm, a constant
current is applied to charge the battery until the battery voltage
rises to a preset maximum charging voltage (
), then the
charging voltage is held constant at
correspondingly the charging current is reduced exponentially.
The charging process stops when the charging current reaches
a preset small current. Fig. 2 shows the charging profile of the
CC/CV [6].
When the CC/CV was used to develop a charger for a
lithium-ion battery, a few protection measures have to be taken
to protect the battery. Fig. 3 shows the flow chart of the
charging process of a charger based on the CC/CV [7]
including the safety and protection check. It shows that the
charging process of the CC/CV consists of three steps. First,
the battery initial conditions, such as temperature and open
circuit voltage (OCV), are checked if they are in the normal
range. If the OCV is less than a preset cutoff voltage (
the battery is charged by trickle charge (TC) mode with small
current (e.g. 0.1C) until the battery voltage rises to the cutoff
voltage, where 0.1C represents the charging current with the C
representing the nominal capacity of the battery. Second, once
the battery voltage exceeds
, the CC mode starts to charge
the battery. The charging current is chosen by referring to the
specification of the lithium-ion batteries, its range can be
varied from 0.5C to 3.2C [8]. Third, when the battery voltage
charges to
(e.g. 4.2V), the charging process switches to
the CV mode, the battery is charged at the constant voltage of
4.2V and the charging current is reduced correspondingly. The
charging period is terminated by either the minimum charging
current (
CI 1.0
<) or the maximum charging time
) is reached.
Based on the charging current in the CC mode, the total
charging time is varied from 1 hour to 2.5 hours. In general,
the lower the charging current of the CC mode is, the higher
the charging efficiency and longer the charging time and the
battery life. Three sensors are usually required to measure the
battery voltage, current and surface temperatures. The CC/CV
charging algorithm is very easy and cheap to implement as it
does not necessarily require a microcontroller.
Fig. 2 Charging profile of CC/CV
Fig. 3 Flow chart of CC/CV charger
B. Variants of CC/CV charging algorithms
Many variants of the CC/CV charging algorithms were
developed. There are two of them by slightly modifying the
standard CC/CV charging algorithm. One is the double-loop
control charger (DL-CC/CV) [9], as shown in Fig. 4. With
1568 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
positive and negative feedback of the battery voltage ( )(sV
the DL-CC/CV can obtain the charging profile very similar to
the standard CC/CV without sensing the charging current. As a
result, the need for a current sensor is eliminated while still
achieving the similar performance of the CC/CV with the
simplest and lowest cost in hardware implementation.
Fig. 4 Block diagram of double-loop control charger
The other is the boost charger (BC-CC/CV) [10], where the
battery is required to be fully discharged before charging. For
the BC-CC/CV, the CV mode of the maximum charging
(e.g. 4.3V which is 0.1V higher than 4.2V) is
initially used to charge the battery in the boost charging period
(e.g. 5 min.) and the charged capacity can reach around
30% of its nominal capacity. It shows that a significant amount
of charge has been stored in the battery within a relatively
short period
. If this period is extended to 10 minutes, about
60 % of its nominal capacity can be charged into the battery.
After this period, the charging algorithm is then switched to the
standard CC/CV. Fig. 5 shows the charging profile of the BC-
CC/CV. Due to the initial higher charging voltage, the BC-
CC/CV can charge the battery faster than the CC/CV, but it is
required to fully discharge the battery before charging which
requires the discharge circuit. This will increase the number of
components and cost. The necessity of discharge before
charging also makes this charging algorithm inefficient. The
effect of initial higher charging voltage on the battery life has
been investigated. It shows that there is no obvious
degradation within 500 testing cycles.
There are other two algorithms which use the advanced
control to implement the CC/CV. In these two charging
algorithms, the fuzzy-logic control and the grey-predicated
control were used to optimize the charging current in the CV
mode of the CC/CV, which are named as the FL-CC/CV [11]
and the GP-CC/CV [12], respectively. The essential part of
these two charging algorithms is to use the open circuit voltage
to replace the voltage in charge as the changeover voltage from
the CC mode to the CV mode so that the charging current of
the CV mode is larger at higher current part and smaller at the
lower current part than the current in the CV mode of the
standard CC/CV charging algorithm. As a result, more
capacity is able to be charged into the battery within the same
period of the CV mode. Fig. 6 shows the schematic
representation of the charging profiles for the both algorithms,
where a fuzzy-controlled active state of charge controller for
the FL-CC/CV and a grey-predicted technique for the GP-
CC/CV have been used to dynamically determine the
appropriate charging current with the OCV of 4.2V in the CV
mode. Thus, the FL-CC/CV and the GP-CC/CV have a shorter
charging time and a higher charging efficiency. Due to their
complexity and the requirement of high computation power, a
microcontroller is more suitable for implementing both
charging algorithms.
There is one more charging algorithm based on the principle
of the phase-locked loop (PLL) control [13]. The PLL process
naturally coincides with the requirement of the charging profile
of the CC/CV (PLL-CC/CV). Fig. 7 shows the block diagram
of the PLL-CC/CV.
Fig. 5 Charging profile of BC-CC/CV
Fig. 6 Charging profile of FL-CC/CV and GP-CC/CV
Fig. 7 Block diagram of PLL-CC/CV
From Fig. 7, the output of the VCO oscillates a feedback phase
that reflects on the battery voltage
is then compared
with the input reference phase
to produce the phase error
. This phase error
is sent to the current pump to
produce a suitable current to charge lithium-ion batteries. The
battery can be fully charged after many cycles.
Under this PLL-CC/CV arrangement, the auto-tracking
process (the frequency-tracking) is corresponding to the bulk
charge which is similar to the CC mode of the CC/CV. The
auto-locking process (from the phase-tracking to the phase-
locked state) is corresponding to the variable current charge
2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) 1569
and float charge which is similar to the CV mode of the
CC/CV. Fig. 8 shows the flow chart for the charging process of
the PLL-CC/CV. Later, an improved PLL-CC/CV [14] was
proposed (IPLL-CC/CV). The complete charging process
consists of the bulk current charge (CC mode) which remains
the same as that of the PLL-CC/CV and the pulsed current
charge and the pulsed float charge (CV mode) which were
modified from the variable current charge and the float charge,
respectively, as shown in Fig. 9. As the internal pressure
charged by a pulsed current is smaller than that charged by
constant current, the charging efficiency of the improved
IPLL-CC/CV is higher than that of the CC/CV. The total
charging time is similar to that of the CC/CV. Both the PLL-
CC/CV and the IPLL-CC/CV can be easily implemented by
using the IC chip with the PLL function.
Fig. 8 Flow chart for charging process of PLL-CC/CV
C. Multistage current charging algorithm
Multistage current charging (MSCC) algorithm was
developed to charge the battery, where various methods were
proposed to determine the optimal charging currents in each
charging stage of the battery. Fig. 10 illustrates the charging
profiles of the MSCC with 5 charging stages. It is clear that
there are two issues in the MSCC. The first issue is that at what
time the charging process switches from one stage into
another. This was solved by setting the maximum charging
(e.g. 4.2V) and whenever the battery voltage is
charged to
, the charging process will switch over to the
next stage.
Fig. 9 Charging profile of IPPL-CC/CV
The second issue is that the appropriate charging current in
each stage should be must be determined to charge the battery.
So far, five approaches were used to determine the optimal
charging current for each stage. The first approach used a
fuzzy logical controller to determine charging current [15],
where the inputs of the fuzzy controller are the temperature
and the change of the temperature, and the output of the
controller is the charging current. The effectiveness of the
approach depends on the knowledge of the user in choosing
the right error computation and membership functions and
coming up a proper rule base table.
The second approach adopted the consecutive orthogonal
array (or Taguchi method) to search an optimal charging
current profile [16], [17]. The third approach applied the ant
colony system to optimize a charging current profile [18]. The
fourth approach used an integer linear programming to search
an optimal charging current profile [19]. The above-mentioned
three approaches were implemented with the properly designed
experiments with a computer. No matter what approaches are
used, the general flow chart to implement each charging
algorithm is shown in Fig. 11. In this flow chart, the block
highlighted by the shaded area may vary from one to another
as various optimization approaches are chosen, the rest of them
are remaining the same. Generally, the implementation of this
charging algorithm is required a microcontroller or a
computer. The charging speed is faster and charging efficiency
is higher than those of the CC/CV.
D. Pulse charge
The pulse charge have been claimed to be a fast and
efficient charging algorithm for lithium-ion batteries. The
effects of pulse charge on lithium-ion batteries were evaluated
using an electronic network model. Simulation results provide
insight into the effect of the pulses on the internal process,
such as diffusion, migration, electrochemical reactions and
heat generation [20]. Also, the effect of pulse charge on the
cycle life of lithium-ion batteries was investigated using the
experimental approach [21].
1570 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
Many pulse charging algorithms were developed. Basically,
they can be divided into two groups. The first group is the
pulse charge with the constant voltage (CV-PC) in the entire
Fig. 10 Charging profile of MSCC
Fig. 11 Flow chart of MSCC
charging process while the frequency of the pulse (FCV-PC)
[22] or the duty cycle of the pulse (DCV-PC) is changed [23].
The basic idea of the FCV-PC is to adjust the frequency of
the pulse within a certain range and obverse the response of the
charging current. The optimal frequency (
) is obtained
when the battery impedance is minimized and the highest
charging current is achieved. Fig. 12 shows the flow chart of
the FCV-PC. The DCV-PC is very similar to the FCV-PC, the
only difference is that, instead of changing the frequency of the
pulse, it changes the duty cycle of the pulse to achieve the
highest charging current. A prototype of these two charging
algorithms was implemented.
Fig. 12 Flow chart of FCV-PC
It demonstrated that the charging time is shorter than that of
the CC/CV [22], [23] and the charging efficiency is higher and
the cycle life is longer.
The second group is the pulse charge with the constant
current in the entire charging process (CC-PC) while the
battery voltage is monitored to make sure that the voltage is
always lower than the preset maximum charging voltage. The
charging profile can be varied by changing the amplitude and
width of the pulse and the relaxation period between the pulses
[24], [25]. Fig. 13 shows the charging profile of the CC-PC.
With the help of the simulation, the charging time (
) is
selected when the maximum concentration is reached and the
relaxation period (
) is determined such that it provides
sufficient time to a reset concentration.
Fig. 13 Charging profile of CC-PC
2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) 1571
As a result, electrochemical reactions neither produce heat nor
cause the accumulation of pressure inside the battery. Since
this charging algorithm is designed to establish the link
between the pulse charging current profile and the chemical
reaction process, in principle it can charge the battery faster
and more efficient as well as extend battery cycle life.
However, no hardware of this charging algorithm for the
lithium-ion battery has been implemented so far [15]-[16].
With so many charging algorithms available to charge
lithium-ion batteries, it might not be obvious for the users to
choose which one better suit their application needs. However,
Table I summarizes the major aspects of these charging
algorithms which should help in choosing an appropriate
charging algorithm.
I. M
or digital
CC/CV Both L L M L V, I, T
DL-CC /CV Analog L L L L V,T
BC-CC/CV Both H L M L V, I, T
FL-CC/CV Digital M M H M V, I, T
GP-CC/CV Digital M M H M V, I, T
PLL-CC /CV Analog L M M L V, I, T
IPLL-CC/CV Analog L M M M V, I, T
MSCC Digital M M H M V, I, T
FCV-PC Digital H H H H V, I, T
DCV-PC Digital H H H H V, I, T
CC-PC Digital H H H H V, I, T
Notes: H: high, M: medium, L: low, Ch.: charging, Eff.: efficiency,
Imp.: implementation, Comp.: complexity, Para.: parameters
This paper discusses and analyzes the existing charging
algorithms for lithium-ion batteries in the literature. Their
major characteristics are compared in terms of implementation,
charging time, charging efficiency, cycle life and sensed
parameters, which serves as a useful guide in choosing the
right charging algorithms for particular applications.
This research work is supported by Commonwealth of
Australia, through the Cooperative Research Centre for
Advanced Automotive Technology (AutoCRC), under the
project of Electric Vehicle Control Systems and Power
Management (C2-801).
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The environmental concerns and reduction in fossil fuels have become a major concern due to which a large number of electric and hybrid vehicles are being built to minimize the contribution of greenhouse gas emissions from the transportation sector and to increase the efficiency of the overall vehicles. Electric vehicles (EVs) play an important role in today’s development of smarter cities and hence, there is a rapid growth of EVs all around the globe. Although they are found to be environmentally friendly and energy-efficient in comparison with internal combustion engine vehicles but lack of availability of a large number of charging stations at present time limits the use of EVs in the wider perspective. The broader use of EVs would require a huge amount of power from the existing power grids that may hit the prevailing distribution system. Further, charging such EVs equipped with huge battery packs, high power charging stations are essential to charge them at a speed comparable to the conventional oil/gas refueling system. The EVs considered in this study restricts to electric ships and electric cars being two major contributors towards greenhouse gas emissions. In order to address the aforementioned concerns, this study, therefore, presents state-of-the-art based on conventional and current technologies relating to EVs and their charging infrastructure. Further, possible configurations based on the integration of renewable energy sources and stationary energy storage systems are presented to aid the existing power grids. Lastly, challenges along with possible solutions and the future perspective are part of this study.
Batteries are complex systems that need to be properly managed to guarantee safe and optimal operations. Model predictive control (MPC) is an advanced control strategy that, thanks to its characteristics, can be embedded into battery management systems (BMS) to derive optimal charging strategies. However, deterministic MPC, which relies on a nominal model only, is not adequate in a realistic scenario in which cells parameters are not known exactly. In this paper, stochastic MPC is proposed for the optimal charging of a Li-ion battery pack to account for the presence of parameter uncertainties. The adopted scheme relies on the polynomial chaos expansion paradigm for the propagation of uncertainties through the model equations and allows to satisfy safety constraints with a guaranteed probability. The results highlight the advantages of stochastic MPC over different scenarios when compared to a deterministic MPC approach.
Nonaqueous sodium- and lithium-oxygen batteries are of interest because of their high theoretical specific energies relative to state-of-the-art Li-ion batteries. However, several challenges limit rechargeability, including instability of the carbon electrode and electrolyte with reactive oxygen species formed during cycling. This work investigates strategies to improve the cycling efficiency of the Na–O 2 system and minimize irreversible degradation of electrolyte and electrode materials. We show that charging cells with a constant current/constant voltage (CCCV) protocol is a promising technique made possible by the slight solubility of sodium superoxide in nonaqueous electrolytes. In addition, the type of carbon electrode has a significant impact on cell performance and efficacy of the cycling protocol. Graphitic carbon electrodes coupled with CCCV charging demonstrate higher reversibility, more efficient oxygen evolution, and less outgassing than conventional cells using a porous carbon paper electrode and only a constant current charge. Graphical abstract
Electric bicycle (EB) is a common short-distance transportation means in China, Recently, fire incidences caused by thermal runaway of EB batteries occur frequently nationwide, drawing people's attention. Although policies have been made that EB charging is forbidden in residential buildings, people may not obey due to various reasons. Therefore, identification of EB charging load (EBCL) in residential buildings, especially the abnormal batteries with fire danger, is beneficial to public safety. To meet this urgent need, an unsupervised EBCL identification and battery status assessment method based on non-intrusive load monitoring technology is proposed in this paper. At first, the specifications of typical EB batteries are introduced with the demonstration of EBCL signals for batteries in either normal or abnormal status. Then, pre-processing steps including signal transformation, multiple filtering steps, and state transition removal, are proposed. Next, the signal sub-sequences related to EBCL characteristics are obtained via piecewise linear representation. Finally, post-processing steps are proposed to refine the EBCL identification results and detect abnormal batteries. Validation is carried out on the power readings containing charging loads for various EB battery types, collected from real Chinese households. The experimental results show the proposed method outperforms two state-of-the-art benchmarking NILM methods in various metrics.
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This thesis describes the subject of Battery Management Systems (BMS), in particular the design of BMS with the aid of simulation models.
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Current pulsing, unlike potential pulsing, is generally considered ineffective in enhancing mass transport However, we show here, that by properly selecting the pulsed current parameters, the depletion of the reactant at the electrode during cathodic reduction, or its excess during anodic dissolution, can be reduced compared to the corresponding dc current application, while still passing the same amount of charge in an identical amount of time. The advantageous pulsed current modes include a sequence of decreasing current density amplitudes, or pulsing the current at the same amplitude, but with increasing relaxation intervals. By comparison, it is also shown that applying a sequence of constant-amplitude square current pulses at constant time intervals leads to an identical concentration profile as in dc. The application to the battery-charging process is briefly discussed.
This chapter gives general information on Battery Management Systems (BMS) required as a background in later chapters. Section 2.1 starts with the factors that determine the complexity of a BMS and shows a general block diagram. The function of each part in a BMS is discussed in more detail in section 2.2 and examples of adding BMS intelligence are given. The BMS aspects of two types of portable devices are discussed in section 2.3. This serves to illustrate the theory presented in sections 2.1 and 2.2.
An efficient, simple and low cost quick charger based on the double-loop controller is proposed for the charging of lithium-ion (Li-ion) batteries. With positive and negative feedback of the battery voltage, charging profile similar to the constant current and constant voltage (CC–CV) charging strategy can be performed without actually sensing the charging current. The charging time can easily be shortened by raising the level of saturation in the primary voltage control loop. Experimental results are included to demonstrate the effectiveness of the battery charger. The charger could be a low cost and high performance replacement for existing Li-ion battery chargers.
The effect of (dis)charge pulses on lithium-ion batteries is evaluated using an electronic network model. Simulations give insight into the effect of the pulses on the internal processes such as diffusion, migration, electrochemical reactions, heat generation, etc. on time scales from microseconds to hundreds of seconds. The simulated results are verified by experimental measurements on a commercial lithium-ion battery. The relevance of the results for battery charging with short pulses and for the occurrence of short circuit is discussed.
The effects of a pulse charging technique on charge–discharge behavior and cycling characteristics of commercial lithium-ion batteries were investigated by comparison with the conventional direct current (dc) charging. The impedance spectra and cycling voltammograms of Li-ion batteries cycled by both protocols have been measured. The individual electrodes in the batteries have also been examined using XRD and SEM. The results show that pulse charging is helpful in eliminating concentration polarization, increasing the power transfer rate, and lowering charge time by removing the need for constant voltage charging in the conventional protocol. Pulse charging interrupts dc charging with short relaxation periods and short discharge pulses during charging, and also improves the active material utilization giving the battery higher discharge capacity and longer cycle life. Impedance measurements show that the magnitude of the interfacial resistance of the batteries cycled both by pulse charging and dc charging is small. However, at the same number of cycles, the interfacial resistance of the pulse charged battery is larger than that of dc charged. The batteries after 300 cycles charged by pulse charging show higher peak currents during both forward and reverse scans indicating higher reversibility of the electrodes. XRD and SEM studies of the individual electrodes indicate that pulse charging maintains the stability of the LiCoO2 cathode better than dc charging and inhibits the increase in the thickness of the passive film on the anode during cycling.
A novel algorithm applying (0, 1)-integer linear programming is proposed to search the optimal charging profile for rechargeable Lithium-ion batteries. The analysis of charging efficiency and charging time is based on equally segmented battery state of charge (SoC) and the results are re-assembled into a charging profile by (0, 1)-integer linear programming. Experimental results show that the proposed algorithm achieves up to 18.25% and 21.38% charging time reduction in comparison to fast- and slow-charging specifications of a commercial product respectively, subject to a charging efficiency constraint at the same time.
Nowadays, commercial lithium-ion (Li-ion) batteries are playing important roles as supplies for mobile phones, laptop computers and other electronics. In order to maximize the performance of Li-ion batteries, advanced charger is required. The main objective of an advanced battery charger includes short recharge times, high charging efficiencies and improved battery cycle life. This paper presents the design and implementation of a dsPIC-based fuzzy five-step Li-ion battery charging system. To obtain the optimal charging performance for the Li-ion battery, fuzzy-control-based five-step charging algorithm and a simple power stage is used in the proposed system. Using this control, the performance of the proposed system can be improved. In addition to the hardware, a graphical user interface is also presented in this paper. According to the experimental results, the proposed charger is capable of charging the Li-ion batteries with higher efficiency and lower temperature rise.
Lithium-ion batteries are typically charged using constant current that is applied until the cell voltage reaches about , at which time, charging continues at a constant voltage until the full battery capacity is attained. This process is slow, typically requiring . Empirically selected pulse-charging sequences have been shown to provide enhanced charging; however, no model exists to explain and optimize the pulse-charging protocols. Modeling the lithium diffusion into a homogeneous intercalant layer indicates that the lithium concentration reaches saturation at the graphite∕electrolyte interface after about under conventional constant current charging, mandating the shift to the lower rate constant voltage charging. It is shown here that charging the lithium battery using non-dc waveforms with properly selected parameters may circumvent this lithium saturation, enabling charging at significantly higher rates. A nonlinearly decreasing current density profile which conforms to the mass transfer coefficient variation was shown to provide complete charging in less than of an hour, faster than any other pulse-charging profile studied.