Lithium-Ion Battery Charge Equalization Algorithm for ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 53, NO. 3, MAY/JUNE 2017

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Lithium-Ion Battery Charge Equalization Algorithm for Electric Vehicle Applications Mohammad Abdul Hannan, Senior Member, IEEE, Md. Murshadul Hoque, Student Member, IEEE, Seow Eng Peng, and M. Nasir Uddin, Senior Member, IEEE

Abstract—The lithium-ion batteries are commonly used in electric vehicle (EV) applications due to their better performances as compared with other batteries. However, lithium-ion battery has some drawbacks such as the overcharged cell which has a risk of explosion, the undercharged cell eventually reduces the life cycle of the battery, and unbalanced charge in series battery gradually reduces overall charge capacity. This paper presents a battery charge equalization algorithm for lithium-ion battery in EV applications to enhance the battery’s performance, life cycle, and safety. The algorithm is implemented in series-connected battery cells of 15.5 Ah and 3.7 V nominal each using a battery monitoring integrated circuit for monitoring and equalization of an 8-cell battery pack using a bidirectional flyback dc–dc converter as the channel for charging and discharging of the battery cell. The obtained results show that the developed charge equalization controller algorithm performs well in equalizing both undercharged and overcharged cells, and equalizes the cell within the safety operation range of 3.81 V. To validate the charge equalizer performance, the proposed algorithm outperforms with other studies in terms of balancing, equalization speed, low power loss, and efficiency. Thus, the proposed battery charge equalization algorithm proves an effective and automated system to modularize the battery charge that improves the safety and life cycle of battery. Index Terms—Charge equalization algorithm, charging and discharging lithium-ion battery, electric vehicle (EV), lithium-ion battery, monitoring integrated circuit (IC).

I. INTRODUCTION OWADAYS, the vehicles on the road are mainly operated with the combustion engine and fossil fuel is used as the power source. However, the use of fossil fuel poses some issues on the environmental problem and has the risk of fuel shortage

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Manuscript received November 4, 2016; revised January 18, 2017; accepted January 29, 2017. Date of publication February 22, 2017; date of current version May 18, 2017. Paper 2016-IACC-1257.R1, presented at the 2016 IEEE Industry Applications Society Annual Meeting, Portland, OR, USA, Oct. 02–06, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Industrial Automation and Control Committee of the IEEE Industry Applications Society. This work was supported by M. A. Hannan under Grant 06-01-02-SF1060 and Grant DIP-2015-012. M. A. Hannan is with the Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia (e-mail: [email protected]). M. M. Hoque and S. E. Peng are with the Department of Electrical, Electronic, and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia (e-mail: [email protected]; [email protected]). M. Nasir Uddin is with the Department of Electrical Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada (e-mail: muddin@ lakeheadu.ca). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIA.2017.2672674

in the future. This contributes to the need for development of the pure electric vehicle (EV). EV is operated based on the energy store in the battery pack and the ultimate power source is the electricity supplied from the generating plant. Moreover, the advanced regenerative braking used in EV helps in reducing the energy waste. The braking process of the vehicle absorbs its energy, converts it back to electrical energy, and returns it to the batteries [1], [2]. In EV, the important characteristic is that the electrical energy would be efficiently used to power electric motors and the other basic systems of a battery-powered EV. The EV is categorized as a system with higher engine efficiency and no polluted emission through tailpipe, fuel evaporation, or fuel refining and make it known as “zero emission vehicle” [3], [4]. There have been many efforts to find and develop a battery pack for EV which can provide long lasting and high total storage capacity of energy. Currently, lithium-ion battery is the well-known battery type used in EV due to its specific characteristics and better performance as compared to its counterparts. Lithium-ion battery has high energy and power density, high energy capacity, no memory effect, long life spam, low selfdischarge, and low temperature effect [5], [6]. However, it has some drawbacks such as overcharged cell has a risk of explosion, undercharged cell eventually reduces the life cycle of the battery, and unbalanced charge in series battery gradually reduces overall charge capacity [5], [7]. A battery management system (BMS) as the battery charge controller is essential to enhance the battery’s performance, life cycle, and safety. Thus, the BMS has to be effective in performing battery charge equalization. A number of developments of BMS with charge equalization control have been serving the existing EV systems with the fulfillment of their minimum requirements [1]–[3]. Nevertheless, the existing BMS still have some problems in cell charge equalization. The more common method is using the resistive current shunt. The battery is discharged through the shunt resistor which is very simple in design, control, and execution. However, the heat energy is lost through resistor and it shortens the battery runtime [7]. The equalization control is simple in the switched capacitor control in terms of control and execution. Switched capacitor method is an energy recovery cell-balancing method where capacitor is used as energy storage to transfer excess energy from high voltage cell to lower voltage cell, but it is imperfect for fully balancing due to the voltage drop at the switches [8]. The inductor and transformer based equalization control methods are effective in EV application. A centralized

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multiwinding transformer with isolated forward converter modules is across each battery cell. When overcharging occurs, the corresponding switch of the cell is turned ON and the voltage across all windings is dictated by the overcharged battery cell. This system has effective low cost but it suffers from magnetizing loss, voltage and current stress, efficiency, high cost, and implementation problem [9]–[11]. The power converters such as buck–boost [13]–[15], resonant [16]–[19], are also the energy recovery cell-balancing methods and highly used in EV due to their high equalization speed, high efficiency, and reliability. They consist of bidirectional dc–dc converter and switches. The systems choose two cells to undergo balancing at one time, the energy flows from the cell with higher energy to the cell with lower energy. However, no more than two cells can be connected and this system requires highly complex procedure, design and control strategy, and it is costly [14], [17]. More research and development [19], [20] toward the betterment of the performance of BMS is still necessary for the efficient energy storage system for EV application. Therefore, this paper proposes the implementation of a charge equalization algorithm using a monitoring integrated circuit (IC). This IC is able to monitor a series of battery up to 12 cells per IC and it is also able to connect and communicate in series with other ICs to monitor up to hundreds of battery cells [21]. For the prototype and algorithm development, one IC is used, and eight battery cells are monitored and equalized. This proposed method is an active equalization that uses two flyback dc–dc converters as the channel for the charge and discharge purpose of the battery pack. The monitoring IC communicates with an NI myRIO as a microcontroller and carries out the modularization algorithm. This project is started with the design of the charge equalization algorithm and simulation using MATLAB/Simulink. Then, it is followed by the model development in the experimental setup. The control program is built in LabVIEW works to fine in controlling the monitoring IC and the overall system. The proposed BMS method is found as an effective and automated system to modularize the battery charge that improves the safety and life cycle of battery. II. OVERVIEW OF BATTERY CHARGE EQUALIZATION In this work, an equalization control algorithm is developed, where the charge is modularized among the battery pack instead of power loss as heat, or charging from external source or discharging to external sink [19]. All the cells are sharing a dc–dc converter and reduce the circuit complexity. A. Battery Charge Equalization The battery charge equalization is necessary to equalize the battery charge level of overcharged cell by transferring the excess energy to cell back or to equalize that of undercharged cell by feeding the required charge from the cell pack. The proposed battery charge equalization algorithm development is divided into two parts, a master board which comprises the microcontroller and flyback dc–dc converter and the module board that consists of the measurement and the cell monitoring device with battery cells and bidirectional cell switches as shown in

Fig. 1. Equalization control system in (a) block diagram and (b) schematic diagram.

Fig. 1. The microcontroller communicates with the monitoring IC where the IC monitors the cell status and switches ON/OFF the bidirectional cell switches. Based on the cell status, the microcontroller executes the charge equalization algorithm and generates regulated pulse-width modulation (PWM) signal for the flyback dc–dc converter to protect and equalize the battery cell by discharging the overcharged cell or by charging undercharged cell accordingly. The flyback dc–dc converter provides the channel of current flow during charging or discharging process. The cell switch allows a specific cell to be connected through the flyback dc–dc converter to cell pack for discharging or charging based on the cell condition as overcharged or undercharged, respectively. The equalization of the cell-1 as undercharged (assume) is performed by switching ON the corresponding bidirectional cell

HANNAN et al.: LITHIUM-ION BATTERY CHARGE EQUALIZATION ALGORITHM FOR ELECTRIC VEHICLE APPLICATIONS

Fig. 3.

Fig. 2.

switch S1 from the respective output control pin of the monitoring IC. Then, the controller generates regulated PWM signal for switch Q1 of the stepdown flyback converter to allow the undercharged cell to be charged from the cell pack. Similarly, the detected overcharged cell-8 can discharge to the cell pack for equalization by activating the corresponding bidirectional cell switch S8 and sending regulated PWM signal to switch Q8 of the stepup flyback converter. The amount of energy carried to the undercharged cell Q1 (Te ) during equalization period Te from the battery pack is contented as 8 1 Q1 (Te ) = Qi (Te ) (1) 7 i=2 which is the average amount of energy released from the battery pack during that time or vice versa, and the average power Pout avg extracted from the pack is equal to the average input power Pin avg multiplied by the converter efficiency η as avg .

Typical circuit diagram of the flyback dc–dc converter.

(SOC) values. This characteristic has a good relationship as defined by (3), which is obtained by curve fitting approximation of Fig. 2. The SOC of batteries can be estimated using

Open-circuit voltage characteristics of the lithium-ion battery.

Pout avg = η × Pin

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(2)

B. Characteristics of the Lithium-Ion Battery Lithium-ion battery is one of the most promising battery technologies now-a-days and has high potential to dominate the battery field for energy storage applications particularly in EVs. Lithium is one of the lightest elements with high reactive and electrochemical potential which makes it an ideal material for battery [5], [6]. Lithium-ion is used instead of metallic lithium so that no metal lithium is formed in the reaction [6]. Lithiumion battery is able to achieve high energy density, good life cycle, higher cell voltage, easier maintenance, and is environmental friendly compared with other type of batteries. Moreover, lithium-ion battery has no memory effect and low self-discharge rate at idle state [2], [4]. Lithium-ion battery has posed some safety concerns regarding the electric, electromechanical, thermal, and mechanical impacts, yet they are still manageable with proper BMS [8], [22], [23]. The high production cost is the main problem for this battery development [1]. Fig. 2 shows the model of 15.5 Ah lithium-ion battery with the experimental results of the open-circuit voltage characteristics at various state of charge

VOC = −8.4306 × 10−10 × SOC5 + 1.9306 × 10−7 × SOC4 −1.4512 × 10−5 × SOC3 + 3.0999 × 10−4 × SOC2 −1.1312 × 10−2 × SOC + 3.4395 (3) which is needed to develop the charge equalization controller (CEC) algorithm. The real-time SOC of the lithium-ion battery can be estimated by 1 Te SOC = SOC0 − i (t) dt (4) C 0 where SOC0 is the initial SOC and C is the nominal capacity of the battery [24]–[26]. C. Flyback Converter Model A bidirectional flyback dc–dc converter is used in this proposed system to transfer energy from the cell to cell pack and vice versa for equalization [7]. The flyback converter model is shown in Fig. 3. The maximum duty of PWM switching signal Dm ax for the flyback dc–dc converter is considered to operate the switch in the safe region of voltage stress expressed as (5) [7]. The turns ratio of transformer n, the filter capacitor Cf of the dc–dc converter, and the mutual inductance of transformer Lm can be calculated using (6)–(8) [7]. The converter efficiency is considered as 85% to regulate the required converter output power by setting the mutual inductance of the flyback transformer, transformation ratio, voltage transmission factor, and filter capacitor of the power converter so that the converter operates with tolerable voltage/current stress for equalization.

Dm ax = n=

Vds nom − Vin m in Vds m in + Vds nom − Vin

N1 ηVin m ax Dm ax = N2 Vout (1 − Dm ax )

(5) m ax

(6)

Cf =

ηPin Dm ax Vout f Vc m ax

(7)

Lm =

(nVout (1 − Dm ax ))2 2ηPin f

(8)

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Fig. 4.


Flowchart of the proposed charge equalization algorithm.

where Vin m in and Vin m ax are the minimum and maximum voltages for the battery cell pack, Vds m in and Vds nom are the minimum and nominal voltage stresses on the switch, N1 and N2 are the number of primary and secondary turns, Vout is the output voltage, Pin is the input power, f is the switching frequency, and η is the converter efficiency. III. CHARGE EQUALIZATION ALGORITHM The modularization method is an active equalization method. The monitoring IC is the sensor to make measurements on the status of the battery cell. The monitoring IC reads the voltage level of the cells and communicates with the microcontroller. The microcontroller collects data from monitoring IC, then estimates SOC and compares the result with the threshold value set, detects the unequalized cell, and sends instruction to the monitoring IC to activate the bidirectional switch of the corresponding detected cell and the operation of the flyback dc–dc converter to carry out charge equalization according to the proposed algorithm. The flowchart of the proposed charge equalization al-

gorithm is shown in Fig. 4. The charge equalization algorithm makes decision based on the SOC of battery cells’ voltage readings obtained from the monitoring IC. The charge equalization algorithm is included the charge equalization for overcharged and undercharged battery, and the unbalanced charge in battery string. The following steps are used for the proposed charge equalization algorithm. 1) Initialize the system. 2) Record the voltage readings, Vi of the battery cells and sort in descending order, V1 –V8 . 3) Check the cells’ status whether they are normal or abnormal. If cells are operated at normal, go to step 2, or if a cell is found as abnormal, go to next step. 4) Check the condition of cell. If V1 > Vm ax , a cell is found as overcharged. 5) Execute overcharge modularization and go to step 7. If V8 < Vm in , a cell is found as undercharged. 6) Execute undercharge modularization and go to step 7. 7) Start the cell equalization process.. 8) Estimate SOCi corresponding to the cells. 9) Check the SOC value for discharge balancing. If SOC1 > OSOC, the respective battery cell of SOC1 is classified as overcharged battery, where SOC1 is the highest SOC reading of the battery cells and OSOC is the threshold value of the overcharged battery SOC, which is corresponding to Vm ax . 10) Execute the discharge mode. 11) Control the bidirectional MOSFET switch corresponding to detected battery cell, generate the PWM signal for discharging the overcharged battery cell, control the stepup flyback dc–dc converter, and allow the equalization current for discharging. 12) Check the balancing of discharging cell. If |SOC1 − SOCavg | > 2%, the respective cell is unbalanced, where SOCavg is the average SOC of all battery cells and |SOC1 − SOCavg | is ΔSOC. Go to step 7, or if the cell is balanced, go to step 2. 13) Check the SOC value for charge balancing. If SOC8 < USOC;the respective battery cell of SOC8 is classified as undercharged battery, where SOC8 is the lowest SOC reading of the battery cells and USOC is the threshold value of the undercharged battery SOC which is corresponding to Vm in . 14) Execute the charge mode. 15) Control the bidirectional MOSFET switch corresponding to detected battery cell, generate the PWM signal for charging the undercharged battery cell, control the stepdown flyback dc–dc converter, and allow the equalization current for charging. 16) Check the balancing of charging cell. If |SOC8 − SOCavg | > 2%, the respective cell is unbalanced, where |SOC8 − SOCavg | is ΔSOC, go to step 7, or if the cell is balanced, go to step 2. The algorithm repeats to maintain the continuous execution of the battery charge equalization process.


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TABLE I SPECIFICATION OF THE PROPOSED SYSTEM Part

Value

Battery cell Flyback Converter switch Bidirectional Cell switch Stepdown Transformer Stepup Transformer

Fig. 5.

Experimental hardware setup.

Cin Cou t Sampling Frequency Microcontroller Monitoring IC Interface

Lithium-ion MOSFET

nMOS Dual pMOS Dual nMOS N1 : N2 Lm N 1 : N 2 Lm

15.5 Ah, 3.7 V FQPF7N10 IRF7811A FDS9958 FDS9945 80:35 25e–6 H 35:80 2e–6 H 25e–5 F 22e–5 F 40 kHz NImyRIO LTC6804-2 LabVIEWworks

IV. PROTOTYPE IMPLEMENTATION A. Hardware Configuration The hardware part is divided into two parts, a master board which is the microcontroller and a dc–dc converter for charge equalization and the module board where the measurement and the cell monitoring devices are located. The circuit diagram in hardware implementation of the system is shown in Fig. 5. The system is initialized by setting up all hardware parts together according to the design requirement and confirming the equalization control switch. The equalization algorithm operation is started by detecting the unprotected or unequalized cell. The microcontroller used in master board is the National Instruments NI myRIO. Its main function is to communicate with the monitoring IC and to generate PWM signal for the dc–dc converters. The flyback dc–dc converters are the channel of current flow during charging and discharging processes. When the MOSFET switch is turned ON at duty ON of PWM signal, the primary coil of the transformer charges from the overcharged cell. When the switch is open at duty OFF stage of PWM signal, the stored energy in primary coil is transferred to the secondary coil and the charge is shifted to the battery pack during overcharged battery discharging. The cycle is repeated to achieve cell balancing [12], [19]. The procedure is same to the undercharged cell where the flyback dc–dc converter is in reverse direction, the equalization charge is transferred from battery string to the selected undercharged cell. In this system, the flyback dc–dc converter is controlled by the PWM signal from the microcontroller with frequency of 40 kHz. The converter operates in a closed loop system where the converter output is maintained and corrected with minimum error through the feedback by auto-adjusting of duty of PWM signal. The main components in module board are the lithium-ion battery cells, the monitoring IC, and the MOSFET cell switches. The monitoring IC used is LTC6804-2 that is specially produced for the BMS by Linear Technology [21]. It can monitor up to 12 cells per IC, and can be cascaded up to 16 ICs together to monitor more number of battery cells. There is a built-in MOSFET switch under each cell inside of IC that can control the external circuit or switch for the battery cell to be charging or discharg-

ing. The MOSFET cell switch performs as the bidirectional switch between the battery cell and the dc–dc converter that allows the detected cell to be connected for charging or discharging. The monitoring IC is configured to monitor the battery cells in such a way that it sends the cell voltage values to the microcontroller, and activates the MOSFET cell switch corresponding to the detected unbalanced battery cell according to the microcontroller instructions. In this prototype, only one slave board and eight battery cells are used. The specification of the proposed charge equalizer is presented in Table I. B. Software Interfacing In the experimental setup, the NI myRIO microcontroller communicates with monitoring IC and LabVIEW in host PC by using serial peripheral interface (SPI) communication interface. The microcontroller is programmed with the equalization control algorithm and the control interface is created in host PC by using LabVIEW. In the control interface, an indicator for the IC registers, battery voltage readings, and charging/discharging status are presented to figure out the overall system performance. A control program is set for the over charged and undercharged threshold values, charging and discharging time, and PWM control for the dc–dc converter. V. RESULTS AND DISCUSSIONS A. System Testing The proposed prototype is built to monitor the equalization among eight lithium-ion battery cells. Table II shows the comparison between the voltage readings obtained by the monitoring IC and manual reading by using a multimeter. A negligible error is found between the two sets of readings of cell voltage values. The program is tested with NI myRIO and the monitoring IC. The switches are controlled by the signal sent from the monitoring IC to construct the bidirectional channel for equalization current flow. However, the charging or discharging process starts only when the dc–dc converter is activated by the PWM signal

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TABLE II BATTERY CELL VOLTAGE READING Battery

Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 Cell 8

Voltage (V) IC Reading

Multimeter Reading

3.596 3.751 3.855 3.828 3.919 3.825 3.828 3.822

3.570 3.730 3.831 3.800 3.890 3.797 3.800 3.796

TABLE III CHARGING/DISCHARGING TIME AT DIFFERENCE SOC Charging

Error

Δ SOC (%) 5 10 15

+0.026 +0.021 +0.024 +0.028 +0.029 +0.028 +0.028 +0.026

Fig. 6.

DC–DC converter waveforms at charging mode.

Fig. 7.

DC–DC converter waveforms at discharging mode.

Time (s) 2412 4865 7321

Discharging Δ SOC (%) 5 10 15

Time (s) 2842 5731 8617

Fig. 8. Results of charge equalization of lithium-ion battery cells at ΔSOC of 5%, 10%, 15% during charging and discharging.

from the microcontroller. At any certain time, only one unbalanced cell undergoes in equalization process. B. Charging Evaluation The flyback dc–dc converter plays an important role in the charge transfer during charging and discharging. There are two flyback dc–dc converters used in two modes, which are charging and discharging. In the charging mode, the converter processes the equalization by transferring the required charge from battery string to the detected undercharged cell. Fig. 6 shows the waveforms of the input and output voltages, and the charging current during charge mode. In Fig. 6, the first waveform is

Fig. 9.

Voltage levels of battery charge equalization.


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TABLE IV COMPARISON ANALYSIS OF THE PROPOSED CHARGE EQUALIZER Type → Parameter ↓ Speed Execution Control Power loss Cost Size Voltage/Current Stress Switch (for 8 cells) Balancing component Efficiency

Shunting Resistor

Switched Capacitor

Multiwindings trans-former

Buck–boost Converter

Resonant converter

Proposed

S E E H L E N/N 8 (1 × 8) 8R L

S VG G L M G L/L 16 (2 × 8) 8C S

G G G L H S H/H 8 (1 × 8) 8T G

E VG S N H G L/L 14 (2 × 8 − 2) 8 BBC G

G S S N H S H/L 14 (2 × 8 − 2) 8 RC G

E E G N M VG L/L 18 (2 × 8 + 2) 2 FC G

E—Excellent; VG—Very good; G—Good; S—Satisfactory; H—High; M—Medium; L—Low; N—Negligible; R—Resistor; C—Capacitor; T—Transformer; BBC—Buck– boost converter; and FC—Flyback converter.

the input voltage supplied by the battery string, about 31.63 V, the second waveform is the charging voltage approximate 4.32 V, the third waveform is the output current of the charging converter, also is the charging current of the cell known as equalization current which is 1.75 A in average.

During the higher charging and discharging equalization current, the charge of the battery flows in or out at a higher rate compared with lower current. During charge equalization operation by charging or discharging, the bidirectional flyback converter operates at the efficiency of approximately 92%, where the losses mainly contribute for the switching, winding, and diode conduction of the converter.

C. Discharging Evaluation In the discharging mode operation of flyback dc–dc converter, the step-up converter performs the equalization by transferring the excess energy from the detected overcharged cell to the battery string. Fig. 7 presents the waveforms of input–output voltage and current for the discharging mode. The first waveform is the input voltage of the converter which is the voltage value of overcharged cell of 3.90 V. The second waveform is the output voltage of converter connected to the battery string, about 31.684 V, slightly higher than the battery string voltage. The third waveform is the input current to the converter which is the discharge current of the unbalanced overcharged cell known as equalization current. The discharge current is at a mean about 1.0 A. The input and output voltages of both converters are not perfect as the open-circuit battery reading due to the connection between two sources and some drops in the circuit components.

D. Charge Equalization Evaluation Table III presents the equalization time for the battery cell at SOC difference (ΔSOC) of 5%, 10%, and 15% to be charged and discharged to the average level of SOC of battery cells. The average SOC is about 38% for the 15.5 Ah lithium-ion battery cell of nominal voltage of 3.70 V. The SOC of the unequalized battery increases when charging and decreases while discharging with longer time as more SOCs differ from the average SOC as depicted in Fig. 8. As data in Table III, with a large ΔSOC, longer time is needed to charge or discharge the unequalized battery to the average value. In comparison between the charging time and discharging time, the battery takes shorter time to be charged than to be discharged at the same ΔSOC. This is due to larger charge equalization current than the discharge equalization current as shown in Figs. 6 and 7.

E. Cell Equalization Profile Fig. 9 shows the cell equalization profile before and after the charge equalization process. The columns in Fig. 9 present the cell voltage values at unequalized state. The area depicts the equalized feature of lithium-ion battery cells in Fig. 9. The undercharged cell (see cell1 in Fig. 9) is allowed to be charged and the overcharged cell (see cell5 in Fig. 9) is considered to be discharged for equalization by means of the proposed charge equalization algorithm. The cell voltage levels are obtained at approximate 3.81 V after charge equalization. F. Comparative Analysis of the Proposed Charge Equalizer The proposed battery charge equalizer is compared with the common equalizer that is illustrated in Table IV [8]–[20]. The comparison analysis is depicted in Table IV based on the speed of balancing, execution, design and control difficulty, electrical stresses, power loss, and efficiency. Moreover, the analysis describes the size and cost of design, and required number of switches and balancing component for 8-cell battery charge equalization. The shunting resistor equalizer is simple and cost effective, nevertheless, its balancing speed and efficiency are low; it loses power into heat and needs temperature control [7], [9]. The switched capacitor equalizer is simple in implementation and cheap, nonetheless, the efficiency and balancing speed are satisfactory [8]. The multiwindings transformer equalizer has good balancing speed and efficiency; however, it has high electrical stresses, magnetizing loss; the design is bulky and expensive; and needs complex control [9]–[11]. The balancing speed, execution, and efficiency are excellent with low stress in the buck–boost converter equalizer; nevertheless, it is costly and complex [13]–[15]. On the contrary, the efficiency and balancing speed are good with low power loss in the resonant converter

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equalizer; however, the voltage stress is high; it needs complex design and is expensive [16]–[18]. The proposed battery charge equalizer has excellent balancing speed, execution, good control, and efficiency. The power loss and stresses are low. It needs less balancing components compared to other equalizers. However, it needs more switch and intelligent control. The proposed battery charge equalizer is a bidirectional CEC for the battery cell charge and discharge balancing and could be applicable for battery storage equalization in EV with high rating and modular design. VI. CONCLUSION A novel battery charge equalization algorithm among lithiumion battery cells for EV applications has been presented in this paper. The detail development of the control algorithm, operating principle, and prototype implementation for the proposed charge equalization system has been provided. Only eight lithium-ion battery cells and one slave board are used for testing and implementation. It has been found from real-time results that the proposed charge equalization algorithm is able to equalize and modulate the battery charge in order to improve the efficiency, reliability, and safety issues of the battery drive system. The results also indicate that utilizing same algorithm more slave boards can be implemented to monitor and equalize the charging among more cells, possibly up to 100 cells, to achieve the real power and voltage supplies in EV applications. However, the cost of the proposed charge equalizer will vary for additional monitoring ICs and bidirectional switches with increase of battery cell which might be acceptable with the cost of battery pack itself. Moreover, the proposed charge equalization algorithm reduces the risk of explosion for overcharged cells and improves the life cycle of undercharged cells. Furthermore, for real-life applications conditions of charging and discharging of the battery may also be taken into consideration based on the battery charge flow difference when the battery is in use during charging or idle condition. A comparison is made to show outperformance of the developed algorithm with the other studies. REFERENCES [1] X. Hu, J. Jiang, B. Egardt, and D. Cao, “Advanced power-source integration in hybrid electric vehicles: Multicriteria optimization approach,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7847–7858, Dec. 2015. [2] M. A. Hannan, M. M. Hoque, A. Mohamed, and A. Ayob, “Review of energy storage systems for electric vehicle applications: Issues and challenges,” Renew. Sustain. Energy Rev., vol. 69, pp. 771–789, Mar. 2017. [3] X. Hu, N. Murgovski, L. M. Johannesson, and B. Egardt, “Optimal dimensioning and power management of a fuel cell/battery hybrid bus via convex programming,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 1, pp. 457–468, Feb. 2015. [4] X. Hu, N. Murgovski, L. M. Johannesson, and B. Egardt, “Comparison of three electrochemical energy buffers applied to a hybrid bus powertrain with simultaneous optimal sizing and energy management,” IEEE Trans. Int. Transp. Syst., vol. 15, no. 3, pp. 1193–1205, Jun. 2014. [5] X. Gong, R. Xiong, and C. C. Mi, “Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells,” IEEE Trans. Ind. Appl., vol. 51, no. 2, pp. 1872–1879, Mar./Apr. 2015.

[6] D. Anseán, M. González, V. M. Garc´ıa, J. C. Viera, J. C. Antón, and C. Blanco, “Evaluation of LiFePO4 batteries for electric vehicle applications,” IEEE Trans. Ind. Appl., vol. 51, no. 2, pp. 1855–1863, Mar./Apr. 2015. [7] M. M. Hoque, M. A. Hannan, and A. Mohamed, “Voltage equalization control algorithm for monitoring and balancing of series connected lithiumion battery,” J. Renew. Sustain. Energy, vol. 8, no. 025703, pp. 1–15, Mar. 2016. [8] M.-Y. Kim, C.-Ho Kim, J.-H. Kim, and G.-W. Moon, “A chain structure of switched capacitor for improved cell balancing speed of lithium-ion batteries,” IEEE Trans. Ind. Electron., vol. 61, no. 8, pp. 3989–3999, Aug. 2014. [9] S. Li, C. Mi, and M. Zhang, “A high-efficiency active battery-balancing circuit using multiwinding transformer,” IEEE Trans. Ind. Appl., vol. 49, no. 1, pp. 198–207, Jan./Feb. 2013. [10] S. M. Lambert, V. Pickert, D. J. Atkinson, and H. Zhan, “Transformerbased equalization circuit applied to n-number of high capacitance cells,” IEEE Trans. Power Electron., vol. 31, no. 2, pp. 1664–1343, Feb. 2016. [11] Y. Chen, X. Liu, Y. Cui, J. Zou, and S. Yang, “A multiwinding transformer cell-to-cell active equalization method for lithium-ion batteries with reduced number of driving circuits,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 4916–4929, Jul. 2016. [12] M. Uno and A. Kukita, “Single-switch single-transformer cell voltage equalizer based on forward–flyback resonant inverter and voltage multiplier for series-connected energy storage cells,” IEEE Trans. Veh. Technol., vol. 63, no. 9, pp. 4232–4247, Nov. 2014. [13] M. Tang and T. Stuart, “Selective buck-boost equalizer for series battery packs,” IEEE Trans. Aerosp. Electron. Syst., vol. 36, no. 1, pp. 201–211, Jan. 2000. [14] M. Uno and K. Tanaka, “Single-switch cell voltage equalizer using multistacked buck–boost converters operating in discontinuous conduction mode for series-connected energy storage cells,” IEEE Trans. Veh. Technol., vol. 60, no. 8, pp. 3635–3645, Oct. 2011. [15] Y. Shang, C. Zhang, N. Cui, and J. M. Guerrero, “A cell-to-cell battery equalizer with zero-current switching and zero-voltage gap based on quasiresonant LC converter and boost converter,” IEEE Trans. Power Electron., vol. 30, no. 7, pp. 3731–3747, Jul. 2015. [16] M. Uno and A. Kukita, “Bidirectional PWM converter integrating cell voltage equalizer using series-resonant voltage multiplier for series-connected energy storage cells,” IEEE Trans. Power Electron., vol. 30, no. 6, pp. 3077–3090, Jun. 2015. [17] I.-O. Lee, “Hybrid PWM-resonant converter for electric vehicle on-board battery chargers,” IEEE Trans. Power Electron., vol. 31, no. 5, pp. 3639– 3649, May 2016. [18] K.-M. Lee, Y.-C. Chung, C.-H. Sung, and B. Kang, “Active cell balancing of Li-Ion batteries using LC series resonant circuit,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5491–5501, Sep. 2015. [19] T. Anno and H. Koizumi, “Double-input bidirectional dc/dc converter using cell-voltage equalizer with flyback transformer,” IEEE Trans. Power Electron., vol. 30, no. 6, pp. 2923–2934, Jun. 2015. [20] Y.-S. Lee and M.-W. Cheng, “Intelligent control battery equalization for series connected lithium-ion battery strings,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1297–1307, Oct. 2005. [21] Multicell Battery Monitors- LTC6804 Datasheet, Linear Technology, 2013. [Online]. Available: http://cds.linear.com/docs/ en/datasheet/680412fb.pdf [22] X. Gong, R. Xiong, and C. C. Mi, “A data-driven bias-correctionmethod-based lithium-ion battery modeling approach for electric vehicle applications,” IEEE Trans. Ind. Appl., vol. 52, no. 2, pp. 1759–1765, Mar./Apr. 2016. [23] M. A. Hannan, M. M. Hoque, S. E. Peng, and M. N. Uddin, “Lithiumion battery charge equalization algorithm for electric vehicle applications,” in Proc. IEEE Ind. Appl. Soc. Annu. Meeting, 2016, pp. 1–8. doi: 10.1109/IAS.2016.7731846. [24] X. Zhang and Z. Li, “Sliding-mode observer-based mechanical parameter estimation for permanent magnet synchronous motor,” IEEE Trans. Power Electron., vol. 31, no. 8, pp. 5732–5745, Aug. 2016. [25] J. Meng, G. Luo, and F. Gao, “Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine,” IEEE Trans. Power Electron., vol. 31, no. 8, pp. 2226–2238, Mar. 2016. [26] C. Zhang, L. Y. Wang, X. Li, W. Chen, G. G. Yin, and J. Jiang, “Robust and adaptive estimation of state of charge for lithium-ion batteries,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 4948–4957, Aug. 2015.


Mohammad Abdul Hannan (M’10–SM’17) received the B.Sc. degree in electrical and electronic engineering from the Chittagong University of Engineering and Technology, Chittagong, Bangladesh, in 1990, and the M.Sc. and Ph.D. degrees in electrical, electronic, and systems engineering from the Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia, in 2003 and 2007, respectively. He was a Professor in the Department of Electrical, Electronics, and Systems Engineering, UKM. Currently, he is a Professor with the Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Malaysia. His research interests include intelligent controller development in different applications, power electronics, energy management and storage systems, and artificial intelligence.

Md. Murshadul Hoque (S’16) received the B.Sc. degree in electrical and electronic engineering from the Chittagong University of Engineering and Technology, Chittagong, Bangladesh, in 2006, and the M.Sc. degree in electrical, electronic, and systems engineering from Universiti Kebangsaan Malaysia, Bangi, Malaysia, in 2016. Currently, he is an Assistant Professor with the Department of Applied Physics, Electronics, and Communication Engineering, University of Chittagong, Chittagon. His research interests include power electronics, power conversion, power system control and automation, alternative energy technologies, and smart intelligent systems.

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Seow Eng Peng received the B.Sc. degree in electrical and electronic engineering (Hons.) from the Universiti Kebangsaan Malaysia, Bangi, Malaysia, in 2015. Currently, he is a Digital Hardware Engineer with National Instruments, Malaysia. His main research interests include power electronics, power conversion, power system control and automation, digital signal processing, and smart intelligent systems.

M. Nasir Uddin (S’99–M’00–SM’04) received the B.Sc. and M.Sc. degrees in electrical and electronic engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, and the Ph.D. degree in electrical engineering from the Memorial University of Newfoundland, St. John’s, NF, Canada, in 1993, 1996, and 2000, respectively. Currently, he is a Professor with the Department of Electrical Engineering, Lakehead University, Thunder Bay, ON, Canada, where he is engaged in teaching and research. His research interests include power electronics, electric motor drives, and the application of neural networks.