基于充电电压的锂离子电池组在线均衡技术

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"在线均衡(online equalization)"是锂离子电池组在充电过程中的一种均衡技术，旨在解决电池组中单个电池电压不一致的问题。该技术的重要性和应用主要集中在提高电池包的整体性能和寿命，特别是在电动汽车(Electric vehicle, EV)领域。文章详细介绍了基于充电剩余容量估计(remaining charging capacity estimation, RCCE)的在线均衡策略。作者Yuejiu Zheng等人提出了一种基于RCCE观察器的消散单元均衡(Dissipation Cell Equalization, DCE)算法。这种算法的目标是在充电过程中实时监测并调整电池组内各单元的电压，以实现电池容量的均衡。他们建立了一个电池包系统模型来验证RCCE和DCE的有效性。研究表明，使用RCCE和DCE的电池包，在均衡后与理论上的电池包容量差异很小，这表明该方法能够实现精确且高效的均衡，同时避免过度均衡的问题。过度均衡可能导致电池过充或过放，从而损害电池性能和安全性。在电动汽车中，电池包的性能至关重要，因为电池单元间的差异会导致整个系统的效率降低和寿命缩短。细胞间的不一致性可能源于制造过程中的微小差异，或者在使用期间的循环老化。因此，细胞均衡技术如RCCE和DCE对于确保电池组的一致性、稳定性和整体可靠性具有重要意义。通过对充电电压的监控和分析，RCCE算法可以估计每个电池单元的剩余充电容量，从而指导均衡策略的执行。这种方法不仅有助于在充电过程中平衡电池组，还能减少不必要的能量损失，因为DCE算法是基于实际容量的估计，而不是简单地通过电流注入或抽取来实现均衡。这项研究提供了一种创新的在线均衡解决方案，对于优化锂离子电池包的充电性能，延长电池寿命，以及提升电动汽车的行驶里程和驾驶体验有着积极的影响。关键词包括：电动车辆、电池包、电池变化、单元均衡、容量估计和充电电压。该研究发表于2013年，展示了在电池管理系统(Battery Management System, BMS)设计中，如何利用先进算法有效解决电池组的均衡问题。

However, the NDCE theoretical pack capacity is achieved with

appropriate EAs which should ensure all the cells were fully

charged and discharged and no energy loss should occur during

energy transfer from cell to cell. If the pack is constructed with

cell screening and cell capacity difference is less than 5% during

cycling, pack capacity difference between DCE and NDCE with an

appropriate EA is less than 2.5% (Section 5.1 shows one example).

The cost of NDCE is high and both hardware and EAs are difﬁcult

to realize, but pack capacity attributed by NDCE is small if cell

capacities are properly screened.

Therefore we suggest that DCE is suitable for on-line battery

pack equalization in EVs and NDCE should be applied for off-line

equalization for the purpose of battery pack maintenance.

2.2. Equalization algorithms

As mentioned in Chapter 1, EAs are divided into three categories,

i.e. voltage-based, SOC-based and pack capacity-based EAs.

Voltage-based EAs are the most feasible to realize because of the

direct measured cell voltages. However, real time voltage-based EAs

[15e21] would suffer from over-equalization when the cell capac-

ities and internal resistances are different: some cell need to be

charged by the equalizer for a while, but it is discharged by the

equalizer later/earlier in the same cycle. To solve the problem,

single-point voltage-based EAs are proposed: when the mean cell

voltage reaches a certain voltage, EAs compare the voltages and

begin to equalize [22]. Fully discharged or fully charged voltage-

based EAs which ensure cells are discharged or charged to the

same target voltage are cases of single-point voltage-based EAs. For

SOC-based EAs, over-equalization would also happen because of

the cell capacity variation: The cell with the minimum cell capacity

tends to have a higher SOC when cells are near fully charged state

and it will have a lower SOC when cells are near the fully discharged

state. Similar to single-point voltage-based EAs, single-point SOC-

based EAs are proposed. Fully discharged and fully charged SOC-

based EAs are also cases of single-point SOC-based EAs.

As voltage- and SOC-based EAs cannot directly represent the

ultimate purpose of the equalization (to maximize pack capacity),

pack capacity-based EAs are proposed [14]. However, pack capacity-

based EAs need accurate cell SOC and capacity estimation and thus is

difﬁcult for on-line application.

3. The RCCEeDCE algorithm

As analyzed in Chapter 2, we suggest that DCE is suitable for on-

line battery pack equalization in EVs. The objective of pack

capacity-based EAs for DCE is to make full use of the cell with the

minimum capacity. Nevertheless accurate cell SOCs and capacities

are difﬁcult to estimate on-line. In this part, by RCCE we try to use

DCE to make all the cells with the same RCCs and ﬁnally fully charge

all the cells. Once all the cells are fully charged, the cell with the

minimum capacity is fully used and pack capacity will be the DCE

theoretical pack capacity.

The key idea of RCCE is based on the uniform CCVC hypothesis

which believes that different internal resistances are eliminated by

CCVC vertical translations, different initial remaining cell capacities

are compensated by CCVC horizontal translations and by horizontal

scaling, inﬂuence of different cell capacities are eliminated [13].

We ﬁrst investigate a schematic scene of CCVCs under constant

current charging in the battery pack as shown in Fig. 1.Cell1inthe

solid black curve in Fig.1 reaches the charge cutoff voltage, and it is set

as the reference cell. The internalresistance of Cell 2 is lower than that

of Cell 1 but they have the same capacities and SOCs. According to the

uniform CCVC hypothesis, CCVC of Cell 2 shown in the solid blue curve

(in the web version) is a downward vertical translation of Cell 1’s

CCVC. Because the internal resistance of Cell 2 is lower, the voltage of

Cell 2 does not reach the charge cutoff voltage when pack is fully

charged at time t

. Nevertheless, the slope of Cell 2’s CCVC at time t

large, and if we continue to charge Cell 2, the charging curve would be

a very short blue curve as shown in Fig.1. That indicates the RCC of Cell

2 is so small that we may ignore the inﬂuence of the different internal

resistance for simplicity. Hence, we do not consider the inﬂuence of

the different internal resistance for Cell 3. Instead, we suppose a scene

thatSOC of Cell 3 islower than thatof Cell 1 and the capacity of Cell 3 is

undetermined. According to the uniform CCVC hypothesis, similarly,

CCVC of Cell 3 shown in the solid red curve is a right horizontal

translation and horizontal scaling of Cell 1’s CCVC. If we continue to

charge Cell 3, the possible charging curve might be the black or red

dashed curves as shown in Fig. 1. The dashed black curve ⓪ is a direct

right horizontal translation of Cell 1’s CCVC. Because the capacity ratio

between Cell 3 and Cell 1 k is unknown, the remaining charging curve

of Cell 3 could be scaled as the dashed red curve ① or ②. N evertheless,

the RCC of Cell 3 can be expressed as follows

¼ kI

(2)

where C

is the RCC, I is the constant charging current and k is the

capacity ratio as well as the scaling factor. Because capacity dif-

ference in a battery pack is generally less than 20%, we can set the

bound of scaling factor k ˛ (0.8 1.2). The calculation of

demonstrated as an example in Fig. 1 with dotted black lines:

the difference between pack fully charged time t

and the time

achieved by interpolation of Cell 3’s voltage at t

in CCVC of Cell 1.

As k is undetermined, RCC cannot be precisely calculated. If the

pack is properly constructed with cell screening, k is approximately

1. Thus, we set k ¼ 1 in Equation (2) for RCCE,

¼ I

(3)

8 cells in the same batch with a nominal capacity of 7.5 Ah are

tested to verify the precision of RCCE. Cells are initially fully charged

and afterward are released approximately 1 Ah. Finally the cells are

charged at the same constant current until they reach the charge

cutoff voltage. CCVCs of the ﬁnal charging process are shown in

Fig. 2. If we connect these 8 cells in series, Cell 1 would be fully

charged at t

as depicted in Fig. 2. Other cells could not be further

charged and the charging curves after t

would be unavailable.

Thus, by independently charging the cells with the same current,

the pack charging process can be easily simulated till t

, and the

charging curves of other cells after t

are also available for RCCE

veriﬁcation. Take Cell 6 for instance in Fig. 2, the precision of RCCE

of Cell 6 can be achieved by comparing

and

t which are

Charge cutoff Voltage

Cell 1

Cell 2

Cell 3

Possible

charge curves

- t

+ t

1 2

Fig. 1. Schematic diagram of RCCE based on CCVCs.

Y. Zheng et al. / Journal of Power Sources 247 (2014) 676e686678

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