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ScienceDirect Energy Procedia 105 (2017) 2837 – 2842

The 8th International Conference on Applied Energy – ICAE2016

A Novel Current Disturbance Estimation Method for Battery Management Systems in Electric Vehicle Jun Xua, *, Shiying Lia, Binggang Caoa a

State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

Abstract Current information is of great importance for battery management systems (BMSs), especially for the estimation of state of charge (SOC), state of health (SOH), etc. However, current information may have large disturbance caused by the current sensor. As a result, the estimated states will suffer large estimation error. This paper tries to estimate the current disturbance, and with the estimated disturbance, the obtained SOC could be accurate. The simplified battery model is utilized to the current disturbance estimation method and the accurate current disturbance could be obtained. The simulation work bench is established to verify the proposed method. The simulation results indicate that the proposed method can accurately estimate the current disturbance. Accordingly, with the accurate estimated current disturbance, the estimated SOC is very accurate too. © Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license The Authors. Published by Elsevier Ltd. ©2017 2016The (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy.

Keywords: Current disturbance; Proportional integral observer; Battery model; State estimation; Battery management system; Electric vehicle

1. Introduction Due to pollution and the energy crisis, Electric vehicles (EVs), including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are of great importance.[1-3] In order to cope with the power and energy demands for such applications, a stable lithium-ion battery pack should be constructed with a large number of battery cells connected in series or parallel. A battery management system (BMS) is utilized to maintain optimum battery performance and ensure safety in EVs. As the key functions of the BMS, the State of Charge (SOC), the State of Health (SOH) should be monitored online. An assortment of techniques has previously been reported to estimate the SOC of the batteries in EVs, * Jun Xu. Tel.: +86-029-8266-8835. E-mail address: [email protected].

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.621

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Jun Xu et al. / Energy Procedia 105 (2017) 2837 – 2842

each having its relative merits. The ampere-hour counting/integral (coulomb counting) method (ACM) is based on current measurement and integration, which is considered as the most common SOC estimation method. However, the prior knowledge of initial SOC is needed and it suffers from accumulated errors of noise and measurement error [4, 5]. Although open-circuit voltage (OCV) method is very accurate, it needs a long rest time to estimate the SOC and thus it cannot be used in real time applications [4]. F.Sun and R. Xiong et.al proposed employed the uncertainty quantification method to solve the uncertainty modeling problems innovatively, which showed excellent performance and high accuracy respectively against uncertain diving cycles.[6] Besides, the sliding mode observer based estimations [7-10], the extended Kalman filtering (EKF) based estimators [11-14], the proportional integral observer(PIO) based estimations [15-18], the support vector based estimators[19], the neural networks (NNs) [20, 21] and the fuzzy logic principle based estimations[22], etc. have been widely applied to estimate the SOC of batteries. Till now, the aforementioned methods have been studied and acceptable achievements have been made in different applications. However, most of the methods stated above are relied on the current information. What if the current disturbance happened? If current disturbance occurs but the system does not know, the estimated SOC will be incredible and useless, which may be dangerous for the batteries as over-charge or over-discharge may occur. This paper tries to estimate the current disturbance and as a result, the accurate SOC can also be obtained. The remainder of this paper is organized as follows: In Section 2, the simplified battery model is introduced and the state space expression of the battery model is deduced. The PIO based current disturbance estimation method are proposed and analyzed in Section 3; Section 4 establishes simulations validation and the results are analyzed. Conclusions are provided in Section 5. 2. Battery Modelling In order to achieve a reliable current disturbance estimation and battery state estimation, an accurate model must be built first. Taking accuracy and computation complexity into consideration, the simplified battery model is introduced to characterize the battery. The schematic diagram for the simplified battery model is shown in Figure 1. As shown in the figure, several commonly used electric components are utilized. The OCV is adopted to describe the voltage source; series resistance ( R1 ) is used to describe the electrical resistance of various battery components; the diffusion resistance ( R2 ) and the diffusion capacitance ( C2 ) consisting of a RC network are adopted to describe the mass transport effects and dynamic voltage performances; the load current I is assumed positive for charge while negative for discharge; Vo and V1 is the terminal voltage and the voltage over R1 respectively, V2 describes the diffusion voltage over the RC network. The battery OCV is denoted as Voc ( z ) in this model to describe the OCV under different SOCs, where z is the abbreviation for battery SOC.

Fig. 1. The simplified battery model.

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The electrical relationship of the different parameters in the model can be described as

V2

1 1 V2 I R2C2 C2

(1)

Vo Voc ( z ) V1 V2

(2)

According to the definition of SOC, which is the ratio of the remaining capacity to the nominal capacity, the mathematical relationship can be written as: t

z (t )

z (0) 'z

Ki I (W ) dW Cn 0

z (0) ³

(3)

The state space function with V2 and z can be rewritten as:

x Ax Bu ® ¯ y Cx Du

1 ª « RC where A « 2 2 «¬ 0

(4)

º 0» ,B » 0 »¼

ª1 º «C » « 2 » ,C « Ki » «C » ¬ n¼

>1

ai @ , D

R1 , x

ªV2 º « z » , y Vo bi , u ¬ ¼

I.

3. Current Disturbance Estimation Method In this section, the current disturbance is assumed to be a delta value between the actual current and the measured current. Such delta current fault is denoted as current disturbance f , as shown in following equation.

u

u +f

(5)

where u is the measured current, u is the true current, f represents the current disturbance. Considering the battery model affected by the current disturbance, the state space equation is given as follows:

x Ax B (u f ) ® ˄u f ) ¯ y Cx D

(6)

where x \ 2u1 represents the battery states, which is V2 and z as stated in the previous section, y \1u1 is the output, u \1u1 is the input, which is the current I . A, B, C and D are known coefficient matrices with appropriate dimensions, which could be identified from the test data of batteries. To estimate the current disturbance, the PIO is applied to the battery model and the observer is designed as follows: x Ax Bu K p ( y y ) K i 2 f ° ° (7) ® f K i1 ( y y ) ° y Cx Du °¯ Note that variable f is defined as the integral of the difference ( y y ) , which represents the 2u1 estimated current disturbance. Vectors K p \ is the proportional gain, K i1 \1u1 and K i 2 \ 2u1 are

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the integral gains. 4. Simulation Validation To verify the effectiveness of the proposed strategy for current disturbance detection and compensation, the simulation and experimental validation are established. The urban dynamometer driving schedule (UDDS) is an automobile industry standard vehicle time velocity profile for urban driving that has been used for a number of years for electric vehicle performance testing. The current profile computed from the UDDS and scaled to percent of peak discharge power is utilized to emulate the real power requirement for the battery when it is equipped in an EV. Firstly, to verify the proposed method, the normal situation without current disturbance is firstly studied, as shown in Fig. 2. In this situation, the battery is charged to full, which means that the initial SOC of the battery is 100%. Fig. 2 is the simulation results of the estimated SOC of the proposed method. The SOC estimation results show that the estimated SOC convergent to the reference SOC, which has been proved in the previous section for the properties of the PIO. In addition, the SOC estimation error results also give the same conclusion. The estimated SOC traces the reference trajectory accurately, with less than 0.01% estimation error, since the battery model are very accurate in this simulation. 100

Estimated SOC Reference SOC

SOC (%)

80 60

zoom figure 70 69

40

68 67 2450

20 0

1000

2500

2550

2000

2600

3000 T ime (s)

4000

5000

6000

Fig. 2. SOC estimation method without current disturbance.

To determine the influence of the current disturbance to the SOC estimation, a special situation is set that the current disturbance is added at 2500 s. At this time, there will be a constant current disturbance caused by the current sensor, which is set to be -20 A in this section, as shown in Fig. 3 (a). Fig. 3 (b) shows the SOC estimation results when current disturbance occurs and no action is taken. It is obvious from the figure that the estimated SOC traces the reference SOC accurately before the current disturbance occurs. At time 2500 s, when the current disturbances is added, the estimated SOC diverges from the reference SOC suddenly and the SOC estimation error becomes bigger and bigger. Since then, the estimated SOC has become not so accurate. What’s more, since the BMS does not know the disturbance, the information provided by the BMS is incredible and it cannot provide as the design and control reference any more. To reduce the influence caused by the current disturbance, the proposed current disturbance estimation method is applied to the simulation and the results are shown in Fig. 4. Fig. 4 (a) is the comparative profiles between the estimated current disturbance and the reference current disturbance. Fig. 4 (b) presents the estimated SOC with the reference SOC when the current disturbance is added. According to Fig. 4 (a), we find that the estimated current disturbance converges to the reference current disturbance after a short time; after the convergence, the estimated current disturbance traces the reference trajectory accurately. It is clear from Fig. 4 (b), when the current disturbance occurs, the estimated SOC deviates

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from the reference SOC suddenly. However, thanks to the PIO based current disturbance estimation method, the estimated SOC converges to the reference SOC in a short time. In addition, since then, even the current disturbance is large, the estimated SOC could stick to the reference SOC, which means the proposed method is not only able to estimate the current disturbance, but also to compensate the fault. With such measures, the SOC estimation is robust to the current disturbance. (a)

(b)

100


0

80

-5

SOC (%)

Current Disturbance (A)

5

-10 -15

60 40

-20 -25

20 0

1000

2000

3000 T ime (s)

4000

5000

6000

0

1000

2000

3000 T ime (s)

4000

5000

6000

Fig. 3. SOC estimation method with current disturbance but without taking any action.

(a) Estimated Current Disturbance

(b) 100


Reference Current Disturbance

80

0

SOC (%)

Current Disturbance (A)

5

-5 -10

60

zoom figure 70

40

-15

65 60

-20

20 0

1000

2000

3000 T ime (s)

4000

5000

6000

2500

0

1000

3000

2000

3500

3000 T ime (s)

4000

5000

6000

Fig. 4. SOC estimation method with current disturbance and the current disturbance estimation method.

5. Conclusions A novel current disturbance estimation method for BMS in EV has been proposed in this paper. The simplified battery model was introduced and the current disturbance estimation method was then proposed and analyzed. The simulation work bench was established to verify the proposed method. The simulation results showed that the proposed method could obtain accurate current disturbance. Additionally, with the estimated current disturbance, the estimated SOC could also be very accurate, and the SOC estimation is robust to the current disturbance. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No.51405374), the Postdoctoral Science Foundation of China (Grant No.2014M560763), the Postdoctoral Science Special Foundation of China (Grant No.2016T90904), the Fundamental Research Funds for the Central Universities and Postdoctoral Science Foundation of Shaanxi.

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