digital twin for battery systems: cloud battery management
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Digital twin for battery systems: cloud battery management
system with online state-of-charge and state-of-health estimation
Weihan Lia,b,∗, Monika Rentemeistera,b, Julia Badedae, Dominik Josta,b, Dominik Schultef,Dirk Uwe Sauera,b,c,d
aChair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics andElectrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17/19, 52066, Aachen, Germany
bJuelich Aachen Research Alliance, JARA-Energy, GermanycInstitute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University,
GermanydHelmholtz Institute Munster (HI MS), IEK-12, Forschungszentrum Julich, Germany
eABO Wind AG, GermanyfBatterieIngenieure GmbH, Germany
Abstract
Battery management is critical to enhancing the safety, reliability, and performance of thebattery systems. This paper presents a cloud battery management system for stationary andmobile battery systems to improve the computational power and data storage capability bycloud computing. With the proposed Internet of Things component, all battery relevant datacan be measured and transmitted in real-time to the cloud platform to build up the digitaltwin for the battery system, where advanced battery diagnostic algorithms evaluate thedata and open the window into the battery’s remaining capacity and aging level. Notably,an adaptive extended H-infinity filter is proposed to estimate the state of charge of thebattery robustly and shows a strong self-regulation ability even with a large initializationerror. Furthermore, a particle swarm optimization-based state-of-health estimation approachis innovatively exploited to estimate both capacity and ohmic resistance of the battery,indicating the capacity fade and power fade during battery aging. The functionalities andstability of both hardware and software of the proposed cloud battery management systemand battery diagnostic algorithms are validated with prototypes under field operation andexperimental validation on both lithium-ion and lead-acid batteries.
Keywords: digital twin, cloud computing, internet of things, battery management system,state of charge, state of health
∗Corresponding author. Chair for Electrochemical Energy Conversion and Storage Systems, Institutefor Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17/19, 52066,Aachen, Germany
Email address: batteries@isea.rwth-aachen.de (Weihan Li)
Preprint submitted to Elsevier April 3, 2020
1. Introduction1
With the rapid advances in energy storage technologies, the battery system has emerged2
as one of the most popular energy storage systems in stationary and mobile applications to3
reduce global carbon emissions [1]. However, without proper monitoring and controlling of4
the batteries by a battery management system (BMS), problems concerning safety, reliabil-5
ity, durability, and cost will appear. The state-of-the-art BMS includes two main modules:6
BMS-Master and BMS-Slave, which can be combined in one embedded system or designed7
as two separate systems with wiring communication [2]. While the BMS-Slave is respon-8
sible for monitoring the battery cells with signal acquisition and filtering, more advanced9
functions, such as battery diagnostic algorithms, which require high computation power, are10
implemented in the BMS-Master. However, with the increase in the number of battery cells11
and the scale of the battery systems, the reliability and cost of the wiring communication12
are becoming challenging.13
As the most important functions of the BMS, accurate estimation of the state of charge14
(SOC) and the state of health (SOH) deliver essential information about the battery’s re-15
maining capacity and aging level, which can be used to perform maintenance or adapt16
operational strategies to extend its service life. Due to the easy implementation and small17
computation power requirement, open-loop algorithms, e.g., Coulomb counting [3] and open-18
circuit voltage method [4], are widely used to estimate the battery’s SOC. Considering the19
high dynamic operation load in mobile battery systems and the tendency of multi-use appli-20
cation of stationary battery systems [5], the significant accumulated error due to current sen-21
sor uncertainties and the rare appearance of relaxation status reduce the reliability of these22
open-loop algorithms. Compared with SOC estimation, SOH estimation is more challenging23
due to the complex nonlinear aging mechanisms of the batteries. Although Electrochemi-24
cal Impedance Spectroscopy [6–8] is commonly applied offline to detect the aging levels of25
the batteries, the strict input-signal requirements are obstacles for their online application26
in a BMS. To achieve accurate battery state monitoring, different model-based approaches27
were developed for SOC [9–13] and SOH [14–16] estimation. However, with the increase28
of battery cell number and algorithm complexity, on-board BMS is faced with problems in29
computation power and data storage for precise estimation and prediction of the battery’s30
states with model-based algorithms.31
To overcome these obstacles, BMS can be revolutionized by applying cloud computing32
[17] and the Internet of Things (IoT) [18] technologies. The computation and data storage33
capabilities increase exponentially, and all battery relevant data can be measured and trans-34
mitted in real-time to the cloud platform to build up the digital twin [19] for battery systems,35
where advanced battery diagnostic algorithms are implemented. With the collection of big36
data in one base, system prognostics and optimizations can be achieved by machine learning37
algorithms, which will revolutionize our understanding of battery aging. Furthermore, the38
system reliability increases by replacing wiring communication with wireless IoT communi-39
cation. The pioneering work has shown several concepts to apply cloud computing and IoT40
in BMS for both stationary and mobile battery systems [20–22]. Tanizawa et al. [20] pro-41
posed a cloud system for electric vehicles to manage the battery information in the battery42
2
replacement system. Harish et al. [21] proposed a battery monitoring system based on IoT43
for batteries in a microgrid system. Most recently, Kim et al. [22] proposed a cloud-based44
battery condition monitoring and fault diagnosis platform for large-scale stationary battery45
systems. However, the aforementioned works suffer from two major drawbacks awaiting46
further improvements. Firstly, the technical details of both software and hardware design of47
the cloud BMS are rarely introduced, and the functionalities of the system are not validated48
with field operation. Secondly, cloud suitable battery diagnostic algorithms are missing,49
which can improve the diagnostic accuracy and benefit from the high computation power50
and data storage capability of the cloud BMS.51
This paper aims to bridge the aforementioned research gap and proposes a cloud BMS52
for stationary and mobile battery systems, releasing the computation and data storage53
limitations of the on-board BMS. The information of the batteries connected with the cloud54
can be shared by the proposed IoT component, enabling the data communication among55
individual battery systems and the cloud platform. Moreover, the proposed model-based56
battery diagnostic algorithms with adaptive extended H-infinity filter (AEHF) and particle57
swarm optimization (PSO) for SOC and SOH estimation, are implemented in the cloud58
BMS in real-time, ensuring continuous and accurate monitoring of the battery’s status with59
the digital twin and intelligent controlling of the battery’s behavior. The AEHF algorithm60
based on the extended Thevenin model provides a robust approach for SOC estimation.61
The PSO-based algorithm estimates all the parameters of the extended Thevenin model,62
providing both information of the battery’s capacity fade and power fade synchronously.63
Furthermore, two prototypes of the cloud BMS are designed and produced for both the field64
test of the system’s functionalities and experimental test of the proposed battery diagnostic65
algorithms with two different battery technologies, i.e., lithium-ion and lead-acid batteries.66
We begin in Section 2 by introducing the operation principles of the cloud BMS and67
the technical details of the subsystems. Battery modeling is presented in Section 3. In68
Section 4 and 5, the model-based SOC estimation approach with the AEHF algorithm and69
SOH estimation approach with the PSO algorithm are introduced, respectively. In Section70
6 and 7, the experimental set-up and validation results of both cloud BMS functionalities71
and battery diagnostic algorithms are discussed. Section 8 draws the conclusions and points72
towards future work.73
2. Cloud battery management system74
In this section, operation principles of the cloud BMS and the functionalities of the75
subsystems are introduced, as depicted in Figure. 1.76
2.1. Stationary and mobile battery systems77
In electricity systems with a high penetration of renewable energy generation, such as78
solar and wind energy, stationary battery systems can be used to balance the power supply.79
High costs and single applications make the stationary battery systems operating under80
inefficient scenarios. To use the energy of the UPS systems efficiently, Badeda et al. [5]81
investigated multi-use applications by delivering additional services, e.g., peak shaving and82
3
Data Generation Data Sensing
Battery Systems BMS-Slave IoT Component Cloud API UI
Data VisualizationData Collection Data Storage Data Analytics
Figure 1: Schematic of the cloud BMS, which consists of six subsystems: the battery systems for datageneration, the BMS-Slave for data sensing, IoT component for data collection, cloud for data storage,application programming interface (API) for data analytics and user interface (UI) for data visualization.
primary containment reserve. Compared with the batteries in single applications, multi-use83
battery systems need continuous real-time state monitoring and diagnostics to ensure the84
availability of power for different use cases, highlighting the benefits of the cloud BMS.85
With the electrification of vehicles, battery systems are playing an essential role in the86
energy storage of electric vehicles. Compared with the stationary battery systems, the87
batteries in mobile applications are usually working with larger depth of discharge (DOD)88
and under more dynamic conditions, which in turn require more advanced algorithms for89
battery diagnostic, prognostic, and optimization. With the emerging new communication90
technologies, e.g., 5G technology, the mobile battery systems can be connected with the91
cloud by the proposed cloud BMS, reducing battery aging and improving the battery’s92
safety, reliability and performance. The data generated by stationary and mobile battery93
systems contain a wealth of information about the operation history, which can be analyzed94
to adapt the operation strategy to extend service life.95
2.2. BMS-Slave96
The main components in the developed BMS-Slave for data sensing are multi-cell battery97
monitors LTC 6804G-2 and LPC 11C14F/301. The LTC measures up to 12 battery cells98
connected in series with a total measurement error of less than 1.2 mV, which can also99
be connected in series to monitor high-voltage battery strings. The LPC is a 32-bit ARM100
Cortex-M0 based microcontroller, which sends the measurement commands to the LTC and101
receives the measured data in hexadecimal values from the LTC. The BMS-Slave performs102
data acquisition by measuring the voltage, current, and temperature of the batteries with103
sensors at different sampling rates.104
4
2.3. IoT component105
The basic idea behind IoT is to make the devices, i.e., the stationary and mobile battery106
systems embedded with electronics and connected with the internet, communicate and in-107
teract with others to be monitored and controlled remotely. Considering the advantages of108
Raspberry Pi as a single-board computer with relatively low price and weight, it is respon-109
sible for collecting data from the batteries and communicating with the cloud platform and110
other battery systems. The measured data of the batteries are sent to the IoT component111
by the BMS-Slave based on the Controller Area Network (CAN) protocol. The software112
programs were developed and implemented with Python in the IoT component to translate113
the CAN signals into physical values. Furthermore, the IoT component is also responsible114
for sending the generated data to the cloud under TCP/IP and Message Queuing Telemetry115
Transport (MQTT) protocol with the assured security and privacy.116
2.4. Cloud117
As IoT devices usually have limited storage and computation capabilities and do not118
allow complex data processing onboard, cloud computing with virtually unlimited storage119
capability and processing power enables the scalable and real-time data analysis of the120
IoT devices. The proposed cloud platform in cloud BMS consists of a data logger and a121
database hosted in Germany. The data logger captures a massive amount of non-structured122
or semi-structured data produced by the battery systems and enables a secure gateway and123
data transfer into the cloud database, which was extensively certified and has distributed124
denial-of-service (DDoS) protection and multi-redundant connections. Only the owners and125
operators of the battery systems have access to the data, and the data privacy and security126
are guaranteed.127
2.5. Application programming interface128
As a part of the cloud BMS, Application Programming Interface (API) is the bridge129
between the cloud database and the data-analytic algorithms, which offers several popular130
programming languages. With the proposed API, the battery diagnostic algorithms, e.g.,131
SOC and SOH estimation algorithms, which will be introduced in Section 4 and 5, run in132
real-time in the cloud. Furthermore, the field data collected from different battery systems133
can be used for precise predictions and system optimization and reducing the wear and tear134
on the batteries through the adaption of battery management.135
2.6. User interface136
User Interface (UI), which is the belt between the cloud platform and the battery system137
operators, is responsible for inspecting and visualizing the data analytic results. Compared138
with the onboard BMS with little data visualization opportunities, the browser-based UI in139
our cloud BMS can provide not only the real-time status of the batteries but also histori-140
cal operation data with numerous display types and options, which helps the operators in141
scheduling maintenance and repair. With different kinds of alarm functions, which can be142
set up for the original and virtual data points, the operators can be informed by the UI as143
soon as the fault of the systems is identified, increasing the chances of preventing damaging144
5
Uoc Ut
C2C1
R1 R2
R0
Figure 2: Schematic of the extended Thevenin model.
effects and thus improving the system reliability and reducing the maintenance cost. In145
order to protect the data, the UI endpoints are accessible only via HTTPS and require user146
authentication.147
3. Battery modeling148
The numerous equivalent circuit models (ECMs), such as Rint model, Thevenin model,149
and nth RC networks model, have attracted a lot of interest in past years for real-time150
battery state estimation. These models use electrical circuit components, such as resistances,151
capacitances, and voltage sources, to mimic the dynamic response of a battery. Considering152
the balance between model accuracy and computation time, an extended Thevenin model,153
as shown in Fig. 2, is proposed in this paper to model the battery dynamics, which can be154
expressed as follows:155
U1 = − U1
R1C1
+I
C1
(1)
156
U2 = − U2
R2C2
+I
C2
(2)
157
Ut = Uoc − U1 − U2 − IR0 (3)
where I denotes the input current, Ut denotes the terminal voltage, Uoc denotes the open-158
circuit voltage which has a measurable nonlinear relationship with battery SOC, R0 is the159
ohmic resistance, R1,2 are polarization resistances, C1,2 are polarization capacitances, and160
U1,2 are voltage drops over R1,2. Based on the extended Thevenin model, state estima-161
tion and parameter identification algorithms will be introduced in the following sections to162
estimate the SOC and SOH of the battery.163
4. State-of-charge estimation with adaptive extended H-infinity filter164
4.1. Adaptive extended H-infinity filter165
H-infinity filters have shown high robustness and are widely studied in battery state166
estimation [12]. The adaptive extended H-infinity filter (AEHF) is an optimal estimator,167
which can be computed recursively using noisy input data in real-time to analyze and solve168
a wide class of state estimation problems. Zhang et al. [12] applied AEHF to estimate the169
SOC of the lithium-ion battery and the AEHF has shown its advantage compared with the170
6
Estim
atio
n P
roce
ss
Pre
dic
tio
n P
roce
ss
Ad
ap
tive
La
w
Linearization matrix
Error covariance
propagation
Stability check
Gain matrix
Output estimation
State estimation
update
Error covariance
update
Process noise
covariance matrix
Measurement noise
covariance matrix
Figure 3: Implementation flowchart of the adaptive extended H-infinity filter.
Kalman filters considering the uncertainty in battery dynamic models and noise statistics.171
Considering the discrete-time model of a general nonlinear system in a state-space form, we172
have173
xk+1 = Axk +Buk + wk (4)
174
yk = Cxk +Duk + vk (5)
where uk and yk represent the system inputs and outputs, xk denotes the system states, wk175
and vk are the system process noise and measurement noise, the matrices Ak, Bk, Ck, and176
Dk describe the system dynamics.177
The aim of H-infinity filters is to estimate the states zk = Lkxk by applying the game178
theory and to improve the algorithm robustness by estimating the covariance values using179
the covariance matching technique [11]. If xk are states to be estimated, then Lk is an180
identity matrix. The implementation flowchart of AEHF for processing state estimation is181
depicted in Figure. 3. The realization of the AEHF is divided into three sub-processes and182
is detailed as below.183
4.1.1. Estimation process184
In this process, the estimation of the states and outputs of the system are updated. The185
prior state estimation x−k and error covariance P−k are calculated by186
x−k = Ak−1x+k−1Bk−1 (6)
187
P−k = Ak−1Pk−1A>k−1 +Qk−1 (7)
where Qk−1 is the weighting matrix. Afterward, the observer stability criteria are checked188
by189 (P−k)−1 − θ−2
k L>k Lk > 0 (8)
where θk is the tuning parameter. Furthermore, the gain matrices are updated as follows:190
Re,k =
[Rk−1 0
0 −θ2kI
]+
[CkLk
]P−k[C>k L>k
](9)
7
191
Hk = P−k[C>k L>k
]R−1e,k (10)
192
Hs,k = P−k C>k
(Rk−1 + CkP
−k C
>k
)−1(11)
where Re,k and Hs,k are transition matrices and Hk is the H-infinity matrix. The estimation193
of the system output yk is provided by194
yk = Cx−k +Duk + vk. (12)
4.1.2. Prediction process195
In the prediction process, the state and error covariance matrix for the next time step is196
calculated by197
xk+1 = Axk +Buk +Hkek (13)
198
Pk =
(I −Hk
[CkLk
])P−k (14)
where ek represents the output estimation error as follows:199
ek = yk − yk. (15)
4.1.3. Adaptive process200
The tuning parameter θk and the weighting matrices Qk and Rk play important roles in201
H infinity filters and are adjusted in each time step as follows:202
θk = α√
max(eig(P−k))
(16)
203
Rk =1
N
k∑j=k−N+1
(eje>j − CjPjC>j
)(17)
204
Qk =1
N
k∑j=k−N+1
(Hs,keke
>j H
>s,j − Aj−1Pj−1A
>j−1
). (18)
4.2. SOC estimation with AEHF205
The SOC of the battery is defined as the ratio of the present remaining capacity to the206
nominal battery capacity CN , given by207
SOCt = SOC0 −1
CN
∫ t
t0
ηIdt (19)
where SOCt and SOC0 are the SOC of the battery at initial time t0 and current time t,208
respectively, η is the Coulomb efficiency, and I denotes the current flowing through the209
8
voltage source. With the transformation of (1) - (3) and (19) into a discrete-time system,210
the input matrix uk, output matrix yk and the state matrix xk as shown in (4) and (5), are211
defined as follows:212
uk = Ik (20)
213
yk = Ut,k (21)
214
uxk =[SoCk U1,k U2,k
]>. (22)
The parameter matrices Ak, Bk, Ck and Dk are defined by215
Ak =
1 0 0
0 exp(−∆tR1C1
)0
0 0 exp(−∆tR1C1
) (23)
216
Bk =
η∆tCN
R1
(1− exp
(− −∆tR1C1
))R2
(1− exp
(− −∆tR2C2
)) (24)
217
Ck =[dUoc,k(SoC)
dSoC| ˆSoCk
−1 −1]
(25)
218
Dk =[−R0
](26)
where Uoc,k (SOC) represents the nonlinearity between SOC and Uoc, which can be measured219
with experiments. The measurement and identification of the parameters in the parameter220
matrices will be introduced in the next sections.221
With the definitions of the system states and parameter matrices in (20) - (26), AEHF222
described with (6)−(18) can be applied to estimate the SOC of the battery.223
5. State-of-health estimation with particle swarm optimization224
5.1. Particle swarm optimization225
As a branch of Artificial Intelligence (AI), PSO is a meta-heuristic algorithm initially226
inspired by the choreography of a bird flock [23]. It can optimize a numeric problem by227
simulating the behaviors of swarms. PSO and its variants are becoming quite popular due228
to their high convergence rate and robustness. They are widely researched and utilized to229
optimize the nonlinear system parameters [16]. The main steps of PSO are summarized as230
follows.231
5.1.1. Random initialization232
The positions xi and velocities vi of all the particles are generated and initialized ran-233
domly, the dimensions of which are depending on the number of the parameters to be234
optimized.235
9
Sw
arm
evalu
atio
nR
andom
initia
lization
Evaluate fitness
function
Update
Pbest particle
Update
Position and velocity
Algorithm
parameters
Stopping criterion
Particle position
and velocity
Update
Gbest particle
Optim
ization
End
Figure 4: Implementation flowchart of the particle swarm optimization.
5.1.2. Swarm evolution236
At each iteration, each particle in the swarm is improved by two values, namely Pbest,237
and Gbest. Pbest represents the best solution in all previous generations of each particle and238
Gbest represents the best solution of all the particles in previous generations, also called the239
best global solution. Each particle updates its location xi and velocity vi by240
xi+1 = vi+1 + xi (27)
241
vi+1 = wvi + c1r1 (Pbest − xi) + c2r2 (Gbest − xi) (28)
where c1,2 are learning rates influencing the algorithm convergence performance, w is the242
inertia weighting factor, r1,2 are random values between 0 and 1. After updating the positions243
and velocities, the fitness function is evaluated for each particle to update the Pbest and244
Gbest. The iterated optimization process will stop when the predefined fitness value or the245
maximum iteration number is reached. The implementation flowchart of applying PSO for246
parameter identification is depicted in Figure. 4.247
5.2. SOH estimation with PSO248
The SOH of the battery is a metric to indicate the battery’s aging level. The commonly249
used SOH indices can be divided into two groups: capacity fade indices and power fade250
indices [24]. With battery aging, the value of the nominal capacity of the battery will de-251
crease because of the capacity loss mechanisms, resulting in the capacity fade of the battery.252
Meanwhile, the value of ohmic resistance and polarization resistances will increase, resulting253
in the power fade of the battery. In this work, the SOH indicating the battery’s capacity254
fade, SOHC , and the SOH indicating the battery’s power fade, SOHR, are determined by255
PSO synchronously.256
The SOHC of the battery is usually defined as follows:257
SOHC,t =CN,tCN,0
(29)
10
SOC
SOHSOH SOH
TT T
Figure 5: Implementation strategy of AEHF-based SOC estimation and PSO-based SOH estimation. TheAEHF will run continuously for all time. The battery parameters and SOH are updated after each periodof T by running the PSO based on the field data in the previous period. This process is repeated for theduration of use.
where CN,0 and CN,t are the nominal capacity of the battery at battery’s Begin of Life (BoL)258
and current time t. The SOHR of the battery is investigated from the ohmic resistance259
increasing, namely260
SOHR,t =R0,t
R0,0
(30)
where R0,0 and R0,t are the ohmic resistance at battery’s BoL and current time t. It is worthy261
of notifying that the identification of the aging level of the battery based on the increase262
of the polarization resistances R1 and R2 can also be implemented, which is not introduced263
in detail in this work. The End of Life (EoL) of the battery regarding the capacity fade is264
reached when the value of SOHC,t reaches 80%, as shown in (29). However, the EoL can also265
be defined as the time that the value of the ohmic resistance increases 30%, i.e., the value of266
SOHR,t reaches 1.3, considering the battery’s power fade, as shown in (30). Therefore, it is267
important to estimate both SOHC,t and SOHR,t at the same time, which were not provided268
by most of the literature.269
The ECM parameters, such as nominal capacity CN , resistances R0,1,2, and capacitances270
C1,2, can be identified based on the measurement data of current and voltage with PSO271
according to the fitness function F as follows:272
F =1
N
N∑i=1
(Ut,i − Ut,i
)2
(31)
where N is the total number of the data points, Ut,i is the measured battery terminal voltage,273
Ut,i is the estimated battery terminal voltage with the identified parameters. The PSO will274
stop the optimization process when the values of the estimated parameters converge. The275
identified CN and R0 in the final Gbest are then used to calculate the SOHC and SOHR of276
the battery in (29) and (30).277
Considering that the aging mechanism is slower compared to the capacity change, the278
parameter identification with PSO was designed to run based on a period of field data. The279
identified parameters CN , R0,1,2, and C1,2 can not only be used to estimate the battery’s280
SOH but can also be injected back into SOC algorithms to improve the estimation accuracy281
with accurate model parameters. Figure. 5 presents a strategy in which the SOC and SOH282
estimation can be implemented together online.283
11
Voltage measurement Temperature measurement
Current measurement BMS-Slave
(a)
(b)
Figure 6: (a) UPS system connected with the cloud BMS prototype A. (b) Battery test bench for lithium-ionand lead-acid batteries connected with the cloud BMS prototype B to validate the proposed SOC and SOHalgorithms.
6. Experimental set-up284
To validate the functionalities and stability of both hardware and software, we designed285
and produced two prototypes of the proposed cloud BMS. As the dominating battery tech-286
nology nowadays for the UPS market is the lead-acid battery, a UPS system consisting of287
lead-acid batteries was chosen to validate the monitoring functionalities of the cloud BMS.288
Furthermore, the proposed battery diagnostic algorithms were validated with different bat-289
tery technologies, i.e., lithium-ion battery and lead-acid battery, considering the increasing290
market share of lithium-ion batteries and application potential of the cloud BMS in mobile291
battery systems, e.g., electric vehicles.292
6.1. Field validation of the cloud BMS functionalities293
The prototype A of the cloud BMS was connected with a UPS system in Denmark, as294
shown in Figure. 6 (a). The battery system consists of four 12 V, 92 Ah Hawker Enersys295
AGM lead-acid batteries connected in series, as summarized in Table. 1. The UPS system296
was under operation and kept at float charge. The voltage and temperature of four batteries297
were monitored in Germany remotely with the developed UI in real-time to validate the298
performance of the system’s functionalities.299
12
Hawker Enersys Yuasa NP7-12 Samsung INR18650-35e
Chemistry Lead-acid Lead-acid Lithium-ionCapacity [Ah] 92 7 3.40Norminal voltage [V] 12 12 3.60Cell dimension [mm] 395 x 105 x 264 151 x 65 x 94 65.25 x 18.55Weight [kg] 27.60 2.20 0.05
Table 1: Specifications of the batteries for experimental validations of the cloud BMS.
0 20 40 60 80 100 120Time in min
−4
−2
0
2
4
Curre
nt in
A
(a)
0 20 40 60 80 100Time in min
−10
−8
−6
−4
−2
0
2
Curre
nt in
A
(b)
Figure 7: Current profile for experimental validation of the AEHF-based SOC estimation algorithm. (a)Dynamic pulse profile for a multi-use stationary battery system. (b) Real-world driving data-based currentprofile for the mobile battery system.
6.2. Experimental validation of the AEHF-based SOC estimation algorithm300
To validate the proposed AEHF-based SOC estimation algorithm experimentally, we301
installed prototype B of the cloud BMS on batteries with different chemistries in our labora-302
tory in Germany. The lead-acid batteries and lithium-ion batteries were applied to verify the303
accuracy and stability of the algorithm in multi-use stationary battery systems and mobile304
battery systems, respectively.305
The stationary battery system is composed of four 12 V, 7 Ah Yuasa AGM lead-acid306
batteries, as summarized in Table. 1, connected in series. This 48 V battery system was con-307
13
0 50 100 150 200 250 300 350Time in min
−10.0
−7.5
−5.0
−2.5
0.0
2.5
5.0
7.5
10.0
Curre
nt in
V
Current
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
Volta
ge in
V
Voltage
(a)
0 100 200 300 400 500 600 700Time in min
−10.0
−7.5
−5.0
−2.5
0.0
2.5
5.0
7.5
10.0
Curre
nt in
V
Current
2.75
3.00
3.25
3.50
3.75
4.00
4.25
4.50
Volta
ge in
V
Voltage
(b)
Figure 8: Characterization test in the cyclic aging test. (a) Pulse test with 0.5 C, 1 C, and 2 C at 80% SOC,50% SOC, and 20% SOC. (b) Capacity test with 0.33 C.
nected with a Digatron battery tester with 100 V, 50 A test circuits, as shown in Figure. 6 (b).308
To validate the proposed AEHF-based SOC estimation algorithm for the mobile application,309
a lithium-ion battery, Samsung INR18650-35e, was connected with a Digatron battery tester310
with 5 V, 20 A test circuit and tested in a temperature chamber. The cathode and anode311
material of the lithium-ion battery is Lithium-Nickel-Cobalt-Aluminium-Oxide (NCA) and312
graphite, respectively. The specifications of the battery are summarized in Table. 1. Before313
the validation test of the cloud BMS diagnostic algorithms, battery characterization tests,314
e.g., capacity tests and OCV tests were conducted for both batteries.315
Considering the multi-use tendency of the stationary battery systems, we applied a316
dynamic current profile consisting of charging and discharging current pulses with amplitudes317
from 1 A to 5 A to validate the proposed SOC estimation algorithm with the lead-acid318
battery, as shown in Figure. 7 (a). In contrast, a real-world driving data-based test profile319
was applied to the lithium-ion battery considering its high demand in automotive battery320
systems. As shown in Figure. 7 (b), the maximum discharging and charging current of the321
test profile is 2 A and 9.6 A, respectively.322
14
6.3. Experimental validation of the PSO-based SOH estimation algorithm323
To validate the PSO-based SOH estimation algorithm, we carried out aging tests to324
age the same lithium-ion battery as introduced in Section 6.2. Cycling of the battery was325
conducted at a constant temperature of 25C. In each aging cycle, the battery was discharged326
with the real-world driving data-based test profile, as depicted in Figure. 7 (b), from 90% to327
10% SOC. Then the battery was charged with 0.33 C constant current from 10% SOC back328
to 90% SOC. After every 30 aging cycles, a characterization test consisting of a pulse test and329
a capacity test was conducted at 25C, as shown in Figure. 8. Within the multi-pulse test,330
a dynamic profile consisting of multi 10s charging and discharging pulses at three different331
SoC levels, i.e., 80%, 50%, and 20%, with 0.5C, 1C, and 2C was conducted. After that,332
the battery cell was discharged and charged under a capacity test with a constant current333
of 0.33 C between its cut-off voltages. The variance of the temperature does influence the334
value of the battery parameters, e.g., CN , R0,1,2, and C1,2. This temperature relationship can335
be modeled by parameterizing the battery model under the same aging level with different336
temperatures, which is out of the scope of this work.337
7. Results and discussions338
7.1. Field validation of the cloud BMS functionalities339
The validation results of the cloud BMS functionalities, as proposed in Section 2, are340
depicted in Figure. 9, where about 160 hours of the voltage and temperature measurement341
data with the cloud BMS are visualized with the UI. It is clear that the data were collected342
continuously, and the sensors have caught the variation of voltage and temperature of each343
battery in the system. The real-time remote monitoring of the battery system with the344
cloud BMS prototype A was verified.345
Within the field validation of the cloud BMS, the hardware of the system, consisting of346
the BMS-Slave, the IoT component and the data logger in the cloud were working together347
with the proposed system’s software, successfully verifying the functionalities of the cloud348
BMS, e.g., data sensing, data collection, data storage, and data visualization. The cloud349
BMS enables direct and real-time visualization and monitoring capability of large scale350
battery systems for the users and battery experts, which can also be adapted with new351
communication technologies, e.g., 5G technology, for the mobile battery systems, reducing352
the battery aging and improving the battery’s safety, reliability and performance.353
7.2. Experimental validation of the AEHF-based SOC estimation algorithm354
The validation results of the AEHF-based SOC estimation algorithm, as proposed in355
Section 4, are depicted in Figure. 10. While the real voltage value was measured directly,356
the reference value of SOC was calculated by Coulomb counting with high-accuracy current357
sensors of battery tester with accurate SOC initial value.358
The validation results for the lead-acid battery are shown in Figure. 10 (a), (b), and359
(c). To check the self-regulation ability and the stability of the SOC algorithm, we applied360
an erroneous initial value of SOC to the cloud BMS. Both voltage estimation and SOC361
estimation can converge to the real value very fast. With a relatively long operation period,362
15
0 20 40 60 80 100 120 140 160Time in h
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
Volta
ge in
V
Ut, 1
16
18
20
22
24
26
28
30
Tempe
rature in
°C
T1
(a)
0 20 40 60 80 100 120 140 160Time in h
12.0
12.5
13.0
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14.0
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15.0
15.5
16.0
Volta
ge in
V
Ut, 2
16
18
20
22
24
26
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30
Tempe
rature in
°C
T2
(b)
0 20 40 60 80 100 120 140 160Time in h
12.0
12.5
13.0
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15.5
16.0
Volta
ge in
V
Ut, 3
16
18
20
22
24
26
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30Te
mpe
rature in
°C
T3
(c)
0 20 40 60 80 100 120 140 160Time in h
12.0
12.5
13.0
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15.0
15.5
16.0
Volta
ge in
V
Ut, 4
16
18
20
22
24
26
28
30
Tempe
rature in
°C
T4
(d)
Figure 9: Voltage and temperature measurement of the stationary battery system with the cloud BMSprototype A. (a) Field data collected from battery 1. (b) Field data collected from battery 2. (c) Field datacollected from battery 3. (d) Field data collected from battery 4.
the voltage and SOC estimation can always follow the reference value of the battery with363
mean absolute error (MAE) of 0.01 V and 0.06%. The validation results for the lithium-364
ion battery under a real-world driving cycle are depicted in Figure. 10 (d), (e), and (f).365
Similarly, an erroneous initial value of SOC was applied to check the self-regulation ability366
and stability of the algorithm for the lithium-ion battery. Both the estimation of voltage and367
SOC converged fast to the reference value. Within 90% DOD range, the voltage and SOC368
estimation of the lithium-ion battery can always match with the reference values with MAE369
of 0.01 V and 0.49%, respectively. The validation results show that the proposed AEHF-370
based SOC estimation algorithm is accurate and stable for both lead-acid and lithium-ion371
batteries in stationary and mobile application scenarios.372
7.3. Experimental validation of the PSO-based SOH estimation algorithm373
The validation results of the PSO-based SOH estimation algorithm, as proposed in Sec-374
tion 5, are presented in Figure. 11. The results of the parameter identification for the battery375
at Begin of Life (BoL) are visualized as an example in Figure. 11 (a), (b), (c), and (d).376
With the identified battery parameters, e.g., CN , R0,1,2, and C1,2, identified by the pro-377
posed PSO approach, the extended Thevenin model is able to estimate the voltage of the378
16
0 20 40 60 80 100 120Time in min
11.0
11.5
12.0
12.5
13.0
13.5
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14.5
15.0
Volta
ge in
V
MeasurementEstimation
(a)
0 20 40 60 80 100 120Time in min
40
50
60
70
80
90
100
SoC in %
ReferenceEstimation
50.0 52.5 55.0 57.5 60.0 62.5 65.05152535455
(b)
0 20 40 60 80 100 120Time in h
0.0
0.2
0.4
0.6
0.8
1.0
Volta
ge in
V
Voltage absolute error
0.0
0.1
0.2
0.3
0.4
0.5So
C in %
SoC absolute error
(c)
0 20 40 60 80 100Time in min
2.50
2.75
3.00
3.25
3.50
3.75
4.00
4.25
Volta
ge in
V
MeasurementEstimation
(d)
0 20 40 60 80 100Time in min
0
20
40
60
80
100
SoC in %
ReferenceEstimation
38 40 42 4455.057.560.062.565.0
(e)
0 20 40 60 80 100Time in h
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Volta
ge in
V
Voltage absolute error
0.0
0.5
1.0
1.5
2.0
2.5
3.0
SoC in %
SoC absolute error
(f)
Figure 10: Validation results of the AEHF-based SOC estimation algorithm with a lead-acid battery and alithium-ion battery. (a) Validation results of the voltage estimation for the lead-acid battery. (b) Validationresults of the SOC estimation for the lead-acid battery. (c) The absolute error of voltage and SOC estimationfor the lead-acid battery. (d) Validation results of the voltage estimation for the lithium-ion battery. (e)Validation results of the SOC estimation for the lithium-ion battery. (f) The absolute error of voltage andSOC estimation for the lithium-ion battery.
battery accurately with the maximum absolute error of 0.11 V as shown in Figure. 11 (a)379
and (b), indicating the accuracy of the parameter identification. As shown in Figure. 11380
(c), the fitness value, as described in (31), decreased very fast with the increase of the itera-381
17
0 20 40 60 80 100Time in min
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
Volta
ge in
V
MeasurementEstimation
(a)
0 20 40 60 80 100Time in min
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Volta
ge abs
olute error in V
(b)
0 200 400 600 800 1000Iteration number
5
10
15
20
25
30
35
40
Fitness v
alue
in 10−
4 V2
(c)
0 200 400 600 800 1000Iteration number
3.300
3.325
3.350
3.375
3.400
3.425
3.450
3.475C N
in Ah
CN
20
25
30
35
40
45
50
55
60
R 0 in
mΩ
R0
(d)
0 100 200 300 400 500N
0.88
0.90
0.92
0.94
0.96
0.98
1.00
SOHC
MeasurementEstimation
(e)
0 100 200 300 400 500N
1.00
1.05
1.10
1.15
1.20
1.25
SOHR
MeasurementEstimation
(f)
Figure 11: Validation results of the PSO-based SOH estimation algorithm. (a) Validation results of thebattery voltage estimation with the parameters estimated by PSO at BoL. (b) The absolute error of voltageestimation with the parameters estimated by PSO at BoL. (c) Convergence performance of the fitness valuefor the battery at BoL. (d) Convergence performance of the capacity and ohmic resistance for the batteryat BoL. (e) Comparison between the measured and estimated SOHC of the battery during aging test. (f)Comparison between the measured and estimated SOHR of the battery during aging test.
tion number and converged to the minimum value within 600 iterations. This phenomenon382
indicates that the PSO-based algorithm is working to adapt the parameter values within383
the parameter boundaries to reduce the error between the measured and modeled voltage384
18
Cycle number 0 10 40 70 100 130 160 190 220 250 280 310 340 370 400 430 460 490 520
CC [Ah] 3.39 3.34 3.31 3.27 3.25 3.23 3.20 3.19 3.19 3.11 3.11 3.10 3.09 3.08 3.07 3.07 3.06 3.05 3.03CN [Ah] 3.43 3.38 3.35 3.29 3.27 3.25 3.24 3.19 3.16 3.14 3.11 3.10 3.07 3.07 3.08 3.05 3.00 3.00 3.01
Table 2: List of cell capacity degradation index CC and CN during cell aging, where CC is the measuredcell capacity with 0.33 C discharging in characterization test and CN is the output of the PSO-based SOHestimation algorithm.
Cycle number 0 10 40 70 100 130 160 190 220 250 280 310 340 370 400 430 460 490 520
RC [mΩ] 26.7 27.8 28.0 28.2 28.5 27.7 29.2 29.6 29.7 30.8 30.9 29.9 31.5 31.8 32.2 31.3 32.7 32.9 33.3R0 [mΩ] 27.2 27.8 27.9 28.5 28.8 29.3 29.8 30.3 29.9 30.1 30.6 30.7 31.1 31.2 32.1 31.0 32.7 32.7 32.6
Table 3: List of cell impedance degradation index RC and R0 during cell aging, where RC is the measuredcell ohmic resistance with 2 C current pulse at 80% SOC in characterization test and R0 is the output ofthe PSO-based SOH estimation algorithm.
of the cell. The capacity of the battery CN,t also converged fast to a stable value within385
600 iterations, as shown in Figure. 11 (d). Meanwhile, the ohmic resistance R0,t of the386
battery, which is used in this work as an index for the power fade, also showed a fast con-387
vergence performance. The final estimated values of CN and R0 are 3.43 Ah and 27.2 mΩ,388
respectively.389
Based on the real-world driving data, as shown in Fig. 7, the cell parameters, i.e., CN390
and R0, are identified with the proposed PSO-based algorithm at 19 different aging status391
within 540 aging cycles. As a benchmarking for the capacity estimation, the measured392
capacity of the battery with Coulomb counting under C/3 discharging was calculated as CC393
from the characterization tests. The measured capacity CC and estimated capacity CN at394
19 different aging status are summarized in Table 2. According to (29), the measured and395
estimated SOHC are depicted in Figure. 11 (e). It is clear that the SOHC of the battery396
decreased continuously from 1 to 0.89 within 520 aging cycles, indicating the capacity fade397
of the battery. The estimated SOHC matches with the measured SOHC with MAE of 0.74%.398
Similarly, the ohmic resistance of the battery under 2 C current pulse at 80% SOC was399
calculated as RC from the characterization tests for benchmarking. The measured ohmic400
resistance RC and estimated ohmic resistance R0 at 19 different aging status are summarized401
in Table 3. Both RC and R0 show the same increasing trend during cell aging. According402
to (30), the measured and estimated SOHR are depicted in Figure. 11 (f). The SOHR of403
the battery increased from 1 to 1.25 within 520 aging cycles, showing the power fade of the404
battery during aging. The estimated SOHR matches with the measured SOHR with MAE405
of 1.70%.406
With the experimental validation, it is clear that the proposed PSO-based SOH estima-407
tion algorithm can provide clear information of both capacity fade and power fade of the408
battery, highlighting its application potential. The limitation of the proposed approach is409
the relatively high computation burden because of the iterated simulation of the battery410
model. However, the ultra-high computation and data storage capabilities of the proposed411
cloud BMS is able to implement this advanced SOH algorithm and open the window into412
19
the battery’s capacity and power fade synchronously, which can be further used to take413
actions to optimize the operation scenarios, enhance the battery’s performance and extend414
their service lives.415
8. Conclusions416
A cloud battery management system was developed based on the concept of the Internet417
of Things and cloud computing in this work to build up the digital twin for battery systems418
in the cloud and improve the computation power, data storage capability and reliability of419
the BMS. We believe that this is the first work that shows both the technical details of a420
cloud BMS and the operation results of the cloud-suited advanced battery diagnostic algo-421
rithms in this platform, which is a big step to bring digital twin from theory to real industrial422
application, ensuring intelligent monitoring and utilizing the stationary and mobile battery423
systems. Furthermore, SOC and SOH estimation algorithms were developed based on an424
extended Thevenin model with AEHF and PSO, respectively, and were validated experi-425
mentally with the cloud BMS prototypes for both lithium-ion and lead-acid batteries. It is426
also the first work providing both capacity fade and power fade of the battery synchronously427
with the PSO. In future work, machine learning algorithms will be developed based on the428
collected data from the battery systems in the cloud for precise lifetime prediction and429
system optimization purposes.430
Acknowledgment431
This work was supported within the project BSMS by the European EFRE program432
(grant number EU-1-1-081) and within the project EVERLASTING from the Horizon433
2020 program by the European Union (grant number EVERLASTING-713771).The authors434
would like to thank Voltia Automotive for the real-world driving profile, Patrick Borycka435
from BatterieIngenieure GmbH for his contributing work and support at hardware design,436
and Erik Brummendorf from aedifion GmbH for his support at the cloud platform. Thanks437
are also given to Decheng Cao and Ruilin Li for their support at simulation works.438
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