# Battery Cloud with Advanced Algorithms

Xiaojun Li  David Jauernig  Mengzhu Gao  Trevor Jones  Gotion Inc, 48660 Kato
Road, Fremont, California, USA
<EMAIL_ADDRESS>
###### Abstract

Energy storage battery plays a key role in modern interconnected energy
networks. Recent development of Internet of Things (IoT) has enabled
traditional battery management system to evolve into Battery Cloud. A Battery
Cloud or cloud battery management system leverages the cloud computational
power and data storage to improve battery safety, performance, and economy.
This work will present the Battery Cloud that collects measured battery data
from electric vehicles and energy storage systems. Advanced algorithms are
applied to improve battery performance. Using remote vehicle data, we train
and validate an artificial neural network to estimate pack SOC during vehicle
charging. The strategy is then tested on vehicles. Furthermore, high accuracy
and onboard battery state of health estimation methods for electric vehicles
are developed based on the differential voltage (DVA) and incremental capacity
analysis (ICA). Using cycling data from battery cells at various temperatures,
we extract the charging cycles and calculate the DVA and ICA curves, from
which multiple features are extracted, analyzed, and eventually used to
estimate the battery’s state of health. For battery safety, a data-driven
thermal anomaly detection method is developed. The method can detect
unforeseen anomalies such as thermal runaways at the very early stage.
Potential applications of battery cloud also include areas such as battery
manufacture, recycling, and electric vehicle battery swap.

###### keywords:

Battery , big data , battery management system , machine learning , renewable
energy, internet of things , state of charge , state of health , thermal
runaway , fault diagnostics

###### MSC:

[2010] 00-01, 99-00

###### Contents

  1. 1 Introduction
  2. 2 Battery in the Cloud
    1. 2.1 Data Sources and Connections
    2. 2.2 Database
    3. 2.3 Data Visualization
    4. 2.4 Algorithms and Analytics
  3. 3 Onboard SOC Estimation with Cloud-trained ANN
    1. 3.1 Requirements Definition and Design
    2. 3.2 ANN Training with Cloud Data
    3. 3.3 HIL and Vehicle Testing Results
  4. 4 Online State-of-Health Estimation
    1. 4.1 Degradation Mechanisms and Modes of Li-Ion Batteries
    2. 4.2 State-of-Health & End-of-Life
    3. 4.3 Advanced Online SOH-Estimation Methods
      1. 4.3.1 Methods
      2. 4.3.2 DVA/ICA-based SOH-Estimation Method
  5. 5 Cloud-based Thermal Runaway Prediction
    1. 5.1 Cause and Effects of Thermal Runaway
    2. 5.2 Methods for Thermal Runaway detection
    3. 5.3 Data-driven Thermal Anomaly Detection
      1. 5.3.1 Workflow
      2. 5.3.2 Case Study
  6. 6 Conclusions

##  1 Introduction

Figure 1: Battery Cloud is at the center of the success of the future battery
industry.

Batteries play an essential role in the rapid development of transportation
electrification and energy storage systems[1]. Lithium-ion batteries are known
for their high energy/power density and low self-discharge. They are becoming
more available as the manufacturing cost continues to improve. Large-scale
energy storage systems consist of MWh/GWh batteries that continuously operate
under different weather conditions. Electric vehicle batteries are subject to
road harshness, different driving behavior, and frequent high current fast
charges. These applications call for batteries to become more reliable, safe,
and predictable. As such, monitoring and control of Li-ion batteries become
more critical. Battery algorithms, such as SOC and SOH, deliver important
information about battery charge and health. This information is critical for
maintaining optimal operations of modern energy networks. For example,
inaccurate estimation of SOC will force the battery system to reduce
charge/discharge power or completely shut off, which subsequently affects the
grid stability. Based on accurate SOH estimation, a modern energy network can
reduce the risk of battery failure. Early thermal anomaly detection can
foresee thermal runaway, which is catastrophic for energy networks.

Table 1: Lithium-ion Battery for Energy Storage Applications

Types |  Specific Energy |  Life Cycle |  Cost
---|---|---|---
NMC |  over 150 Wh/kg |  1200 |  $ 403/kWh
LFP |  over 110 Wh/kg |  2000 |  $ 274/kWh

  * 1.

Note: cost estimation based on 10MW, without warranty, insurance,
maintenance.[2]

As of now, conventional onboard battery management systems (BMS) are used for
monitoring and control. A BMS includes embedded micro-controllers (µC) and
peripheral integrated circuitry (IC). Usually, the BMS collects voltage,
current, and temperature measurement with dedicated sensing ICs that
communicates with a main µC, which process the measurements and perform
various functions, such as SOX estimation, and diagnostics, protection,
control, and thermal management. Nevertheless, the micro-controllers are
designed to handle simple tasks and have the minimal computing power and
memory size. It prevents the onboard BMS from executing advanced algorithms.
For example, artificial neural networks (ANN) are frequently used for SOC
estimation[3]. As we will show later in this chapter, an onboard BMS might run
a trained neural network. However, the ANN must be carefully designed to
reduce CPU and RAM impact. Although the BMS receives numerous data from the
measurements of hundreds of cells that it monitors, these data are not stored
due to the lack of onboard data storage, making incremental learning
impossible for the onboard BMS.

With the further development of IoT[4], future BMS is expected to be cloud-
connected (Battery Cloud). As a result, battery data can be seamlessly
uploaded and stored in a cloud data platform[5, 6], and the power of cloud
computing resources can be leveraged. The cloud computational power and data
storage can support advanced algorithms, such as machine learning algorithms
improve battery safety, performance, and economy. There are several
significant advantages. Firstly, the cloud database has battery data from not
just one pack but numerous EV/ESS battery packs, allowing a massive amount of
data to be used for extensive data analysis and machine learning. Secondly,
cloud computing allows complicated algorithms to be executed in real-time,
which is not possible for onboard µC. Thirdly, the cloud platform allows data
collection and feedback from batteries throughout the entire life cycle. The
other battery processes and applications also benefit from the battery cloud,
such as manufacturing, second life usage, and recycling.

This chapter aims to provide an overview of Battery Cloud, including the
essential infrastructure and software components. We also discuss important
topics for batteries, including the underlying causing mechanism, effects, and
corresponding algorithms. The remaining chapter is organized as follows. The
first section discusses the critical components of a Battery Cloud. Then, in
the following sections, we overview the critical areas regarding battery
performance, health, and safety: State-of-Charge estimation, State-of-Health
estimation, and thermal runaway/anomaly detection. We also present
corresponding algorithms that were developed with the Battery Cloud. In the
first section, we train and validate an artificial neural network (ANN) to
estimate pack SOC during vehicle charging using remote vehicle data. The ANN
is then implemented and tested by onboard BMS. It gives high accurate (¡3%)
real-life vehicle testing results. In the second section, high accuracy (¡%5)
and onboard battery state of health estimation (SOH) methods for electric
vehicles are developed based on the differential voltage (DVA) and incremental
capacity analysis (ICA). We extract the charging cycles and calculate the DVA
and ICA curves using cloud data. Multiple features are extracted and analyzed
to estimate the SOH. A data-driven thermal anomaly detection method is
developed for battery safety in the last section. The method can detect
unforeseen thermal anomalies at an early stage, more than 1hr ahead of the
event.

##  2 Battery in the Cloud

Figure 2: Key hardware and software components and data flow of the Battery Cloud: data storage includes but not limit to (from top to bottom): noSQL database, data storage, binary files, SQL database, spreadsheets. Table 2: Battery Data Sources Item |  Stages |  Type of data
---|---|---
Cell |  manufacture testing |  manufacture metadata battery timeseries data
Pack |  assembly testing |  assembly metadata battery timeseries data
EV&ESS |  operating service |  battery timeseries data vehicle/grid timeseries data vehicle/grid metadata service record
Charger |  operating |  charger timeseries data
Pack |  recycle |  recyle metadata

This section covers essential components of a Battery Cloud, including the
database, data visualization, and algorithm/analytics.

###  2.1 Data Sources and Connections

Data are collected during different stages of the battery’s life cycle,
ranging from cell manufacture and module/pack assembly to vehicle
driving/charging and pack recycling. There are numerous procedures for cell
manufacture alone, including electrode mixing, coating, laser cutting, stack,
and so many others[7], during which a significant amount of data is generated.
Table 2 summarizes battery-related data based on the different devices and
scenarios. The EV battery pack is equipped with a BMS, a wireless IoT
component that transmits the collected data to the cloud via the 4G/5G
network. These data will be collected via the internet, online or private
gateways for charging stations. Because ESS power plants affect grid
stability, they are subject to more stringent cybersecurity regulations. As a
result, usually, ESSs are connected through a one-way, local gateway to ensure
maximum security. Similarly, battery data from cell/pack testing equipment are
uploaded via a secured, one-way gateway. However, the equipment may be
controlled securely via the company’s intranet.

###  2.2 Database

##### Choosing the right database

For production big data platforms, Hadoop[8] is the prevailing choice. Hadoop
is based on HDFS (Hadoop files system) and MapReduce (the programming model)
that ensure good scalability, robustness, and high availability, all of which
are essential requirements for a battery database. Besides, Hadoop has a
complete ecosystem, including software stacks like Spark, Hbase, Kafka, Hive,
and many others, making it easier to use and expand functionalities. There are
also dedicated timeseries databases (TSDB), such as Influxdb, Timescale, and
Prometheus. TSDB has built-in features for timeseries data, such as time-
domain queries (integration, differential), retention policy, and others. This
makes TSDB ideal for a small R&D battery database. As TSDBs are being
developed and improved actively, they will become more competitive against
traditional databases in the future.

##### Database deployment

The database can be hosted on primes or on the cloud. Although on-primes
deployment will theoretically give better control and security, it is often
more expensive to maintain and scale. For cloud deployment, there are several
models to consider. Iaas (Infrastructure as a Service) let the cloud provider
handle hardware resources, where the company has complete control of software
stacks. Popular IaaS providers are Amazon Web Services (AWS)[9], Microsoft
Azure[10], and Google Cloud Platform (GCP)[11]. In the PaaS (Data Platform as
a Service) model, such as AWS EMR, the cloud provider also hosts basic
software stacks, except for application software. The provider manages all
software stacks in the SaaS (Software as a Service) model, such as
Cloudera[12] and Influxdata Cloud[13].

###  2.3 Data Visualization

Most end-users are data analysts or operators who monitor EV/ESS in real-time.
It is vital to have a responsive and interactive data visualization tool where
essential data are displayed in real-time. Users can create a dashboard and
add custom processing/query to explore statistical insights. Other features
include: 1) adding a signal threshold, which can trigger quick alarms to the
ESS site operator. 2) Options to trigger an ML pipeline from the frontend.
Widespread data visualization tools are web-based, such as Grafana, Datadog,
and Kibana. Figure 3 depicts an example battery data display dashboard.

Figure 3: A typical dashboard for displaying battery data, developed by
Gotion[6].

###  2.4 Algorithms and Analytics

With the data platform built, advanced algorithms that leverage big data and
machine learning[3] can be applied to increase battery performance, safety and
economy. Another interesting topic is the digital twin [14]. Based on
sophisticated electrochemical modeling, the digital twin can give insight into
the internal states of its physical twin. The battery cloud platform will need
API (application programming interface) for popular programming languages,
such as Python and Matlab®, based on the developers’ preferences. It may also
provide a more interactive computing platform like the Jupyter notebook/Lab,
commonly used for data analytics. After the algorithms/analytics are
developed, they should be optimized and incorporated into a data processing
engine, such as Spark, Kafka, and Airflow. Life data of all the battery cells
are used to analyze the manufacture, assembly process, and facility to improve
quality management. Similarly, these data can be used as references during
battery second life application, recycling, and refurbishing, eliminating the
need for extra testing/calibration.

##  3 Onboard SOC Estimation with Cloud-trained ANN

State of Charge (SOC) estimation is one of the essential functions of battery
software. It has been researched extensively. There are mainly three different
methods for SOC estimation. The commonly used, basic method is coulomb
counting, which calculates the accumulated charge by current integral, given
as

$z(t)=\frac{1}{C}\int_{0}^{t}{i(\tau)}d\tau+z(0),$ (1)

where $C$ is the battery capacity. This method is susceptible to accumulated
error generated from $i(t)$ or data loss from $z(0)$. As such, estimation
accuracy degrades if coulomb counting is used without correction over a
prolonged period of time. The other two methods are model-based and data-
driven. Both have self-correction features to correct SOC. The model-based
approach utilizes a battery model, either ECM (equivalent circuit model) or
electro-chemical model, to establish the connection between battery
measurements, such as voltage, temperature, and current, and immeasurable
internal states. Then an estimator, such as Kalman Filter, is applied to
estimate the SOC. Because those batteries are highly nonlinear systems,
modified Kalman filters such as extended Kalman filters and unscented Kalman
filters are often used in practice. Model-based approaches require an accurate
model. The model can be calibrated accurately by long-term cell and pack
testing. But it is also prone to over-fitting, meaning it can not tolerate
individual cell/pack variations. Making an accurate and well-generalized model
is very challenging and time-consuming. Data-driven approaches range from
simple voltage-based correction[15] to deep neural networks[16]. More detailed
reviews of data-driven SOC estimation methods are covered by [3, 17]. Most of
these methods are resource-consuming and can not be easily applied to an
onboard BMS.

This section will present a data-driven SOC estimation method that fuses the
onboard BMS with the battery cloud. A neural network is firstly trained with
cloud battery data. The neural network is designed to reduce its computational
and memory footprint to be fit into a micro-controller.

###  3.1 Requirements Definition and Design

Figure 4: The neural network model used for SOC estimation

When designing SOC algorithms, there are several typical requirements to be
considered. For example:

  1. 1.

SOC estimation should be 100% when the battery is fully charged.

  2. 2.

SOC estimation should not change suddenly, including power cycles.

  3. 3.

SOC estimation should have a maximum error of less than 5%.

  4. 4.

SOC estimation should have an average error of less than 3%.

The neural network presented is designed to meet requirements #1,#3, and #4.
Other requirements, like #2 are implemented by a different software component,
such as the SOC initialization function.

As depicted in Figure 4, the neural network includes an input layer, hidden
layers, and an output layer. The inputs are measurable battery signals,
including voltage, current, and temperature. Both present and historical
measurements are used. Historical measurements are critical for the feed-
forward ANN to infer the internal SOC of the battery, which is a dynamical
system.

###  3.2 ANN Training with Cloud Data

Figure 5: The ANN training flowchart for SOC estimation

The ANN is developed using Matlab® and Simulink®. Cloud battery data are
fetched through Matlab® API and used to train the neural network. DC charging
data of NMC cells are used for training. The training data includes the cells
being charged at a range of temperatures, including -10°, 0°, 25°, 40°, and
50°, during which the voltage and current signals are recorded. As depicted in
Figure 5, these data are split into training data (75%), validation data
(15%), and test data (15%). During the training, Levenberg-Marquardt is
configured as the optimization algorithm. The training results are depicted in
Figure 6.

After acquiring the parameters, the ANN is implemented as Matlab® code and
integrated into the SOC software component, a Simulink® model. Using the
embedded coder, the Simulink® model is converted to C code, integrated with
other software components, and eventually become executable binaries.

Figure 6: ANN training results

###  3.3 HIL and Vehicle Testing Results

The ANN is first tested using the hardware-in-the-loop (HIL) system, during
which basic functions of the software component and SOC accuracy are evaluated
using cloud data. More importantly, as the ANN executes in the BMS real-time
operating system (RTOS), the impact on CPU and RAM usage is evaluated. It is
found that the ANN takes approximately 50 µs of execution time. Its RAM usage
is also small.

Finally, the algorithm is tested on a vehicle BMS. The ANN is deployed as a
shadowing strategy in addition to existing software for several passenger EVs.
To verify its robustness, the ANN is tested under the AC charge scenario to
verify its robustness. Even though it is only trained with DC charge data, the
ANN performed satisfactorily during AC. For example, Figure 7 depicts the SOC
comparison, current, voltage, and temperature plots of one test. As shown in
the SOC comparison plot, for most of the time, the true SOC (solid blue) falls
into the +/-5% bracket of the SOC estimation (solid red). The RMSE of all
testing results is 1.9%, well below the 3% target.

Figure 7: Onboard Vehicle Test: AC charging

##  4 Online State-of-Health Estimation

Li-ion batteries and many other secondary cells are subject to different
degradation mechanisms that lead to a loss of removable energy or power, which
lead to a decrease in range and acceleration for battery electric vehicle
[18]. For that reason, it is essential to monitor the state of health of Li-
ion batteries. The degradation mechanisms are briefly discussed and organized
into three categories in the following section. Furthermore, the concept of
the end-of-life (EOL) and the SOHC will be presented. Conclusively, an
overview of SOHC estimation methods will be given, with a closer look at the
voltage analysis (DVA) and the incremental capacitance analysis (ICA).

###  4.1 Degradation Mechanisms and Modes of Li-Ion Batteries

The components of a Li-ion battery are subject to different degradation
mechanisms. In general, the degradation mechanisms can be classified into
three modes [18, 19, 20]:

  * 1.

Loss of Lithium Inventory (LLI): The Li-ions are consumed by side reactions
and are no longer available for intercalation/deintercalation in the anode and
cathode.

  * 2.

Loss of active material of the anode (LAMA): The anode active material is no
longer available for lithium intercalation due to particle cracking and loss
of electrical contact or active areas being blocked by solid surface layers.

  * 3.

Loss of active material of the cathode (LAMC): The cathode active material is
no longer available for lithium intercalation due to structural failure,
particle cracking, or loss of electrical contact.

These modes can be attributed to different degradation mechanisms in the
components of a Li-ion cell. In the following, the primary degradation
mechanisms on the components of a Li-ion cell are named and briefly explained.
An overview of these degradation mechanisms is presented in the Figure 8.

##### Anode

The main degradation modes at the negative electrode are Lithium Plating and
the formation of solid electrolyte interphase (SEI). Lithium plating is a
commonly recognized and inherently damaging degradation mechanism in Li-ion
batteries, which describes the deposition of lithium metal on the surface of
the anode as soon as the anode potential exceeds the threshold of 0V (vs.
Li/Li+) [21]. On the other hand, the solid electrolyte interphase (SEI) is a
protective layer on the surface of the anode particles due to the
decomposition of the electrolyte, which is formed mainly during the first
cycles [22]. Both degradation modes are the main contributor for LLI and LAMA
[18].

##### Cathode

On the other side of the battery, the degradation modes of the cathode are
still growing in interest and therefore not thoroughly documented yet. It is
considered that structural changes and mechanical stress are the main
contributors to LLI and LAMC. Due to various cathode materials, the Li-ion
battery suffers from different side reactions based on the cathode material
composition. For example, an Mn-based cathode is more prone to the dissolution
of the active material due to Mn dissolution. In contrast, the degradation of
the LFP cathode is more likely to be defined by Fe dissolution, which
generates HF as a by-product and attacks the surface of the cathode particles
[21].

##### Separator, Electrolyte and Current Collectors

The separator, electrolyte and current collectors also suffer from various
degradation mechanisms. Although the porous separator of a Li-ion cell is
electrochemically inactive, the separator can negatively affect the
performance of the cell. According to various studies, deposits from the
electrolyte decomposition can clog the pores of the separator. This can
increase the impedance and also can reduce the accessible active surface of
the electrodes (LAMA and LAMC, respectively) [19, 23].

Figure 8: Degradation mechanisms in Li-ion cells adopted from Birkl et al.
[18]

On the other hand, the electrolyte is involved in side reactions that lead
mainly to the formation of a surface film on the negative electrode, but also
partly on the positive electrode. Electrolyte oxidation at the cathode does
not directly affect any of the three modes of degradation, but it does cause
re-intercalation into the active material, also known as self-discharge of the
cell. In contrast, electrolyte reduction at the anode results in a loss of
cyclable lithium, which leads to a capacity loss. [24, 25]. The current
collectors of a Li-ion cell suffer mainly from two degradation mechanism.
First, the current collectors can corrode electrochemically. This occurs
particularly at the aluminum collector of the positive electrode when acidic
species, such as HF, are present [26]. The copper current collector of the
negative electrode may dissolve during deep discharge when the anode potential
increases to $1.5\text{\,}\mathrm{V}$ in reference to Li/Li^+ [27]. Second,
the current collector foils may deform due to mechanical stress. This can
disrupt the contact between the electrodes and the separator so that specific
active areas can no longer participate in the intercalation process of Li-ions
in to the electrodes, which causes a loss in capacity [28].

Figure 9: Overview of different degradation mechanisms, their cause and their
effect on the performance of the Li-ion cell adopted from Birkl et al. [18]

Based on the Figure 9, it can be seen that all degradation modes can be
organized by their effect on the electric characteristics of a Li-ion battery,
the capacity and power fade. For further elaborations, we will focus primarily
on the capacity fade.

###  4.2 State-of-Health & End-of-Life

Due to the degradation mechanisms, Li-ion batteries have a limited lifetime.
The end of life (EOL) of a Li-ion battery is reached when the battery can no
longer provide the power or energy intended for its application [29]. However,
as of today, there is no uniform standard that defines a clear EOL criterion
for Li-ion batteries in the new energy industry [24]. The USABC consortium is
the only one to define two EOL criteria in its manual of test procedures for
electric vehicle batteries. According to this manual, the EOL of a Li-ion
battery is reached when:

  * 1.

the net capacity delivered is less than $80\%$ of the rated capacity CN or,

  * 2.

the peak capacity is less than $80\%$ of the rated capacity at a DOD of $80\%$
[30].

Also, in many publications, the EOL for the Li-ion-based traction battery of a
BEV at a capacitive aging condition of $SOH\textsubscript{C}\leq 80\%$ assumed
[31, 32, 33]. The capacitive aging state SOHC can be calculated using equation
2. Here, CN corresponds to the nominal capacity of the Li-ion battery, and
Qdis,max corresponds to the maximum charge quantity that can be removed from a
Li-ion battery, which is also known as the net capacity.

$SOH_{C}=\frac{Q_{dis,max}}{C_{N}}\cdot 100\%$ (2)

###  4.3 Advanced Online SOH-Estimation Methods

The capacitive aging state SOHC has an impact on two critical factors of a
BEV, the maximum range and the charging time during fast charging. Based on
the capacitive aging state SOHC, the maximum range can be predicted to the
driver, and the fast charging function can be adjusted to find the optimal
compromise between minimum charging time and damage of the anode by lithium
plating [34]. Therefore, an online SOHC estimation is essential for automotive
applications.

Figure 10: Methods for SOHC estimation adopted from Xiong et al. [35]

####  4.3.1 Methods

There are several options for determining the capacitive aging state, which
can first be divided into experimental and model-based methods, respectively.
As shown in Figure 10 111This figure was published in Journal of Power
Sources, 405, Rui Xiong, Linlin Li, Jinpeng Tian, Towards a smarter battery
management system: A critical review on battery state of health monitoring
methods, 18-29, Copyright Elsevier (2018)., these categories can be subdivided
into further subcategories. For example, experimental methods can be divided
into direct methods, such as capacity measurement (coulomb counting), and
indirect methods, such as differential voltage analysis. In contrast, model-
based methods can be divided into SOHC determination based on adaptive battery
models or data-driven methods.

At this point, the most popular methods regarding the capacitive aging
condition SOHC are mentioned and briefly discussed. More detailed summaries
about methods for SOHC estimation were given by Berecibar et al. [36] and
Xiong et al. [35].

##### Direct measurements

are the most straightforward method to determine the SOHC. One prevalent
method is the direct measurement of the current battery capacity. However,
this method requires an enormous expenditure due to the low charging current
during the capacity measurement, which is why this method is only used for R&D
purposes.

##### Model-based estimation methods

utilize algorithms like Kalman Filter or Neural Networks to model the battery
cell parameters. These methods achieve relatively high accuracy and can be
implemented in a Cloud-BMS. Yet, these algorithms require a high development
effort. For example, the accuracy of the Kalman filter is highly dependent on
the accuracy of the applied battery model, whereby high accuracy is only
achieved with complex battery models. Also, training a neural network requires
a large amount of data, which can only be generated by cost-intensive testing
of battery cells. In addition, these algorithms need extensive validation. For
example, the neural network is considered a black box, and the output can not
be generally predicted based on unexpected input data.

##### Indirect analysis methods

utilize various battery parameters to correlate the capacity fade with various
features of the Li-ion battery. For example, the charge curve can characterize
the SOHC of the battery as it changes throughout the battery degradation.
Constant current followed by constant voltage with current limiting (CCCV)
charging mode is commonly used for batteries. Eddahech et al.[37] developed a
method for SOHC estimation using the CV stage as a health indicator. Since
minimal intrinsic information about the battery can be obtained directly from
the voltage curves, Dubarry et al. [38, 39], for example, used electrochemical
characterization and analysis techniques, incremental capacitance analysis
(ICA), and differential voltage analysis (dV/dQ) (DVA). These methods are
often applied in laboratories since a low current rate is required to record
these differential curves. However, due to the increasing energy of the
battery packs and the lower power of AC charging, it is also possible to
record the differential curves during an AC charging process in a BEV. For
this reason, the basics of the DVA and ICA and their correlation with capacity
fade will be discussed in more detail in the following section.

Figure 11: The differential curve of ICA during a CC charge with C/10 of a Li-
ion battery consisting of a graphite anode and NMC cathode.

####  4.3.2 DVA/ICA-based SOH-Estimation Method

As mentioned before, it is essential to estimate the SOHC of battery packs in
BEV. In the following section, a DVA and ICA-based estimation method is
introduced. Therefore, the DVA and ICA will be presented and discussed in
detail to present a simple SOH-estimation implementation, which could be
realized on a cloud platform.

The DVA and ICA are commonly known analysis methods for Li-ion batteries in
laboratories. The IC curves can be calculated from equation 3 during a low-
current charging or discharging process. The division of an infinitesimal
charge change due to the charge/discharge current by the resulting voltage
change is calculated during the charge/discharge process. This process
converts the low-slope regions of the OCV curve, also known as voltage
plateaus of the two-phase transition, into detectable IC peaks. Another
method, is the differential voltage analysis (dV/dQ) (DVA). The DV curves can
be calculated by the reciprocal of the IC curve as shown in equation 4. The
distance between two peaks of the DV curve represents the amount of charge
involved in the two-phase transition, so it is easier to analyze the capacity
degradation quantitatively using the DV curves [40].

$\frac{dQ}{dV}\approx\frac{Q(t)-Q(t+\Delta t)}{V(t)-V(t+\Delta t)}$ (3)
$\frac{dV}{dQ}=(\frac{dQ}{dV})^{-1}\approx\frac{V(t)-V(t+\Delta
t)}{Q(t)-Q(t+\Delta t)}$ (4)

The result of calculating the IC curve during a low current rate charging
process is shown in Figure 11. It should be noted that the DV curves can be
represented using the half-cell potentials due to the superposition behavior
of the anode and cathode (see Equation 4). Thus, the peaks and valleys of the
DV curve can be assigned to the anode and cathode, respectively.

As mentioned before, Li-ion batteries suffer from various degradation
mechanisms, which lead to LLI, LAMC and LAMA. Due to these degradation modes,
a change in the DV and IC curves can be observed. Figure 12 shows the shift of
the DV and IC curve due to cyclic aging. Based on the change of these
features, the SOHC can be estimated by correlation, for example, the distance
between two peaks in the DV curve with the capacity fade of the Li-ion
battery. Another possible feature is the height or depth of the peaks or
valleys of the IC curve, which also shift throughout the ongoing degradation
of battery materials.

Figure 12: The course of the differential curves of the (1) ICA and (2) DVA
during a C/2 of a cyclically aged Li-ion battery (graphite anode/LFP cathode).
Figure 13: Correlation of selected features with Capacity Fade: (a) Height of
selected Feature (b) Distance between two features

In order to implement a DVA/ICA-based SOH estimation on a Battery Cloud the
following workflow should be included:

  1. 1.

The platform monitors the typical battery cell parameters, voltage, current,
and temperature.

  2. 2.

Whenever the battery is charging, it determines if the charging data has
satisfied feature detection based on several conditions. The conditions
include the C rates, amount of charge, and so on.

  3. 3.

If the conditions are met, proceed with the following steps. Otherwise, abort
and watch for the next window.

  4. 4.

Calculate and filter the differential curves (dV/dQ) based on the
measurements.

  5. 5.

Apply feature detection algorithm, i.e., a peak detection algorithm, to
extract the features. Based on the scenarios, different features may be
extracted and used. Since the features themselves do not indicate the SOHC,
they will be further processed.

  6. 6.

Apply a mapping function that relates the features with the SOHC. Typically,
the reference is represented by a Look-Up-Table (LUT) that is based on the
correlation between features and SOHC, extracted from existing cyclic aged
battery data.

As depicted in Figure 13, the real SOHC has strong correlations with DVA and
ICA features. For example, the distance of two features or the hight of a
feature. The correlations also depend on the temperature. Higher charging
currents will affect the estimation accuracy. However, this method can
generally achieve 5% SOH accuracy when charging at C/2 or less.

##  5 Cloud-based Thermal Runaway Prediction

###  5.1 Cause and Effects of Thermal Runaway

One significant disadvantage of batteries is the narrow operating temperature
range. The safety and stability of the battery cells are dependent on keeping
interior temperatures under certain limits. A thermal runaway can occur if the
temperature surpasses the critical level, killing the battery or, even worse,
causing a fire. Thermal runaway is a chain reaction that can be very difficult
to stop once it has begun within a battery cell. During a thermal runaway, the
temperature rises incredibly fast (milliseconds), and temperature can be
higher than 752°F/400℃. At such elevated temperatures, electrolytes in the
battery cell can be vaporized and combustive when exposed to oxygen. Such
battery fires are hard to extinguish with conventional ways.

Figure 14: Thermal Runaway explanation based on heat generation and
dissipation models[41]

The heat generated by the electro-chemical reactions is critical as it can
lead to thermal runaway. The heat generation is caused by chemical/electro-
chemical reactions and joule heating inside the battery. Radiation and
convection dissipate heat to the surroundings. The process of thermal runaway
can be explained by the plot Figure 14. The heat generation because of an
exothermic reaction assuming Arrhenius law, an exponential function, is shown
in curved line 4. In comparison, the heat dissipation is represented by
straight lines, which follow Newton’s cooling law at different coolant
temperatures. For the lithium-ion battery, curve 4 is the combined results of
reactions in the cell during the thermal runaway process and the energy
balance between the heat generation. Heat dissipation is shown as the
following equation Eqn. (5)

$\frac{\partial\left(\rho C_{p}T\right)}{\partial t}=-\nabla(k\nabla
T)+Q_{\mathrm{ab}-\mathrm{chem}}+Q_{\mathrm{joul}}+Q_{\mathrm{S}}+Q_{\mathrm{P}}+Q_{\mathrm{ex}}+\cdots,$
(5)

where $\rho\left(\mathrm{g}\mathrm{cm}^{-3}\right)$ is the composite/average
density of the battery, $C_{p}\left(\mathrm{Jg}^{-1}\mathrm{~{}K}^{-1}\right)$
the composite/average heat capacity per unit mass under constant pressure,
$T(\mathrm{K})$ the temperature, $t(\mathrm{s})$ the time,
$k\left(\mathrm{W}\mathrm{cm}^{-1}\mathrm{K}^{-1}\right)$ the thermal
conductivity. $Q_{\mathrm{ab}-\mathrm{chem}}$ the abuse chemical reaction in
the battery, $Q_{\text{joul}}$ Joule heat, $Q_{s}$ the entropy heat, $Q_{P}$
the overpotential heat, and $Q_{ex}$ the heat exchange between the system and
the ambient.

Generally, thermal runaway can be triggered by various types of abuse in a
battery shown in Figure 15 [42], including:

Figure 15: Abuses that cause thermal runaway [42]

##### Internal short circuit

Internal short circuit caused by physical damage to the battery or poor
battery maintenance.

##### Mechanical abuse

Vehicle collision and consequent crush or penetration of the battery pack are
the typical conditions for mechanical abuse.

##### Electrical abuse

  * 1.

Overcharging: the voltage that exceeds the maximum safety operation voltage
range will damage the battery and lead to thermal runaway. Because of the
extra energy filled into the battery during overcharge, the overcharge-induced
TR can be more severe than other abuse conditions.

  * 2.

Rapid charging can lead to excessive currents, therefore, causing thermal
runaway

  * 3.

External short circuit: External short circuit happens when the electrodes
with voltage difference are connected by conductors, which could also kick off
the TR chain reaction.

##### Thermal abuse

  * 1.

Over/Under temperatures: either the low or high side of the safety ranges
degrades battery health, leading to irreversible damage that may eventually
trigger the TR reaction.

  * 2.

Contact loose of the cell connector can lead to overheating.

###  5.2 Methods for Thermal Runaway detection

For the typical applications of batteries, including micro-grids and Electric
Vehicles, they are connected and packed in modules and packs. Suppose one or a
few batteries experience thermal runaway due to the limited space for heat
exchange. In that case, the heat will rapidly go up, leading to thermal
runaway propagation among all surrounding batteries. Therefore, it’s essential
to detect thermal runaways at an early stage to ensure operation safety.
Lithium-ion batteries may experience a voltage and current anomaly, a
temperature rise, or a gas venting during a thermal runaway process. Those are
the indicators that can be detected at the early stage of thermal runaway to
ensure the operation safety of batteries[43]. Methods of thermal runaway
detection include:

##### Terminal voltage

The terminal voltage can be detected by using voltage sensors within the
battery management system.

##### Mechanical deformation

Mechanical deformation can be detected by creep distance sensors.

##### Internal temperature

Since the core temperature directly represents the thermal condition within
batteries, it can be either:

  * 1.

measured by temperature sensor inserted in the batteries

  * 2.

estimated in terms of the measured surface temperature of batteries

##### Gas component

Some gas components can be identified during the thermal runaway process, such
as Carbon monoxide, hydrocarbons, and Hydrogen. Gas sensors like thermal
conductivity detectors (TCD) can be used for this purpose.

###  5.3 Data-driven Thermal Anomaly Detection

Here we give a cloud-based and data-driven method for detecting battery
thermal anomalies[44]. Because that this method is based on the measurements’
shape-similarities, which is less affected by cell deterioration or
environmental variation, it is robust to battery aging or environment
variations. As a result, this method can be applied to different battery
configurations. The shape-based distance measurement handles the asynchronous
data issue. It also needs very little reference data. This method is based on
K-shape clustering[45]

$SBD(\vec{x},\vec{y})\ =\
1-\underset{\omega}{max}{\left(\frac{CC_{\omega}\left(\mathbf{x},\mathbf{y}\right)}{\sqrt{R_{0}\left(\mathbf{x},\mathbf{x}\right)R_{0}\left(\mathbf{y},\mathbf{y}\right)}}\right)},$
(6)

where $\vec{x},\vec{y}$ are two time-series measurements that used for
comparing similarity, and $R_{0}$ the Rayleigh Quotient.

Figure 16: Flowchart for the thermal anomaly detection algorithm

####  5.3.1 Workflow

As depicted in Figure 16, the proposed anomaly detection method contains the
following steps. At first, data is continuously buffered and segmented. During
the pre-processing stage, invalid/faulted data points are removed. Signals are
normalized. Segments with static signals are filtered out.During the Anomaly
Confirmation stage, the K-shape algorithm is applied to each segment for the
distances ($SBD(x_{i},c_{j})$) for each cluster. Two criteria are used for
determining anomaly. 1) When at least one of the measurements change the
membership. 2) When no change was found in cluster membership, we check for
any noticeable increase in the fitting errors. During each iteration, the ith
cluster is compared to the reference cluster, which captures the accumulated,
long term changes that result from anomalies caused by gradual deterioration.
Such as thermal anomalies caused by increased battery impedance. It’s also
compared to the predecessor for anomalies that developed abruptly, such as
short-circuit. In the final stage of Anomaly Isolation, we use the change of
membership or increase in fitting error to isolate the signals that have
caused the anomaly.

####  5.3.2 Case Study

(a) two months of temperature signals, over-temperature highlighted (b)
anomaly detected 90min ahead (c) one of the signal has different shape

Figure 17:  Test Case (a) temperature measurement which shows the over-
temperature fault. (b) The zoom-in view of the fault occurrence, this method
detects the temperature anomaly 90min before the onboard BMS. (c) Further
zoom-in view of the segment’s shapes plot where the anomaly is detected, which
is also highlighted with blue in (b)

We apply the proposed anomaly detection method to an EV battery. The data was
collected and transmitted from an onboard BMS. As shown in Figure 17(a),
temperature measurement from sensor #13 increased to over 70 ℃on Oct 30th. The
onboard BMS detects the over-temperature anomaly around 3:45 pm, during which
the temperature was over 55 ℃. Mean whole, the proposed method was able to
detect anomalies around 2:15 pm, which is about 90 minutes earlier. The
detailed comparisons are illustrated in Figure 17(b). As it shows, the
proposed method detect the anomaly when sensor #13 just started to behave
differently from the other measurements. Figure 17(c) is the shape plot of the
segment that detects the anomaly. In this figure, sensor #13 is flagged as the
outlier for its rising shape.

##  6 Conclusions

In this chapter, we present a Battery Cloud (Cloud Battery Management System)
which is aimed at improving battery performance, safety, and economy by
utilizing Cloud Computing and the Internet of Things. The major component of a
battery cloud, include the data sources during the stages of the battery life
cycle, the different choices of databases, and deployment for battery data and
data visualization. In addition, we discuss core algorithms for the Battery
Cloud. Firstly, an artificial neural network is trained with cloud battery
data for state-of-charge (SOC) estimation. The ANN is eventually deployed to
the onboard BMS and tested on the vehicle. The successful testing results show
that cloud battery data is essential for developing advanced battery
algorithms. Secondly, we discuss the degradation mechanisms of battery health
and different algorithms for state-of-health (SOH). We develop a SOH
estimation method based on DVA/ICA, which shows ¡5% accuracy under different
operating temperatures. At last, we review one important safety issue for
batteries, thermal runaway. Its cause and effects are discussed. A data-driven
battery anomaly detection method is developed to give early warnings.

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