<2024 new edition> Technology Status and Outlook of
Battery Management System (BMS) for EV/ESS
(Battery
condition (SOx) estimation technology and AI-linked next-generation BMS
technology)
As the demand for eco-friendly energy increases
and high fuel efficiency means of transportation are expanding due to high oil
prices, EVs, PHEVs, HEVs, E-Bikes, E-Scooters, etc. are gaining popularity, and
existing lead-acid batteries and Ni-MH are being replaced with Li-ion batteries
that have high performance, high output, light weight, and long lifespan.
However, the risk of fire and explosion in abnormal situations is becoming an
increasing social problem. BMS emerged as an essential system for
balance of safety and cell.
Although global
electric vehicle sales have been slowing down recently due to chasm, the
electrification trend is expected to continue. Global EV sales have been
steadily increasing from 2015 to 2024, growing by more than 20% from 13.27
million units in 2023 to 15.87 million units in 2024, according to SNE
Research. It is expected that electric vehicles will account for 49% of all
vehicles worldwide and more than 60% of new car sales by 2030. Accordingly, the
BMS market is expected to grow from USD 6.8 billion in 2025 to USD 22 billion
in 2035, recording a CAGR of more than 22%.
At the heart of
EV, PHEV, HEV, and ESS is a complex battery management system (BMS). BMS acts
as the brain, ensuring the safety and reliability of secondary batteries that
supply the power required for the driving system. Although the cost of BMS in a
battery pack is only 4-5%, it is no exaggeration to say that it accounts for
more than half of the battery pack performance.
The importance of BMS is becoming more
necessary as battery fire and explosion accidents increase, and it is composed
of hardware and software to ensure the stability of the system. It monitors the
voltage, current, and temperature of the system to maintain it in an optimal
state, and provides alarms and preventive safety measures for the safe
operation of the system.
It prevents
overcharge and over-discharge during battery charging and discharging, and
increases energy efficiency and battery lifespan by equalizing the voltage
between cells, and it is possible to save alarm-related history status and
diagnose through an external diagnostic system or monitoring by preserving data
and diagnosing the system. The domestic BMS market is expected to record a CAGR
of more than 16% from 2024 to 2029.
BMS plays a
role in improving driving range and securing safety by optimizing battery
control of electric vehicles. BMS technology can be largely divided into
thermal management control that evenly cools batteries that are vulnerable to
heat to enable the same performance, and battery state of charge (SOC) control
that determines each state of the battery and operates at the optimal
efficiency point.
BMS monitors the voltage, current, and
temperature of the system to maintain it in an optimal state, and provides
alarms and preventive safety measures for the safe operation of the system. In
other words, it prevents overcharge and over-discharge during battery charging
and discharging, and increases energy efficiency and battery lifespan by
uniformly adjusting the voltage between cells. It also preserves data and
diagnoses the system, enabling the storage of alarm-related history status and
diagnosis through an external diagnostic system or monitoring.
The rated
voltage of a single cell of the latest Hi-Ni LIB is 3.7V, and the charging
voltage is 4.5V. When connected in series, a voltage of over 600V is generated.
When multiple cells are connected in series, if even one of them breaks down or
deteriorates, the entire battery pack is affected. Therefore, the BMS applied
to the latest EVs, PHEVs, and HEVs has the function of preventing overcharge,
over-discharge, and overheating of individual cells and optimizing their
lifespan.
BMS achieves
this by maintaining all cells in an even state of charge at all times through
cell balancing. Furthermore, BMS comprehensively analyzes various changing
factors to predict the remaining driving range and provides the information to
the upper vehicle ECU (Electronic Control Unit). For in-vehicle communication,
CAN (short for Controller Area Network, a communication system developed by
Bosch for data sharing between in-vehicle ECUs) is generally used. The hardware
configuration of BMS consists of VITM (Voltage, Current, Temperature Measure)
module, cell balancing module, microprocessor, etc.
The software of
BMS provides advanced information to the user based on the control and
management of the battery's SOx state. Various methods for estimating the state
based on the battery's electrical equivalent circuit model are being proposed,
but as the importance of collecting big data during the application's driving
is emphasized, various AI algorithms based on data analysis are also being
developed.
In order to
improve BMS performance, a deep learning model was introduced to
complement the limitation of machine learning, which is the human-input feature
extraction into the computer. Recurrent neural network (RNN) and long
short-term memory (LSTM) algorithms are used to predict time series data of the
battery, and convolution neural network (CNN) is
utilized to detect battery faults (failures). Various preprocessing processes
are required to apply these deep learning algorithms.
The final HI is selected through correlation
analysis between data preprocessing and remaining life, and the battery life is
predicted based on the learning model. And by learning
the characteristics and patterns of driving history data, abnormal operation of
the battery system is detected, and in situations where it is difficult to
secure a large amount of failure data, a model that learns normal data through
unsupervised learning and anomaly scores are used to detect failures. In
addition, considering the EV driving environment, factors are selected based on
the Urban Dynamometer Driving Schedule (UDDS), and an SOH estimation algorithm
is installed on the embedded board. The technology developed in this way can
improve the performance of the BMS and enhance the stability of the battery
system.
The introduction of wireless BMS enables real-time data collection in
a different way than existing wired systems, providing battery status
information to users. This performs the functions of existing BMS in real time
in virtual space (Cloud). Battery status estimation algorithms are executed,
and the results are visualized and transmitted to users. However, there are
several limitations when collecting data in the Cloud. As the amount of data
increases, transmission delays occur, and to solve this, the Edge computing
concept is being introduced, which is connected to the vehicle-mounted BMS and
enables immediate control.
In addition, encryption technology and blockchain-based
data forgery/alteration prevention technology have been introduced for
security issues. Battery data information for battery life management and
system improvement should be disclosed as a public blockchain, and personal
information (path, ID, etc.) should be kept private as a private blockchain. If
a reliable data history management system with excellent performance in
preventing forgery and tampering is established, the wireless BMS market is
expected to grow further and expand to various vehicle platforms.
Finally, since
there are different characteristics according to the environmental temperature
for each type of battery, an appropriate thermal management system is
required for each battery. In battery-based applications, there is a
battery pack composed of a number of battery cells in series and parallel, and
the imbalance in the temperature distribution inside the battery pack affects
the performance/lifespan of the battery pack. The surface temperature of the
battery is determined by three heat transfer phenomena: irreversible heat,
reversible heat, and heat loss. Battery heat generation can be estimated using
an equivalent circuit model, an electro-thermal model, and an artificial
intelligence model. Since the cooling method and arrangement are different
depending on the shape and internal structural characteristics of the cells
that make up the battery pack, the thermal management system must be designed
to enable sufficient heat dissipation by reflecting this.
This report
introduces in detail the basic technologies surrounding BMS, a very important
component in battery packs and modules, as well as recent technology trends
such as deep learning, AI-linked next-generation technologies, and wireless
BMS, which are expected to be helpful in developing safer and longer-lasting
packs and modules.
Strong Points of this
Report
① Growth of various LIB-based applications and development of
next-generation batteries and increasing importance of BMS
② Importance of BMS and status of domestic BMS market due to rapid
increase in battery safety issues
③ Increased need for BMS H/W, S/W and AI algorithms
④ Battery life prediction and abnormality detection based on deep learning
model
⑤
Improvement of BMS
reliability and scalability using Cloud BMS and Blockchain technology
⑥
Need to design
appropriate thermal management system according to battery type and
environmental temperature
Contents
1. Applications of Lithium-Ion Battery and Next-Generation Batteries
1.1 Overview of LIB
1.1.1 Basic Terms of LIB
1.1.1.1 Voltage
1.1.1.2 Coulomb, Current
1.1.1.3 Capacity
1.1.1.4 Power, Energy
1.1.1.5 C-rate
1.1.1.6 OCV, Battery upper and lower
cut-off voltages
1.1.1.7 Series-Parallel Connection of
Batteries
1.1.1.8 Battery Pack
1.1.1.9 Location of Battery Pack and
BMS in EV
1.1.2 LIB
Composition and Operating Principle
1.1.2.1 Battery Structure
1.1.2.2 Battery Type
1.1.2.3 Operating Principle
1.2 LIB Application Trends
1.2.1 Energy Storage System (ESS)
1.2.1.1 ESS Overview
1.2.1.2 Domestic/Overseas Trends of
ESS
1.2.2 Electric Vehicle (EV)
1.2.2.1 EV Overview
1.2.2.2 Domestic/Overseas Trends of EV
1.2.3 Electric Ship
1.2.3.1 Electric Ship Overview
1.2.3.2 Domestic/Overseas Trends of
Electric Ship
1.2.4 Urban Air Mobility (UAM)
1.2.4.1 UAM Overview
1.2.4.2 Domestic/Overseas Trends of
UAM
1.3 Next-Generation Batteries
1.3.1 Technology Development of
Next-Generation Batteries
1.3.1.1 Need for Technology
Development
1.3.2 Technology Development Trends of
Next-Generation Batteries
1.3.2.1 Lithium-Sulfur Battery
1.3.2.2 All-Solid-State Battery
1.3.2.3 Vanadium Flow Battery
1.3.2.4 Lithium Air Battery
1.3.2.5 Sodium-ion Battery
1.3.2.6 Lithium Metal Battery
1.3.2.7 Sodium-Sulfur Battery
1.3.2.8 Hydrogen Bromide Flow Battery
1.3.2.9 Iron Flow Battery
1.3.3 Development Trends of
Next-Generation Battery Application
1.3.3.1 ESS Trends
1.3.3.2 EV Trends
1.3.3.3 Drone Trends
2. Introduction of Battery Management System (BMS)
2.1 Introduction and Necessity of BMS
2.1.1 Need for BMS Due to the Expansion
of the Battery Market
2.1.1.1 Expansion of the EV Market
2.1.1.2 Expansion of the ESS Market
2.1.2 Need for BMS Due to Battery Fire
2.1.2.1 Fire Accident in EV
Application
2.1.2.2 Fire Accident in ESS
Application
2.1.2.3 Cause of Fire Accident
2.1.3 Architecture and Function of BMS
2.1.3.1 BMS Architecture
2.1.2.2 Classification of BMS by
Function - S/W
2.1.3.3 Classification of BMS by
Function – H/W
2.1.3.4 Function of BMS by Application
- EV
2.1.3.5 Function of BMS by Application
– ESS
2.2 Technology Trends of BMS
2.2.1 Domestic/Overseas Trends of BMS
Technology
2.2.1.1 Changes in BMS Technology
Trends
2.2.2 Domestic/Overseas BMS Technology
2.2.2.1 State estimation
2.2.2.2 Fault diagnosis
2.2.2.3 Balancing
2.2.2.4 Screening
2.2.2.5 Retired battery
2.3 BMS Market Trends
2.3.1 Domestic BMS Market Trends
2.3.2 Overseas BMS Market Trends
2.3.2.1 Global BMS Market Trends
2.3.2.2 BMS Market Trends in US
2.4 BMS H/W Configuration and
Design Process
2.4.1 BMS H/W Configuration and Function
2.4.1.1 BMS H/W Overview
2.4.1.2 BMS H/W Function – Protection
2.4.1.3 BMS H/W Function – Measurement
2.4.1.4 BMS H/W Function – Communication
2.4.1.5 BMS H/W Function – Control
2.4.2 BMS H/W Design Process
2.4.2.1 Determining the Battery
Combination Structure According to the Application Specifications
2.4.2.2 Selecting the BMS H/W Topology
According to the Requirements
2.4.2.3 BMS H/W Design – Measurement Section
2.4.2.4 BMS H/W Design – Protection
Section
2.4.2.5 BMS H/W Design – Control
Section
2.4.2.6 BMS H/W Design – Telecom Section
2.4.2.7 BMS H/W Operation Verification
and Validation
2.4.3 BMS F/W(Firmware) Configuration and
Function
2.4.3.1 BMS F/W Configuration
2.4.3.2 BMS F/W Driver
2.4.3.3 BMS F/W Module
2.4.3.4 BMS F/W Engine
3. BMS Status Estimation Technology Trends
3.1 BMS S/W Definition and Function
3.1.1 Main Functions of BMS S/W Status
Estimation (SOx)
3.1.1.1 BMS S/W Necessity
3.1.1.2 Introduction of BMS S/W Status
Indicators
3.1.2 Battery Model-Based Status
Estimation Technology
3.1.2.1 Necessity of Electrical
Equivalent Circuit Model
3.1.2.2 Introduction of Electrical
Equivalent Circuit Modeling
3.1.2.3 Types of Electrical Equivalent
Circuit Models
3.1.3 Electrical Equivalent Circuit
Modeling Technology Trends
3.1.3.1 Equivalent Circuit Model
Considering Battery Accumulated Current
3.1.3.2 Equivalent Circuit Model
Considering Battery Available Capacity
3.2 SOC Estimation Algorithm Technology Trends
3.2.1 Introduction of Battery SOC
Estimation Algorithms
3.2.1.1 Necessity of Battery SOC
Estimation
3.2.1.2 Battery SOC Estimation Methods
3.2.1.3 SOC Estimation Based on
Coulomb Counting
3.2.1.4 SOC Estimation Based on
Adaptive Control
3.2.1.5 SOC Estimation Based on Data
3.2.1.6 Comparison and Analysis of
Advantages/Disadvantages for Each SOC Estimation Method
3.2.2 Adaptive Control Model-Based SOC
Estimation Algorithm
3.2.2.1 Extended Kalman Filter-Based
SOC Estimation Algorithm
3.2.2.2 Offline Parameter and Extended
Kalman Filter-Based Battery SOC Estimation
3.2.2.3 Online Parameter and Extended
Kalman Filter-Based Battery SOC Estimation
3.2.2.4 Dual Extended Kalman
Filter-Based Battery SOC Estimation
3.2.3 SOC Estimation Algorithm Technology
Trend
3.2.3.1 SOC Estimation Algorithm
According to Variable Conditions (Temperature/Aging)
3.3 SOH Estimation Algorithm Technology Trends
3.3.1 Accelerated Life Test and Battery
Degradation Mechanism
3.3.1.1 Definition of Battery
Degradation
3.3.1.2 Battery Degradation Mechanism
3.3.1.3 Accelerated Life Test
3.3.2 Introduction of Arrhenius
Model-Based Battery Degradation Model
3.3.2.1 Arrhenius-Based Battery
Degradation Model Design Method
3.3.2.2 Arrhenius-Based Battery
Degradation Model Factor Derivation Method
3.3.3 Resistance Information-Based SOH
Estimation Algorithm
3.3.3.1 EIS Impedance-Based SOH
Estimation Algorithm
3.3.4 Adaptive Control-Based SOH
Estimation Algorithm
3.3.4.1 Model-Based Battery
Degradation Analysis Method
3.3.5 SOH Estimation Algorithm Technology
Trend
3.3.5.1 Stress Factor-Based
Degradation Model
4. AI-linked Battery Management System
(BMS)
4.1 Necessity of Introducing AI
Based on Big Data in BMS
4.1.1 Expansion of Big Data Collection Infrastructure Based on Cloud
Server
4.1.1.1 Status of Vehicle Data Collection Based on Cloud Server
4.1.2 Necessity of Next-Generation BMS Based on AI Due to Establishment
of Big Data Platform
4.1.2.1 Need to Estimate and Predict Nonlinear Characteristics of
Batteries Due to Diversification of Applications
4.1.2.2 Providing Integrated Solutions Based on Big Data Collection and
Analysis and Providing Solutions Within BMS
4.1.2.3 Necessity of Linking BMS and AI Technology
4.2 Introduction of AI for
Battery Management System
4.2.1 Introduction of AI Model
4.2.1.1 Early Artificial Neural Network (ANN) Model – Perceptron
4.2.1.2 Statistical-based Secondary AI Model – Machine Learning
4.2.1.3 Current Artificial Intelligence (AI) Model – Deep Learning
4.2.1.4 Recurrent Neural Network (RNN)
4.2.1.5 Long-short-term Memory (LSTM)
4.2.1.6 Convolution Neural Network
(CNN)
4.2.2 Data Preprocessing for AI Algorithm
Application
4.2.2.1 Data Processing
4.2.2.2 Data Cleaning
4.2.2.3 Data Transformation and
Correlation Analysis
4.2.2.4 Data Labeling
4.2.2.5 Data Compression
4.2.3 Battery
Deterioration Data Analysis and Health Indicator Extraction Study
4.2.3.1 Battery Deterioration Data Analysis
4.2.3.2 Battery Health Indicator (HI) Extraction
4.2.3.3 Battery Health Indicator (HI) Selection
4.2.4 Experimental Data Decomposition and Compression Study through
Signal Interpretation
4.2.4.1 Concept of Wavelet Transform (WT)
4.2.4.2 Discrete Wavelet Transform (DWT)
4.2.5 Feature Extraction for Building Learning Data Set and Correlation
Analysis
4.2.5.1 Importance of Feature Extraction
4.2.5.2 Types of Feature Extraction
4.2.5.3 Principal Component Analysis (PCA)
4.2.6 AI-Based BMS Algorithm
4.2.6.1 AI-Based Battery Life
Prediction Algorithm
4.2.6.2 AI-Based Battery Failure
Diagnosis Algorithm
4.3 AI-based BMS Advanced
Algorithm
4.3.1 Random Forest-based Data Missing
Value Compensation and Discharge Capacity Prediction Study
4.3.1.1 Random Forest-based Data Set
Compensation for Battery Capacity Prediction
4.3.1.2 Random Forest-based Important
Factor Selection Process
4.3.1.3 Case Classification by
Important Factor and Case-by-Case Capacity Prediction Results
4.3.2 CNN-based External Environment
Classification Study through EIS Image Input
4.3.2.1 Battery Deterioration Data
(EIS) Collection According to External Environment Diagnosis
4.3.2.2 EIS Image Pattern Change
Analysis for CNN Learning Data Set Composition
4.3.2.3 EIS Image Conversion for CNN
Learning Data Set Composition
4.3.2.4 CNN Model Learning and
External Environment Classification According to EIS Image Input
4.3.3 Study on Data Patternization for
Battery Fault Diagnosis
4.3.3.1 Need for Abnormality Detection
and Data Patternization Research Specialized for Time Series Data
Characteristics
4.3.3.2 Autoencoder-based Time Series
Data Compression
4.3.3.3 Input Data Set Composition of
Classification Model
4.3.3.4 Compressing Time Series Data
of Each Abnormality Type into One Signal
4.3.3.5 Pattern Classification Model
Learning and Battery Abnormality Pre-Diagnosis through Compressed Abnormality
Type Signals
4.3.4 Study on Real-Time SOH Estimation
Considering EV Driving Environment
4.3.4.1 Indicator Selection for
Improving SOH Estimation Performance in Dynamic Profile
4.3.4.2 Building Long-Short Term
Memory Network for Time Series Data Prediction
4.3.4.3 Embedded Linux-Based Real-Time
SOH Estimation Algorithm Installation
4.3.4.4 Building Experimental
Evaluation Environment and Verifying Real-Time SOH Estimation Algorithm
Performance
5. The Future of BMS
5.1 Cloud BMS
5.1.1 IoT-based BMS
5.1.1.1 Concept of Internet of things(IoT)
5.1.1.2 IoT-based Real-time Data
Collection
5.1.1.3 IoT-based Data Transmission-
OBD-II to Cloud
5.1.1.4 Integrated Battery Management
Service Using IoT-based BMS
5.1.2 Building Cloud BMS for Optimal
Battery Operation
5.1.2.1 Definition of Battery Status
Diagnosis Platform (Cloud BMS) Based on Collected Data
5.1.2.2 Cloud BMS Structure
5.1.2.3 BMS Operation through Cloud
Server Construction
5.1.2.4 Cloud BMS-based Real-time
Battery Monitoring - Data Analysis and Result Visualization
5.1.2.5 On-board BMS Performance Improvement
Using Cloud BMS
5.1.3 IoT-Based Cloud BMS Limitations and
Supplementary Techniques
5.1.3.1 Limitations in Data Collection
and Server Storage (1)
5.1.3.2 Edge Computing Technology
5.1.3.3 Limitations in Data Collection
and Server Storage (2)
5.1.3.4 Introduction of Encryption
Technology for Data Security
5.1.4 Wireless BMS
5.1.4.1 Definition and Necessity of
Wireless BMS
5.1.4.2 Wireless BMS Structure
5.1.4.3 Wireless BMS Market Trends
5.1.5 Blockchain
5.1.5.1 Concept of Blockchain
5.1.5.2 Classification of Blockchain
5.1.5.3 Necessity of Blockchain
Technology
5.1.5.4 Blockchain-Based Cybersecurity
Solution
5.1.5.5 Blockchain-Based Personal
Information Protection
5.1.5.6 Blockchain-Based Full-Cycle Management
System
5.2 Digital-Twin Model
5.2.1 Digital Twin
5.2.1.1
Concept of Digital Twin and Expected Effects
5.2.1.2
Components of Digital Twin
5.2.1.3
Digital Twin Implementation and Utilization
5.2.1.4 Key Technologies for
Implementing Digital Twin
5.2.1.5
Digital Twin Optimization
5.2.2 Trends in Battery Status Estimation
Based on Digital Twin Model and Cloud BMS
5.2.2.1 Combining Digital Twin Model
and Cloud BMS
5.2.2.2 Utilization of Digital Twin
and Cloud BMS: Virtual Battery Model
5.3 Battery Replacement System
5.3.1 Battery Replacement Technology
Trends
5.3.1.1 Battery Swapping Technology
5.3.1.2 Battery Swapping Technology
Characteristics and Process
5.3.1.3 Battery Swapping System Market
Size
5.3.1.4 Battery Swapping Technology
Development Trends by Company
5.3.1.5 Domestic Battery Swapping
Technology Trends
5.3.2 Battery Replacement History
Tracking Platform Trends
5.3.2.1 Battery Passport
5.3.2.2 Overseas Battery Passport
Trends
5.3.2.3 Domestic Battery Passport
Trends
5.4 Fast Charging System
5.4.1 Overview and Trends of EV Battery
Charging Technology
5.4.1.1 Classification of EV Charging
Method
5.4.1.2 Changes in EV Charging System
5.4.1.3 Current Status of EV
Development Applying 800V System
5.4.2 Battery Fast Charging Technology
Trends and Strategies
5.4.2.1 Battery-related Issues
According to Fast Charging Application
5.4.2.2 Fast Charging Optimization
Research Using Optimal Charging Profile
5.5 V2G System
5.5.1 Vehicle to Grid
5.5.1.1 Definition and Necessity of V2G
5.5.2 Domestic V2G Technology Trends
5.5.2.1 Domestic V2G Demonstration
Projects and Systems
5.5.3 Overseas V2G Technology Trends
5.5.3.1 US
5.5.3.2 UK
6. Battery Thermal Management System
6.1 Overview of Battery
Thermal Management System
6.1.1 Battery Thermal Runaway and
Necessity of Thermal Management System
6.1.1.1 Battery Thermal Runaway
6.1.1.2 Prevention Solution of Battery
Thermal Runaway
6.1.1.3 Flame Retardancy Required in
Battery Pack
6.1.1.4 Flame Retardancy Solution in
Battery Pack
6.1.1.5 Necessity of Battery Thermal
Management System
6.2 Battery Thermal Management Model
6.2.1 Battery Heating Model
6.2.1.1 Battery Heating
Characteristics
6.2.1.2 Heat Loss
6.2.2 Battery Heat Generation Model
Technology Trend
6.2.2.1 Battery Heat Generation Estimation
Through Equivalent Circuit Model
6.2.2.2 Electro-Thermal Model
6.2.2.3 Thermal Model Through AI
Technology
6.3 Battery Thermal Management
System Design
6.3.1 Battery Thermal Management System
Design and Battery Cooling Technology
6.3.1.1 Battery Thermal Management
System Configuration
6.3.1.2 Main Devices of Battery
Thermal Management System
6.3.1.3 Battery Thermal Management
System Design Process
6.3.1.4 Battery Cooling Technology
7. Battery Pack and BMS Market Outlook
7.1 Electric LV (Passenger + Pick-up) Share Outlook
7.2 Global EV Battery Demand Outlook
7.3 Global EV Battery Cell Market Outlook
7.4 Global EV Battery Pack Market Outlook
7.5 EV Battery and Battery Pack Price
Outlook
7.6 Cost Composition of Main EV Battery Pack
Components
7.7 Market Outlook of Global Battery Pack
Components
7.8 Market Outlook of BMS for EV
8. Status of Battery
Pack and BMS Companies
8.1 LG Innotek
8.2 SK On
8.3 Hyundai KEFICO
8.4 NEXCON Technology
8.5 WONICK PNE
8.6 Elentec
8.7 POWERLOGICS
8.8
CTNS
8.9 SL Corp
8.10 Hunate
8.11 MISUMSYSTECH
8.12 YoungHwa TECH
8.13 INZI e-Solution
8.14 Blue Sigma
8.15
LAONTECH
8.16 STMicroelectronics
8.17 dSPACE
8.18 Freudenberg e-Power Systems(FEPS)
8.19 Infineon Technologies AG
8.20 FORVIA Hella
8.21 Renesas Electronics Corp.
8.22
Elithion
8.23 Eberspaecher Vecture Inc
8.24 Texas Instruments
8.25 ELEMENT Energy
8.26 Intel
8.27 Shenzhen Tritek Limited
8.28 Octilion
8.29
GuoCHUANG
8.30 CATL
8.31 BYD
8.32 Sensata Technologies
8.33 Panasonic Corp