<2025> Latest Technology Development Trends and Market Outlook of Battery Management Systems (BMS) for EV & ESS
(Focusing on Next-Generation AI-Integrated BMS)
At
the heart of EVs, PHEVs, HEVs, and further ESS lies a complex Battery
Management System (BMS). The BMS serves as the brain that ensures the safety
and reliability of the secondary batteries supplying power to the drivetrain.
Although the BMS accounts for only about 4–5% of the total cost of a battery
pack, it is no exaggeration to say that it determines more than half of the
overall battery pack performance.
The importance of BMS has become increasingly evident
with the rise in battery fire and explosion incidents. Comprising both hardware
and software, the BMS secures system stability through state estimation, fault
diagnosis, cell balancing, and continuous monitoring of system voltage,
current, and temperature to maintain optimal operating conditions. It also
provides alarms and proactive safety measures to ensure safe system operation.
In other words, the BMS prevents overcharge and overdischarge during battery
charging and discharging, equalizes voltages among cells to improve energy
efficiency and extend battery life, and preserves data while enabling system
diagnostics by storing alarm histories and supporting diagnostics through
external diagnostic systems or monitoring platforms.
Despite the recent passage of the U.S. OBBBA Act and a
slowdown in global EV sales excluding China, the overall electrification trend
is expected to continue. According to SNE Research, the global xEV battery
market is projected to expand from 898 GWh in 2024 to 2,098 GWh in 2030 and
further to 4,279 GWh by 2035. In line with this growth, the market for cell and
pack components is expected to increase from USD 28.2 billion in ’25 to USD
50.8 billion in ’30, reaching USD 97.6 billion by ’35. Accordingly, the BMS
market is forecast to grow from USD 5.1 billion in ’25 to USD 17.6 billion by
’35.
BMS software provides advanced information to users
based on the control and management of battery states of X (SoX). While various
methods for state estimation based on electrical equivalent circuit models of
batteries have been proposed, the growing importance of large-scale data
collection during real-world operation has led to the active development of
diverse AI algorithms based on data analytics.
In other words, to improve BMS calibration and
performance, deep learning models have been introduced to complement the
limitation of machine learning, where feature extraction must be manually
performed by humans and provided to computers. For time-series prediction of
battery data, recurrent neural networks (RNN) and long short-term memory (LSTM)
algorithms are used, while convolutional neural networks (CNN) are applied for
battery anomaly (fault) detection. The application of these deep learning algorithms
requires various data preprocessing processes.
Meanwhile, as electrification advances and large-scale ESS deployment
expands, the role of the BMS is evolving beyond simple state-of-charge display
toward a focus on prediction, protection, and connectivity. The most notable
trend is the commercialization of AI/ML-based state estimation. By enhancing
traditional OCV methods, equivalent circuit models, and extended Kalman filters
(EKF), sequence learning models such as LSTM, regression models, and hybrid
models combining physics and data are used to improve the accuracy of SOC, SOH,
and RUL prediction, while enabling early warnings for cell imbalance and aging
modes such as lithium plating and increased resistance. Recent academic and
industry reports commonly emphasize the division of roles between lightweight
inference within the BMS or at the edge and cloud-based analytics, along with
the growing adoption of physics-informed neural networks to compensate for data
scarcity.
ESS BMS is evolving from internal pack-level protection toward integrated
safety management that extends to facility-level operation, site conditions,
and code compliance. BMS events are linked with fire codes such as NFPA 855 and
requirements coordinated with the authority having jurisdiction (AHJ) to
trigger ventilation, smoke exhaust, fire suppression, and isolation sequences,
while thresholds are recalibrated using data from large-scale fire testing
(LSFT). From a project developer and operator perspective, incorporating the
latest UL 9540A methodologies and expanding test coverage has become a key
factor in reducing permitting and insurance risks.
From a connectivity standpoint, system-wide monitoring through cloud-based
battery analytics and digital twins is becoming increasingly widespread. Across
both vehicles and ESS, BMS logs and on-site operational data are collected and
learned to enable remaining useful life-based maintenance (PHM), OTA
optimization of operating constraints such as temperature, current, and
voltage, and early warning of abnormal conditions. This two-tier “edge
(BMS)–cloud” architecture also integrates naturally with regulatory compliance
requirements, including cybersecurity and update traceability.
Meanwhile, with the introduction of wireless BMS, real-time data can be
collected and battery state information can be provided to users in a manner
different from conventional wired systems. In this approach, functions
traditionally performed by a BMS are executed in real time within a virtual
space (cloud). Battery state estimation algorithms are run, and the results are
visualized and delivered to users. However, cloud-based data collection has
several limitations. As data volumes increase, transmission delays occur, and
to address this issue, an edge computing concept has been introduced in which
the system is connected to the vehicle-mounted BMS to enable immediate control.
In addition, to address security concerns, encryption technologies and
blockchain-based mechanisms to prevent data tampering and forgery have been
introduced. Battery data used for lifetime management and system improvement
should be disclosed on a public blockchain, while personal information such as
routes and IDs should be kept confidential on a private blockchain. Once a
reliable data history management system with strong resistance to forgery and
tampering is established, the wireless BMS market is expected to grow further
and expand across a wider range of vehicle platforms.
This report is expected to support the development of
safer and longer-lifetime battery packs and modules by providing a detailed
overview of not only the fundamental technologies surrounding BMS, a critical
component in battery packs and modules, but also recent technology trends such
as deep learning, AI-integrated next-generation technologies, and wireless BMS.
The strong points of this report are as follows.
① Growth of various
lithium-ion battery–based applications and
next-generation battery development, leading to the increasing importance of
BMS
② Increasing battery safety
issues and the resulting importance of BMS, along with the current status of
the domestic BMS market
③ Growing need for BMS
hardware, software, and AI algorithms
④ Battery lifetime
prediction and anomaly detection based on deep learning models
⑤ Enhancement of BMS
reliability and scalability through cloud BMS and blockchain technologies
⑥ Need for appropriate
thermal management system design according to battery type and operating
temperature conditions
[Comparison of fault diagnosis flow in
conventional systems and AI-based battery system fault diagnosis approaches]
[Examples of application of AI-based battery fault diagnosis algorithms]
Contents
1. LIB Applications and Next-Generation Batteries
1.1 Basic Overview of LIB
1.1.1 Basic Terminology of LIB
1.1.1.1 Voltage
1.1.1.2 Charge (Coulomb) and Current
1.1.1.3 Capacity
1.1.1.4 Power and Energy
1.1.1.5 C-rate
1.1.1.6 OCV, Upper and Lower Cut-off Voltage
1.1.1.7 Series and Parallel Connection of Batteries
1.1.2 Configuration and Operating Principles of LIB
1.1.2.1 Cell Structure
1.1.2.2 Battery Types
1.1.2.3 Operating Principles
1.2 Application Trends of LIB
1.2.1 Energy Storage System (ESS)
1.2.1.1 Overview of ESS
1.2.1.2 Domestic and Global ESS Trends
1.2.2 Electric Vehicle (EV)
1.2.2.1 Overview of EV
1.2.2.2 Domestic and Global EV Trends
1.2.3 Electric Ship
1.2.3.1 Overview of Electric Ships
1.2.3.2 Domestic and Global Electric Ship Trends
1.2.4 Urban Air Mobility (UAM)
1.2.4.1 Overview of UAM
1.2.4.2 Domestic and Global UAM Trends
1.3 Next-Generation Batteries
1.3.1 Development of Next-Generation Battery
Technologies
1.3.1.1 Necessity for Next-Generation Battery
Development
1.3.2 Development Trends of Next-Generation Battery
Technologies
1.3.2.1 Lithium–Sulfur Batteries
1.3.2.2 All-solid-state Batteries
1.3.2.3 Vanadium Redox Flow Batteries
1.3.2.4 Lithium–Air Batteries
1.3.2.5 Sodium-ion Batteries
1.3.2.6 Lithium-metal Batteries
1.3.2.7 Sodium–Sulfur Batteries
1.3.2.8 Hydrogen Bromide Flow Batteries
1.3.2.9 Iron Flow Batteries
1.3.3 Application Development Trends of
Next-Generation Batteries
1.3.3.1 ESS Trends
1.3.3.2 EV Trends
1.3.3.3 UAV Trends
1.3.3.4 Drone Trends
2. Introduction to Battery Management Systems (BMS)
2.1 Overview and Necessity of BMS
2.1.1 Necessity of BMS with Battery Market Expansion
2.1.1.1 Expansion of the EV Market
2.1.1.2 Expansion of the ESS Market
2.1.2 Necessity of BMS Due to Battery Fires
2.1.2.1 Fire Accidents in EV Applications
2.1.2.2 Fire Accidents in ESS Applications
2.1.2.3 Causes of Fire Accidents
2.1.3 BMS Architecture and Functions
2.1.3.1 BMS Architecture
2.1.3.2 Classification of BMS Functions – Software
2.1.3.3 Classification of BMS Functions – Hardware
2.1.3.4 BMS Functions by Application – EV
2.1.3.5 BMS Functions by Application – ESS
2.2 BMS Technology Trends
2.2.1 Domestic and Global BMS Technology Trends
2.2.1.1 Changes in BMS Technology Trends
2.2.2 BMS Technologies
2.2.2.1 State Estimation Technology
2.2.2.2 Fault Diagnosis Technology
2.2.2.3 Balancing Technology
2.2.2.4 Screening
2.2.2.5 Retired Batteries
2.3 BMS Hardware Configuration and Design Process
2.3.1 BMS Hardware Configuration and Functions
2.3.1.1 Overview of BMS Hardware
2.3.1.2 BMS Hardware Function – Protection
2.3.1.3 BMS Hardware Function – Measurement
2.3.1.4 BMS Hardware Function – Communication
2.3.1.5 BMS Hardware Function – Control
2.3.2 BMS Hardware Design Process
2.3.2.1 Determination of Battery Configuration Based
on Application Specifications
2.3.2.2 Selection of BMS Hardware Topology Based on
Requirements
2.3.2.3 BMS Hardware Design – Measurement Unit
2.3.2.4 BMS Hardware Design – Protection Unit
2.3.2.5 BMS Hardware Design – Control Unit
2.3.2.6 BMS Hardware Design – Communication Unit
2.3.2.7 Verification and Validation of BMS Hardware
Operation
2.3.3 BMS Firmware Configuration and Functions
2.3.3.1 BMS Firmware Structure
2.3.3.2 BMS Firmware Driver
2.3.3.3 BMS Firmware Module
2.3.3.4 BMS Firmware Engine
3. Trends in BMS State Estimation Technologies
3.1 Definition and Functions of BMS Software
3.1.1 Key Functions of BMS Software State Estimation
(Sox Estimation)
3.1.1.1 Necessity of BMS Software
3.1.1.2 Introduction to BMS State Indicators
3.1.2 Model-based Battery State Estimation
Technologies
3.1.2.1 Necessity of Electrical Equivalent Circuit
Models
3.1.2.2 Introduction to Electrical Equivalent
Circuit Modeling
3.1.2.3 Types of Electrical Equivalent Circuit
Models
3.1.3 Trends in Electrical Equivalent Circuit
Modeling Technologies
3.1.3.1 Equivalent Circuit Models Considering
Cumulative Current
3.1.3.2 Equivalent Circuit Models Considering
Available Battery Capacity
3.2 Trends in SOC Estimation Algorithm Technologies
3.2.1 Introduction to 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 Adaptive Control-based SOC Estimation
3.2.1.5 Data-driven SOC Estimation
3.2.1.6 Comparison and Analysis of Advantages and
Limitations of SOC Estimation Methods
3.2.2 Adaptive Control Model-based SOC Estimation
Algorithms
3.2.2.1 Extended Kalman Filter-based SOC Estimation
Algorithm
3.2.2.2 Battery SOC Estimation Based on Offline
Parameters and Extended Kalman Filter
3.2.2.3 Battery SOC Estimation Based on Online
Parameters and Extended Kalman Filter
3.2.2.4 Dual Extended Kalman Filter-based Battery
SOC Estimation
3.2.3 Trends in SOC Estimation Algorithm Technologies
3.2.3.1 SOC Estimation Algorithms Under Variable
Conditions (Temperature/Aging)
3.3 Trends in SOH Estimation Algorithm Technologies
3.3.1 Accelerated Life Testing and Battery
Degradation Mechanisms
3.3.1.1 Definition of Battery Degradation
3.3.1.2 Battery Degradation Mechanisms
3.3.1.3 Accelerated Life Testing
3.3.2 Introduction to Arrhenius Model-based Battery
Degradation Models
3.3.2.1 Design Approaches for Arrhenius-based
Battery Degradation Models
3.3.2.2 Parameter Derivation Methods for
Arrhenius-based Battery Degradation Models
3.3.3 Resistance-based SOH Estimation Algorithms
3.3.3.1 Parameter Derivation Methods for EIS
Impedance-based SOH Estimation
3.3.4 Adaptive Control-based SOH Estimation
Algorithms
3.3.4.1 Model-based Battery Degradation Analysis
Approaches
3.3.5 Trends in SOH Estimation Algorithm Technologies
3.3.5.1 Stress Factor-based Degradation Models
4. AI-integrated BMS
4.1 Necessity of Big Data-based AI Adoption in BMS
4.1.1 Expansion of Cloud Server-based Big Data
Collection Infrastructure
4.1.1.1 Current Status of Cloud Server-based Vehicle
Data Collection
4.1.2 Necessity of AI-based Next-generation BMS
Driven by Big Data Platform Development
4.1.2.1 Necessity of Estimation and Prediction of
Nonlinear Battery Characteristics Due to Diversification of Applications
4.1.2.2 Provision of Integrated Solutions and In-BMS
Solutions Based on Big Data Collection and Analysis
4.1.2.3 Necessity of Integrating BMS with Artificial
Intelligence Technologies
4.2 AI Adoption for BMS
4.2.1 Introduction to AI Models
4.2.1.1 Early Artificial Neural Network (ANN) Models
4.2.1.2 Statistical Second-generation AI Models –
Machine Learning
4.2.1.3 Current Artificial Intelligence Models –
Deep Learning
4.2.1.4 Recurrent Neural Networks (RNN)
4.2.1.5 Long Short-Term Memory (LSTM)
4.2.1.6 Convolutional Neural Networks (CNN)
4.2.2 Data Preprocessing Process 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 Encoding
4.2.2.6 Data Splitting
4.2.2.7 Data Sampling
4.2.2.8 Data Augmentation
4.2.2.9 Data Compression
4.2.3 Analysis of Battery Degradation Data and
Extraction of Health Indicators
4.2.3.1 Battery Degradation Data Analysis
4.2.3.2 Extraction of Battery Health Indicators (HI)
4.2.3.3 Selection of Battery Health Indicators (HI)
4.2.4 Experimental Data Decomposition and Compression
via Signal Analysis
4.2.4.1 Concept of Wavelet Transform (WT)
4.2.4.2 Discrete Wavelet Transform (DWT)
4.2.5 Feature Extraction and Correlation Analysis for
Training Dataset Construction
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 Algorithms
4.2.6.1 AI-based Battery Lifetime Prediction
Algorithms
4.2.6.2 AI-based Battery Lifetime Prediction
Algorithm Study – ESS Lifetime Prediction Case Study
4.2.6.3 AI-based Battery Fault Diagnosis Algorithms
4.2.6.4 AI-based Battery Fault Diagnosis Algorithm
Case Study – Fault Diagnosis Based on RLS Deviation and LSTM-Autoencoder
4.3 Advanced AI-based BMS Algorithms
4.3.1 Random Forest-based Missing Data Compensation
and Discharge Capacity Prediction
4.3.1.1 Dataset Compensation for Battery Capacity
Prediction Using Random Forest
4.3.1.2 Important Factor Selection Process Using
Random Forest
4.3.1.3 Case Classification by Important Factors and
Capacity Prediction Results by Case
4.3.2 CNN-based External Environment Classification
Using EIS Image Inputs
4.3.2.1 Collection of Battery Degradation Data (EIS)
Based on External Environment Diagnosis
4.3.2.2 Analysis of EIS Image Pattern Variations for
CNN Training Dataset Construction
4.3.2.3 EIS Image Transformation for CNN Training
Dataset Construction
4.3.2.4 CNN Model Training and External Environment
Classification Based on EIS Image Inputs
4.3.3 Data Patternization Research for Battery Fault
Diagnosis
4.3.3.1 Necessity of Anomaly Detection and Data
Patternization Research Specialized for Time-series Data Characteristics
4.3.3.2 Time-series Data Compression Using
Autoencoder
4.3.3.3 Construction of Input Datasets for
Classification Models
4.3.3.4 Compression of Time-series Data of Each
Fault Type into a Single Signal
4.3.3.5 Pattern Classification Model Training Using
Compressed Fault-type Signals and Early Battery Fault Diagnosis
4.3.4 Real-time SOH Estimation Considering EV Driving
Environments
4.3.4.1 Indicator Selection for Improving SOH
Estimation Performance in Dynamic Profiles
4.3.4.2 Construction of Long Short-Term Memory
Networks for Time-series Data Prediction
4.3.4.3 Implementation of Real-time SOH Estimation
Algorithms Based on Embedded Linux
4.3.4.4 Construction of Experimental Evaluation
Environments and Performance Verification of Real-time SOH Estimation
Algorithms
5. 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 Services Using
IoT-based BMS
5.1.2 Development of Cloud BMS for Optimal Battery
Operation
5.1.2.1 Definition of Data-driven Battery State
Diagnosis Platform (Cloud BMS)
5.1.2.2 Cloud BMS Architecture
5.1.2.3 BMS Operation through Cloud Server
Deployment
5.1.2.4 Real-time Battery Monitoring Based on Cloud
BMS – Data Analysis and Result Visualization
5.1.2.5 Enhancement of On-board BMS Performance
Using Cloud BMS
5.1.3 Limitations and Complementary Techniques of
IoT-based Cloud BMS
5.1.3.1 Limitations in Data Collection and Server
Storage
5.1.3.2 Edge Computing Technologies
5.1.3.3 Adoption of Encryption Technologies for Data
Security
5.1.4 Wireless BMS
5.1.4.1 Definition and Necessity of Wireless BMS
5.1.4.2 Architecture of Wireless BMS
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 Solutions
5.1.5.5 Blockchain-based Personal Data Protection
5.1.5.6 Blockchain-based Lifecycle Management
Systems
5.2 Digital Twin Model
5.2.1 Digital Twin
5.2.1.1 Concept and Expected Benefits of Digital
Twin
5.2.1.2 Components of Digital Twin
5.2.1.3 Implementation and Utilization of Digital
Twin
5.2.1.4 Key Technologies for Digital Twin
Implementation
5.2.1.5 Digital Twin Optimization
5.2.2 Trends in Battery State Estimation Based on
Digital Twin Models and Cloud BMS
5.2.2.1 Integration of Digital Twin Models and Cloud
BMS
5.2.2.2 Utilization of Digital Twin and Cloud BMS:
Virtual Battery Models
5.3 Battery Swapping Systems
5.3.1 Trends in Battery Swapping Technologies
5.3.1.1 Battery Swapping Technology
5.3.1.2 Characteristics and Processes of Battery
Swapping Technology
5.3.1.3 Market Size of Battery Swapping Systems
5.3.1.4 Development Trends by Companies with Battery
Swapping Technologies
5.3.1.5 Domestic Trends in Battery Swapping
Technologies
5.3.2 Trends in Battery Swapping History Tracking
Platforms
5.3.2.1 Battery Passport
5.3.2.2 Overseas Trends in Battery Passport
5.3.2.3 Domestic Trends in Battery Passport
5.4 Fast Charging Systems
5.4.1 Overview and Trends of EV Battery Charging
Technologies
5.4.1.1 Classification of EV Charging Methods
5.4.1.2 Changes in EV Charging Systems
5.4.1.3 Development Status of EVs Applying 800V
Systems
5.4.2 Trends and Strategies in Battery Fast Charging
Technologies
5.4.2.1 Battery-related Issues Arising from Fast
Charging Applications
5.4.2.2 Optimization Studies for Fast Charging Using
Optimal Charging Profiles
5.5 V2G Systems
5.5.1 Vehicle to Grid
5.5.1.1 Definition and Necessity of V2G
5.5.2 Domestic Trends in V2G Technologies
5.5.2.1 Domestic V2G Demonstration Projects and
Policies
5.5.3 Overseas Trends in V2G Technologies
5.5.3.1 United States
5.5.3.2 United Kingdom
5.5.3.3 Netherlands
5.5.3.4 Germany
5.5.3.5 Australia
6. Battery Thermal Management Systems
6.1 Overview of Battery Thermal Management Systems
6.1.1 Battery Thermal Runaway and the Necessity of
Thermal Management Systems
6.1.1.1 Battery Thermal Runaway
6.1.1.2 Necessity of Battery Thermal Management
Systems
6.2 Necessity of Battery Thermal Management Models
6.2.1 Battery Heat Generation Models
6.2.1.1 Characteristics of Battery Heat Generation
6.2.1.2 Heat Loss
6.2.2 Technology Trends in Battery Heat Generation
Models
6.2.2.1 Battery Heat Generation Estimation Using
Equivalent Circuit Models
6.2.2.2 Electro-thermal Models
6.2.2.3 Thermal Models Using Artificial Intelligence
6.3 Design of Battery Thermal Management Systems
6.3.1 Design of Battery Thermal Management Systems
and Battery Cooling Technologies
6.3.1.1 Configuration of Battery Thermal Management
Systems
6.3.1.2 Key Components of Battery Thermal Management
Systems
6.3.1.3 Design Process of Battery Thermal Management
Systems
6.3.1.4 Battery Cooling Technologies
7. Battery Pack and BMS Market Outlook
7.1 Global xEV Battery Market Outlook
7.2 Global xEV Battery Market Size Outlook
7.3 Global xEV Battery Demand Outlook
7.4 Share Outlook of Electric Passenger Vehicles (LV)
7.5 EV Battery Price Outlook
7.6 Cost Breakdown of Major Components in EV Battery
Packs
7.7 Global Market Size Outlook for Major Battery Pack
Components
7.8 Global BMS Market Outlook
7.9 Global EV/ESS BMS Market Outlook (Research Firms)
7.10 BMS Market Trends
7.10.1 Domestic BMS Market Trends
7.10.2 U.S. BMS Market Trends
7.10.3 China BMS Market Trends
7.10.4 Europe BMS Market Trends
7.10.5 Japan BMS Market Trends
8. Patent and Company Trends Related to BMS
8.1 Analysis of Recent BMS Patent Trends
8.1.1 Patent Analysis
8.1.2 Trends in Patent Filings for EV BMS
8.2 Comparison between EV BMS and ESS BMS
8.2.1 Comparison of Technologies, Functions, and
Algorithms
8.2.2 Comparison of Key Technologies and Application
Status
8.3 BMS Development Trends of Global Battery Companies
8.3.1 BMS Development Status of K-3 Companies and
Global Battery OEMs
8.3.2 LG Energy Solution (LGES)
8.3.3 Samsung SDI
8.3.4 SK On
8.3.5 CATL
8.3.6 BYD
8.3.7 CALB
8.3.8 Gotion High-Tech
8.3.9 EVE Energy
8.3.10 SVOLT Energy
8.3.11 Panasonic
8.3.12 Envision AESC
8.3.13 GS Yuasa
8.4 BMS Development Trends of Global Semiconductor,
Sensor, and Module Companies
8.4.1 Texas Instruments
8.4.2 Intel
8.4.3 Infineon Technologies AG
8.4.4 STMicroelectronics
8.4.5 Renesas Electronics Corporation
8.5 BMS Development Trends of Global Automotive
Electronics Companies
8.5.1 LG Innotek
8.5.2 Hyundai Kefico (KEFICO)
8.5.3 Mitsubishi Electric Corporation
8.5.4 SL Corporation
8.5.5 YoungHwa TECH
8.5.6 dSPACE
8.6 BMS Development Trends of Battery Pack and BMS
Companies
8.6.1 NEXCON Technology
8.6.2 WONIK PNE
8.6.3 Elentec
8.6.4 POWERLOGICS
8.6.5 CTNS
8.6.6 Hunate
8.6.7 MISUMSYSTECH
8.6.8 e-Solution
8.6.9 Blue Sigma
8.6.10 Freudenberg e-Power Systems (FEPS)
8.6.11 FORVIA Hella
8.6.12 Elithion
8.6.13 Eberspächer Venture Inc.
8.6.14 ELEMENT Energy
8.6.15 Shenzhen Tritek Limited
8.6.16 Octilion Power Systems
8.6.17 GuoCHUANG
8.6.18 Sensata Technologies
8.6.19 LAONTECH
8.7 BMS Development Trends of Global Automotive OEMs
8.7.1 Toyota, Volkswagen
8.7.2 Hyundai Motor, General Motors
8.7.3 Stellantis, Ford Motor
8.7.4 Mercedes-Benz, BMW
8.7.5 Honda, Nissan
8.8 BMS Development Trends of Chinese EV OEMs
8.8.1 BYD, NIO
8.8.2 XPeng, Li Auto
8.8.3 Geely / Zeekr, SAIC (IM Motors, MG, etc.)
8.8.4 GAC Aion, Great Wall (ORA)
8.8.5 Changan (Deepal / AVATR, etc.), Leapmotor