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Battery, EV, Energy Storage System

<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

 

      Growth of various LIB-based applications and development of next-generation batteries and increasing importance of BMS

 

      Importance of BMS and status of domestic BMS market due to rapid increase in battery safety issues

 

      Increased need for BMS H/W, S/W and AI algorithms

 

      Battery life prediction and abnormality detection based on deep learning model

 

      Improvement of BMS reliability and scalability using Cloud BMS and Blockchain technology

 

      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