Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems

1. Overview and Applications of Lithium-ion Batteries

1.1 Operating principles of lithium-ion batteries

1.2 Performance testing methods

1.2.1 Energy test experiments

1.2.2 Hybrid pulse power characterization

1.2.3 Charge-discharge tests under various C-rates

1.2.4 Capacity calibration experiments

1.2.5 Aging tests

1.3 Performance characteristics

1.3.1 Basic electrochemical properties

1.3.2 Dynamic response characteristics

1.3.3 Temperature dependence

1.3.4 Aging behavior

1.3.5 Safety boundary characteristics

1.4 Application scenarios

1.4.1 Electric vehicles

1.4.2 Energy storage systems

1.4.3 Portable electronic devices

1.4.4 Aerospace and military

1.4.5 Industrial automation and robotics


2. Fundamentals of Artificial Intelligence and Algorithms

2.1 Basics of machine learning

2.1.1 Principles of supervised learning

2.1.2 Overview of unsupervised learning

2.1.3 Fundamentals of reinforcement learning

2.2 Battery Data Types and Preprocessing for Intelligent Modeling

2.2.1 Input Features: Voltage, Current, Temperature

2.2.2 Denoising and outlier handling

2.2.3 Data standardization and normalization

2.2.4 Data enhancement and balance

2.2.5 Time series alignment and windowing

2.3 Fundamentals of deep learning

2.3.1 Artificial neural network structures

2.3.2 Convolutional neural networks (CNN)

2.3.3 Recurrent neural networks (RNN) and LSTM

2.4 Other AI techniques 2.4.1 Fuzzy logic and expert systems

2.4.2 Particle swarm optimization

2.4.3 Introduction to genetic algorithms

2.5 Trends in AI applications

2.5.1 Multi-state coupling modeling

2.5.2 Product life-cycle management

2.5.3 Physics-informed AI


3. Modeling Methods for Lithium-ion Batteries

3.1 Equivalent circuit models

3.1.1 Model structures

3.1.2 Parameter identification

3.1.3 Applications and improvements

3.2 Electrochemical models

3.2.1 Theoretical foundations

3.2.2 Porous electrode theory

3.2.3 Numerical solution methods

3.3 AI-based modeling approaches

3.3.1 Limitations of traditional models

3.3.2 Machine learning for battery modeling

3.3.3 Deep learning-based modeling techniques

3.3.4 From physical to hybrid modeling


4. State of charge (SOC) estimation

4.1 Definition and significance

4.2 Traditional SOC estimation methods

4.3 SOC estimation based on CKF and improved algorithms

4.4 SOC estimation based on CNN and improved algorithms

4.5 Whole-life-cycle remaining capacity estimation


5. State of health (SOH) estimation

5.1 Definition and significance

5.2 Empirical models from aging data

5.3 Machine learning-based SOH estimation

5.4 Advances in deep learning for SOH prediction

5.5 Whole-life-cycle remaining capacity estimation


6. State of power (SOP) estimation

6.1 Definition and significance

6.2 SOP estimation based on UKF and improved algorithms 6.3 SOP estimation based on LSTM and improved algorithms 6.4 Multi-factor coupled SOP strategies


7. Joint Estimation of Battery States

7.1 Theoretical background

7.1.1 Coupled characteristics of battery states

7.1.2 Limitations of independent estimation

7.1.3 Full lifecycle health management

7.2 Traditional joint estimation methods

7.2.1 Extended Kalman filter-based methods

7.2.2 Particle filter-based methods

7.3 AI-based joint estimation approaches

7.3.1 Gaussian process regression

7.3.2 Support vector machines

7.3.3 Bayesian inference methods


8. Experimental Design and Validation

8.1 Development of experimental platform

8.1.1 Electrochemical Impedance Spectroscopy Instruments

8.1.2 High-Precision Charge–Discharge Testing Systems

8.1.3 Temperature-Controlled Environmental Chambers

8.2 Design of test protocols under varying conditions

8.2.1 Hybrid Pulse Power Characterization (HPPC) Protocol

8.2.2 Bursts of Balanced Dynamic Stress Test (BBDST) Protocol

8.2.3 Dynamic Stress Test (DST) Protocol

8.3 Visualization and analysis of experimental data

8.3.1 Data Cleaning and Preprocessing Techniques

8.3.2 Multi-Dimensional Data Visualization Approaches

8.3.3 Visualization Tools and Application Scenarios


9. Future Directions and Societal Impact

9.1 Technical future directions in battery management

9.1.1 AI-Driven advanced modeling

9.1.2 Adaptive parameter reconstruction

9.1.3 Real-time learning and edge intelligence collaboration

9.2 Societal impact and industrial transformation

9.2.1 Economic impacts on energy ecosystems

9.2.2 Environmental and sustainability implications

9.2.3 Promoting the dual-carbon goals and the transition to green energy

9.3 Challenges and mitigation strategies

9.3.1 Limited model generalization and challenging transferability

9.3.2 Underdeveloped continual learning mechanisms