Introduction 1

1.1 Research background and significance 1
1.2 Battery manufacturing 2
1.3 Battery operating status analysis 4
1.4 Principle of filter design methods 6
1.5 Artificial intelligence strategy 8
1.6 Outlines 9

2 Advances in battery grouping by artificial intelligence strategy 11
2.1 Introduction to battery grouping 11
2.2 Problem formulation and model establishment 12
2.3 Chromosome encoding 13
2.4 Crossover modes designing 13
2.5 Population mutations 14
2.6 Convergence analysis 16
2.7 Case study on battery grouping 18
2.8 Concluding remarks 20

3 Advances in forecasting of battery manufacturing capacity by artificial intelligence strategy 21
3.1 Introduction to battery manufacturing capacity 21
3.2 Problem formulation 24
3.3 Q-learning principle 24
3.4 Variational mode decomposition 27
3.5 Long short-term memory 29
3.6 Design of the forecasting method 30
3.7 Case study on forecasting of battery manufacturing capacity 34
3.8 Concluding remarks 39

4 Advances in battery operating status analysis by zonotopic filtering 41
4.1 Introduction to battery operating status analysis 41
4.2 Problem formulation 43
4.3 Zonotopic filter 47
4.4 Zonotope and Gaussian Kalman filters 53
4.5 Orthotope-search-expansion-based extended zonotopic Kalman filter 61
4.6 Constrained zonotopic Kalman filtering 73
4.7 Case study on battery operating status analysis 79
4.8 Concluding remarks 92

5 Advances in battery operating status analysis by filtering and artificial intelligence 93
5.1 Problem formulation 93
5.2 Introduction to particle filter and particle swarm optimization 95
5.3 Particle swarm optimization based orthometric hyperparallel space filtering 98
5.4 Improved particle filter algorithm based on the parallelotope 107
5.5 Projected particle-confinement-based zonotopic space filtering 119
5.6 Zonotopic feasible set optimized filter based on differential evolution 130
5.7 Case study on battery operating status analysis 136
5.8 Concluding remarks 151

6 Advances in battery remaining useful life analysis by filtering and artificial intelligence 153
6.1 Introduction to battery remaining useful life analysis 153
6.2 Dynamic complexity reduction zonotopic Kalman filter 156
6.3 Double exponential empirical particle filter 163
6.4 Anti-aliasing filter and LSTM 169
6.5 Case study on battery remaining useful life analysis 173
6.6 Concluding remarks 186

7 Summary and future outlook 189
7.1 Summary 189
7.2 Future outlook 191

References 195