From Electrochemical Energy Storage to Next-Generation Intelligent Battery Technologies for Electric Vehicles: A Survey

Abstract

This comprehensive survey examines electrochemical energy storage advancement for electric vehicles, covering conventional lithium-ion technology alongside emerging sodium-ion, metal-ion, and metal-air batteries. The authors emphasize electrode engineering innovations, electrolyte development, and solid-state transitions. Critically, the survey integrates machine learning, digital twins, and large language models into intelligent battery management systems, addressing performance enhancement, safety protocols, and lifecycle extension. The work synthesizes material science, systems engineering, and artificial intelligence perspectives.

Key Contributions

Electrochemical Domain:

• Systematic analysis of lithium inventory loss and cathode degradation mechanisms in real-world aging
• Comprehensive examination of silicon-based anodes, including mechanical fracture and interfacial instability barriers
• Investigation of NCM cathode regeneration through in-situ surface modification and protective coating strategies
• Review of lithiated materials enhancing ionic transport across electrolytes, separators, and interphases
• Diagnostic approaches for identifying lithium plating during fast-charging operations
• Integrated thermal-electrochemical design principles for high-rate battery packs

Electrode Engineering:

• Reduced graphene oxide with optimized defect density improving electron-ion transport
• Carbon nanotube architectures accommodating volumetric expansion in silicon anodes
• Electronic-ionic hybrid additives enabling rapid capacity recovery in thick electrodes
• Multidimensional conductive networks reducing contact resistance in nickel-rich cathodes
• Spray-dried carbon nanotube coatings supporting uniform conductive networks in dry-processed electrodes
• Biomass-derived nitrogen-doped porous carbon additives providing supplementary lithium storage
• Non-carbon conductive oxides (Ti₄O₇) minimizing polarization in demanding electrochemical environments

Electrolyte & Solid-State Innovation:

• Fluorinated electrolyte formulations extending electrochemical stability windows beyond 5V
• Nitrile-based electrolytes with tailored solvation structures enhancing thermal stability
• Solid polymer and composite electrolytes demonstrating mechanical integrity and lithium-metal compatibility
• HELENA project advancing lithium-metal halide solid-state batteries with nickel-rich cathodes

Machine Learning for Battery Management:

• AI-powered digital twins synthesizing state-of-charge trajectories via time-series GANs
• Multimodal sensing integration (X-ray CT, electrochemical impedance spectroscopy) with model-free estimation
• Adaptive particle filters achieving substantial reductions in state estimation and RUL prediction errors
• Gradient boosting, SVMs, and bagging regressors for degradation forecasting
• Deep transfer learning capturing aging dynamics across diverse operating conditions
• Bidirectional LSTM architectures with metaheuristic optimization for capacity fade prediction
• Hybrid CNN-GRU-LSTM deep learning models enabling computationally efficient onboard implementation
• Q-learning reinforcement learning strategies dynamically adjusting operating policies

Large Language Model Integration:

• Multimodal LLM frameworks detecting cyber threats in EV charging infrastructure
• Transformer-based LLM frameworks achieving 0.87% MAE for lithium titanate SOH prediction
• Automated ML pipelines reducing MAPE by 52% for SOH forecasting
• LLM-driven multi-agent systems optimizing vehicle-to-grid interactions through behavioral simulation
• GPT-based charging state prediction achieving 55.52% accuracy improvement over conventional LSTM

Methodology

Systematic literature search across Web of Science, Scopus, IEEE Xplore, ScienceDirect, and Google Scholar, combining domain-specific keywords with Boolean operators. Synthesizes evidence from more than 30 high-quality review articles and over 150 peer-reviewed papers published between 2023–2025. Framework integrates five major knowledge domains: electrochemical foundations, materials engineering, solid-state transitions, machine learning applications, and intelligent systems architectures.

Results

• Digital twin SOC estimations achieve errors below 0.14% under dynamic stress protocols
• CNN-LSTM hybrid models maintain MAE below 1.5% across variable temperature conditions
• AI-IoT-DT systems achieve 95% anomaly detection precision with 76% detection rate
• GPT-based charging prediction demonstrates 55.52% improvement over traditional LSTM baselines
• Q-learning strategies demonstrate measurable improvements in energy efficiency and battery lifespan
• Phase change material–liquid cooling systems significantly improve temperature uniformity
• Multimodal LLM-transformer fusion substantially reduces RMSE, MAE, and MAPE relative to nine benchmarks
• Sodium-ion batteries show lower cost-per-kilometer compared to NMC/LFP for lower-capacity vehicles
• HELENA solid-state project achieves pre-industrial prototype development with integrated recycling strategies

Limitations

• SEI diagnostic methods validated under controlled conditions; real-time onboard implementation not addressed
• Silicon anode studies focused on specific configurations; full-cell systems not consistently evaluated
• Digital twin accuracy depends heavily on data availability and calibration quality
• Transfer learning frameworks show questionable transferability across battery chemistries and usage patterns
• LLM-SOH estimation validation primarily on benchmark datasets; real-world variability effects uncertain
• LLM-based energy management recommendations remain qualitative requiring experimental validation
• Thermal management system integration costs and large-scale deployment feasibility incompletely evaluated
• Limited quantitative benchmarking across different BMS architectures and AI-driven implementations

Source: From Electrochemical Energy Storage to Next-Generation Intelligent Battery Technologies for Electric Vehicles: A Survey by Bahi et al., Chadli Bendjedid University / RMIT University / University of Windsor