ANALYSIS, DESIGN AND DEVELOPMENT OF ENERGY STORAGE SYSTEM FOR ELECTRIC VEHICLES

Abstract

The rapid adoption of electric vehicles requires the development of energy storage systems with high energy density, enhanced safety, and long cycle life. However, the performance of lithium-ion batteries in electric vehicle applications is significantly influenced by operating and environmental conditions, including material degradation, temperature variations, charge/discharge rates, dynamic drive profiles, and thermal stresses. These factors introduce nonlinear electrochemical behaviour that accelerates battery aging and hinders the accurate estimation of critical battery states, such as state of charge, state of energy, and state of health. To address these challenges, this thesis aims to develop advanced artificial intelligence driven frameworks combining advanced cathode material development, AI-based state estimation, and Green AI–based control strategies. to address the challenges of nonlinear battery behaviour, ageing, and safety. The proposed approach significantly improves the accuracy, reliability and sustainability of lithium-ion battery systems in electric vehicle applications. A Ni-rich layered NMC811 cathode was synthesised through a solid-state reaction route and characterized through thermogravimetric analysis, Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy and energy dispersive x-ray spectroscopy analysis. These results confirmed a hexagonal α-NaFeO₂ phase (R-3m space group), crystallite size ~22 nm. Thermal and structural analyses confirmed phase formation, while scanning electron microscopy and energy dispersive x-ray spectroscopy revealed homogeneous morphology and elemental composition, validating its suitability for high-energy-density EV batteries. To address battery monitoring challenges, advanced AI-based algorithms were developed for accurate estimation of state of charge and state of energy. A novel filtering technique was proposed to reduce redundant data by maintaining original critical patterns. The method achieved up to 80% reduction in dataset size, and 79-80% memory consumption, 59-66% computational efficiency, and 61-82% energy consumption without losing critical lithium-ion battery dynamic information. The filter technique with convolutional neural network and bidirectional long short-term memory model achieved significantly higher V estimation accuracy compared to traditional models for battery states estimation under different dynamic drive cycles, improving RMSE, MSE, and MAE by up to 99.98%, 99.97%, and 99.96%, respectively. However, the filter technique with gated recurrent unit model improved state of health estimation accuracy by over 93% in RMSE and maintained R² values above 0.999 across multiple aging datasets. These findings validate the proposed energy efficient AI frameworks for battery health monitoring. This research introduces a Green AI framework for accurate, scalable, and efficient battery state estimation and health monitoring under dynamic conditions. Further, the impact of the environmental and operational influences, such as temperature variations and dynamic load profiles, was systematically investigated. Condition-aware AI driven control strategies were developed for adaptive charging behaviour, thermal management, and degradation mitigation. The proposed models were tested across −10 °C to 40 °C, confirmed by the convolutional neural network, which achieved superior accuracy i.e. RMSE of 0.0043 and 67% faster computation, while long short-term memory performed best at 25–40 °C and showed higher error at −10 °C due to electrochemical limitations. CNN based SOH and SOC estimation frameworks outperform long short-term memory and gated recurrent unit in terms of accuracy, flexibility, and efficiency under diverse thermal conditions. This interdisciplinary research contributes a unified framework that integrates materials engineering, Green AI-driven modelling, condition-aware architecture and sustainable computational techniques. The proposed methodology significantly improves battery states prediction accuracy, computational efficiency, and operational reliability. The outcomes of this research deliver strong foundation for the development of intelligent, energy-efficient, condition-aware BMS for the next generation of electric mobility and energy storage applications. Furthermore, the incorporation of Green AI principles ensures reduction in computational and energy demands, improved resource efficiency. This development supports sustainable and energy-efficient energy storage technologies for greener EVs aligned with global decarbonization goals.