IJAIEM

Hari Prasad Bhupathi, Srikiran Chinta, Dr Vijayalaxmi Biradar, Dr Sanjay Kumar Suman

Abstract

Global warming and global pollution can consequence in many severe changes to the environment, ultimately challenging environmental problems and impacting human health. Human activities that result in an electronic-based population are the primary causes of the sharp rise in waste, particularly battery waste. Massive releases of heavy metals from battery waste impact health and ecosystems as a whole. Hence there is need to work on battery lifecycle and its affecting measuring factors. However, there are many challenges to enhance the lifecycle of battery and to sustain environmental balance. Machine learning is a form of artificial intelligence that is fundamentally present in almost every area of our lives. It increased automation and increased productivity. This research article provides an overview of exemplary research efforts for efficient Machine Learning-based Battery Management Systems (BMS) to improve battery life cycles and address their measurement factors in the