Abstract:

The transportation sector is responsible for the majority of greenhouse gas emissions and pollution. The implementation of e-mobility applications such as electric vehicles (EVs), hybrid locomotives, and other battery-energy storage systems will boost the transportation sector.  One of the most important components of electric vehicles and smart grid technology is the energy storage system. Electricity transmission and distribution lines are being transformed by smart grid technology. The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR will help design the optimum battery management system for electric vehicles based on SoC predictions.The transportation sector is responsible for the majority of greenhouse gas emissions and pollution. The implementation of e-mobility applications such as electric vehicles (EVs), hybrid locomotives, and other battery-energy storage systems will boost the transportation sector.  One of the most important components of electric vehicles and smart grid technology is the energy storage system. Electricity transmission and distribution lines are being transformed by smart grid technology. The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR will help design the optimum battery management system for electric vehicles based on SoC predictions.