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 are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

This study presents an extremum seeking‐proportional–integral and derivative (ES‐PID) controller design for brushless direct current motors and its implementation in electric vehicles. The ES‐PID controller aims to simultaneously maintain a speed set‐point and reduce torque ripples in the presence of load‐torque disturbances. The proposed ES‐PID combines the simplicity of a PID controller with an extremum seeking approach, a model‐free optimisation approach, thereby resulting in an optimal controller that can be realised on simple hardware (with limited computing power and memory). In addition, a theoretical analysis of the proposed ES‐PID control scheme in terms of stability and convergence is also presented. Performance evaluation of the proposed ES‐PID controller using both simulations and hardware experiments are presented. The results clearly illustrate the ability of the controller to track speed set‐point and reduce torque ripples in the presence of load‐torque variations.

No abstract