State of charge estimation for electric vehicles using random forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating conditions.The electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery management.This study applies a Random Forest(RF)machine learning algorithm to improve SOC estimation.Traditionally,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions.Previous methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV applications.In contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world scenarios.By leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC values.This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions.Comprehensive comparative analyses showcase the superiority of the RF over ELM.The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV industry.The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3.This integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV technology.Importantly,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a lower Mean Absolute Error(MAE)of 4.432,1%versus 5.111,2%for ELM across rigorous k-fold cross-validation testing,reaffirming its superiority in quantitative SOC estimation.

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Hybrid firefly algorithm-neural network for battery remaining useful life estimation

Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and resources.This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN.The performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a battery.Specifically,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098.Additionally,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction inaccuracies.This study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.