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.

Cascaded Multilevel Inverter Based Power and Signal Multiplex Transmission for Electric Vehicles

Power&signal multiplex transmission(P&SMT)is a technique that uses power electronic circuits for communication signal transmission.In this paper,a three-phase cascaded multilevel inverter-based P&S MT system is proposed.The proposed method can transmit communication signals without using a Controller Area Network bus,thereby reducing the wiring cost of the conventional electric vehicle(EV)communication system.The designed system can achieve motor speed regulation and battery balance discharging for EVs.With the combined pulse width modulation scheme and frequency shift keying method,both power and communication signals are transmitted successfully in a simulation model implemented in Matlab/Simulink.By evaluating the bit error rate of the transmitted signal,the maximum signal rate of the proposed system is determined as 600 bit/s.

Energy Management Strategy for Hybrid Electric Vehicle Based on System Efficiency and Battery Life Optimization

A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.