The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehiclemass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and vari...The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehiclemass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variableslope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicleshifting strategy was formulated according to the identification results. The co-simulation results showed that,compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-timevehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had thefollowing advantages, e.g., avoiding frequent shifting of vehicles up the hill, making full use ofmotor braking downthe hill, and improving the overall performance of vehicles.展开更多
Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make pr...Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.展开更多
基金funded by the Innovation-Driven Development Special Fund Project of Guangxi,Grant No.Guike AA22068060the Science and Technology Planning Project of Liuzhou,Grant No.2021AAA0112the Liudong Science and Technology Project,Grant No.20210117.
文摘The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehiclemass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variableslope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicleshifting strategy was formulated according to the identification results. The co-simulation results showed that,compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-timevehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had thefollowing advantages, e.g., avoiding frequent shifting of vehicles up the hill, making full use ofmotor braking downthe hill, and improving the overall performance of vehicles.
文摘Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.
