A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation

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.

Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration

Recently,ambient vibration test(AVT)is widely used tu estimate dynamic characteristics of large civil struc- tures.Dynamic characteristics ean be affected by various envirnnmental factors such as humidity,intensity of wind,and temperature.Besides these environmental conditions,tire mass of vehicles may change the measured valnes when traffic-in- duced vibration is used as a source of AVT tor bridges.The effect of vehicle mass on dynamic characteristics is investigated through traffic-induced vibration tests on three bridges;(1)three-span suspension bridge(128m+404m+128m),(2) five-span continuous steel box girder bridge(59m+3@ 95m+59m),(3)simply supported plate girder bridge(46m). Acceleration histories of each measurement location under normal traffic are recorded for 30 minutes at field.These recor- ded histories are divided into individual vibrations and are combined into two groups aceording to the level of vibration;one by heavy vehicles such as trucks and buses and the other by light vehicles such as passenger cars.Separate processing of the two groups of signals shows that,for the middle and long-span bridges,the difference can be hardly detected,but,for the short span bridges whose mass is relatively small,the measured natural frequencies can change up to 5.4%.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

Mass estimation method for intelligent vehicles based on fusion of machine learning and vehicle dynamic model

Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.