A Novel Estimating Algorithm of Critical Driving Parameters for Dual-Motor Electric Drive Tracked Vehicles Based on a Nonlinear Observer and an Adaptive Kalman Filter

Zhaomeng Chen, Songhua Hu, Haoliang Lv () and Yimeng Fu ()
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Zhaomeng Chen: School of Automotive and Transportation Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
Songhua Hu: School of Automotive and Transportation Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
Haoliang Lv: Suzhou Sc-Solar Equipment Co., Ltd., Suzhou 215000, China
Yimeng Fu: BYD Auto Industry Co., Ltd., Shenzhen 518118, China

Energies, 2024, vol. 17, issue 18, 1-20

Abstract: High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms.

Keywords: tracked vehicle; emergency braking; estimation; nonlinear observer; adaptive Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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