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Estimation of Vehicle Mass for Distributed Drive Electric Vehicles
FENG Yingxia1
, LI Kai1
, LI Huiling1
, LU Guo1
, ZHENG Run1
, HAN Yinfeng2*
, LENG Bo3
2025, 50(23):
1-8,19.
DOI: 10.16638/j.cnki.1671-7988.2025.023.001
The total vehicle mass is a critical parameter in vehicle dynamics modeling and control
system design, directly influencing the dynamic performance, energy efficiency, and safety control
capabilities of electric vehicles. In distributed-drive electric vehicles, due to the rapid response and
precise torque regulation of electric drive systems, the impact of total vehicle mass on control accuracy is particularly pronounced. However, vehicles frequently encounter complex operating
conditions such as variable payloads during operation. Fixed-mass assumptions can easily lead to
model mismatch, thereby compromising the stability and robustness of control systems. To enhance
model adaptability and estimation accuracy, this study develops an adaptive Kalman filter to achieve
robust vehicle speed estimation, thereby providing reliable speed reference information for mass
estimation. Subsequently, based on the vehicle longitudinal dynamics model, an online vehicle mass
estimation method is constructed using a recursive least squares (RLS) algorithm with a forgetting
factor. By approximating resistance terms, this method reduces dependence on unmeasurable
parameters and improves practical feasibility. Simulation results demonstrate that the proposed
method achieves high-precision, rapidly convergent mass estimation under typical acceleration
scenarios, providing theoretical support for optimizing control strategies in distributed-drive systems.
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