| Online Vehicle Velocity Prediction Based on an Adaptive GRNN with Various Input Signals |
| Dongwei Yao1,2, Junhao Shen1, Jue Hou1, Ziyan Zhang1, Feng Wu1 |
1College of Energy Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, Zhejiang Province, China
2Key Laboratory of Smart Thermal Management Science & Technology for Vehicles of Zhejiang Province, Taizhou 317200, China |
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Received: March 11, 2024; Revised: July 12, 2024 Accepted: October 1, 2024. Published online: November 5, 2024. |
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| ABSTRACT |
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To improve the prediction accuracy and computational speed of vehicle velocity prediction (VVP) strategies for energy management, an online VVP strategy based on general regression neural network (GRNN) is proposed and optimized. First, a GRNN was employed to achieve online VVP, with an evaluation of the effects of order and σ on prediction accuracy. Then, the impact of various input signals on the VVP prediction effect was compared, and the minimal ARMSE was found under the input signal combination of vehicle velocity, driving motor torque, and brake pedal opening degree. Subsequently, a GRNN structure determination method (SDM) based on the Akaike information criterion (AIC) was proposed to construct an online VVP model based on adaptive-structure GRNN. Simulation results using real vehicle test data indicate that the online VVP strategy based on GRNN is feasible under various urban driving conditions. Additional case studies have demonstrated that, compared with the GRNN relying solely on historical velocity data, the optimized GRNN with adjusted structure and input signals reduced prediction error by at least 26.3%. |
| Key Words:
Neural network · Vehicle velocity prediction · Energy-management strategy · Neural network structure determination · General regression neural network · Prediction accuracy |
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Corresponding Author.
Dongwei Yao , Email. 
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