Energy Management Strategy for Unmanned Tracked Vehicles Based on Local Speed Planning

TL;DR

This paper introduces a novel energy management strategy for unmanned tracked vehicles that integrates local speed planning, CNN-LSTM based velocity prediction, and model predictive control to enhance efficiency and reduce fuel consumption.

Contribution

It presents a new integrated approach combining local speed planning, CNN-LSTM prediction, and model predictive control for unmanned tracked vehicles.

Findings

CNN-LSTM improves velocity prediction accuracy by 20%.

Energy management reduces fuel consumption by 7%.

Method validated through real-world field experiments.

Abstract

The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on driver's operation. Secondly, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the planned and the historical velocity series to improve the prediction accuracy. Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management. The validity of the method is verified by simulation using collected data from actual field experiments of our unmanned tracked vehicle. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves the prediction accuracy by 20%. Compared with the traditional regular energy management strategy, the energy management strategy based on model predictive control reduces fuel consumption by 7%.