An efficient energy management method for plug-in hybrid electric vehicles based on multi-source and multi-feature velocity prediction and improved extreme learning machine

The dynamic changes in vehicle status and road conditions pose a challenge to plug-in hybrid electric vehicles in achieving real-time optimal energy management, leading to increased energy consumption and pollutant emissions. In this paper, an efficient energy management control architecture is proposed based on the principle of model predictive control (MPC) to balance optimization effectiveness and operational efficiency. First, a multi-source and multi-feature velocity predictor based on long short-term memory networks is established using information from the driver, vehicle, and road. Subsequently, the latest bio-inspired algorithm, namely the white shark optimizer, is utilized to optimize the input weights and hidden biases of the extreme learning machine. Additionally, the concept of environmental treatment cost is introduced, and a comprehensive driving cost estimation model based on the enhanced extreme learning machine is established to improve computational efficiency by replacing complex nonlinear operations in optimization. The results demonstrate that the proposed velocity predictor and cost estimation model outperform existing methods. The proposed control framework reduces driving costs and emissions by 73.4 % and 78.4 %, respectively, compared to the rule-based method. Meanwhile, the optimization performance of the proposed method within the 5 s prediction horizon is 19.6 % better than that of the conventional MPC method. Although the inclusion of the cost estimation model leads to a slight 4.5 % reduction in optimization performance compared to the method using only the proposed velocity predictor, this is balanced by a 64.6 % improvement in computational efficiency, allowing the proposed method to maintain a significant overall advantage over conventional MPC.