In this article, an adaptive cruise control algorithm with braking energy recovery is proposed. First, the influence of the working characteristics of motor and battery on the energy recovery is analyzed in the braking energy recovery system. Considering the requirements of the braking regulations, the genetic algorithm is used to optimize the economy and safety during the braking energy recovery process. Braking force allocation strategy results can be obtained by offline lookup table. Based on the model predictive control theory with particle swarm optimization algorithm, an adaptive cruise control strategy is constructed to recover the braking energy as much as possible under the premise of satisfying vehicle tracking, safety, and comfort performance. Use Carsim as the simulation platform, and then co-simulate it with MATLAB/Simulink which is embedded with control algorithm. Simulation results show that distance error ratio and speed error ratio are mostly within 10%, braking energy recovery rate can up to 43.65% or more.The stability and comfort performance can also meet the control requirement.
The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.
At the request of the Publisher, the following articles have been retracted. The Advances in Mechanical Engineering Editorial Office became aware that the peer review process organised by the Guest Editors of the Special Collections Advanced Control and Analysis of Mechatronics Systems with Modelling Uncertainty and Advanced Control of Robotics and Mechatronics Systems with Nonlinearity and Modelling Uncertainty 2016 did not meet the journal's usual rigorous standards of peer review. Due to this, all articles in these Special Collections were re-reviewed by a new set of independent referees.
Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.
Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.
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