Abstract:The main challenge for the pure electric vehicles (PEVs) with a hybrid energy storage system (HESS), consisting of a battery pack and an ultra-capacitor pack, is to develop a real-time controller that can achieve a significant adaptability to the real road. In this paper, a comprehensive controller considering the traffic information is proposed, which is composed of an adaptive rule-based controller (main controller) and a fuzzy logic controller (auxiliary controller). Through analyzing the dynamic programming (DP) based power allocation of HESS, a general law for the power allocation of HESS is acquired and an adaptive rule-based controller is established. Then, to further enhance the real-time performance of the adaptive rule-based controller, traffic information, which consists of the traffic condition and road grade, is considered, and a novel method combining a K-means clustering algorithm and traffic condition is proposed to predict the future trend of vehicle speed. On the basis of the obtained traffic information, a fuzzy logic controller is constructed to provide the correction for the power allocation in the adaptive rule-based controller. Ultimately, the comparative simulations among the traditional rule-based controller, the adaptive rule-based controller, and the comprehensive controller are conducted, and the results indicate that the proposed adaptive rule-based controller reduces battery life loss by 3.76% and the state of change (SOC) consumption by 3.55% in comparison with the traditional rule-based controller. Furthermore, the comprehensive controller possesses the most excellent performance and reduces the battery life loss by 2.98% and the SOC consumption of the battery by 1.88%, when compared to the adaptive rule-based controller.

Abstract:The strict operational condition of driving motors for vehicles propels the development of more complicated configurations in pure electric vehicles (PEVs). Multi-power-source powertrain configurations are one of the efficient technologies to reduce the manufacturing difficulty of driving motors. However, most of the existing studies are predominantly focused on optimal designs of powertrains and power distribution between the engine and motor of hybrid electric vehicles, which are not appropriate for PEVs. This paper proposes a novel dual-motor coupling-propulsion powertrain system that improves the dynamic and economic performance of the powertrain system in PEVs. The proposed powertrain system can realize both the single-motor driving mode and dual-motor coupling driving mode. The driving modes are divided and a power distribution strategy for the different driving modes based on an optimal system efficiency rule is employed, which enhances the performance of the proposed system. Further, a mode-switching strategy that ensures driving comfort by preventing jerk during mode switching is incorporated into the system. The results of comparative evaluations that were conducted using a dual-motor electric vehicle model implemented in MATLAB/Simulink, indicate that the mileage and dynamic performance of the proposed powertrain system are significantly better than those of the traditional single-motor powertrain system.

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In order to complete the reasonable parameter matching of the pure electric vehicle (PEV) with a hybrid energy storage system (HESS) consisting of a battery pack and an ultra-capacitor pack, the impact of the selection of the economic index and the control strategy on the parameters matching cannot be ignored. This paper applies a more comprehensive total cost of ownership (TCO) of HESS as the optimal target and proposes an optimal methodology integrating parameters and control strategy for the PEV with HESS. Through the integrated optimal methodology, the application value of HESS is analyzed under various types of driving cycles and the results indicate that the HESS can significantly improve the economic performance of PEVs under both urban and suburban driving cycles. Due to the poor adaptability of traditional control strategies to different driving cycles, a novel extreme learning machine (ELM) based controller is established. Firstly, a dynamic programming (DP) based controller is applied for the offline optimization of the HESS power allocation under several typical driving cycles. Then, an analytical method combining correlation analysis and mean impact value (MIV) is employed to deal with offline sample data from DP and obtain the characteristic variables of the ELM model. Ultimately, the instantaneous power allocation strategy of HESS is acquired by utilizing ELM to learn offline data of HESS. Comparative simulations between the ELM-based controller and the rule-based controller are conducted, and the simulation results show that compared to the rule-based controller (RBC), the ELM-based controller reduces the electricity consumption by 3.78% and battery life loss by 6.51%.

In order to effectively extend the mileage of pure electric vehicles, the influence of the electromechanical energy conversion principle and the dynamic control of a permanent magnet synchronous motor (PMSM) on the performance of pure electric vehicles are studied with a dual-motor drive system as a carrier in this paper. A vector control strategy based on the speed-torque-current map of a motor is proposed, considering the bus voltage fluctuation influenced by battery charge and discharge. By constructing a complete vehicle model including the dynamic control model of the motor, the power distribution control strategy of a dual-motor coupling mode considering the dynamic characteristics of the motor is developed and simulated on the MATLAB platform. The results show that the dynamic model of the motor is closer to the actual running conditions. The proposed control strategy effectively reduces the demand power, decreases the energy consumption of the electric drive system, and extends the mileage of the vehicle.