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The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. [ABSTRACT FROM AUTHOR]

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV. [ABSTRACT FROM AUTHOR]

Path tracking plays an essential role in autonomous vehicles. To ensure tracking accuracy and improve tracking adaptability in different velocities, a path tracking strategy based on an improved model predictive control (MPC) method is presented in this research. First, a path tracking controller based on improved MPC based an online Updating algorithm is constructed. The update mechanism is triggered by using the cosine similarity, when the cosine similarity is lower than the predefined threshold value, making the state space and cost function of MPC match real-time conditions to rectify the sensitivity of MPC to vehicle speed. Additionally, to further enhance the controller’s performance, a fuzzy control is employed to determine the horizon factor for optimizing the prediction horizon and control horizon online. Also, the weighting factors of the prediction horizon and control horizon are discussed to improve the adaptability at varying velocities. Next, the improved MPC controller is compared with the traditional MPC controller for a double lane change maneuver. The validation results demonstrate that the proposed strategy achieves good adaptability with satisfactory tracking accuracy at various velocities. Finally, the feasibility of the proposed strategy is verified in a real prototype vehicle test. [ABSTRACT FROM AUTHOR]

An intelligent energy management strategy (EMS) based on an improved Reinforcement Learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (EF) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RL strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with EF makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV. [ABSTRACT FROM AUTHOR]

In this article, an online recursive power management strategy based on the reinforcement learning technique is proposed to achieve an optimal power distribution and excellent economic performance of a plug-in fuel cell hybrid electric vehicle. First, the optimal control algorithm of the power management strategy is formulated. Then, an online recursive algorithm using cosine similarity and a forgetting factor to achieve the adaptability of the proposed strategy to various driving conditions is formulated. The parameters updating framework is triggered once the change rate of cosine similarity exceeds the threshold value of the corresponding driving cycle. Some of the important parameters such as the learning rate, discount factor, forgetting factor, and the change rate of cosine similarity are analyzed to optimize the online updating capability of the strategy. Finally, an experimental study is conducted to validate the effectiveness of the proposed strategy. Both a rule-based strategy and an equivalent consumption minimization strategy are implemented to establish the benchmark framework used to analyze the performance of the proposed strategy. The simulation and experimental results corroborate the effectiveness and economic performance of the proposed strategy. A comparison with the existing standard benchmark strategy indicates the superior performance of the developed strategy reaching a remarkable reduction in energy consumption at a long driving distance. [ABSTRACT FROM AUTHOR]

Summary: The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV. [ABSTRACT FROM AUTHOR]

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV's energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV. [ABSTRACT FROM AUTHOR]

An appropriate control strategy can play an important role in further improving the fuel economy performance of hybrid electric vehicle (HEV). This research developed a novel adaptive control strategy to achieve optimal power distribution for a series-parallel hybrid electric bus (SPHEB) to adapt driving pattern instantaneously. First, a methodology of extracting mode transition control and power distribution strategy from dynamic programming (DP) solution is proposed for the development of the hierarchical energy management strategy. A SPHEB energy management problem under the Chinese typical bus driving schedule at urban district (CTBDS_UD) is investigated as a case study. Second, an approach of driving pattern recognition (DPR) module is developed. For adaptive learning, four typical driving patterns are selected as the database of driving condition and using the extraction method described above to acquire optimal control strategies for four driving patterns. Third, a framework of adaptive control strategy has been proposed based on the extracted hierarchical energy management strategy from DP and combined with DPR. Finally, the simulation results demonstrate the proposed adaptive strategy can make power distribution proper adjustments in real time and be capable of improving significantly the fuel efficiency of the SPHEB. [ABSTRACT FROM AUTHOR]

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF. [ABSTRACT FROM AUTHOR]