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The collaborative optimization of speed and energy management for fuel cell hybrid electric vehicles (FCHEVs) in car-following scenario is an important and effective means to achieve vehicle energy conservation. In view of the unclear coupling relationship between dual energy source degradation and energy consumption in existing strategies, and the difficulty of considering both global optimization and real-time performance, this paper proposes a Stackelberg game based FCHEV following energy management strategy. Firstly, models for energy consumption and performance degradation of fuel cells/lithium batteries are established and incorporated into a unified dimensional comprehensive vehicle cost function. Secondly, a hierarchical decoupling based energy management strategy for vehicle following is proposed to achieve decoupling control of vehicle following speed and power distribution. Thirdly, considering safety, comfort, fuel economy, and energy durability, a bi-level programming model corresponding to the car-following control layer and energy management layer is established. Based on the Stackelberg game theory, a bi-level differential genetic algorithm is designed to offline optimize the core parameters of the strategy. Finally, simulation and experimental results show that compared to the model predictive control method, this method can reduce average vehicle spacing error by 37.7%, average impact by 2.4%, equivalent hydrogen consumption by 9.3%, and energy degradation cost by 13.9%, achieving a balance between optimized performance and real-time performance. [ABSTRACT FROM AUTHOR]

In this paper, the issue of finite‐time prescribed performance control (PPC) is addressed for stochastic nonlinear systems with the effect of dead‐zone. Fuzzy logic systems are utilized for identifying unknown nonlinear characteristics. A model consisting of linear and disturbance‐like terms is constructed to approximate the nonlinearity of dead‐zone. Meanwhile, a state observer is employed to estimate unmeasured states. To improve the convergence rate, a finite‐time performance function is introduced, which transforms the tracking error of original system to error of converted system, thus prescribing the transient and steady‐state performance of the system. Eventually, based on backstepping technique, a finite‐time PPC strategy is proposed, which ensures the boundedness of all signals in closed‐loop system. Simultaneously, the tracking error converges to a prescribed region in finite‐time. Finally, two simulation examples are provided to verify the effectiveness of presented control strategy. [ABSTRACT FROM AUTHOR]

For a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell (FC), a battery (BAT) and an ultracapacitor (UC), an improved convex-optimization-based energy management strategy (EMS) is proposed in this article. To protect the fuel cell and the battery from peak power, an adaptive fuzzy filter is used to complete the frequency decoupling of the power demand. Subsequently, an adaptive equivalent consumption minimization strategy (A-ECMS) is introduced to improve fuel economy. To improve the optimality of the EMS obtained, an improved convex optimization method is utilized by introducing a slack variable and a barrier function. Finally, based on experimental data and an improved convex optimization algorithm, the optimal EMS is calculated and verified by a series of simulations under different driving cycles. Simulation results confirm that, compared with the traditional ECMS-based EMS, the proposed EMS can improve fuel economy and achieve lower power fluctuation and optimal FC efficiency for the FCHEV. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

For the complex topology of fuel cell hybrid electric vehicle (FCHEV), an energy management strategy based on improved soft actor-critic (SAC) was proposed. Firstly, an adaptive fuzzy filter was utilized to complete frequency-based decoupling of power demand for achieving power stratification, and the ultracapacitor was employed to provide/absorb the peak power in the power demand. Then, the idea of the equivalent consumption minimization strategy was introduced to construct the reward function of SAC, which aims at accomplishing the goal of reducing hydrogen consumption and improving fuel cell efficiency, and optimizing the power distribution between fuel cell and lithium battery. In the process of training, the heuristic experience replay mechanism was introduced to enhance the convergence performance and optimization effect of SAC algorithm. Finally, the simulation and platform test were carried out under various typical driving cycles. The result shows that compared with the traditional SAC strategy, the fuel economy of the proposed strategy is improved by 6.4%. Furthermore, in the face of the extreme driving cycle, the proposed strategy can still guarantee the smooth operation of fuel cell and realize the energy management of FCHEV with three energy sources. [ABSTRACT FROM AUTHOR]

In visual simultaneous localization and mapping (SLAM) systems, the limitations of the assumption of scene rigidity are usually broken by using learning-based or geometry-based methods. However, learning-based methods usually have a high time cost, and geometry-based methods usually do not result in clean maps which are useful for advanced robotic applications. In this paper, an RGB-D SLAM in indoor dynamic environments with two channels that classifies frames as slightly and highly dynamic scenarios based on matching accuracy is proposed. And a geometric constraint based on Hamming distance is proposed to improve the effectiveness of matching accuracy as a basis for scenario classification. Dynamic features are detected by affine consistency constraint and semantic method. The semantic method is only used for highly dynamic scenarios to reduce the time cost of dynamic feature detection and provide a basis for mapping. Furthermore, an improved adaptive threshold algorithm is proposed to improve the robustness of feature matching. The proposed method is evaluated in the TUM RGB-D dataset and a real scenario. The experimental results demonstrate that the proposed method achieves highly accurate tracking with appreciable time cost in both slightly and highly indoor dynamic environments while obtaining effective maps. [ABSTRACT FROM AUTHOR]

For three energy sources hybrid electric vehicles equipped with fuel cell, lithium battery and ultracapacitor, a hierarchical energy management strategy based on the improved deep deterministic policy gradient (DDPG) algorithm was proposed to reduce hydrogen consumption, improve fuel cell efficiency and maintain the state of charge (SoC) of lithium battery. First, an adaptive low-pass filter based on fuzzy rules was adopted to layer the power, and the peak power was assumed by the ultracapacitor. Secondly, an energy management framework based on DDPG was designed, and the optimization function was constructed by using the calculation idea of the equivalent consumption minimization strategy. A penalty factor related to fuel cell efficiency and lithium battery SoC deviation was added to optimize the power distribution of fuel cells and lithium battery. In addition, to avoid the frequent occurrence of extreme action values caused by noise exploration, the optimal solution of dynamic programming was used to assist strategy training to improve the optimization effect. Finally, the simulation was carried out under different driving conditions, and a test platform was built for verification. The results show that, compared with the traditional DDPG-based strategy, the proposed strategy can effectively reduce the consumption of lithium battery SoC, better ensure that the fuel cell works in the high-efficiency range, and significantly reduce hydrogen consumption, with an average increase of 19% in fuel economy. [ABSTRACT FROM AUTHOR]