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Eco-cruising is considered an effective approach for reducing energy consumption of connected vehicles. Most eco-cruising controllers (ECs) do not comply with real-time implementation requirements when a short sampling interval is required. This paper presents a solution to this problem. Model predictive control (MPC) framework was applied to the speed-planning problem for a power-split hybrid electric vehicle (HEV). To overcome the limitations of time-domain MPC (TMPC), a nonlinear space-domain MPC (SMPC) was proposed in the space domain. A real-time iteration (RTI) algorithm was developed to accelerate nonlinear SMPC computations via generating warm initializations and subsequently forming the SMPC-RTI. Proposed speed controllers were evaluated in a hierarchical EC, where a heuristic energy management strategy was selected for powertrain control. Simulation results indicated that the proposed SMPC yields comparable fuel savings to the TMPC and the globally optimal solution. Meanwhile, SMPC reduced MPC computation time by 41% compared to TMPC, and SMPC-RTI further reduced MPC computation time without compromising optimization. During the hardware-in-loop (HIL) test, the mean computation time was 9.86 ms, demonstrating potential for real-time applications. [ABSTRACT FROM AUTHOR]

This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time. [ABSTRACT FROM AUTHOR]

Power-split hybrid technologies have been transformed from single mode to multimode to realize even greater savings in fuel and more powerful output. However, the optimization procedure of the mode shift map for multimode hybrid electric vehicles (HEVs) has not been formalized. In this paper, the types of mode shift are first classified as direct/indirect and conditional/unconditional. The optimal shift path with minimum energy variation for the indirect mode shift is formulated as a shortest path problem and solved by Dijkstra’s algorithm. By taking the energy loss during mode shift into consideration, the method for designing a mode shift map is presented from the perspective of the normalized system efficiency. The derived mode shift map is combined with a novel energy management strategy, referred to as normalized efficiency maximum strategy, to formulate an integrated real-time control strategy for the multimode HEV. Finally, by comparing two other commonly used strategies, dynamic programing and equivalent consumption minimization strategy, the proposed control strategy shows great potential in improving fuel economy while maintaining smooth engine operation, thus leading to better ride comfort of the vehicle. [ABSTRACT FROM AUTHOR]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [ABSTRACT FROM AUTHOR]

• Bionic thin-walled tubes are designed inspired by dragonfly wings. • Parametric Analysis Inspired by Dragonfly Wings Morphology. • The theoretical model of bionic tube is established. • Bionic tube under multi-angle impact is optimized by NSGA-Ⅱ algorithm. Nature's creatures have developed remarkable geometric structures for environmental adaptation. Dragonflies have evolved a rapidly flipping symmetrical four-wing system to enhance hunting efficiency. Inspired by the unique double-symmetric wing structure of the dragonfly, a group of multi-cell elliptic tubes (METs) are developed to enhance the energy absorption capacity of thin-walled tube for multiple load cases. The finite element models of MET with different cross-sectional configurations are established and validated by quasi-static axial crushing tests. The crashworthiness of MET under different load angles θ have been explored and extensively studied by simulation method through LS-DYNA. The results indicate that the crashworthiness of MET could be influenced by the number of basic corner elements. Moreover, MET demonstrates higher energy absorption efficiency in oblique compression as compared to axial compression. Theoretical models of axial and oblique compressions are developed to predict the mean crushing force, which are obtained by summarizing the deformation modes of MET and employing simplified super folded unit theory. Finally, multi-objective particle optimization (MOPSO) algorithm is adopted to explore the MET crashworthiness optimization under oblique impact, with peak crushing force (PCF) and specific energy absorption (SEA) as targets and geometric parameters L, α, F and thickness t regarded as design variables. A new knee-point selection method is used to select MET from the pareto set of solutions of different schemes. The results show that different weight coefficients have great influences on the optimization results and further study is needed. [Display omitted] [ABSTRACT FROM AUTHOR]

The stringent fuel economy and emission standards enforce sport sedan and sport utility vehicle employ four-wheel-drive (4WD) hybrid powertrain to achieve better fuel economy and acceleration performance simultaneously. This paper reviews and conducts a comparative study of four 4WD hybrid configuration types: series, series-parallel, power-split and compound-split configurations. To make a fair comparison of fuel economy and acceleration performance, an integrated hierarchical optimization framework is presented, which solves the parameters sizing and energy management problem simultaneously. The pareto-front optimal design of each configuration is derived. According to detailed energy analysis, a novel multi-mode configuration is proposed by adding clutches between planetary gear nodes of compound-split configuration. By combining multiple modes, the proposed configuration achieves better fuel-saving and acceleration performance than initial configuration. Moreover, this comparative study takes into consideration the cost, control complexity and off-road performance. The results show that the power-split one adopting three motor/generators (MGs) shows superior fuel economy and acceleration performance than others, while the proposed multi-mode configuration maintains a competing performance with less MGs. In addition, the series-parallel configuration has great comprehensive potential. • Parameters sizing is conducted by integrated hierarchical optimization approach. • Four 4HWD configurations are comparatively investigated in energy efficiency. • A novel multi-mode 4HWD configuration is proposed. • All five 4HWD configurations are comprehensively examined in several aspects. [ABSTRACT FROM AUTHOR]

Multi-mode hybrid electric vehicles (HEVs) have become popular in recent years because of their high efficiency and excellent overall performance. However, the introduction of multiple operating modes has led to some issues, e.g., mode shift problems, which are not observed in conventional HEVs with a single mode. Similar to a gear shift map in conventional vehicles, the mode shift map in multi-mode HEVs needs to be properly designed. In this study, the energy management algorithm of a multi-mode HEV is optimized using dynamic programming (DP) considering the mode shift frequency and energy losses. A mode shift map is then extracted from the operating points of the DP results using the support vector machine (SVM). By combining the derived mode shift map and the equivalent consumption minimization strategy (ECMS), a real-time control strategy is proposed for the online energy management of the multi-mode HEV. The results of a case study show the effectiveness of the proposed mode shift map and energy management strategy. [ABSTRACT FROM AUTHOR]

This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53 ms. Our results demonstrate that the proposed ADMM is capable of real-time control. • An ADMM algorithm is proposed for power-split HEVs to optimize engine switching. • Local convexification and approximation are made to simplify the primary problem. • The algorithm tuning and its convergence performance are discussed in detail. • ADMM combined MPC method is developed for receding horizon control. • Fuel efficiency is evidently promoted with high computational efficiency. [ABSTRACT FROM AUTHOR]