The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers' driving style. This paper proposes a driving style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, firstly, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on EMS are discussed by analyzing the performance of equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS.

The path planning of unmanned differential steering vehicles (UDSVs) in the off-road environment not only needs to consider the non-complete constraints of vehicles but also faces the challenges of complex off-road terrains and obstacles. In this paper, an integrated path planning system is proposed to handle the influence of kinematic vehicle model, off-road terrains and obstacles systematically for the path planning of UDSVs. To improve the planning efficiency, a Pre-planning is designed and carried out using the Voronoi diagram established in the 3D environment with obstacles. By combining the potential field functions (PFF) related to passable obstacles and 3D terrains, an integrated PFF is defined to represent the movement cost of UDSV in the nonlinear optimal control (NOC) problem. Based on the NOC, a channel path planning (CPP) problem is formulated to avoid the untraceable path caused by the traditional line path planning (LPP). Simulation results show that the proposed system can plan a feasible path fast with the constraints from vehicle kinematics, obstacle avoidance and off-road terrains.

Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV.

In recent years, there has been a significant increase in braking energy regeneration for hybrid electric vehicles. To improve performance and reduce fuel consumption, a better control strategy composed of braking regeneration and gear shifting is needed. This work presents a braking energy regeneration control strategy for a hybrid electric vehicle (HEV). The mathematical model for the vehicle dynamic system is established, and the objective function of braking energy regeneration is presented based on system analysis. Taking the increased electric energy of a battery as the objective function of the economic downshift law, the multi-island genetic algorithm (MIGA) is used to solve the shifting condition factors corresponding to different deceleration speeds and motor torques and the optimal downshifting speed. The presented control strategy of braking energy regeneration is validated in a typical city cycle form in China, and the results show better energy efficiency.

The pneumatically automatic mechanical transmission (AMT) system has been developed for a pure electric garbage truck. The buildup principle and control framework of the pneumatically AMT is introduced. In order to realize the coordinate control, the CAN (Controller Area Network) bus technology is adopted to realize the communications of AMT electric control unit (ECU) and induction motor controller (MC), and then the control of the motor's rotate speed and torque is realized with the instructions from AMT system. Owing to the speed control of the induction machine during the period of the gear-shifting, the synchronization is always guaranteed. Furthermore, the time of shifting to target gear is reduced greatly using the pneumatically automatic mechanical transmission system. All those shorten the whole shifting time. The experimental results show that pneumatic gear-selecting and gear-shifting is quick and reliable and proposed pneumatically AMT improves some performances of the vehicle.

Controllability is the fundamental concept for system analysis and control synthesis. In this paper, the properties of controllability, including its duality to observability, are studied for the linearized dynamic systems. Furthermore, a novel criterion for determining the degree of controllability (DoC) based on the eigenvalues of the Controllability Gramian is proposed, and certain normalization is made to constrain the range of the controllability index. Then, the proposed criterion is implemented to the controllability analysis of Mars entry guidance. The simulation results show that the DoC criterion can reveal the dominating factor in the longitudinal dynamics during the entry phase, and can thus help us to gain information about the controllability characteristics of the system along the nominal trajectories. Hence, the DoC criterion can function as an indicator for entry trajectory design the control system optimization.