Xiaohua Zeng

Research on the optimal power management strategy for a hybrid electric bus

The power management strategy for a hybrid electric vehicle uses expert knowledge and experience to determine the control rules and thresholds, but it is difficult to obtain good performance. This paper presents a dynamic programming algorithm based on a powertrain system to find the optimal solution in the specific speed cycle, but this method cannot be applied to a real-time control system. Therefore, the optimal control rules and thresholds are extracted for the rule-based strategy; the simulation results show that the refined strategy can reduce the fuel consumption by about 14.41% compared with that obtained by the original logic rule control algorithm in the Chinese urban driving cycle. An online neural network energy controller based on the dynamic programming results is provided by choosing a reasonable network structure and control parameters to improve the control precision; the numerical simulation results show that the neural network controller improves the vehicle fuel economy by 15.05%, which is closer to the dynamic programming algorithm simulation result. This control algorithm not only can be used in real-time control and reduces the computational complexity but also allows new design ideas to be proposed for the control algorithm and the vehicle fuel consumption to be reduced.

Powertrain parameter matching of a plug-in hybrid electric vehicle

Powertrain parameter matching principles and specific method for a given plug-in hybrid electric vehicle is presented in detailed. And a co-simulation of Matlab/Simulink and AVL Cruise is conducted to verify the correctness and rationality of the powertrain parameter matching. Co-Simulation results show that the plug-in hybrid electric vehicle with the matched parameters meets the design requirements of power and fuel economy performance.

The Development and Verification of a Novel ECMS of Hybrid Electric Bus

This paper presents the system modeling, control strategy design, and hardware-in-the-loop test for a series-parallel hybrid electric bus. First, the powertrain mathematical models and the system architecture were proposed. Then an adaptive ECMS is developed for the real-time control of a hybrid electric bus, which is investigated and verified in a hardware-in-the-loop simulation system. The ECMS through driving cycle recognition results in updating the equivalent charge and discharge coefficients and extracting optimized rules for real-time control. This method not only solves the problems of mode transition frequently and improves the fuel economy, but also simplifies the complexity of control strategy design and provides new design ideas for the energy management strategy and gear-shifting rules designed. Finally, the simulation results show that the proposed real-time A-ECMS can coordinate the overall hybrid electric powertrain to optimize fuel economy and sustain the battery SOC level.

Control strategy of four-wheel drive plug-in hybrid electric vehicle

To promote the industrialization of plug-in hybrid electric vehicles (PHEV), a practical four-wheel drive hybrid electric vehicle configuration is introduced. A control strategy based on engine high efficient area is proposed for the PHEV. Simulink model of the control strategy and AVL cruise model of the PHEV are built. The results of co-simulation show that the PHEV has excellent power and fuel economy performance.

Control rules extraction and parameters optimization of energy management for bus series-parallel AMT hybrid powertrain

Abstract A rule-based energy management strategy, that the control rules are extracted from acknowledged optimal algorithms and its control parameters are optimized offline and corrected online, for a series-parallel hybrid powertrain with an automatic mechanical transmission (AMT) is proposed in this paper to achieve near optimal fuel economy and battery state-of-charge (SOC) balance. Firstly, the dynamic programming (DP) global optimization method is applied to extract driving-mode transition rules and gear shifting rules. Furthermore, an instantaneous equivalent fuel consumption minimizing optimization method (ECMS) is utilized to determinate the engine torque distribution rules during its parallel driving mode. Then selected control parameters of driving-mode switching rules and torque split distribution are optimized based on genetic algorithm (GA) for further fuel consumption improvement. And the adaptive correction of optimized control parameters based on online driving cycle recognition method is discussed also. The simulation results show that this real-time rule-based energy management control strategy associated with the series of optimization approaches comprehensively can achieve a relatively close fuel consumption results to global optimal results and sustain the battery SOC balance after the end of driving cycle without much cycle-depending care.

Hardware-in-the-loop validation of speed synchronization controller for a heavy vehicle with Hydraulics AddiDrive System

A novel Hydraulics AddiDrive System consisting of a variable displacement pump and two in-wheel motors is presented. The new Hydraulics AddiDrive System installed on the front axle of a traditional rear-wheel drive heavy vehicle aims to offer better mobility and improve traction efficiency in rough driving condition. To synchronize speeds between front in-wheel motors and rear wheels for optimal traction efficiency, a speed synchronization controller composed of feedforward and feedback control strategy is proposed for displacement adjustment of the variable displacement pump. The feedforward strategy is designed based on the relationship between the displacement coefficients of variable displacement pump and the gears. The feedback strategy utilizes proportional–integral algorithm to correct the dynamic errors. The compound speed synchronization controller helps to improve the control accuracy and adaptability under varied external conditions. Simulations are conducted on AMESim and MATLAB/Simulink to validate the proposed control strategy. The hardware-in-the-loop test allows for a more realistic evaluation of the proposed strategy, providing guidance of its application in real vehicle. Simulation and experiment results indicate that the maximum gradeability and traction force can be separately increased by 14.4%–17.2% and 13.4%–15.6% at low adhesion coefficient roads. The speed of front and rear wheel can be matched accurately with small difference below 1.31%.

Traction control–integrated energy management strategy for all-wheel-drive plug-in hybrid electric vehicle:

Plug-in hybrid electric vehicle is one of the potential candidates to tackle the stringent fuel economy standards. Many previous studies focus on optimal fuel economy by coordinating primary power source and assistant power source in plug-in hybrid electric vehicles, which usually neglects vehicle dynamic performance. All-wheel-drive plug-in hybrid electric vehicle has advantages both in fuel economy and dynamic performance. However, when all-wheel-drive plug-in hybrid electric vehicle is traveling on low cohesive roads, how to balance the conflict between traction performance and fuel economy undoubtedly will be the crucial problem. To address the issue, an integrated control strategy is proposed for all-wheel-drive plug-in hybrid electric vehicle in this article, namely, traction control system–integrated energy management strategy. A charge depletion/charge sustaining strategy is employed to realize the torque distribution mainly according to the engine optimal working region to achieve better fuel economy. Traction control system is integrated to prevent slipping of the wheels during starting and acceleration by applying engine throttle control, motor torque control, and active braking control, which improves the vehicle dynamic performance. The traction enhancement is beneficial to the improvement of fuel efficiency. By the co-simulation of MATLAB/Simulink and AMESim, the results demonstrate the effectiveness of the proposed integrated control strategy, which increases the vehicle velocity greatly and optimizes fuel economy as well.

Fault detection and confirmation for hybrid electric vehicle

Compared with traditional vehicle, hybrid electric vehicle (HEV) is a more complicated system. With a series of high voltage components, HEV has an increasing failure rate. In order to detect the possible fault, a fault detection for HEV is proposed. The fault detection is conducted in three aspects, namely input signal of main components, torque response of power sources and logical rationality of key signals. Then time based fault confirmation mechanism is used to confirm the detected fault. Finally, hardware in the loop test result shows that the proposed method is able to detect faults rationally.

Design and Experiment of a Differential-Based Power Split Device

Hybrid electric vehicles have excellent energy efficiency and emission performance. Power split device (PSD) is a key component that directly affects the control strategy of power systems, the economic consumption of fuel, and the dynamic performance of vehicles. A differential-based PSD was proposed in this paper. A traditional differential was taken as the prototype and a new design method is proposed to retrofit the differential into a PSD. First, a comprehensive approach that includes theoretical analysis and software simulation was used to analyze the possibility as well as the necessity of retrofitting the differential into PSD. Then the differential was retrofitted. Finally, finite element analysis and bench test were conducted. Results showed that applying the retrofitted differential as PSD is practicable.