Chengning Zhang

Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic

Fuzzy logic is used to define a new quantity called the battery working state (BWS), which is based on both battery terminal voltage and state of charge (SOC), to overcome the problem of battery over-discharge and associated damage resulting from inaccurate estimates of the SOC. The BWS is used by a fuzzy logic energy-management system of a plug-in series hybrid electric vehicle (HEV) to make a decision on the power split between the battery and the engine, based on the BWS and vehicle power demand, while controlling the engine to work in its fuel economic region. The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real battery in the loop. Simulation results are presented to demonstrate the performance of the proposed fuzzy logic energy-management system under different driving conditions and battery SOCs. The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging.

Optimal Control Strategy Design Based on Dynamic Programming for a Dual-Motor Coupling-Propulsion System

A dual-motor coupling-propulsion electric bus (DMCPEB) is modeled, and its optimal control strategy is studied in this paper. The necessary dynamic features of energy loss for subsystems is modeled. Dynamic programming (DP) technique is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement in reducing energy loss due to the dual-motor coupling-propulsion system (DMCPS) running is realized without increasing the frequency of the mode switch.

A Pseudospectral Strategy for Optimal Power Management in Series Hybrid Electric Powertrains

This paper examines the problem of optimizing hybrid electric vehicle (HEV) power management for fuel economy. This paper begins by presenting a pseudospectral algorithm to solve this optimization problem. Compared with traditional dynamic programming (DP)-based optimal power management approaches, this algorithm has two key advantages: It is numerically more efficient, and it furnishes both the optimal state and costate trajectories. Building on the second advantage, this paper proposes a two-level strategy for optimal power management in vehicles commuting along fixed routes. The upper level of the proposed strategy is a costate adaptation algorithm employing pseudospectral optimization, whereas the lower level is an instantaneous optimization controller employing Pontryagins minimum principle (PMP). This paper shows its pseudospectral optimization algorithm and two-level strategy using numerical simulation for a series hybrid school bus. Parameters of the bus powertrain model are obtained from experimental component tests performed at the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. Simulation results show that the pseudospectral method reaches a solution close to DP with higher computational efficiency. Furthermore, the proposed two-level strategy is capable of adapting vehicle power management based on road-grade predictions, i.e., an attractive feature compared with more traditional online hybrid power management approaches such as the use of proportional integral derivative (PID) control for adaptive equivalent fuel consumption minimization [PID equivalent consumption minimization strategy (PID-ECMS)].

Power Management Comparison for a Dual-Motor-Propulsion System Used in a Battery Electric Bus

The efficiency performance of multi-motor-driven system highly depends on the power management. Three aspects of contribution have been made in this study. 1) A predictive power management for a DMPS is developed. To improve the performance of the predictive power management, an adaptive velocity predictor is proposed and the coefficients of proposed predictor can update its parameters according to identified driving patterns. Simulation results show that the new velocity predictor have best prediction performance compared with traditional predictors. 2) A neural network based power management is proposed. According to the optimization results of dynamic programming, radial-basis-function neural network is trained. The input dimensions and the number of hidden layer neurons of the neural network are optimized. 3) The performance of proposed control strategies are compared with three different drive cycles including MANHATTAN cycle, Japanese 1015 cycle, and UDDSHDV cycle. Simulation results indicate that compared with original control strategy, the predictive control strategy and neural network based control strategy show better efficiency performance. The neural network based strategy is verified by hardware-in-loop experiment and experiment results indicate that the control performance in real hardware shows similar property with simulation results.

Analysis of optimal power management strategy for series plug-in hybrid electric vehicles via Dynamic Programming

This paper examines two optimal power management strategies (i.e. BL strategy and CD/CS strategy) for a series plug-in school bus. The fuel performance of these two strategies is compared. Dynamic Programming approach which is able to achieve global optimal solution is applied in this study to guarantee a fair comparison. Simulation results are presented, showing that difference between the optimal fuel economy obtained by these two power management strategies is very marginal for a plug-in series powertrain.

Loss analysis of rotor magnet with PWM controller switching frequency variation

When high-power Permanent Magnet Synchronous Motor (PMSM) is operating with large stator current and high rotating speed there will be large amount of harmonics component in stator winding and deduce significant eddy current loss in rotor magnet. A detailed analytical method was proposed which calculated the eddy current loss in rotor magnet based on FFT analysis of stator current. A Finite Element Method (FEM) was founded to evaluate the relation of eddy current loss in rotor magnet and stator winding harmonics which were caused by different switching frequency. The flux densities in air gap, eddy current distribution and eddy current loss in rotor magnet were investigated. At last a testing method was applied to measuring the eddy current loss in rotor magnet which is based on testing descending of back Electromotive Force (EMF), temperature of rotor magnet. The three results have a good agreement with each other.

Fuzzy PID control for boom energy recovery on hybrid hydraulic excavator

To study the hybrid hydraulic excavator boom energy recovery process, multiple dynamics software Recurdyn is used to establish realistic models of hydraulic excavator working devices, Simulink is used to establish the hydraulic circuit and the energy recovery system model. Boom Energy Recovery of hybrid excavator energy recovery process is simulated by the joint using of Recurdyn and Simulink software. Energy recovery process is controlled by fuzzy PID control strategy, so that the process of energy recovery can achieve higher efficiency. Result of co-simulation shows that the energy recovery system can be controlled well in fuzzy PID control strategy, with which the energy recovery system can achieve comparatively higher efficiency.

State Monitor for Lithium-ion Power Battery Pack

Management technology for the lithium-ion traction batteries is the key research point for electric vehicles (EV). The states of temperature, current, single voltage, state of charge (SOC) and state of energy (SOE) should be acquired and evaluated timely. This paper has designed a battery state monitor system for the EV batteries based on LabView. Combined with the Extended Kalman Filter (EKF) of the optimal estimation theory and system parameter identify method, evaluate the SOC and SOE for lithium-ion batteries. The experiment results indicate that the EKF method is suitable for estimating the states of batteries, the virtual instrument technology could provide multiple message of the batteries.

Design for Battery Energy Manage System Based on LabVIEW

Battery energy manage system is the key technology in the research of electrical vehicle. Signal batteries parameters like voltage and temperature should be acquired accurately and in time in order to show the working state and SOC of battery. Based on the distributed battery signal getting system and virtual instrument LabVIEW, the battery energy management system is developed. Virtual instrument can provide direct signal of battery, and show the changing of state accurately and intimae.

Analysis of losses and thermal model in a surface-mounted permanent-magnet synchronous machine over a wide- voltage range of rated output power operation

This paper shows the results of permanent magnet eddy current loss, iron core loss and Copper loss of a surface permanent magnet synchronous machine. A thermal model for the temperature of all motor components is also presented in the paper. This PMSM will be operated at the voltage 700-1000V powered by the battery pack. So the losses are calculated separately at four different DC link voltages (700V, 800V, 900V, 1000V). The analysis shows that this motor should stop working to avoid damage at very low voltage.