Liangfei Xu

Energy management of plug-in hybrid electric vehicles with unknown trip length

Abstract This paper proposes a novel control strategy for plug-in hybrid electric vehicles (PHEV). The minimization of the utility factor weighted fuel consumption (FC UFW ), which represents the average fuel consumption in numerous trips, is firstly proposed as the objective of the energy management. In previous studies, the trip length is usually assumed to be known. Then, if it is shorter than the all-electric range (AER), a Charge Depleting–Charge Sustaining (CDCS) strategy leads to the minimum fuel consumption; otherwise, a blended strategy that spends down battery energy almost uniformly brings the minimum fuel consumption. Nevertheless, the trip length is not always known before trip in real life. To deal with the cases of unknown trip length, this paper proposes a Range ADaptive Optimal Control (RADOC) strategy to minimize the FC UFW , which utilizes the statistical information of the trip length. The RADOC strategy was verified by dynamic programming and was found to be somewhere in between the blended and CDCS strategies. Depending on the nature of the trips, the RADOC strategy was found to improve FC UFW between 0.10% and 4.07% compared with the CDCS strategy. The RADOC strategy is very close to the CDCS strategy when the PHEV is used in regular daily driving. On the contrary, the RADOC solution exhibits a “uniform battery discharging” behavior similar to the blended strategy for urban utility vehicles or taxis. The behavior of the RADOC strategy is also studied for different battery sizes and driving cycles.

Dynamic Programming Algorithm for minimizing operating cost of a PEM fuel cell vehicle

A PEM (Proton Exchange Membrane) fuel cell city bus utilizes a PEM fuel cell engine as the primary source, and a li-ion battery system as the auxiliary power source. By optimizing the power split strategy and recycling braking energy, this kind of power-train has advantages of zero emission and high energy efficiency. However, the cost of hydrogen gas is far more expensive than that of the electric energy. How to split the power between the two power sources so as to minimize the operating cost, as well as guarantee the vehicle dynamic performance, becomes an important topic. This paper proposes a Dynamic Programming Algorithm (DPA) to solve the minimizing problem. Some details of the DPA are discussed, e.g. the principles of selecting parameters for the algorithm. The effectiveness of the algorithm is verified by comparing simulating results of different algorithms. Results show that, 1) by using the DPA algorithm, we can find the optimal control strategy in an objective way. 2) The constraints of vehicle dynamic performance on the optimal problem have great influences on the optimal results. 3) To predict the power requirement in the near future is very important to achieve an optimal real-time strategy.

Power management and economic estimation of fuel cell hybrid vehicle using fuzzy logic

Fuel cell hybrid vehicles (FCHVs) have been attracting a lot of attention as well as electric vehicles (EVs) and various hybrid electric vehicles (HEVs) for environmental issues and energy crises. One of the advantages of using foregoing vehicles is saving energy during electric motors braking regeneration. In this paper, a fuzzy logic based power management strategy considering a regenerative braking control of the electric motor is presented for proton exchange membrane (PEM) fuel cell and Nickel-Metal Hydride (Ni-MH) battery hybrid vehicular power system. The fuel economic of the FCHV is estimated by analyzing the fuel consumption of hydrogen usages. Simulation results show that the fuel economic is improved by using the proposed fuzzy logic controller for regenerative braking management.

Control strategy optimization of a hybrid fuel cell vehicle with braking energy regeneration

This paper deals with the optimized control strategy within the vehicle which is driven by a fuel cell stack hybrid with a NiMH battery. The aim is the instantaneous energy management to optimize the hydrogen consumption. At first the powertrain was modeled using data on testbed. Then the optimization control problem basing on an equivalent hydrogen consumption strategy is defined, where the motor target torque and DC/DC target current should be calculated. According to the shift signals and pedal position signals, the vehicle operation can be divided into two modes, non-brake mode (idle, drive backward, drive forward, slide) and brake mode. The optimization problem was solved respectively. Results in ldquoChina typical city bus cyclerdquo testing show the robustness and effectiveness of the strategy in improving fuel economy while maintaining drivability. The fuel economy was improved from 9.6 kg/100 km to 7.9 kg/100 km and the battery SOC was kept around 47%.

Equivalent Consumption Minimization Strategies of Series Hybrid City Buses

With ever growing concerns on energy crisis and environmental issues, alternative clean and energy efficient vehicles are favoured for public applications. Internal combustion engine(ICE)-powered series hybrid buses and fuel cell (FC) hybrid buses, respectively as a near-term and long-term strategy, have a very promising application prospect. The series hybrid vehicle utilizes an ICE/FC as the main power source and a battery/ultra capacity (UC) as the auxiliary power source. The main power source supplies the average vehicle power, and the auxiliary power source functions during accelerating and decelerating. Because the battery/UC fulfills the transient power demand fluctuations, the ICE/FC can work steadly. Thus, the durability of the fuel cell stack could be improved compared with a pure FC-powered bus in the FC series hybrid bus. And the PM and NOx can be greatly lowered in the ICE series hybrid bus compared with a traditional city bus. Besides, the ability of the energy storage source to recover braking energy enhances the fuel economy greatly. The hybrid configuration raises the question of energy management strategy, which chooses the power split between the two. The strategy is developed to achieve system-level objectives, e.g. fuel economy, low emission and battery charge-sustaining, while satisfying system constraints. Energy management strategies in the recent literature can be generally categorized into two types: rule-based strategies and optimal strategies. A rule based strategy can be easily implemented for the real-time applications based on heuristics (N.Jalil, N.A.Kheir & M.Salman, 1997). Such a strategy could be further improved by extracting optimal rules from optimal algorithms (S.Aoyagi, Y.Hasegawa & T.Yonekura, 2001). Optimal strategies differ from each other in the time range. Fuel consumption in a single control cycle is minimized in an instantaneous optimal strategy (G.Paganelli, S.Delprat & T.M.Guerra, 2002). And a global optimal strategy minimises it over a whole determined driving cycle using determined dynamic programming method (DDP) (Chan Chiao Lin et al., 2003), or over a undetermined driving cycle using stochastic dynamic programming method (SDP) (Andreas Schell et al., 2005). Other strategies minimize fuel consumption over an adaptive time span, which could be adjusted on the basis of vehicular speed, pedal 7

Modeling and Experimental Study of PEM Fuel Cell Transient Response for Automotive Applications

Abstract This paper presents an analysis of the dynamic response of a low pressure proton exchange membrane (PEM) fuel cell stack to step changes in load, which are characteristic of automotive fuel cell system applications. The goal is a better understanding of the electrical and electrochemical processes when accounting for the characteristic cell voltage response during transients. The analysis and experiment are based on a low pressure 5 kW proton exchange membrane fuel cell (PEMFC) stack, which is similar to those used in several of Tsinghuas fuel cell buses. The experimental results provide an effective improvement reference for the power train control scheme of the fuel cell buses in Olympic demonstration in Beijing 2008.

Control algorithm of fuel cell/battery hybrid vehicular power system

In this paper, a control strategy for a vehicular power system combined with a proton exchange membrane fuel cell and a battery energy system (BES) has been presented. The control, witch takes into account the slow dynamic of fuel cell and the state of charge (SOC) of BES, is investigated based on the proposed fuzzy logic control (FLC) for the vehicular power system. Fuel cell output power was determined according to the driving load requirement and the SOC, using fuzzy dynamic decision-making and fuzzy self-organizing concepts. Analysis of simulation results is discussed by Matlab/Simulink software to verify the effectiveness of the proposed control strategy. The control scheme can be used to improve the operational efficiency of hybrid power system.

Interaction of In-wheel Permanent Magnet Synchronous Motor with Tire Dynamics

Drive wheel systems combined with the in-wheel permanent magnet synchronous motor (I-PMSM) and the tire are highly electromechanical-coupled. However, the deformation dynamics of this system, which may influence the system performance, is neglected in most existing literatures. For this reason, a deformable tire and a detailed I-PMSM are modeled using Matlab/Simulink. Furthermore, the influence of tire/road contact interface is accurately described by the non-linear relaxation length-based model and magic formula pragmatic model. The drive wheel model used in this paper is closer to that of a real tire in contrast to the rigid tire model which is widely used. Based on the near-precise model mentioned above, the sensitivity of the dynamic tire and I-PMSM parameters to the relative error of slip ratio estimation is analyzed. Additionally, the torsional and longitudinal vibrations of the drive wheel are presented both in time and frequency domains when a quarter vehicle is started under conditions of a specific torque curve, which includes an abrupt torque change from 30 N · m to 200 N · m. The parameters sensitivity on drive wheel vibrations is also studied, and the parameters include the mass distribution ratio of tire, the tire torsional stiffness, the tire damping coefficient, and the hysteresis band of the PMSM current control algorithm. Finally, different target torque curves are compared in the simulation, which shows that the estimation error of the slip ratio gets violent, and the longitudinal force includes more fluctuation components with the increasing change rate of the torque. This paper analyzes the influence of the drive wheel deformation on the vehicle dynamic control, and provides useful information regarding the electric vehicle traction control.

Wheel Slip Control Using Sliding-Mode Technique and Maximum Transmissible Torque Estimation

This paper presents the analysis and design of a novel traction control system (TCS) based on sliding-mode control (SMC) and maximum transmissible torque estimation (MTTE) technique, which is employed in four-wheel independent drive electric vehicles (EVs) without detecting the vehicle velocity and acceleration. The original MTTE technique is effective with regard to the antislip control; however, it cannot sufficiently utilize the adhesive force from the tire–road surface. In the proposed TCS algorithm, only front wheels are equipped with the MTTE technique, while rear wheels are equipped with the SMC technique. As a result, the front wheel is critically controlled by the MTTE technique. Thus, its rotary speed can be used to approximately estimate the chassis velocity and acceleration, which are key input parameters of the SMC. The rear wheel slip ratio can be therefore controlled by the SMC which is robust against uncertainties and disturbances of parameters for exploiting more transmissible friction force. In addition, the stability of MTTE is analyzed in this paper because an important parameter is neglected in the original MTTE technique. As a result, the stability condition is changed, and the MTTE is modified in the proposed TCS according to the new conclusion. A half four-wheel drive (4WD) EV model is initially built using matlab/simulink. This paper investigates the proposed TCS for various adhesive conditions involving abrupt change in road friction. Compared with the original MTTE technique, the comprehensive performance, particularly the acceleration ability, is significantly improved by the proposed controller. The simulation result validates the effectiveness and robustness of the proposed TCS.

Real-Time Estimation of Vehicle Mass and Road Grade Based on Multi-Sensor Data Fusion

Vehicle mass and road grade are two key parameters for New Energy Vehicles. It plays an important role in the power distribution of multi-energy power systems and braking energy recovery. Using a 4-wheel drive (4WD) electric mini-car as an experimental platform, a road grade and vehicle mass estimation algorithm based on multi-data fusion technology is studied. Firstly, a Simulink model for GPS (Global Positioning System)/INS (Inertial Navigation System)/wheel-speed data fusion is established, taking advantage of the characteristics of a 4WD electric vehicle. An off-line simulation is conducted with data from a real vehicle test to verify the model. Then the verified algorithm is downloaded and successfully implemented in the Vehicle Control Unit based on MPC561 digital core by Simulink Automatic Code Generation technology. Finally, a hardware-inloop simulation based on CANoe and CANalyzer is conducted for the testing and evaluation of the VCU. The result shows that the real-time multi-data fusion algorithm produces a good estimation of the road grade and vehicle mass with an error of 5%, and the convergence and steady-state error meet the need of real vehicle applications.