Federal Urban Driving Schedule Research Articles

Performance Evaluation of Electric Vehicles in Macau

Being a city with small geographical size limiting the travel range of vehicles, Macau has great potential for implementation of electric vehicles (EVs). With an urbanized city and limited land space, Macau has been faced with problems of road congestion due to rapid growth in car population. Air pollution is also another important concern. EVs provide low emission urban transportation. Researchers and engineers have concentrated on the improvement of EV performance through the advances in batteries, motors, converters, controllers and relevant auxiliaries, with enormous successes. Now, it comes to the stage of commercialization! Before adapting EVs, it is important to understand their performances in Macau. Several projects were launched to investigate the performance of EV, specifically for sub-tropical environment of Macau. Due to the high temperature and humidity, performance of EVs operated in Macau was yet to be understood. Previous experimental studies conducted in the US, Europe or Japan might not reflect the actual local real-road driving conditions. This paper aims to ana lyze the performance of EVs and compared with internal combustion engine vehicles (ICEVs) and hybrid electric vehicles (HEVs). Viability of EVs will be simulated using both Federal Urban Driving Schedule (FUDS) and Macau Driving Cycle (MDC).

Dynamic Simulation of Large Scale PEM Fuel Cell under Driving Cycle

A dynamic response of large scale PEMFC model has been studied using computational fluid dynamics (CFD) technique. The power density required during dynamic cycle was taken from one of Federal Urban Driving Schedule (FUDS) cycles to enhance an understanding of flow-field design. The transient simulation of 200-cm2 active area with two different flow-fields showed different response even though those two designs give similar results under steady state operation. Further, the influence of operating condition and flow direction to dynamic performance and distributions were also revealed. The prediction also showed that both operations given in this study can achieve power required from FUDS cycle but automotive condition (cathode dry) shows more non uniform distributions than stationary condition (well humidified condition). Semi counter-flow direction also gives more non-uniform in distributions than co-flow direction under transient operation.

Electro-thermal analysis of Lithium Iron Phosphate battery for electric vehicles

Lithium ion batteries offer an attractive solution for powering electric vehicles due to their relatively high specific energy and specific power, however, the temperature of the batteries greatly affects their performance as well as cycle life. In this work, an empirical equation characterizing the battery's electrical behavior is coupled with a lumped thermal model to analyze the electrical and thermal behavior of the 18650 Lithium Iron Phosphate cell. Under constant current discharging mode, the cell temperature increases with increasing charge/discharge rates. The dynamic behavior of the battery is also analyzed under a Simplified Federal Urban Driving Schedule and it is found that heat generated from the battery during this cycle is negligible. Simulation results are validated with experimental data. The validated single cell model is then extended to study the dynamic behavior of an electric vehicle battery pack. The modeling results predict that more heat is generated on an aggressive US06 driving cycle as compared to UDDS and HWFET cycle. An extensive thermal management system is needed for the electric vehicle battery pack especially during aggressive driving conditions to ensure that the cells are maintained within the desirable operating limits and temperature uniformity is achieved between the cells.

State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures

Ambient temperature is a significant factor that influences the accuracy of battery SOC estimation, which is critical for remaining driving range prediction of electric vehicles (EVs) and optimal charge/discharge control of batteries. A widely used method to estimate SOC is based on an online inference of open-circuit voltage (OCV). However, the fact that the OCV–SOC is dependent on ambient temperature can result in errors in battery SOC estimation. To address this problem, this paper presents an SOC estimation approach based on a temperature-based model incorporated with an OCV–SOC–temperature table. The unscented Kalman filtering (UKF) was applied to tune the model parameters at each sampling step to cope with various uncertainties arising from the operation environment, cell-to-cell variation, and modeling inaccuracy. Two dynamic tests, the dynamic stress test (DST) and the federal urban driving schedule (FUDS), were used to test batteries at different temperatures. Then, DST was used to identify the model parameters while FUDS was used to validate the performance of the SOC estimation. The estimation was made covering the major working range from 25% to 85% SOC. The results indicated that our method can provide accurate SOC estimation with smaller root mean squared errors than the method that does not take into account ambient temperature. Thus, our approach is effective and accurate when battery operates at different ambient temperatures. Since the developed method takes into account the temperature factor as well as the complexity of the model, it could be effectively applied in battery management systems for EVs.

Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries

To resolve the problems that the initial state of charge (SOC) and the available capacity of batteries are difficult to estimate when using the Ah counting method, in this paper An improved SOC estimation method was proposed that combined with the open circuit voltage (OCV) method and Ah counting method based on the analysis and consideration of the battery available capacity variation caused by charge and discharge current, environment temperature and battery state of health (SOH). The precision of the proposed method was validated by using Federal Urban Driving Schedule (FUDS) test of a Lithium iron phosphate (LiFePO4) power battery. The SOC estimate error using the proposed method relative to a discharge test was better than the Ah counting method.

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

Design an Unscented Kalman Filter-Based SoC Estimator for HEV Application

An unscented Kalman filter (UKF) is adopted to estimate the state of charge (SoC) of a lithium ion battery for application in hybrid electric vehicles (HEVs). Generally, the extended Kalman filter (EKF) can be selected to estimate a non-linear system state. However, it may leads to large errors since the strong non-linear and stochastic performance. In this paper, the performance of the lithium-ion battery is tested by a design of experiment, such as hysteresis, polarization, coulomb efficiency, etc. And a combined battery model is selected for SoC estimation, while the model parameter was identified by using UKF algorithm. Finally, the federal urban driving schedule (FUDS) is used to evaluate the proposed method accuracy. And the results show that the maximum SoC estimation error is less than 3%.

Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.

An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack

Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as “AEKFAh”, is proposed. This method uses the adaptive Kalman filtering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.

Modelling of a battery pack for electric vehicles using a stochastic fuzzy neural network

A dynamic simulation model for a battery pack is an important prerequisite for the simulation of electric vehicles. As the battery pack is a highly non-linear system, and their dynamic behaviour depends on different parameters, it is difficult to establish the relationship between the load voltage and the current under different temperatures and states of charge. Furthermore, the input and output data usually contain noise; therefore, the traditional neural-network-based model is affected while training the parameters. To solve the problem, a stochastic fuzzy neural network (SFNN) which has a filtering effect on the noisy input is used to model the battery non-linear dynamics in this paper. In the parameter-learning algorithm for the SFNN, a novel cost function which contains the error variables is studied. Then, the modelling test is performed on an 80A h nickel—metal hydride (Ni—MH) battery pack and the SFNN-based model for the Ni—MH battery pack is set up. The Federal Urban Driving Schedule cycle is performed to test the model. Compared with the traditional neural-network-based model, the SFNN model can simulate the battery dynamic better and has a filtering effect on the noisy input which is more suitable for practical applications.

Dynamic Simulator and Controls for a PEM Fuel Cell Power System

Controls of a PEM fuel cell stack are one of the crucial issues for securing efficient and durable operations of the stack. When the air is excessively supplied, the efficiency of the system drops. Conversely, insufficient supply of the air causes oxygen starvation at a dynamically varying load. In particular, proper cooling strategy ensures rejecting heat produced and prevents any thermal stress on thin layers of cells. Excessive cooling decreases the working temperature and consequently drops efficiency of the stack. In contrast, the thermal stress imposed by insufficient cooling may reduce the lifespan of the layers.Design of controls needs a model for the plant that sufficiently represents its dynamics. Current models available are either empirical or computationally intensive, which do not allow for analysis of a stack behavior and associated controls. The paper addresses development of a high dynamic model for a stack that is based on transport of charges, flow of fuels and byproducts taken into temperature effects. The stack is constructed with single cells composed of sandwiched multiple layers that are thermally and electrically coupled. Air is supplied by a blower, which voltage is regulated. Two representing control strategies for the air supply system are designed and compared. Heat is rejected by a thermal circuit that consists of a pump, a three-way valve, and a radiator with a fan and a reservoir. In order to control the coolant flow rate, a linear cascade and a state feedback control are designed and compared, which includes a feed-forward function that is derived from load profile. In addition, the temperature effect on air flow rate is compensated, so that a deviation of the oxygen excess ratio can be suppressed.The dynamics and performance of the designed controllers are evaluated and analyzed by simulations using dynamic fuel cell system models at a multi-step current and a current profile measured at the Federal Urban Driving Schedule. The results show that the control strategy proposed reduces not only temperature rise in the catalyst layer but also the parasitic power needed for operation of the air and coolant pumps maintaining the oxygen excess ratio set.

Coolant controls of a PEM fuel cell system

When operating the polymer electrolyte membrane (PEM) fuel cell stack, temperatures in the stack continuously change as the load current varies. The temperature directly affects the rate of chemical reactions and transport of water and reactants. Elevated temperature increases the mobility of water vapor, which reduces the ohmic over-potential in the membrane and eases removal of water produced. Adversely, the high temperature might impose thermal stress on the membrane and cathode catalyst and cause degradation. Conversely, excessive supply of coolants lowers the temperature in the stack and reduces the rate of the chemical reactions and water activity. Corresponding parasitic power dissipated at the electrical coolant pump increases and overall efficiency of the power system drops. Therefore, proper design of a control for the coolant flow plays an important role in ensuring highly reliable and efficient operations of the fuel cell system. Herein, we propose a new temperature control strategy based on a thermal circuit. The proposed thermal circuit consists of a bypass valve, a radiator with a fan, a reservoir and a coolant pump, while a blower and inlet and outlet manifolds are components of the air supply system. Classic proportional and integral (PI) controllers and a state feedback control for the thermal circuit were used in the design. In addition, the heat source term, which is dependent upon the load current, was feed-forwarded to the closed loop and the temperature effects on the air flow rate were minimized. The dynamics and performance of the designed controllers were evaluated and analyzed by computer simulations using developed dynamic fuel cell system models, where a multi-step current and an experimental current profile measured at the federal urban driving schedule (FUDS) were applied. The results show that the proposed control strategy cannot only suppress a temperature rise in the catalyst layer and prevent oxygen starvation, but also reduce the parasitic power dissipated for operation of the air blower and coolant pump.

Combined state of charge estimator for electric vehicle battery pack

Ah counting is not a satisfactory method for the estimation of the state of charge (SOC) of a battery, as the initial SOC and coulomb efficiency are difficult to measure. To address this issue, an equivalent coulomb efficiency is defined and a new SOC estimation method, denoted as “KalmanAh”, is proposed. This method uses the Kalman filtering method to correct for the initial value used in the Ah counting method. A Ni/MH battery test, consisting of 8.08 continuous federal urban driving schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.5% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.

Experimental investigation of fuel cell dynamic response and control

An experimental study of the dynamic response of a commercial fuel cell system is presented in this work. The primary goal of the research is an examination of the feasibility for using fuel cells in a load-following mode for vehicular applications, where load-following implies that the fuel cell system provides the power necessary for transient responses without the use of additional energy storage elements, such as batteries or super-capacitors. The dynamic response of fuel cell systems used in the load-following mode may have implications for safe and efficient operation of vehicles. To that end, a DC–DC converter was used to port the power output of the fuel cell to a resistive load using a pulse-width-modulating circuit. Frequency responses of the system were evaluated at a variety of DC offsets and AC amplitudes of the PWM duty cycle from 1 out to 400 Hz. Open-loop transient responses are then evaluated using transitions from 10% to 90% duty cycle levels, followed by dwells at the 90% level and then transitions back to the 10% level. A classical proportional–integral controller was then developed and used to close the loop around the system, with the result that the fuel cell system was driven to track the same transient. The controller was then used to drive the fuel cell system according to a reference power signal, which was a scaled-down copy of the simulated power output from an internal combustion engine powering a conventional automobile through the Federal Urban Driving Schedule (FUDS). The results showed that the fuel cell system is capable of tracking transient signals with sufficient fidelity such that it should be applicable for use in a load-following mode for vehicular applications. The results also highlight important issues that must be addressed in considering vehicular applications of fuel cells, such as the power conditioning circuit efficiency and the effect of stack heating on the system response.

Support vector machine based battery model for electric vehicles

The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%.

A virtual prototype for a hybrid electric vehicle

A virtual prototype of a hybrid electric vehicle (HEV) is created within the virtual test bed (VTB) environment, which has been developed for modeling, simulation, analysis and virtual prototyping of large-scale multi-technical dynamic systems. Attention is focused on the electric system, which is composed of (i) a fuel cell system as a prime power source, (ii) battery and super capacitor banks as energy storage devices for high and intense power demands, (iii) DC-to-DC power converters to control the flow of power, (iv) a three-phase inverter-fed permanent magnet synchronous motor as a drive, and (v) a common DC bus. The simulation of the proposed system is conducted using two types of driving cycles. These are: (i) rapid acceleration and deceleration, and (ii) Federal Urban Driving Schedule (FUDS). The parameter values chosen for the components and the numerical results obtained by simulation are consistent with the practical HEV applications.

Fuel economy and life-cycle cost analysis of a fuel cell hybrid vehicle

The most promising vehicle engine that can overcome the problem of present internal combustion is the hydrogen fuel cell. Fuel cells are devices that change chemical energy directly into electrical energy without combustion. Pure fuel cell vehicles and fuel cell hybrid vehicles (i.e. a combination of fuel cell and battery) as energy sources are studied. Considerations of efficiency, fuel economy, and the characteristics of power output in hybridization of fuel cell vehicle are necessary. In the case of Federal Urban Driving Schedule (FUDS) cycle simulation, hybridization is more efficient than a pure fuel cell vehicle. The reason is that it is possible to capture regenerative braking energy and to operate the fuel cell system within a more efficient range by using battery. Life-cycle cost is largely affected by the fuel cell size, fuel cell cost, and hydrogen cost. When the cost of fuel cell is high, hybridization is profitable, but when the cost of fuel cell is less than 400 US$/kW, a pure fuel cell vehicle is more profitable.

The application of neural networks to fuel processors for fuel-cell vehicles

Passenger vehicles fueled by hydrocarbons or alcohols and powered by proton exchange membrane (PEM) fuel cells address world air quality and fuel supply concerns while avoiding hydrogen infrastructure and on-board storage problems. Reduction of the carbon monoxide concentration in the on-board fuel processor's hydrogen-rich gas by the preferential oxidizer (PrOx) under dynamic conditions is crucial to avoid poisoning of the PEM fuel cell's anode catalyst and thus malfunction of the fuel-cell vehicle. A dynamic control scheme is proposed for a single-stage tubular cooled PrOx that performs better than, but retains the reliability and ease of use of, conventional industrial controllers. The proposed hybrid control system contains a cerebellar model articulation controller artificial neural network in parallel with a conventional proportional-integral-derivative (PID) controller. A computer simulation of the preferential oxidation reactor was used to assess the abilities of the proposed controller and compare its performance to the performance of conventional controllers. Realistic input patterns were generated for the PrOx by using models of vehicle power demand and upstream fuel-processor components to convert the speed sequences in the Federal Urban Driving Schedule to PrOx inlet temperatures, concentrations, and flow rates. The proposed hybrid controller generalizes well to novel driving sequences after being trained on other driving sequences with similar or slower transients. Although it is similar to the PID in terms of software requirements and design effort, the hybrid controller performs significantly better than the PID in terms of hydrogen conversion setpoint regulation and PrOx outlet carbon monoxide reduction.

Dynamic performance measurements of Na/NiCl 2 cells for electric vehicle applications

A method to measure the dynamic-performance of sodium/nickel chloride research cells under various dynamic load profiles, such as the Simplified Federal Urban Driving Schedule (SFUDS) has been described. The dynamic performance of various Na/NiCl 2 research cells is measured in various power profiles in which the duration, intensity, and ratios of the power levels (mW/cm 2) in the SFUDS-like profiles are varied. Performance parameters such as area-specific impedance and specific-area energy are obtained by discharging these cells under actual conditions that exist in the full-size battery during the intended applications. The performance parameters measured by this method are employed in relatively simple scale-up calculations to project performances of full-size battery.

Mechatronic design and control of hybrid electric vehicles

The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV). Toward these ends, four issues are explored. First, the development of HEV is presented. This synopsis includes a novel definition of degree of hybridization for automotive vehicles. Second, a load-leveling vehicle operation strategy is developed. In order to accomplish the strategy, a fuzzy logic controller is proposed. Fuzzy logic control is chosen because of the need for a controller for a nonlinear, multidomain, and time-varying plant with multiple uncertainties. Third, a novel technique for system integration and component sizing is presented. Fourth, the system design and control strategy is both simulated and then implemented in an actual vehicle. The controller examined in this study increased the fuel economy of a conventional full-sized vehicle from 40 to 55.7 mi/h and increased the average efficiency over the Federal Urban Driving Schedule from 23% to 35.4%. The paper concludes with a discussion of the implications of intelligent control and mechatronic systems as they apply to automobiles.