Figure 1 - uploaded by Abbas Fotouhi
Content may be subject to copyright.
Source publication
Computer simulation tools can give early indicators of key vehicle characteristics. In traditional hybrid vehicles, this is important in designing for optimal fuel consumption; in plug-in hybrids and pure electric vehicles, it is critical for accurate prediction of range, a key market qualifier. There are a variety of techniques, typically operatin...
Contexts in source publication
Context 1
... readers will be familiar with the 'New European Driv- ing Cycle' or 'NEDC' used to give standardized fuel con- sumption measures for passenger cars; the NEDC is also used to define electric vehicles' ranges ( Figure 1). In energy consumption tests, the NEDC is repeated twice and the amount of energy required to charge the battery is calcu- lated; in range tests, the NEDC is repeated again and again until either the vehicle displays a warning indicator or the vehicle cannot 'keep up' with the cycle. ...
Context 2
... similar analysis has been performed for PI and PID control- lers. The results are illustrated in Figures 7 to 12. For the PI controller the results can be analysed same as the proportional controller. ...
Context 3
... cycle-following error is an indicator of power fade. With our C-segment vehicle model following the WLTP class 3 driving cycle, it was found that the maximum cycle-following error was initially unaffected by reductions in the power capability of our battery, but after a point, it began to have a very signif- icant effect indeed, as illustrated in Figure 13. On the face of it, this standard measure is much easier to re- late to in-vehicle use. ...
Similar publications
In this paper, a parallel regenerative braking control strategy and fuzzy PID control algorithm for series-parallel hybrid electric vehicles are developed based on the analysis of the existing regenerative braking control strategy and control algorithm, indicating a reasonable and effective regenerative braking control strategy for series-parallel...
Citations
... For the development and especially for the model-based validation of an energy management system, knowledge about the vehicle and the route resulting in the energy consumption of the vehicle is needed [8]. Many studies work with synthetic, standardized driving cycles (e.g., New European Driving Cycle (NEDC), Worldwide Harmonised Light-Duty Vehicles Test Procedure (WLTP)) and the energy consumption data collected there [9][10][11][12][13][14]. Due to the simplified representation of the influences on energy consumption that occur in reality (traffic, gradient, weather, etc.), these data show a partly, not insignificant, difference to real measured energy consumption data [15,16]. ...
... A similar approach is described in [9]. In this work, Newton's laws are also chosen as the basis of the physical model. ...
... The approach of [19], like [2,9], also uses Newton's laws as the basis for the physical model. For the simplified modeling of the HESS, it is assumed that the battery, the supercapacitor and the electric power have constant energy transmission/conversion. ...
Hybrid energy storage systems (HESS) for electric vehicles, which consist of lithium-ion batteries and supercapacitors, have become an increasing focus of research and development in recent years. The combination of the two combines the advantages of each storage technology (high energy density in batteries and high power density in supercapacitors) in one system. To effectively manage the energy flow between these two different storage technologies, an intelligent energy management system (EMS) is required. In the development of the EMS, it is usual to run preliminary checks in a simulation environment that is as close to reality as feasible already during the development process. For this purpose, this paper presents a concept for the creation of a simulation environment consisting of realistic routes and a holistic vehicle model. The realistic route data are generated by a route-generating algorithm, which accesses different map services via application programming interfaces (API) and retrieves real route data to generate a simulated route. By integrating further online services (e.g., OpenWeather API), the routes are further specified with, for example, real weather data, traffic data, speed limits and altitude data. For the complete vehicle model, components including the suspension, chassis and auxiliary consumers are simulated as blackbox models. The components that can be accessed during the simulation are simulated as white box models. These are the battery, the supercapacitor, the DC/DC converter and the electric motor. This allows the EMS to control and regulate the HESS in real time during the simulation. To validate the simulation environment presented here, a real BMW i3 was driven on a real route, and its energy demand was measured. The same route was simulated in the simulation environment with environmental conditions that were as realistic as feasible (traffic volume, traffic facilities, weather) and the vehicle model of the BMW i3. The resulting energy demand from the simulation was recorded. The results show that the simulated energy consumption value differs by only 1.92% from the real measured value. This demonstrates the accuracy of the simulation environment presented here.
... Test benches for EV and HEV help to provide a comparative lifecycle analysis of separate parts of the vehicle, as motors [37], insulation [32], etc., and especially batteries [6], [33], [38], [39]. As more EVs and HEVs appear on road, the disposal of the batteries has become a concern, [33] estimates that there will be nearly one million retired EV battery packs in the US alone by 2020. ...
The goal of the proposed concept is to develop a specialized unsupervised prognosis and control platform for electrical vehicle propulsion drive performance estimation. This goal requires the development of several subtasks and related objectives, therefore the state-of-the-art analysis of the current development in electric vehicle propulsion drives is presented in the paper. Digital Twin as modern technology trend covers a wide range of services, such as efficiency improving, minimizing failure rates, shorten development cycles, and provides new opportunities for remote control and maintenance of the device. In this paper, the general description of requirements for creating a Digital Twin is discussed. The construction of electric vehicle propulsion drive, as physical devices models of Digital Twin, can be carried out using the well-established modeling techniques, the possible solutions are also presented. Different physical models of separate parts of electric vehicle components (power controller, motor(s), gearbox(es), etc.), and the related reduced models of these components (test benches) are proposed.
... Spark timing in internal combustion engine has a significant influence on emission, vibration engine efficiency and durability during ignition period. Spark timing monitoring and controlling is to achieve optimum combustion without affecting the existing engine performance while maintaining operation consistency [5]. Condition monitoring of ICE consists significant data with respect the functioning of its parts. ...
... In such manner, it has also been observed that working in the time domain poses many problems such as lack of information and noise embedding in certain frequency pockets. This is settled by taking the Fourier transform of the signal to analyse it in the frequency domain [5]. ...
This paper provides an application of fundamental statistical analysis named Z-freq technique about fault detection in a gasoline engine. In engine condition monitoring, early detection of failure symptom is an important function to prevent decreasing of engine performance during operation. Signal was measured using hardware named SO signal analyzer which is capable of recording voltage in milliVolt (mV), and validation was made to determine the engine speed and misfire fault. The data were analyzed using modified statistical analysis Z-notch filter based on frequency content (Z-freq) concerning the variation of engine speeds and misfire on one of the engine cylinders. This output is to determine the degree of data scattering for the vibration signals. Based on calculated results, vibration characteristic can be presented using voltage generated and 2D graphical representation of Z-freq. Experimental results of two parameters showed that the values of Z-freq coefficient increased as the speed increased from low to high and identical value for normal and misfire condition. It was found that diagnosis of the engine cylinder wall using novel signal analysis-based was successful using low cost and space saving piezo-film sensors, and analyzing with Z-freq technique as a result shown an identical pattern of data measured.
... The driver model uses the drive cycle and velocity as inputs, and calculates an acceleration/deceleration command (D a or D b ) as output. The driver model is a proportional-integral (PI) controller tuned using Ziegler-Nichols method (Ziegler and Nichols, 1942) as discussed by Fotouhi et al. (2014). The output is scaled between 0 and 1 showing released and pushed pedal statuses. ...
... The driver model uses the drive cycle and velocity as inputs, and calculates an acceleration/deceleration command (D a or D b ) as output. The driver model is a proportional-integral (PI) controller tuned using Ziegler-Nichols method (Ziegler and Nichols, 1942) as discussed by Fotouhi et al. (2014). The output is scaled between 0 and 1 showing released and pushed pedal statuses. ...
... In order to simulate the NiMH battery pack under real conditions in an EV, Nissan Leaf EV is simulated [25] over two real world driving cycles: 1) the worldwide harmonized light vehicles test procedure (WLTP) class 3 and 2) the urban dynamometer driving schedule (UDDS). Then the power demand is scaled down (500:1) to be applicable for the small battery pack [26]. ...
This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery's ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.
... The first part of the EV model is a PI controller which is used as a driver to follow a driving cycle as illustrated in Figure 5. The PI controller's gains are tuned using Ziegler-Nichols method as discussed in [14]. At each time step, vehicle's velocity is compared to the driving cycle and a tracking error is calculated. ...
This paper describes a graphical user interface (GUI) tool designed to support cell design and development of manufacturing processes for an automotive battery application. The GUI is built using the MATLAB environment and is able to load and analyze raw test data as its input. After data processing, a cell model is fitted to the experimental data using system identification techniques. The cell model's parameters (such as open-circuit-voltage and ohmic resistance) are displayed to the user as functions of state of charge, providing a visual understanding of the cell's characteristics. The GUI is also able to simulate the performance of a full battery pack consisting of a specified number of single cells using standard driving cycles and a generic electric vehicle model. After a simulation, the battery designer is able to see how well the vehicle would be able to follow the driving cycle using the tested cells. Although the GUI is developed for an automotive application, it could be extended to other applications as well. The GUI has been designed to be easily used by non-simulation experts (i.e. battery designers or electrochemists) and it is fully automated, only requiring the user to supply the location of raw test data.
... It should be noted that battery degradation has important implications on the efficiency of an EV powertrain and should be taken account into the optimization, Auger et al. (2014), Fotouhi et al. (2014). This paper focuses on dynamic optimisation strategies for PM of a battery-SCs powertrain by implementing MPC to minimize battery degradation. ...
This paper examines the application of Regularized Model Predictive Control (RMPC) for Power Management (PM) of Hybrid Energy Storage Systems (HESSs). To illustrate, we apply the idea to the PM problem of a battery-supercapacitors (SCs) powertrain to reduce battery degradation in Electric Vehicles (EVs). While the application of Quadratic MPC (QMPC) in PM of HESS is not new, the idea to examine RMPC here is motivated by its capabilities to prioritize actuator actions and e ciently allocate control e ort, as advocated by recent works in the control and MPC literature. Thorough simulations have been run over standard urban test drive cycles. It is found out that QMPC and RMPC, compared to rule-based PM strategies, could reduce the battery degradation over 70%. It is also shown that RMPC can slightly outperform QMPC in reducing battery degradation. Moreover, RMPC, compared to QMPC, could potentially extend the range of that SCs can be used, thus exploiting the degree of freedom of the powertrain to a larger extent. We also make some discussions on the feasibility issues and tuning challenges that RMPC faces, among others.
... EV range forecasting relies on the application of suitable modelling techniques. There are a variety of techniques, typically operating at different levels of fidelity and employing different modelling philosophies [46]. The battery model, as a part of the whole vehicle model, plays a significant role in the EV range calculation. ...
Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.
... Note that battery degradation has important implications on the efficiency of an EV powertrain and should be taken account into the optimization, Auger et al. (2014), Fotouhi et al. (2014). This paper focuses on dynamic optimisation strategies for PM of a battery-SCs powertrain by implementing MPC to minimize battery degradation. ...
This paper examines the application of Regularized Model Predictive Control (RMPC) for Power Management (PM) of Hybrid Energy Storage Systems (HESSs). To illustrate, we apply the idea to the PM problem of a battery-supercapacitors (SCs) powertrain to reduce battery degradation in Electric Vehicles (EVs). While the application of Quadratic MPC (QMPC) in PM of HESS is not new, the idea to examine RMPC here is motivated by its capabilities to prioritize actuator actions and efficiently allocate control effort, as advocated by recent works in the control and MPC literature. Thorough simulations have been run over standard urban test drive cycles. It is found out that QMPC and RMPC, compared to rule-based PM strategies, could reduce the battery degradation over 70%. It is also shown that RMPC can slightly outperform QMPC in reducing battery degradation. Moreover, RMPC, compared to QMPC, could potentially extend the range of that SCs can be used, thus exploiting the degree of freedom of the powertrain to a larger extent. We also make some discussions on the feasibility issues and tuning challenges that RMPC faces, among others.
