Special Issue "Vehicle to Grid"

Abstract: A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.

Abstract: Battery peak power capability estimations play an important theoretical role for the proper use of the battery in electric vehicles. To address the failures in relaxation effects and real-time ability performance, neglecting the battery’s design limits and other issues of the traditional peak power capability calculation methods, a new approach based on the dynamic electrochemical-polarization (EP) battery model, taking into consideration constraints of current, voltage, state of charge (SoC) and power is proposed. A hardware-in-the-loop (HIL) system is built for validating the online model-based peak power capability estimation approach of batteries used in hybrid electric vehicles (HEVs) and a HIL test based on the Federal Urban Driving Schedules (FUDS) is used to verify and evaluate its real-time computation performance, reliability and robustness. The results show the proposed approach gives a more accurate estimate compared with the hybrid pulse power characterization (HPPC) method, avoiding over-charging or over-discharging and providing a powerful guarantee for the optimization of HEVs power systems. Furthermore, the HIL test provides valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms.

Abstract: This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operators’ planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

Abstract: In this paper, the performances of various lithium-ion chemistries for use in plug-in hybrid electric vehicles have been investigated and compared to several other rechargeable energy storage systems technologies such as lead-acid, nickel-metal hydride and electrical-double layer capacitors. The analysis has shown the beneficial properties of lithium-ion in the terms of energy density, power density and rate capabilities. Particularly, the nickel manganese cobalt oxide cathode stands out with the high energy density up to 160 Wh/kg, compared to 70–110, 90 and 71 Wh/kg for lithium iron phosphate cathode, lithium nickel cobalt aluminum cathode and, lithium titanate oxide anode battery cells, respectively. These values are considerably higher than the lead-acid (23–28 Wh/kg) and nickel-metal hydride (44–53 Wh/kg) battery technologies. The dynamic discharge performance test shows that the energy efficiency of the lithium-ion batteries is significantly higher than the lead-acid and nickel-metal hydride technologies. The efficiency varies between 86% and 98%, with the best values obtained by pouch battery cells, ahead of cylindrical and prismatic battery design concepts. Also the power capacity of lithium-ion technology is superior compared to other technologies. The power density is in the range of 300–2400 W/kg against 200–400 and 90–120 W/kg for lead-acid and nickel-metal hydride, respectively. However, considering the influence of energy efficiency, the power density is in the range of 100–1150 W/kg. Lithium-ion batteries optimized for high energy are at the lower end of this range and are challenged to meet the United States Advanced Battery Consortium, SuperLIB and Massachusetts Institute of Technology goals. Their association with electric-double layer capacitors, which have low energy density (4–6 Wh/kg) but outstanding power capabilities, could be very interesting. The study of the rate capability of the lithium-ion batteries has allowed for a new state of charge estimation, encompassing all essential performance parameters. The rate capabilities tests are reflected by Peukert constants, which are significantly lower for lithium-ion batteries than for nickel-metal hydride and lead-acid. Furthermore, rate capabilities during charging have been investigated. Lithium-ion batteries are able to store about 80% of the capacity at current rate 2I_t, with high power cells accepting over 90%. At higher charging rates of 5I_t or more, the internal resistance impedes charge acceptance by high energy cells. The lithium titanate anode, due to its high surface area (100 m²/g compared to 3 m²/g for the graphite based anode) performs much better in this respect. The behavior of lithium-ion batteries has been investigated at different conditions. The analysis has leaded us to a new lithium ion battery model. This model will be compared to existing battery models in future research contributions.

Abstract: On the basis of the shifting process of automated mechanical transmissions (AMTs) for traditional hybrid electric vehicles (HEVs), and by combining the features of electric machines with fast response speed, the dynamic model of the hybrid electric AMT vehicle powertrain is built up, the dynamic characteristics of each phase of shifting process are analyzed, and a control strategy in which torque and speed of the engine and electric machine are coordinatively controlled to achieve AMT shifting control for a plug-in hybrid electric vehicle (PHEV) without clutch is proposed. In the shifting process, the engine and electric machine are well controlled, and the shift jerk and power interruption and restoration time are reduced. Simulation and real car test results show that the proposed control strategy can more efficiently improve the shift quality for PHEVs equipped with AMTs.

Abstract: Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. On the other hand, EVs have the potential to support the grid under various conditions. This paper studies possible potential and applications of Vehicle to Grid (V2G) services, including active power services, which discharge the EV batteries, and power quality services, which do not engage the battery or require only small amounts of battery charge. The advantages and disadvantages of each service and the likelihood that a given service will be effective and beneficial for the grid in the future are discussed. Further, the infrastructure cost, duration, and value of V2G services are compared qualitatively.

Abstract: Recently the technology development and increasing amounts of investment in renewables has led to a growing interest towards design and optimization of green energy systems. In this context, advanced Computational Intelligence (CI) techniques can be applied by engineers to several technical problems in order to find out the best structure and to improve efficiency in energy recovery. This research promises to give new impulse to using innovative unconventional renewable sources and to develop the so-called Energy Harvesting Devices (EHDs). In this paper, the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from vehicles to grid is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

Abstract: Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid through an increase in electricity demand. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold were developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques were employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models were compared and the most accurate one was selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.

Abstract: State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO₄ battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.