Query: electric vehicle optics

title: Alternative fuelled hybrid electric vehicle (AF HEV) with hydrogen enriched internal combustion engine
abstract: In this paper, hybrid electric vehicle (HEV) that powered by hydrogen (H2) enriched internal combustion (ICE) engine was studied both simulation and experimental. As an alternative fuel, the usage of H2 as additional fuel on an ICE that used for HEV, investigated in this partially simulational study for the first time. The study was consisted two parts. In the first part, the effects of 10% H2 enrichment on performance and emissions were experimented in a 1.8 L Ford Spark Ignition (SI) engine. Then, AVL Boost tool was used for simulation and validation under this ICE's properties and has compared with experimental results. After the simulations and experiments results were found consistent each other, AVL Cruise was used for hybridization of H2 enriched ICE, for the second part. Combustion, performance and emission values are given comparatively with selected driving cycle of model vehicle were realized with simulation tools. Hybrid mode's ICE becomes more environmentally friendly due to H2 enrichment with increasing performance. The model HEV has delivered promising results on performance and emission values and this improvement added to literature with this study. Results showed that, enrichment of H2 is presented 3.56% improvement in ICE torque and 2.37% for ICE power. Cumulative fuel consumption and emission pollution decreased by 12.6% and 14 33% respectively, for hybrid mode.

title: Retail and Wholesale Electricity Pricing Considering Electric Vehicle Mobility
abstract: We consider the operation of multiple charging network operators (CNOs) each serving a population of electric vehicle (EV) drivers who subscribe to them for charging services. The CNOs set retail prices for the charging stations they operate, which effectively control the electricity demand and queues formed at their charging stations. The stations' electricity demand also ties the CNOs' retail optimization problem to their position in the wholesale market of electricity, creating a coupling between the electric transportation systems and the bulk power grid, which is managed by an independent system operator (ISO) In this paper, we provide retail pricing mechanisms and iterative wholesale market clearing algorithms that together maximize social welfare in this coupled system. Specifically, our algorithm would enable the CNO to reduce its electricity costs and the customers' travel delays (including charging station queueing times) and the ISO to reduce societal electricity costs by tapping into the EVs' flexibility as mobile electric loads. Our theoretical analysis relies on the application of congestion pricing techniques to a capacitated user equilibrium problem on an energy and time expanded transportation network model. This expanded network allows us to track the travel costs of individual EVs, including their limited battery charge, the cost of waiting in queues at public charging stations, as well as the temporally variant nature of travel times.

title: Lateral stability enhancement based on a novel sliding mode prediction control for a four wheel independently actuated electric vehicle
abstract: This study presents a novel direct yaw moment control scheme to improve the lateral stability for a four wheel independently actuated electric vehicle. The proposed scheme adopts a cascaded structure that consists in an upper and a lower controller. A novel sliding mode prediction controller is proposed and used in the upper controller for deriving the desired additional yaw moment for lateral stability enhancement. By using the historic and current sliding mode information for the futuristic sliding mode dynamic prediction, the synthesised control law exhibits better robustness to matched/unmatched uncertainties and significantly mitigates the chattering phenomenon. An optimisation based torque allocation algorithm is presented in the lower controller to optimally appropriate the driving torques to each in wheel motor based on selected criteria. The effectiveness of the proposed method is verified through simulation and hardware in the loop tests, which yields better performance compared to the rule based method.

title: Incentive Based Distributed Scheduling of Electric Vehicle Charging Under Uncertainty
abstract: We consider a power system with <inline formula><tex math notation="LaTeX"$N$/tex math>/inline formula> charging stations, each of which has local renewable generation and electric vehicle (EV) arrivals with deadline constraints for their requests. To reduce the adverse impact brought by uncoordinated charging, the utility company requires to adopt advanced load management to control the load supplied to stations but without collecting many details. The major challenge is to incentivize them to coordinate charging for its system wide optimization while meeting all charging requests, regarding inherent uncertainties. We model a game that aims to minimize the total electricity cost at the utility company meanwhile maximize the payoff of each station. We combine <inline formula><tex math notation="LaTeX"\epsilon$/tex math>/inline formula> Nash equilibrium and Lyapunov optimization to design a low complexity distributed scheduling scheme of EV charging. It achieves at most <inline formula><tex math notation="LaTeX"$O(1/V)/tex math>/inline formula> more than the optimal cost where <inline formula><tex math notation="LaTeX"$V$/tex math>/inline formula> is a controllable parameter that balances between the cost and average fulfillment ratio of charging requests. The payoff of each station is around <inline formula><tex math notation="LaTeX"$O(1/V)/tex math>/inline formula> less than the optimal payoff if using a small <inline formula><tex math notation="LaTeX"$V$/tex math>/inline formula> Simulation results under real world traces show that, compared with a state of the art scheduling algorithm, our algorithm reduces the peak to average ratio and charging cost, respectively, by 94.32% and 44.21% and increases the payoff by 52.27% averagely.

title: Double layer home energy supervision strategies based on demand response and plug in electric vehicle control for flattening power load curves in a smart grid
abstract: The supervision of energy consumption applied to a smart home, seems to be one of the main challenges for the future smart grid. The purpose of this study was to investigate a double layer supervision strategy in order to ensure a flattened power load curve in a residential application. This control is mainly divided into two levels. The first consists of a demand response algorithm, which plays an important role in scheduling the operation of home appliances by moving the shiftable appliances from peak hours, when electricity prices are high, to off peak hours when prices are low and thus contributes to improving the daily load profile. The first strategy was coupled with the emergence of Plug in Electric Vehicle (PEV) which is a new electrical load that must be considered in a smart home. The second consists of a PEV power management, which aims to ensure the bi directional power flow from the smart home to PEVs (H2V) and from the Vehicle to Home (V2H) This procedure results in monitoring the power of each PEV connected to the home to determine its power reference and thereby, enhance again the power load curve. Simulation results highlight the performance of the two layer home energy supervision strategy to achieve the smoothness for the daily power demand curve.

title: Efficient and privacy preserving supplier matching for electric vehicle charging
abstract: Vehicle (EV) charging takes longer time and happens more frequently compared to refueling of fossil based vehicles. This requires in advance scheduling on charging stations depending on the route of the demander EVs for efficient resource allocation. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where any two EVs can charge each other through a charging cable. In such cases, the matching of these EVs is typically done in a centralized manner which exposes private information to third parties which do the matching. To address this issue, in this paper, we propose an efficient and privacy preserving distributed matching of demander EVs with charge suppliers (i.e. public/private stations, V2V chargers) using bichromatic mutual nearest neighbor (BMNN) assignments. To this end, we use partially homomorphic encryption based BMNN computation through local communication (e.g. DSRC or LTE direct) between users while hiding their locations. The proposed matching algorithm provides not only a satisfactory assignment for all parties but also achieves an efficient matching in dynamic environments where new demanders and suppliers show up and some leave. The simulation results indicate that the proposed matching of suppliers and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users. Moreover, due to the nature of its design, it provides a more efficient matching process for dynamic environments compared to standard stable matching algorithm, reducing the average waiting time for users until matching.

title: Rating a Stationary Energy Storage System Within a Fast Electric Vehicle Charging Station Considering User Waiting Times
abstract: The use of stationary energy storage at the fast electric vehicle (EV) charging stations can buffer the energy between the electricity grid and EVs, thereby reducing the maximum required grid connection power and potentially mitigating the need for grid infrastructure upgrade. In this paper, a method is presented that sizes the stationary energy storage based on an acceptable average waiting time of drivers arriving at a fast charging station. The novelty of this paper is the focus on the relationship between the size of stationary energy store and the user waiting time. This relationship is often ignored; however, it is critical to obtain the optimum capacity of stationary energy store. An example charging station location is chosen where there are currently eight chargers capable of 120 kW charging and a 500 kW grid connection. It is demonstrated that the method can be used at this location to design a charging station with stationary energy storage to support future 400 kW charging without upgrading the current grid connection infrastructure. With future charging, using a stationary energy storage with a capacity of 1000 kWh reduces the maximum grid power from 1800 to 500 kW.

title: Model Predictive Iterative Learning Control for Energy Management of Plug In Hybrid Electric Vehicle
abstract: A novel optimal energy management strategy (EMS) for plug in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch wise iterative learning control (ILC) and time wise model predictive control (MPC) referred to as 2D MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period to period improvements. Its performances will approach dynamic programming (DP) based method after a learning process with satisfying real time processing capacity. The results in real world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV.

title: A method to improve an electric vehicle's range: Efficient Cruise Control
abstract: This article proposes a method to improve the range of an electric vehicle (EV) by controlling its speed depending on the upcoming traffic signal status. A conventional Adaptive Cruise Control (ACC) controls an EV's speed such that there is a certain distance between the EV and the preceding vehicle. However, if the preceding vehicle drives at a non optimum speed given the upcoming traffic signal status, as it nears the upcoming traffic signal the preceding vehicle will have to dissipate the unnecessarily gained kinetic energy by braking. If the EV blindly follows such a preceding vehicle using a conventional ACC, the EV will also have to dissipate a part of its kinetic energy by braking. In this work, this problem is addressed by proposing an Efficient Cruise Control (ECC) In the proposed ECC, if the EV is within a certain distance of the upcoming traffic signal, its speed reference is calculated based on the traffic signal status, while maintaining a minimum safe inter vehicular distance from the preceding vehicle. The ECC is designed using Model Predictive Control theory and it is studied in a simulation environment using a Tesla S vehicle model. The simulation results show that the proposed controller improves the EV's energy efficiency and therefore range, where the latter is an important bottleneck in widespread adoption of EVs.

title: Acceptability, energy consumption, and costs of electric vehicle for ride hailing drivers in Beijing
abstract: The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride hailing popularity in mega cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride hailing electric vehicle fleets. Average daily travel distance and travel time for ride hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h) Assuming slow level 1 (1.8 KW) or moderate level 2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride hailing drivers and electrify up to 20% or 55% of total distance driven by the ride hailing fleet. With level 2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level 2 charging infrastructure are needed for large scale electrification of ride hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride hailing vehicle travel, our study outlines the importance of individual level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.