Concerns over the impact of the increasing use of fossil fuels on the environment have led to multiple initiatives to provide electric vehicles (EVs) for many modes of automotive transportation. Critical considerations include the design and implementation of EV automotive drive trains, battery technology suitable for powering EVs, technology for charging such batteries, and the impact of widespread use of EV's on power generation and distribution of power necessary to meet the demand that increased use of EV's will present. Another important consideration is the management of EV traffic flow on roadways and highways to ensure acceptable performance of automotive transportation with increased EV usage.
It has been estimated that the worldwide use of EV's reached around 700,000 in 2015 with 275,000 EV's in the United States. Commercial models include the Nissan LEAF and Chevrolet Volt. An important goal of EV programs is a reduction of air pollution caused by fossil fuel transportation means. EV offers several advantages, including lower CO2 emissions, low petroleum usage and lower operating noise.
The price paid for these advantages is decreased automotive operating range. It has been reported that pure electric vehicles powered only by battery have a range of up to about 100 miles. Plug-in hybrid electric vehicles have a battery range of about 10 miles, but revert to a standard internal combustion engine when that range is reached. Extended range electric vehicles have a battery range of about 50 miles and include internal combustion engine driven generator to increase to increase that range. See, e.g., T. Denton, “Electric and Hybrid Vehicles,” Routledge, 2016.
This limited driving range is a particular concern sometimes referred to as “range anxiety.” Drivers are concerned that they may not have enough stored energy to reach their destination or even to carry out every day routine driving to and from multiple locations.
Lithium-ion technology is currently the preferred battery technology for EV's. Lithium-ion batteries have been the battery of choice for many consumer electronic products, including mobile cell phones, laptop computers and tablets. The automotive application is particularly challenging requiring system control technology that ensures safe operation and mechanical design to ensure proper operation in the hostile automotive environment. Thermal design considerations are important to keep operation within specified temperature ranges. See, id., and, e.g., T Horiba, “Lithium-Ion Battery Systems,” Proceeding of the IEEE, June 2014, pp. 939-950.
Clearly, extending the range of EV's requires systems and methods for recharging or replacing of the vehicle batteries. Multiple considerations are involved and various alternatives exist for such charging. Most EV's are charged at home. Businesses may also offer charging stations for employees and/or visitors. Public charging stations along road ways are also being considered and in some cases implemented. AC charging is the standard charging method. Chargers may be based on single phase AC (alternating current), three phase AC or higher power DC (direct current) technology. Charging time for a 100-km range for lower power single phase AC systems has been reported at 6-8 hours. More powerful three phase AC systems may provide comparable charging in 20-30 minutes. High power DC systems may provide such charging in as little as 10 minutes. Multiple charging cable configurations have been standardized by the IEC (International Electrotechnical Commission). See, e.g., T. Denton, “Electric and Hybrid Vehicles,” Routledge, 2016, pp. 107-110.
Another potential technology for EV battery charging is Wireless Power Transfer (WPT). Possible implementations include stationary WPT where the vehicle is parked and dynamic WPT for use along roadways when the vehicle is in motion. WPT relies upon magnetic induction and requires no cabling between the vehicle and the WPT charging mechanism. Charging is accomplished from a fixed or roadside primary coil to a secondary coil of a stationary or moving vehicle. See Id. pp. 116-122; see also, N. Shinohara, “Wireless Power Transfer via Radio Waves,” John Wiley and Sons, 2014; see also V. Prasanth, et. al. “Green Energy based Inductive Self-Healing Highways of the Future,” IEEE Transportation Electrification Conference and Expo (ITEC), 2016.
An important new development in automotive vehicle transportation is that of autonomous or driverless cars. Such driverless or self-driving cars are capable of sensing their environment and navigating with limited and sometimes no human driver control. Driverless cars make use of various technologies for sensing roadways, obstacles, traffic control signals, signage and other vehicles that may share a roadway being traveled. While such driverless vehicles are just now being introduced, predictions are that this mode of transportation will grow in the near future. EV driverless vehicles may require special considerations when choosing routes of travel to avoid more challenging roadways or congestion that may present difficult or more challenging sensory issues for the vehicle. Appropriate routes of travel for vehicles with drivers may not be appropriate for driverless vehicles. At the same time, the systems and methods of the present invention are applicable to such driverless vehicles with appropriate databases and navigation programs that account for the safety requirements of such vehicles.
The critical needs for improved systems and methods for managing charging of electric vehicles has led to various technological suggestions for allocation and placement of charging stations, integration with navigation systems, the use of Wireless Power Transfer (WPT), and the use of mathematical modeling of system design and operation. In addition to the above citations, exemplary prior art systems and methods attempting to address certain aspects these needs include the following:                1. Fouad Baouche, et. al., “Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network,” IEEE Intelligent Transportation Systems Magazine, Fall 2014. This paper addresses the problem of optimizing the location of electric vehicle charging stations in a particular area such as the Lyon, France metropolitan area. The model purportedly includes trip OD mileage, vehicle energy consumption, and routing tools with elevation information parameters as inputs to an integer linear optimization program for the location of charging stations.        2. Jyun-Yan Yang, et. al., “Electric Vehicle Navigation System Based on Power Consumption,” IEEE Transactions on Vehicular Technology, 2015. This paper purportedly describes an electric vehicle navigation system (EVNS) whose architecture is based on autonomic computing and hierarchical architecture proposed to improve the growing complexity of navigation systems. The electric vehicle sends the traffic information center (TIC) aggregated traffic information during a trip or a navigation request at the start of its travel. The TIC processes the traffic information and plans routes. The electric vehicle receives a suggested route that guides the driver. Traffic information, including state of charge (SOC), traffic flow, average speed, travel time, and vehicle route, is provided by the navigation systems.        3. Sepideh Pourazarm, et. al., “Optimal Routing of Electric Vehicles in Networks with Charging Nodes: A Dynamic Programming Approach,” IEEE Electronic Vehicle Conference, 2014. This paper purportedly seeks to minimize the total elapsed time for vehicles to reach their destinations considering both traveling and recharging times at nodes using a dynamic programming approach when the vehicles do not have adequate energy for the entire journey.        4. Venugopal Prasanth, et. al. “Green Energy based Inductive Self-Healing Highways of the Future,” IEEE Transportation Electrification Conference and Expo (ITEC), 2016. This paper investigates the use of Inductive Power Transfer (IPT) for recharging electric vehicles. The use of solar and wind energy to power such systems is discussed.        5. F. Tianheng, et. al., “A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview,” IEEE Transactions on Vehicular Technology, May 2015, pp. 1691-1700. This paper purportedly presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. A neural network is used to predict the energy demand of the vehicle and an adaptive equivalent consumption minimization strategy is used to optimally distribute energy between the engine and the motor to achieve an optimal torque split.        6. U.S. Pat. No. 6,487,477, J. T Woestman, et. al. “Strategy to use an on-board navigation system for electric and hybrid electric vehicle energy management,” Assignee—Ford Global Technologies, Inc., Nov. 26, 2002. This patent purportedly integrates an on-board navigation system to provide energy management for an electric vehicle (EV) and a hybrid electric vehicle (HEV). The vehicle location is continuously monitored, expectations of driver demand are determined, and vehicle accommodations are made. The system can be configured to include location data on road patterns, geography with date and time, altitude changes, speed limits, driving patterns of a vehicle driver, and weather. The vehicle accommodations can purportedly be configured to use discrete control laws, fuzzy logic, or neural networks.        7. U.S. Pat. No. 9,103,686, B. Pettersson, “Method and guidance-unit for guiding battery-operated transportation means to reconditioning stations,” Assignee—LEICA GEOSYSTEMS AG, Aug. 11, 2015. This patent purportedly describes methods and apparatus for guiding a mobile transportation means of a set of transportation means to a selected reconditioning station of a set of reconditioning stations, comprising determining a position of the battery, determining a condition of the battery, forecasting a consumption characteristic of the transportation means, evaluating an achievable range of mobility of the transportation means, assigning the selected reconditioning station of the set of reconditioning stations, which is located within the range of mobility of the transportation means along a path to a desired target and guiding the transportation means to the selected reconditioning station. An optimization of the assignment and/or the path is executed by a search algorithm for assigning the set of transportation means to the set of reconditioning stations and batteries, based on actual and/or forecasted information about multiple entities of the sets of transportation means, stations and batteries as well as their conditions. In addition to the “search engine,” the '686 Patent states: “For the optimization, certain conditions and aspects of the influencing parameters can be comprised by a usage of abstracted mathematical models of the underlying physical or logical background, which can be comprised in lookup tables, statistical, historical or forecasted data. Those models can be overall, global models of the behavior of the whole set of resources as well as models for subsystems such as e.g. a single battery or engine of a transportation means. For the modeling, a plurality of methods are known to a skilled person, as e.g. physical models, differential equations, Fuzzy-Logic models, logical models, statistics models, forecasting models, etc.” See '686 Patent, 4:47-58.        8. U.S. Pat. No. 9,199,548, R. A. Hyde, et. al., “Communication and control regarding electricity provider for wireless electric vehicle electrical energy transfer,” Assignee—Elwha LLC, Dec. 1, 2015. This patent purportedly describes a computationally implemented system and method that is designed to electronically assess electricity provider detail information associated with providing electrical energy to one or more electric vehicle wireless electrical energy chargers configured for wirelessly charging one or more electric vehicles with electrical energy from the one or more electric vehicle wireless electrical energy chargers to the one or more electric vehicles, the one or more electric vehicles including one or more electric motors to provide motive force for directionally propelling the one or more electric vehicles.        9. U.S. Pat. No. 9,333,873, K. Mori, et. al., “Electric motor vehicle management system,” Assignee—Mitsubishi Electric Corporation, May 10, 2016. This patent purportedly describes an electric motor vehicle management system with a portable terminal that is owned by a user and is located in an electric motor vehicle and transmits vehicle condition information of the electric motor vehicle including position information of the portable terminal that has been detected by a position detector of the portable terminal to a vehicle condition receiver of an energy management system (EMS) installed in a customer. A battery charging-and-discharging plan creating unit of the EMS creates a charging and discharging plan for a battery through the use of the vehicle condition information of the electric motor vehicle. A charging and discharging device performs at least one of charging and discharging of the battery of the electric motor vehicle in accordance with the battery charging-and-discharging plan for the battery.        10. U.S. Pat. No. 9,335,179, A. Penilla, et. al., “Systems for providing electric vehicles data to enable access to charge stations,” May 10, 2016. This patent purportedly describes a cloud system for interfacing with an electric vehicle, wherein the electric vehicle has a battery that is rechargeable. The electric vehicle further has an on-board computer and a wireless communication system that is interfaced with the on-board computer. The on-board computer is configured to monitor a charge level of the battery and display the level on a display screen of the electric vehicle. The electric vehicle has global positioning system (GPS) logic for identifying geo-location of the electric vehicle. The cloud system is configured to manage a user account for the electric vehicle and store data associated with the user account. The data includes information regarding charge parameters received from the user. The cloud system is thus configured to interface with on-board computer of the electric vehicle via the wireless communication system. The cloud system is configured to access information regarding charging stations that are available and send to the electric vehicle one or more options of charge stations in response to processing received geo-location of the electric vehicle and received data regarding the charge level of the battery of the electric vehicle and the charge parameters of the user. The charge stations presented as options are located along a driving path that is reachable before the charge level of the electric vehicle reaches an empty state.Additional prior art directed to technologies useful in some embodiments of the present invention includes:        11. Chen, C. H., “Fuzzy Logic and Neural Network Handbook,” McGraw-Hill, New York, 1996.        12. Cox, C., “The Fuzzy Systems Handbook,” Academic Press Inc., 1994.All of the above are incorporated herein by reference.        
The above cited art demonstrates the industry recognition of the importance of deriving optimal routes of travel for Electric Vehicles (EVs) with the goal of improving EV operational usefulness through determination of preferred routes of travel wherein such preferred routes include intermediate charging or replacement of EV batteries as required. What is needed and is missing in the prior art are specific, more efficient routing algorithms that may be employed in real-time without excessive and complex computation and that consider multiple factors such as battery charging-replacement station locations, required time of travel, roadway conditions, traffic congestion, including congestion for charging stations and minimization of required energy usage to travel between EV changing positions and destination locations.
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