Electric Car Route Planning With Charging and Driver Factors
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Solution Overview
Problem
Current navigation route planning systems for electric cars do not adequately consider environmental, car, and driver factors, leading to suboptimal route decisions that may impact efficiency and safety.
Innovation Solution
A system that integrates environmental, car, and driver factors into navigation route planning using a computer system connected to the internet, utilizing sensors and data analytics to optimize routes based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation systems are used for electric cars, then the system complexity is low, but the route planning does not account for environmental, car, and driver factors leading to suboptimal efficiency and safety
Solution Approach 1:
The patent combines multiple data sources including environmental conditions, vehicle state sensors, driver behavior data, and charging infrastructure information into a unified navigation system. This integration allows the system to optimize routes by considering all relevant factors simultaneously, improving route planning efficiency while managing complexity through systematic data fusion
Solution Approach 2:
The navigation system is designed to perform multiple functions: traditional route guidance, energy consumption optimization, charging station identification, driver behavior adaptation, and real-time route adjustment. This multi-functionality allows a single system to address various aspects of electric car navigation, improving overall productivity without requiring separate specialized systems
2Measurement precision
If real-time data collection from multiple sensors is implemented, then route optimization accuracy is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the navigation system into distinct functional modules: environmental data acquisition, vehicle state monitoring, driver behavior analysis, charging infrastructure integration, and route optimization algorithms. Each module processes specific types of data independently before integration, reducing overall data processing complexity while maintaining high route optimization accuracy through specialized processing pipelines
Solution Approach 2:
The system employs intermediary processing layers that aggregate and pre-process data from multiple sensors before feeding it to the route optimization algorithms. These intermediaries filter, validate, and standardize data from diverse sources, reducing the computational burden on the core optimization engine while preserving measurement precision
3Reliability
If the system integrates multiple factors for route planning, then the safety and efficiency of navigation are improved, but the ease of operation decreases due to more complex system interactions
Solution Approach 1:
The navigation system automatically collects and processes data from vehicle sensors, environmental sources, and charging infrastructure without requiring manual user input. The system autonomously optimizes routes based on real-time conditions, adjusts for driver behavior patterns, and manages charging stops, thereby improving navigation safety and efficiency while maintaining ease of operation through automation
Data Source
AI summary
A method for planning an optimal geographical route by an electric car from origin to destination locations, using an objective of minimizing estimated time, minimizing distance to travel, or minimizing charging price, while arriving at all user-defined multiple locations along the route. The method uses time or trip sensitive data and non-time/trip sensitive data, the data include environmental factors (such as roads map, charging stations location, and weather), car related factors, and driver or user factors. The optimal route planning may be based on Bayesian network or optimization, such as by using a Travelling Salesman Problem (TSP), a Linear Programming (LP) problem, using unsupervised clustering such as K-Means clustering or algorithm, or using casual inference methodology or process that is based on Bayesian inference or Frequentist statistical inference. Any data item used may be obtained from a database, a server, or from a local or remote sensor.