EV Charging Cost Optimization via Route Prediction
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Solution Overview
Problem
Electric and plug-in hybrid vehicle owners face challenges in determining cost-efficient charging plans due to varying energy costs at different locations and times, leading to inefficient charging practices.
Innovation Solution
A system and method that utilize an electronic control unit (ECU) to predict routes and determine charge planning data, including energy requirements and costs at multiple destinations, to optimize battery charging based on predicted routes and available energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users charge their vehicle at any location with available power source regardless of cost, then the reliability of having sufficient electrical energy is improved, but the charging cost increases
Solution Approach 1:
The system performs preliminary actions by predicting the vehicle's route and identifying charging opportunities in advance. It determines the minimum necessary charge at each location before the vehicle arrives, rather than charging reactively when needed. This allows the system to secure sufficient energy reliability while optimizing for lower costs by planning the charging strategy ahead of time based on predicted routes and location-specific energy costs.
2Loss of energy
If users attempt to reduce charging costs by selecting specific locations and times, then the charging cost decreases, but the complexity of managing charge planning increases
Solution Approach 1:
The system enables self-service by automatically performing route prediction, identifying charging locations, determining energy requirements, and calculating optimal charging amounts without user intervention. The ECU autonomously manages the entire charge planning process, using the vehicle's existing navigation and battery management systems to generate cost-effective charging strategies based on predicted routes and location-specific energy costs.
3Loss of energy
If the system creates a detailed charging plan for multiple destinations, then the charging cost efficiency is improved, but the computational complexity increases
Solution Approach 1:
The system applies segmentation by breaking down the multi-destination route into individual segments between consecutive destinations. For each segment, it calculates the specific energy requirement and identifies appropriate charging locations. This segmented approach allows the system to manage computational complexity by processing one segment at a time rather than solving the entire multi-destination problem simultaneously, while still achieving overall cost efficiency across the complete route.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces overall charging costs by strategically charging at less expensive locations, ensuring sufficient battery energy for destinations while minimizing cost, and allows for flexibility in case of detours or traffic delays.
Implementation Method 1
a battery having a state of charge (SOC) corresponding to a current amount of electrical energy stored by the battery
Data Source
AI summary
A system for cost-effective charge planning for a battery of a vehicle includes a battery having a SOC corresponding to an amount of energy stored by the battery. The system also includes an internal electric vehicle charger capable of receiving energy from a charging station and transferring the energy to the battery to increase the SOC. The system includes an electronic control unit (ECU) that can predict a route set including a first destination and a second destination and an amount of time spent at each. The ECU can determine charge planning data including an amount of energy required to reach the first and second destinations and a cost of energy at the first and second destinations. The ECU can determine how much to charge the battery at the first destination and at the second destination based on the predicted route set and the determined charge planning data.