EV Charging Plan Optimization Using User and Energy Information
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Solution Overview
Problem
Existing charging methods for electric and hybrid vehicles lack the ability to optimize the charging process efficiently without complex infrastructure measures, failing to consider user preferences and energy information effectively.
Innovation Solution
A method that utilizes user and energy information within the vehicle to autonomously optimize the charging process, using standardized communication protocols to determine a charging plan that minimizes or maximizes cost, green electricity usage, power loss, and carbon dioxide emissions, without requiring external communication infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the charging process is started immediately with connection to the charging arrangement, then the charging speed is improved, but the optimization of charging costs and energy efficiency deteriorates
Solution Approach 1:
The control device receives energy information from the charging arrangement before the charging process starts, performs optimization calculations to determine the optimal charging plan, and then executes the charging process. This preliminary action allows the system to identify the most cost-effective and energy-efficient charging windows, balancing immediate charging needs with long-term optimization goals.
Solution Approach 2:
The charging plan is dynamically optimized based on received energy information that may include time-varying electricity prices, available charging power, and renewable energy availability. The control device adjusts the charging schedule in real-time to exploit favorable conditions, allowing the system to adapt between speed and cost optimization based on current energy market conditions.
2Adaptability or versatility
If complex infrastructure measures are implemented for charging optimization, then the charging optimization capability is improved, but the device complexity deteriorates
Solution Approach 1:
The motor vehicle's control device autonomously receives energy information from the charging arrangement, performs optimization calculations independently, and determines the charging plan without requiring external optimization infrastructure. This self-service approach enables sophisticated charging optimization while keeping the infrastructure simple, as the intelligence is distributed to the vehicle rather than centralized in the charging infrastructure.
Solution Approach 2:
The control device performs multiple functions: it manages the charging process, receives and processes energy information, optimizes charging plans based on multiple criteria (cost, speed, green energy), and controls the charging power. This multi-functionality consolidates optimization capabilities within the vehicle's existing control architecture, avoiding the need for separate dedicated optimization infrastructure.
3Ease of operation
If the charging process is limited only by battery capacity or charging arrangement properties, then the simplicity of control is maintained, but the optimization potential is lost
Solution Approach 1:
The system receives energy information parameters from the charging arrangement (such as time-varying electricity prices, available power levels, and renewable energy availability) and uses these parameters to dynamically adjust the charging plan. This allows the system to optimize charging efficiency by changing charging parameters based on external energy conditions while maintaining relatively simple control through standardized information exchange protocols.
Data Source
AI summary
A method for operating a motor vehicle for a charging process of a traction battery of the motor vehicle. In a control device assigned to the charging device and installed inside the motor vehicle, the user information describing at least one user request with regard to the charging process is received, energy information related to the electrical energy provided by the charging arrangement is received from the charging arrangement via a communication connection to the charging arrangement, a charging period that is likely to be available for the charging process is ascertained, a charging plan for the charging period that is optimized with respect to at least one optimization goal which is ascertained from the user information is ascertained using time-resolved optimization information of the energy information relating to the optimization goal, and the charging process is carried out according to the charging plan.