EV Battery Charging Terminal Scheduling for Variable Electricity Cost
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Solution Overview
Problem
Existing methods for charging electric vehicle batteries at public stations fail to guarantee timely readiness and optimal cost efficiency due to unpredictable electricity pricing and power availability, requiring high computational power and complex predictions.
Innovation Solution
A method for charging electric vehicle batteries that iteratively selects time slots with minimal electricity cost, considering battery state and power availability, using a simplified computational approach to ensure timely readiness and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high computational power is used to predict battery charge progression and select optimal time slots, then charging timeliness and cost-optimization are improved, but implementation cost and device complexity increase significantly
Solution Approach 1:
The patent transforms the complex prediction problem into a simpler parameter-based selection process. Instead of using high computational power to predict battery charge progression, the system discretizes time into slots and power into slices, then selects optimal combinations based on predefined parameters (electricity cost, power availability, duration) stored in lookup tables. This parameter transformation approach maintains charging optimization while dramatically reducing computational requirements.
Solution Approach 2:
The system performs preliminary discretization of time slots and power slices before the actual charging decision-making process. By pre-defining and storing electricity cost data, power availability data, and duration data for various time slots and power slices in lookup tables, the system eliminates the need for complex real-time predictions. The charging terminal simply needs to query these pre-prepared tables and select the optimal combination, significantly simplifying the computational task.
2Measurement precision
If electricity cost varies by power level within the same time slot, then pricing accuracy is improved, but calculation complexity and data processing requirements increase
Solution Approach 1:
The patent segments both time and power dimensions into discrete units. Time is divided into slots (e.g., hourly or half-hourly intervals), and power is divided into slices (e.g., 0-7kW, 7-14kW, 14-21kW bands). This segmentation creates a two-dimensional grid of time slots × power slices, where each cell contains pre-calculated electricity cost, duration, and power availability data. This structured segmentation allows the system to handle variable pricing by power level without increasing calculation complexity, as all combinations are pre-computed and stored in lookup tables.
Solution Approach 2:
The system creates simplified copies of the complex electricity pricing structure in the form of lookup tables. Instead of performing complex calculations during operation, the system pre-computes and stores representative data for each time slot and power slice combination. These lookup tables serve as simplified copies that capture the essential pricing variations by power level while enabling fast, simple queries during actual charging operations.
3Loss of energy
If nearest off-peak time slots are selected for charging, then electricity cost is reduced, but charging completion before user departure cannot be guaranteed
Solution Approach 1:
The system incorporates feedback mechanisms that consider both electricity cost and charging duration. The lookup tables store not only electricity cost data but also duration data indicating how long each time slot-power slice combination will take to charge the battery. The charging terminal queries these tables and selects combinations that satisfy both constraints: minimizing electricity cost while ensuring the charging duration allows the battery to be fully charged before the user's scheduled departure time. This feedback-based selection ensures both cost optimization and reliability.
Data Source
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AI summary
The invention relates to a method for charging an accumulator battery (12) equipping a motor vehicle (10) that is electrically connected to a charging terminal (20), comprising a step of acquiring data discretised by time slots in the form of power stages each associated with a distinct electricity cost. According to the invention, the following additional steps are provided, which can be executed in a loop at successive time steps: - selecting a power stage of a time slot, which is associated with a minimum electricity cost; - deselecting this other power stage, if, at a preceding time step, another power stage of said time slot has been selected; - estimating a charge level that the accumulator battery will have once it is charged; - comparing the charge level with a target charge level; then - charging the accumulator battery if the charge level is greater than or equal to the target charge level; or, otherwise - executing said additional steps during a new time step.