EV Charging Controller Optimizing Energy Mix via Weather Forecasting
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Solution Overview
Problem
Existing systems for charging electrified vehicle battery packs rely solely on the electrical grid or environmental energy harvesting, without effectively utilizing weather forecasts and real-time conditions to optimize energy sourcing.
Innovation Solution
A system and method that uses a controller to predict energy harvesting potential based on weather forecasts and real-time conditions, adjusting the allocation of charging between energy harvesting devices and the grid to ensure timely and efficient battery pack charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the battery pack is charged solely from the electrical grid, then charging reliability is ensured, but energy cost and environmental impact increase
Solution Approach 1:
The system performs preliminary assessment of weather forecasts and energy harvesting potential before initiating charging operations. The controller evaluates predicted harvested energy amounts in advance and pre-determines the optimal charging strategy, combining grid charging with anticipated renewable energy generation to reduce energy costs while ensuring charging reliability.
Solution Approach 2:
The charging system dynamically adjusts the mix of grid energy and harvested renewable energy based on real-time weather conditions and forecast updates. The controller continuously monitors actual weather conditions against forecasts and modifies charging strategies to optimize energy cost while maintaining reliable charging, transitioning between grid-dependent and renewable-energy-dependent modes as conditions change.
2Loss of energy
If energy harvesting devices are used to charge the battery pack, then energy cost decreases, but charging time may increase under less-than-ideal weather conditions
Solution Approach 1:
The system evaluates weather forecasts in advance to predict the amount of harvestable energy before charging begins. Based on these predictions, the controller pre-determines whether to rely primarily on harvested energy, grid energy, or a combination, ensuring that charging time requirements are met while still maximizing renewable energy utilization when conditions are favorable.
Solution Approach 2:
The controller dynamically adjusts the charging strategy by continuously monitoring actual weather conditions and comparing them to forecasts. When weather conditions are favorable for energy harvesting, the system maximizes renewable energy usage; when conditions deteriorate, the system dynamically increases grid energy supplementation to maintain charging speed, thus optimizing both energy cost and charging time in real-time.
Solution Approach 3:
The system implements continuous feedback by monitoring actual weather conditions during charging operations and comparing them to forecasted conditions. Based on this feedback, the controller adjusts the charging strategy to optimize the balance between harvested energy and grid energy, ensuring charging time requirements are met while minimizing energy costs through intelligent renewable energy utilization.
3Productivity
If the system continuously monitors weather conditions and adjusts charging strategy, then energy optimization improves, but system complexity increases
Solution Approach 1:
The controller performs multiple functions within a single integrated system: it receives and processes weather forecasts, monitors actual weather conditions through sensors, predicts energy harvesting potential, determines optimal charging strategies, and executes charging control. This multi-functional approach achieves comprehensive energy optimization without requiring separate specialized systems for each function, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The system uses onboard weather sensors to autonomously monitor local weather conditions and combines this data with forecast information to self-determine optimal charging strategies. The controller independently evaluates energy harvesting potential and adjusts charging operations without requiring complex external decision-making systems, achieving energy optimization through self-service capabilities that minimize additional system complexity.
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
Enables battery pack charging without sole reliance on grid energy, optimizing energy use by leveraging environmental energy sources while minimizing charging time, even under less-than-ideal weather conditions.
Implementation Method 1
the at least one energy harvesting device includes at least one of a solar panel and a wind turbine
Implementation Method 2
the at least one energy harvesting device includes at least one of a solar panel and a wind turbine
Data Source
AI summary
A method according to an exemplary aspect of the present disclosure includes, among other things, charging a battery pack of an electrified vehicle using an amount of harvested energy from an energy harvesting device and an amount of energy from a grid. The amount of harvested energy is predicted based on at least a weather forecast.