EV Adaptive Charge Override via External Server
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Solution Overview
Problem
Existing electrified vehicles lack adaptive battery charging settings that can override on-board programming, limiting the ability to optimize charging based on user behavior, historical data, and external factors like calendar events, which can lead to inefficient charging and frequent software updates impacting vehicle hardware.
Innovation Solution
An electrified vehicle system that includes a transceiver for wireless communication with an external server to receive adaptive charging settings, an interface module to override the vehicle charger, and a controller to manage both manually entered and adaptive settings, allowing for dynamic charging optimization using machine learning and AI, while minimizing the impact on vehicle hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive charging settings are implemented using external server communication, then charging optimization based on user behavior and historical data is improved, but device complexity and software update requirements increase
Solution Approach 1:
An external server acts as an intermediary between the vehicle's charging system and the cloud-based adaptive charging algorithms. The server receives vehicle data, processes charging optimization using machine learning models, and transmits updated charging settings to the vehicle. This mediator approach allows complex adaptive charging functionality without burdening the vehicle's onboard computing resources and software architecture.
2Ease of operation
If on-board programming is used for charging control, then ease of operation is improved, but adaptability to changing user behavior and external factors deteriorates
Solution Approach 1:
The charging control system transitions from static on-board programming to dynamic adaptive programming. The system continuously receives updated charging settings from the external server based on real-time analysis of user behavior patterns, historical charging data, and external factors such as electricity rates and calendar events. This dynamic approach allows the charging parameters to automatically adapt while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The system implements a feedback loop where vehicle charging data and usage patterns are transmitted to the external server, which processes this information and generates optimized charging settings. These settings are then fed back to the vehicle's charging control system, creating a continuous improvement cycle that adapts to changing user needs and external conditions while maintaining simple operation.
3Adaptability or versatility
If frequent software updates are implemented for adaptive charging features, then adaptability is improved, but reliability of vehicle hardware deteriorates due to update impacts
Solution Approach 1:
The complex adaptive charging algorithms and machine learning models are extracted from the vehicle's onboard system and relocated to the external server infrastructure. This extraction allows the vehicle to receive updated charging strategies without requiring frequent software updates to the vehicle's critical hardware and control systems, thereby maintaining hardware reliability while still providing adaptability through remote parameter updates.
Data Source
AI summary
A vehicle includes a traction battery configured to be charged from an external power source via a vehicle charger, a transceiver configured to wirelessly transmit vehicle data to an external server and to wirelessly receive adaptive traction battery charging settings from the external server, an interface module configured to selectively override control of the vehicle charger based on the adaptive traction battery charging settings wirelessly received via the transceiver from the external server, a human-machine interface (HMI), and a controller in communication with a persistent on-board vehicle memory, the vehicle charger, the override module, and the HMI, the controller configured to receive manually entered traction battery charging settings via the HMI, store the manually entered traction battery charging settings in the persistent on-board vehicle memory, and selectively control the vehicle charger using the manually entered traction battery charging settings in response to the adaptive traction battery charging settings being unavailable.