Battery State-of-Charge Estimation via Server AI During EV Charging
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Solution Overview
Problem
The frequent wake-up/sleep operations of on-board electronic components in electrified vehicles to estimate the state of charge of electric batteries lead to significant stress and premature aging, particularly due to repetitive requests from users and energy management systems.
Innovation Solution
A method that uses a computer server with an artificial intelligence module to estimate the state of charge of an electric battery through data communication, employing calculation factors determined from session data, reducing the need for frequent wake-ups by providing an estimated state of charge during charging sessions, and correcting it through closed-loop adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If frequent wake-up/sleep operations are performed to retrieve state of charge information, then information availability is improved, but component durability deteriorates
Solution Approach 1:
The server performs preliminary calculations of the estimated state of charge based on charging parameters and battery characteristics before the vehicle actually charges. This pre-computed information is made available to users in advance, eliminating the need for frequent wake-up operations during charging to retrieve state of charge data.
Solution Approach 2:
A server acts as an intermediary between the vehicle's battery management system and the user's smartphone application. The server handles all calculations and information processing, allowing the vehicle's electronic components to remain in sleep mode while still providing real-time state of charge information to users through the communication network.
2Ease of operation
If multiple on-board components wake up frequently to check battery charge status, then user information access is improved, but energy consumption increases
Solution Approach 1:
The server serves as an intermediary that handles all computational tasks and communicates with both the vehicle and the user's smartphone. This allows the vehicle's electronic components to remain powered down while users can still access real-time battery information through the server, significantly reducing energy consumption.
Solution Approach 2:
The server maintains a copy of the battery's state of charge information by receiving charging parameters from the vehicle and performing calculations independently. This copied information is then provided to users, eliminating the need for the vehicle's components to wake up and process requests, thus reducing energy consumption.
3Measurement precision
If real-time state of charge monitoring is implemented through frequent wake-ups, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The server acts as an intermediary that performs all complex calculations for estimating state of charge based on charging parameters and battery characteristics. This transfers the computational complexity from the vehicle's on-board components to the server, maintaining measurement accuracy while reducing the complexity of the vehicle's electronic systems.
Solution Approach 2:
The patent replaces the mechanical/electronic wake-up mechanism with a computational system on the server. Instead of physically waking up components to calculate state of charge, the server performs all calculations using received charging data, substituting a simpler communication-based approach for a complex wake-up/sleep control mechanism.
Data Source
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AI summary
The method comprises, at the start of a charging session, a first data communication from the electrified vehicle (VE) to a computer server (NS1, SRC), this data communication comprising charging session data (DSC) including vehicle identification data (VE/USER), an effective state of charge of the electric battery (BAT), a state of health of the electric battery (BAT) and a charging mode, and, during the charging session, estimating and providing by the computer server an estimated state of charge (SOCE), the estimated state of charge being determined from the charging session data, an energy capacity of the electric battery and an estimation calculation factor, the estimation calculation factor being determined by selection in a database of estimation calculation factors (DBα) accessible to the computer server and from the charging session data.