Traction Battery Deep Discharge Prediction via External Computing
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Solution Overview
Problem
Existing methods for monitoring traction batteries in motor vehicles are not sufficiently reliable, particularly under varying operating conditions, as they rely on functional monitoring systems and wireless communication links, which may not always be reliable when a critical state of charge is reached, leading to potential irreversible battery damage and operational issues.
Innovation Solution
A method and device that utilize a battery monitoring system to determine current battery data, including state of charge and health, which are transmitted via a wireless or wired communications link to an external computer for predicting deep discharge states, allowing for timely warnings to be sent to the driver or other monitoring instances, even when the vehicle is parked or out of network range, ensuring accurate prediction and prevention of deep discharging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the monitoring system and wireless communication link are used to detect and alarm critical state of charge, then the battery can be monitored and alarms can be sent, but the reliability is insufficient when the vehicle is parked or out of network range
Solution Approach 1:
The system performs preliminary calculations of the expected deep discharge point in time using basic battery data (capacity, voltage, temperature, state of charge) before the actual deep discharge occurs. The external computer calculates this point in time based on historical data and environmental conditions, enabling advance warning without requiring continuous complex monitoring during parking or network outages.
Solution Approach 2:
The patent extracts only the essential basic data (battery capacity, voltage, temperature, state of charge) needed for deep discharge prediction and transmits this minimal dataset to the external computer. This extraction approach reduces the complexity of continuous monitoring while maintaining prediction reliability, as only critical parameters are continuously tracked and transmitted.
2Reliability
If continuous monitoring and communication are implemented, then real-time warnings can be provided, but energy consumption increases and reliability decreases when communication is unavailable
Solution Approach 1:
The system implements periodic transmission of basic battery data to the external computer instead of continuous monitoring. The external computer receives updated basic data at intervals and recalculates the expected deep discharge point in time periodically. This periodic action reduces energy consumption while maintaining reliable prediction capability.
Solution Approach 2:
The external computer autonomously calculates the expected deep discharge point in time and determines when to send warning signals without requiring continuous vehicle-network communication. The system serves itself by using stored basic data and environmental information to perform predictions independently, reducing dependency on continuous power and communication resources.
3Loss of time
If basic battery data are transmitted to external computer for prediction, then deep discharge can be predicted in advance, but data transmission requires reliable communication infrastructure
Solution Approach 1:
The system transmits basic battery data to the external computer in advance, before the vehicle is parked or before network connectivity is lost. The external computer uses this pre-transmitted data to calculate the expected deep discharge point in time, enabling predictions even when communication infrastructure becomes unreliable during parking or in remote areas.
Solution Approach 2:
The external computer acts as an intermediary that stores and processes basic battery data locally. Instead of requiring continuous real-time communication between the vehicle and remote servers, the external computer maintains a local copy of basic data and performs predictions independently, mediating between the vehicle's monitoring system and the warning signal transmission.
Data Source
AI summary
A method for monitoring at least one traction battery of a motor vehicle includes the steps of determining basic data for the prediction of a deeply discharged state of the at least one traction battery by a battery monitoring system associated with the motor vehicle, transmitting the basic data to an external computer via a first communications link, determining an expected point in time of the deep discharge by the external computer, and transmitting a first warning signal to a first monitoring instantiation of the motor vehicle that can be specified if the determined point in time of the deep discharge is reached apart from an early warning time. A device for monitoring at least one traction battery of a motor vehicle is also disclosed.