Predictive Battery Charge Control for Vehicle Traction Assistance
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Solution Overview
Problem
Current slip or traction control systems in vehicles are reactive and do not predict or prepare for future slip conditions, potentially leading to loss of traction, especially in adverse road conditions.
Innovation Solution
A computer-implemented predictive traction control system that uses a cloud computing system and vehicle data to determine slip conditions and configure the electric drive powertrain to maintain traction by optimizing the state of charge of the battery system before encountering slippery areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current reactive slip control systems are used, then the system complexity is low, but the reliability of traction control is insufficient because the system cannot predict or prepare for future slip conditions
Solution Approach 1:
The system performs preliminary actions by predicting future slip conditions using cloud-based analytics and vehicle sensor data. Before slip conditions occur, the system determines the likelihood of traction loss and pre-charges the battery system to ensure sufficient power is available for immediate traction control intervention, rather than waiting reactively for slip to occur
Solution Approach 2:
A cloud computing system acts as an intermediary between vehicle sensors and the electric drive powertrain controller. The cloud system receives vehicle data, analyzes slip condition likelihood using external data sources (weather, road conditions), and sends back control commands for battery charge management, enabling predictive capabilities without adding complex local processing hardware to the vehicle
2Reliability
If the battery system is kept at high state of charge to ensure power availability for traction control, then the reliability of traction control improves, but the energy efficiency deteriorates due to increased parasitic losses and reduced regenerative braking opportunities
Solution Approach 1:
The battery charge state is made dynamic rather than static. The system continuously adjusts the target state of charge based on real-time predictions of slip condition likelihood, vehicle operating conditions, and route characteristics. When slip risk is low, the battery operates at lower charge states for efficiency; when slip risk increases, the system proactively charges the battery to ensure power availability, minimizing energy losses while maintaining reliability
Solution Approach 2:
The system changes the charge state parameter of the battery dynamically based on predicted traction control needs. By using cloud-based predictions to determine when high power availability is necessary, the system optimizes the charge state parameter to balance between having sufficient power for traction control and minimizing energy losses from maintaining high charge levels during normal operation
Data Source
AI summary
Systems and methods of providing traction assistance in a vehicle are disclosed that include determining whether a slip condition exists on a route of the vehicle based on vehicle data, external data, or a combination thereof, determining an electric drive powertrain configuration that includes a minimum state of charge (SoC) for a battery system of the vehicle based on the slip condition, and communicating the electric drive powertrain configuration to an electric drive powertrain controller, which operates the electric drive powertrain in accordance with the electric drive powertrain configuration to provide the minimum SoC to the battery system prior to the vehicle travelling over a portion of the route where the slip condition exists.