Dynamic Mission Planning for Powertrain Torque Optimization
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Solution Overview
Problem
Current powertrain systems lack efficient dynamic mission planning optimization, leading to suboptimal torque management and driveline response, particularly in hybrid vehicles, which affects fuel efficiency and battery state of charge (SOC) during varying terrain and driving conditions.
Innovation Solution
The method involves advanced driver-assistance system (ADAS) dynamic mission planning that computes a torque request plan using external sensing, behavioral planning, and energy planning algorithms to determine an optimal torque range, which is communicated to the powertrain for closed-loop optimization, considering factors like terrain, traffic, and operator inputs to adjust SOC targets for efficient fuel and battery power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If dynamic mission planning optimization is implemented, then powertrain efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the powertrain control into multiple independent modules: ADAS perception module, mission planning module, torque management module, and execution controllers. Each module handles specific functions independently, allowing complex optimization without proportionally increasing overall system complexity.
Solution Approach 2:
The mission planning module pre-computes optimal torque trajectories and SOC targets based on predicted driving conditions (terrain, traffic, weather) before actual execution. This preliminary action allows the powertrain to operate optimally without real-time complex calculations during dynamic driving.
2Power
If SOC target is increased for climbs, then battery power availability is improved, but fuel consumption increases
Solution Approach 1:
The SOC target is dynamically adjusted based on real-time driving conditions, vehicle state, and predicted terrain. During climbs, the system temporarily increases SOC target to ensure power availability, while during downhill or flat sections, it reduces target to allow regenerative charging, optimizing the trade-off between battery power and fuel consumption.
Solution Approach 2:
The system changes the SOC target parameter adaptively rather than maintaining a fixed value. The torque management module continuously modifies SOC targets based on mission planning outputs, allowing the powertrain to operate in different optimization modes (battery-charging, battery-discharging, fuel-saving) as conditions change.
3Use of energy by moving object
If SOC target is decreased for downhill driving, then regenerative braking efficiency is improved, but battery charge capacity is reduced
Solution Approach 1:
The system converts the normally wasted energy during braking (a harmful loss) into useful electrical energy through regenerative braking. By lowering SOC targets during downhill driving, the system creates optimal conditions for maximum regenerative charging, transforming energy that would be lost as heat into stored battery energy.
4Loss of energy
If torque request plan is optimized for fuel efficiency, then fuel consumption is reduced, but driveline response quality may deteriorate
Solution Approach 1:
The mission planning module pre-calculates optimal torque trajectories that balance fuel efficiency and driveline response requirements. By planning torque requests in advance based on predicted driving conditions, the system ensures smooth, efficient torque delivery without sudden changes that would compromise response quality.
Solution Approach 2:
The torque management system continuously monitors actual driveline response and powertrain operating conditions, feeding this information back to adjust torque requests in real-time. This closed-loop control ensures that fuel-efficient torque planning maintains adequate driveline response quality by compensating for actual vehicle dynamics.
Data Source
AI summary
A powertrain optimization method is used to identify the optimal torque operating range. The method for controlling the vehicle includes: receiving, by a planning controller, a trip plan based on an input from a vehicle-operator, wherein the trip plan is indicative of a planned trip; determining, by the planning controller, a current location of the vehicle using a Global Navigation Satellite System (GNSS) of the vehicle; determining, by the planning controller, a geography of the planned trip using map data from a map database; determining, by the planning controller, a target speed profile for the vehicle as a function of the trip plan, the geography of the planned trip, and a predetermined, optimal acceleration range; determining, by an adaptive cruise controller, a torque request as a function of the target speed profile, a predetermined-optimal torque range, and a current speed of the vehicle.