Hybrid Engine Activation Planning Around Regenerative Braking
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Solution Overview
Problem
Hybrid electric vehicles face inefficiencies in engine activation policies that lead to wasted energy, reduced battery life, and increased noise levels due to sub-optimal engine activation, which is often disconnected from driving conditions and route planning.
Innovation Solution
An intelligent engine activation planner that uses a Markov decision process to optimize engine on/off decisions based on vehicle behavior patterns, road conditions, and navigation maps, minimizing energy consumption and noise by predicting regenerative braking opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the engine is activated frequently to charge the battery, then the battery charge level is improved, but the energy consumption and noise levels increase
Solution Approach 1:
The system performs preliminary analysis of the navigation route to identify upcoming regenerative braking opportunities before they occur. By predicting these opportunities in advance, the system can plan engine activation strategically, charging the battery during identified optimal windows rather than frequently activating the engine throughout the drive cycle.
Solution Approach 2:
The system leverages regenerative braking events to recharge the battery automatically during normal driving operations. By capturing energy during deceleration and braking events that would otherwise waste kinetic energy, the system reduces dependency on engine activation for battery charging, thereby reducing overall energy consumption.
2Quantity of substance
If the engine is activated frequently to charge the battery, then the battery charge level is improved, but the noise levels increase
Solution Approach 1:
The system analyzes the navigation route in advance to predict regenerative braking opportunities and plans battery charging strategies accordingly. This preliminary planning allows the system to charge the battery during specific optimal windows rather than continuously activating the engine, thereby reducing noise exposure during quiet periods such as nighttime or residential areas.
Solution Approach 2:
The system utilizes regenerative braking events to recharge the battery during normal driving operations. By capturing energy during deceleration and braking events, the system reduces the need for engine activation, thereby significantly reducing noise levels generated by the engine while maintaining battery charge levels.
3Device complexity
If simple engine activation policies are used, then the device complexity is reduced, but the energy efficiency and battery life are worsened
Solution Approach 1:
The system merges route navigation data with engine activation control logic into a unified planning framework. By integrating the navigation system's route information with the engine control system, the platform creates a coordinated approach that optimizes both battery charging and energy efficiency without requiring separate complex control systems.
Solution Approach 2:
The system continuously monitors actual driving conditions, battery charge levels, and regenerative braking events, then adjusts engine activation strategies in real-time based on this feedback. This closed-loop control allows the system to adapt to changing conditions while maintaining optimal energy efficiency and battery health without requiring overly complex predetermined policies.
4Device complexity
If simple engine activation policies are used, then the device complexity is reduced, but the battery life is worsened
Solution Approach 1:
The system integrates navigation route information with battery management control to create a unified platform that optimizes charging strategies. By combining these functions, the system can plan battery charging during optimal windows identified through route analysis, reducing unnecessary engine activation cycles and promoting healthier battery operation patterns that extend battery life.
Solution Approach 2:
The system monitors battery charge levels and driving conditions continuously, adjusting engine activation strategies based on real-time feedback to maintain battery health. This feedback mechanism ensures the battery operates within optimal charge ranges and avoids excessive cycling, thereby extending battery life without requiring complex management systems.
Data Source
AI summary
A method for planning an activation action for an engine of a vehicle is disclosed. The method includes planning, according to a model, an activation action of an engine of a vehicle, and activating the engine according to the activation action. The model includes a state space comprising a current charge level of the battery and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.