Regenerative Braking Control via Predictive Battery Depletion
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Solution Overview
Problem
Current regenerative braking systems in hybrid/electric vehicles do not effectively optimize energy recapture by failing to predict and prepare for regenerative braking opportunities based on driver behavior and road segment characteristics, leading to suboptimal battery recharging.
Innovation Solution
A vehicle system that includes an electric machine and a controller programmed to classify driver behavior and road segments, identify regenerative braking opportunities, and schedule battery recharging by cross-referencing these classifications, and if necessary, deplete the battery to increase its capacity before the opportunity, ensuring maximum energy recapture during regenerative braking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the battery charge capacity is maintained at high levels continuously, then the battery is ready to recapture energy during regenerative braking, but the ability to recoup maximum regenerative braking energy is limited due to insufficient battery capacity availability
Solution Approach 1:
The controller schedules a battery depletion event before the regenerative braking opportunity to create capacity space in the battery. This preliminary action of reducing battery charge allows the battery to accept and store the maximum amount of regenerative braking energy when the opportunity arises, thereby resolving the contradiction between maintaining ready capacity and maximizing energy recapture.
2Loss of energy
If the controller operates the electric machine to increase battery charge capacity before a scheduled regenerative braking event, then maximum energy recapture is enabled, but additional energy consumption occurs during the preparation phase
Solution Approach 1:
The controller performs preliminary battery depletion scheduling that strategically reduces battery charge before the regenerative braking event. This preliminary action consumes energy but creates the necessary capacity space to maximize energy recapture during regenerative braking, resulting in net energy benefit.
Solution Approach 2:
The energy consumption during battery preparation is converted into a benefit by enabling maximum regenerative braking energy recapture. The temporary energy expenditure creates capacity availability that allows the system to capture significantly more energy during the regenerative braking event, transforming the preparatory energy cost into a net energy gain.
3Productivity
If regenerative braking opportunities are identified without predictive classification, then the system responds to immediate braking events, but energy recapture optimization is lost due to lack of advance preparation
Solution Approach 1:
The controller performs preliminary classification of driver behavior and road segment characteristics to predict upcoming regenerative braking opportunities in advance. This predictive classification enables the system to prepare optimally by scheduling battery depletion events beforehand, maximizing energy recapture efficiency while the added complexity of classification provides necessary predictive capability.
Solution Approach 2:
The system uses feedback from driver behavior patterns and road segment characteristics to continuously refine predictions of regenerative braking opportunities. This feedback mechanism enables the controller to adaptively optimize battery management strategies, improving energy recapture efficiency while the complexity of the classification system provides the intelligence needed for accurate predictions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach optimizes regenerative braking by predicting and preparing for upcoming opportunities, thereby enhancing battery recharging efficiency and improving fuel economy and emissions reduction.
Implementation Method 1
an electric machine may operate as a generator to convert the kinetic energy of the vehicle into electrical energy which is in turn used to charge a battery
Data Source
AI summary
A vehicle includes an electric machine and a controller. The electric machine is configured to draw energy from a battery to propel the vehicle and to recharge the battery during regenerative braking. The controller is programmed to, in response to identifying a regenerative braking opportunity along an upcoming road segment based on a classification of driver behavior and a classification of the upcoming road segment, operate the electric machine to recharge the battery along the upcoming road segment.