Selective Driver Coaching for Hybrid Electric Vehicle Energy Recovery
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Solution Overview
Problem
Existing driver coaching systems for hybrid electric vehicles primarily focus on deceleration efficiency without considering the energy needed for subsequent reacceleration, which can lead to overall operating inefficiency.
Innovation Solution
A selective driver coaching system that monitors and learns driver behavior, determining when to enable regenerative braking and coaching cues based on location and deceleration events, and adjusts coaching advice based on comparisons of driver-controlled and coached operating efficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If regenerative braking is enabled during deceleration events, then energy recovery is improved, but overall operating efficiency deteriorates when subsequent reacceleration energy exceeds recovered energy
Solution Approach 1:
The system dynamically changes the operational parameters of regenerative braking based on learned driver behavior patterns and contextual conditions. By adjusting when and how aggressively regenerative braking is applied according to accumulated data about driver preferences and efficiency outcomes, the system optimizes the balance between energy recovery and overall operating efficiency
Solution Approach 2:
The system implements feedback loops where coaching responses are evaluated based on their impact on overall operating efficiency. The controller learns from the results of previous coaching interventions and adjusts future coaching strategies accordingly, using feedback about actual energy consumption and driver behavior changes to refine the regenerative braking activation criteria
2Loss of energy
If driver coaching is provided to improve deceleration efficiency, then energy recovery is improved, but driver acceptance deteriorates when coaching overrides efficient driver-controlled operation
Solution Approach 1:
The coaching system transitions from a static, predetermined coaching strategy to a dynamic adaptive approach. The controller continuously learns driver behavior patterns and adjusts coaching interventions in real-time based on the specific driving context and individual driver preferences, making the system flexible and responsive to changing conditions
Solution Approach 2:
Instead of the traditional approach where the system dictates optimal behavior and the driver must comply, the system inverts the relationship by learning from the driver's natural behavior and adapting to their preferences. The coaching is tailored to support rather than override the driver's established patterns, increasing acceptance while maintaining efficiency benefits
3Loss of energy
If coaching cues are generated based on learned deceleration patterns, then deceleration efficiency is improved, but system complexity increases due to continuous learning and comparison processes
Solution Approach 1:
The system performs self-learning and self-optimization without requiring external intervention or complex infrastructure. The controller automatically accumulates data from sensor inputs, learns driver behavior patterns, and refines coaching strategies independently, reducing the need for additional system components or external computational resources
Solution Approach 2:
The system merges multiple functions into the existing vehicle controller: deceleration monitoring, driver behavior learning, coaching cue generation, and efficiency evaluation are integrated into a single control unit. This consolidation reduces overall system complexity by eliminating the need for separate dedicated systems for each function
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
The system optimizes overall operating efficiency by enabling regenerative braking only when it results in net energy savings, overriding coaching when driver-controlled operation is more efficient, and reducing fuel consumption and energy waste.
Implementation Method 1
a motor generator (MG) selectively engaged to function as a generator during vehicle deceleration to recharge the battery
Implementation Method 2
An HEV's electric motor can be powered by a battery, which requires recharging
Data Source
AI summary
Systems and methods of selective driver coaching are provided. Driver coaching systems learn the characteristics of a deceleration event. With the goal of increasing recouped energy while operating a hybrid electric vehicle (HEV), driver coaching systems predict when the HEV can begin coasting at the start of the deceleration event. In this way, the amount of time during which regenerative braking can be applied may be increased. Coaching cues are provided to the driver so that the HEV can be operated in way that achieves the goal of increasing recouped energy. However, engaging in excessive regenerative breaking can negate its advantages if the amount needed to reaccelerate the HEV to a cruising/steady speed is too great. Selective driver coaching provides coaching cues only if the operating efficiency of the HEV exceeds the operating efficiency of the HEV when controlled by the driver without coaching cues.