BEV Eco Mode Control for Route-Aware Battery Discharge Management
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Solution Overview
Problem
Existing battery electric vehicles (BEVs) face inefficiencies in energy use and battery life due to sub-optimal eco mode activation, which is often based on hard-coded rules that do not account for predictive road conditions or driver behavior, leading to wasted energy and reduced battery performance.
Innovation Solution
An intelligent eco mode activation planner using a multi-objective Markov decision process (MOMDP) that predicts road conditions and driver behavior to dynamically adjust eco mode activation, optimizing energy use and battery life by anticipating sections where more responsive performance is needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If eco mode is activated using hard-coded rules, then battery energy consumption is reduced, but acceleration performance and driver responsiveness deteriorate
Solution Approach 1:
The system dynamically adjusts eco mode activation based on real-time driving conditions, road gradients, and predicted driver behavior rather than using fixed hard-coded rules. The drive mode controller continuously monitors vehicle state and environmental factors to determine optimal eco mode timing, allowing the system to adapt between energy-saving and performance-oriented modes as conditions change.
Solution Approach 2:
The system uses predicted route information and machine learning models to anticipate upcoming conditions (such as steep gradients or high-speed sections) and proactively adjusts drive mode before the driver requests acceleration. This preliminary action allows the system to pre-charges the battery or select performance mode in advance, ensuring responsive acceleration is available when needed while maintaining eco mode during predictable low-demand periods.
2Loss of energy
If eco mode is activated based on current conditions only, then immediate energy savings are achieved, but future energy opportunities (regenerative braking) are missed
Solution Approach 1:
The system analyzes predicted route data to identify upcoming downhill sections or traffic conditions favorable for regenerative braking. Before reaching these sections, the controller may deactivate eco mode to allow the battery to charge from regenerative braking, or adjust drive mode to optimize energy recovery. This forward-looking approach ensures the system captures future energy opportunities rather than focusing solely on immediate savings.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual energy consumption, battery state of charge, and regenerative braking events. Machine learning models are trained on this feedback data to improve predictions of optimal eco mode timing. The system learns from past driving patterns and energy recovery opportunities to refine its strategy, balancing immediate energy savings with future regenerative potential based on accumulated experience.
3Ease of operation
If aggressive acceleration is allowed to meet driver demand, then driver satisfaction is improved, but battery discharge rate increases reducing overall efficiency
Solution Approach 1:
The system modifies acceleration parameters dynamically by adjusting the relationship between accelerator pedal position and motor torque output. In eco mode, the system limits maximum acceleration rate and torque delivery while maintaining responsive feel through smooth power delivery curves. The drive mode controller selects between different acceleration profiles (aggressive, moderate, eco) based on predicted route conditions, battery state of charge, and learned driver preferences, optimizing the balance between driver satisfaction and energy conservation.
Solution Approach 2:
The system introduces an intelligent drive mode controller as an intermediary between the driver's acceleration requests and the motor's power delivery. This controller acts as a mediator that interprets driver intent while applying energy optimization strategies. It can smooth acceleration requests, delay peak power delivery until optimal moments, or coordinate with regenerative braking to recover energy during deceleration phases, thereby reducing overall battery discharge rate while maintaining acceptable driver experience.
Data Source
AI summary
Intelligent eco mode optimization in a battery electric vehicle (BEV) includes collecting data from one or more systems of a vehicle in which the vehicle includes a battery. A predicted route is generated based on the collected data. The collected data includes a navigation map for a portion of a vehicle transportation network. A state of the vehicle is determined based on the collected data and the predicted route. A drive mode is determined, using a decision-making model, for the vehicle based on the state of the vehicle and the predicted route. The drive mode is either a first drive mode having a first acceleration curve or a second drive mode have a second acceleration curve and the second drive mode reduces a rate of discharge of the battery as compared to the first drive mode. The vehicle is set to use the drive mode.