EV Power Management System for Fuel Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automotive vehicles lack precise methods for managing fuel consumption, leading to inefficiencies in energy use, as drivers rely on imprecise techniques such as slowing down or carrying lighter loads, with existing assisting devices not optimizing power consumption based on various parameters.
Innovation Solution
A system comprising an interface, memory, and processor that receives sensor data to analyze patterns and manage brake application, calculating optimal power for the vehicle engine based on external, operational, and command inputs, enabling precise control of power consumption and suggesting efficient speeds to optimize fuel use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If drivers use traditional imprecise methods (slowing down, carrying lighter loads) to manage fuel consumption, then fuel usage can be reduced to some extent, but the control precision and energy efficiency optimization are insufficient
Solution Approach 1:
The system changes multiple operating parameters simultaneously (speed, acceleration, braking force, engine power) based on real-time sensor data and predictive algorithms, rather than relying on single-parameter adjustments like traditional methods. This enables precise control of fuel consumption by optimizing the combination of parameters.
Solution Approach 2:
The system performs preliminary actions by predicting future road conditions, traffic patterns, and elevation changes using sensor data and algorithms, then proactively adjusts operating parameters in advance. For example, it may reduce speed before an upcoming hill or prepare regenerative braking before a downhill section, optimizing fuel efficiency before the situation arises.
2Ease of operation
If operational assisting devices (cruise control) are used to maintain constant speed, then driving ease is improved, but power consumption optimization based on multiple parameters is not achieved
Solution Approach 1:
The system dynamically adjusts the vehicle's operating parameters based on real-time conditions rather than maintaining fixed settings. It continuously monitors sensor data (speed, acceleration, brake pressure, engine power, road grade, traffic conditions) and adapts the optimal operating point, enabling both ease of operation and power consumption optimization.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring actual operating parameters and sensor data, comparing them with optimal values calculated by the algorithm, and making real-time adjustments. This feedback mechanism enables the system to optimize power consumption while maintaining ease of operation through automated control.
3Loss of energy
If the system calculates and adjusts optimal power based on multiple parameters (external, operational, command inputs), then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single integrated control unit that processes multiple sensor inputs (speed, acceleration, brake pressure, engine power, road grade, traffic conditions) and simultaneously optimizes multiple operating parameters (speed, acceleration, braking force, engine power). This universal approach improves energy efficiency without proportionally increasing complexity.
Solution Approach 2:
The system performs self-service by automatically collecting sensor data, processing it through algorithms, calculating optimal operating parameters, and executing control adjustments without requiring external intervention. This self-contained approach manages complexity by integrating all functions within the vehicle's existing control architecture.
4Productivity
If real-time sensor data analysis is performed to detect patterns and manage brake application, then braking optimization and energy recovery are improved, but processing requirements and system complexity increase
Solution Approach 1:
The system implements periodic action by analyzing sensor data at optimized intervals rather than continuously processing all data streams. It uses predictive algorithms to determine when pattern detection is necessary based on driving conditions, reducing processing requirements while maintaining brake management efficiency.
Data Source
AI summary
An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.