Predictive HVAC Load Shedding for Electric Transport Vehicles
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Solution Overview
Problem
Electric and hybrid vehicles face challenges with unexpected battery discharge events due to unpredictable power consumption, such as traffic delays, which can jeopardize route completion and lead to out-of-service events or cargo loss, especially in HVAC and refrigeration systems.
Innovation Solution
Implementing a predictive modeling system that uses vehicle and route data, including traffic and weather information, to anticipate energy consumption and perform smart load shedding operations, such as adjusting climate control settings or increasing cooling, to manage energy storage effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If load shedding operations are performed to manage energy consumption, then energy efficiency is improved, but passenger comfort deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-cooling the passenger compartment before high energy consumption events occur. The predictive model identifies upcoming high energy demand periods (such as hot weather conditions or extended routes) and advances the cooling process beforehand, storing cooling capacity in the building structure itself. This allows the HVAC system to reduce or suspend operation during high energy consumption periods while maintaining passenger comfort.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring energy consumption patterns, weather conditions, and passenger comfort levels. The predictive model uses this feedback to refine its predictions and adjust load shedding operations in real-time. Passenger comfort sensors and energy consumption data feed back into the system to optimize the balance between energy efficiency and comfort maintenance.
2Use of energy by moving object
If predictive modeling is implemented to anticipate energy consumption, then energy management is improved, but system complexity increases
Solution Approach 1:
The predictive model serves multiple functions: it predicts energy consumption, identifies high demand periods, determines optimal pre-cooling strategies, and adjusts HVAC operations. By consolidating these functions into a single multi-functional system rather than separate dedicated systems for each function, the overall system complexity is managed while achieving improved energy management.
Solution Approach 2:
The system performs self-service by using its own operational data and environmental sensors to generate predictions and make control decisions autonomously. The predictive model learns from historical energy consumption patterns and automatically adjusts operations without requiring external intervention or complex manual control systems, thereby managing complexity while improving energy management.
Data Source
AI summary
Systems and methods for providing load shedding in a vehicle are provided. Particularly, the vehicle may be an electric vehicle and the loads that are shed may be HVAC or refrigeration loads. The load shedding methods and systems may include a predictive model of energy consumption, determining a predicted energy consumption and comparing it to a stored energy at the vehicle. If the predicted energy consumption exceeds the stored energy, load shedding operations may be performed at a transport climate control system, such as adjusting a set point, adjusting an operating mode of the transport climate control system, increasing a dead band of a compressor of the transport climate control system, utilizing free cooling such as ambient air to provide climate control in the vehicle, or increasing cooling provided by the transport climate control system to an energy storage of the vehicle.