TRU Battery Charge Limits Based on Weather and Duty Cycle
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Solution Overview
Problem
Existing battery-powered refrigerated trailers face reduced battery life due to high states of charge, which accelerate plating reactions, and there is a need for improved energy management to optimize battery usage based on weather and duty cycle information.
Innovation Solution
A system that utilizes weather forecasts and historical data to determine the required battery capacity and set the maximum charging point, adjusting battery capacity by adding or removing modules, and incorporating a controller to manage charging based on thermal loss quotients and onboard power generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the battery is charged to maximum capacity to ensure sufficient energy for hot days, then the energy availability is improved, but the battery life is reduced due to accelerated plating reactions at high states of charge
Solution Approach 1:
The system dynamically adjusts the maximum state of charge threshold based on forecasted weather conditions and actual duty cycle. On cooler days, the maximum charge level is reduced to minimize plating reactions and extend battery life. On hotter days or when high energy demand is predicted, the maximum charge level is increased to ensure sufficient energy availability. This dynamic adjustment resolves the contradiction by making the charge level adaptive rather than static.
Solution Approach 2:
The system changes the state of charge parameter based on environmental conditions and operational requirements. By monitoring temperature forecasts, duty cycle patterns, and energy consumption rates, the system adjusts the maximum charge threshold parameter to optimize both battery life and energy availability for each specific operating scenario.
2Use of energy by moving object
If additional battery modules are added to increase capacity for high energy demand, then the energy capacity is improved, but the weight penalty increases and carrying capacity is reduced
Solution Approach 1:
Instead of always maintaining maximum battery capacity, the system uses partial charging strategies when full capacity is not needed. By charging only to the required level based on forecasted demand, the system avoids the weight penalty of carrying excess battery energy that would not be utilized, thereby improving the energy-to-weight ratio.
Solution Approach 2:
The system dynamically determines the optimal battery capacity configuration based on predicted energy needs. When high energy demand is forecasted, additional battery modules can be connected. When demand is lower, fewer modules are active, reducing the effective weight penalty while maintaining sufficient capacity for actual operational requirements.
3Reliability
If the maximum charging point is set high to ensure energy availability for all conditions, then the reliability is improved, but the battery degradation accelerates due to prolonged exposure to high states of charge
Solution Approach 1:
The system performs preliminary assessment of energy requirements using weather forecasts and duty cycle analysis before setting the maximum charge point. By predicting future energy needs in advance, the system charges the battery to the appropriate level only when necessary, avoiding prolonged exposure to high states of charge that cause degradation, while still ensuring energy availability when needed.
Solution Approach 2:
The system continuously monitors actual energy consumption, temperature conditions, and duty cycle patterns, then adjusts the maximum charge point accordingly. This feedback mechanism ensures that the battery is charged to the minimum necessary level to maintain reliability under current conditions, thereby reducing unnecessary degradation from high state of charge exposure.
Data Source
AI summary
A method and system for intelligent and variable charging of a power supply for a transport refrigeration unit (“TRU”) installed on a transport. The method and system varies a daily state of charge or total battery capacity based upon expected TRU need. The system includes at least one battery and a charging connector for an EV vehicle charger. A controller controls a charge level of the at least one battery. The controller has a location and/or a travel route for the TRU and access external weather data for that location or route. The controller analyzes a weather pattern for the location and/or the travel route to determine an expected environment, and determines the battery capacity required and maximum state of charge for the battery system based upon the expected environment. The system charges the at least one battery via the charging connector to obtain the maximum state of charge.