Thermal Analysis of an Energy Storage System for a Battery Electric Switcher Locomotive

2015 ◽  
Author(s):  
James A. Kreibick ◽  
Marc Serra Bosch ◽  
Timothy P. Cleary ◽  
Brent Ballew
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Computer Aided Design ◽  
Cooling System ◽  
Storage System ◽  
Air Flow ◽  
Energy Storage System ◽  
Battery Pack ◽  
Area Network ◽  
Data Logger

Often, available power from an in-vehicle energy storage system is governed by thermal limitations. Modeling of battery pack thermal response is crucial to managing its cooling system energy consumption and estimating available charge/discharge power for future locomotive tractive and regenerative effort. Active cooling through forced air flow was simulated using computer-aided design of the battery pack and its enclosure. Module scaled (series string of 54 12V batteries) testing and modeling of both air flow and temperature distribution was performed and validated for sealed lead acid carbon batteries. A controller area network and data logger collected temperature data from 218 sensors placed throughout a battery pack module during electrical loading for both switcher and over-the-road cycles while under various environmental thermal loadings. A blower on-off control algorithm was optimized to minimize energy consumption and implemented based on temperature array statistics.

Modeling and Simulation of Building Cooling System With Supercooling-Based Ice Energy Storage

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Yili Zhang ◽  
Sean Kissick ◽  
Hailei Wang
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Cooling System ◽  
Low Cost ◽  
Storage System ◽  
Electric Energy ◽  
Energy Storage System ◽  
Heavy Load ◽  
Electricity Cost ◽  
Hourly Temperature

Abstract City’s electricity power grid is under heavy load during on-peak hours throughout summer cooling season. As the result, many utility companies implemented the time-of-use rate of electricity leading to high electricity cost for customers with significant cooling needs. On the other hand, the need for electricity and/or cooling decreases greatly at night, creating excess electricity capacity for further utilization. An innovative ice energy storage system is being developed leveraging a unique supercooling-based ice production process. During off-peak hours, the proposed system stores the low-cost electric energy in the form of ice; during on-peak hours, the system releases the stored energy to meet extensive home cooling needs. Thus, it can not only reduce energy and cost of cooling, but also increase the penetration of renewable energies (especially wind energy). In this paper, the working principles of the system is presented along with the modeling details of the overall system and several key components. The simulink model takes in hourly temperature and peak/off peak electricity cost data to dynamically simulate the amount of energy required and associated cost for cooling an average home. Both energy consumption and cost for homes using the cooling system with ice energy storage in two US cities have been compared with those using conventional HVAC cooling system. According to the model, huge reduction in energy cost (up to 3X) can be achieved over 6 months of cooling season in regions with high peak electricity rates. While only moderate reduction on energy consumption is predicted for the ice energy storage system, further energy reduction potentials have been identified for future study.

Dynamic Modeling and Simulation of Home Cooling System With Supercooling-Based Ice Energy Storage

2019 ◽  
Author(s):  
Yili Zhang ◽  
Sean Kissick ◽  
Hailei Wang
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Cooling System ◽  
Low Cost ◽  
Storage System ◽  
Electric Energy ◽  
Energy Storage System ◽  
Heavy Load ◽  
Electricity Cost ◽  
Hourly Temperature

Abstract City’s electricity power grid is under heavy load during on-peak hours throughout summer cooling season. As the result, many utility companies implemented the time-of-use rate of electricity leading to high electricity cost for customers with significant cooling needs. On the other hand, the need for electricity and/or cooling decreases greatly at night, creating excess electricity capacity for further utilization. An innovative ice energy storage system is being developed leveraging a unique supercooling-based ice production process. During off-peak hours the proposed system stores the low-cost electric energy in the form of ice; during on-peak hours the system releases the stored energy to meet extensive home cooling needs. Thus, it can not only reduce energy and cost of cooling, but also increase the penetration of renewable energies (especially wind energy). In this paper, the working principles of the system is presented along with the modeling details of the overall system and several key components. The Simulink model takes in hourly temperature and peak/off peak electricity cost data to dynamically simulate the amount of energy required and associated cost for cooling an average home. Both energy consumption and cost for homes using the cooling system with ice energy storage in two US cities have been compared with those using conventional HVAC cooling system. According to the model, huge reduction in energy cost (up to 3X) can be achieved over six months of cooling season in regions with high peak electricity rates. While only moderate reduction on energy consumption is predicted for the ice energy storage system, further energy reduction potentials have been identified for future study.

scholarly journals A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 600
Author(s):  
Kevin Mallon ◽  
Francis Assadian
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Energy Management ◽  
Storage Systems ◽  
Storage System ◽  
Energy Storage System ◽  
Trade Off ◽  
Hybrid Energy ◽  
Hybrid Energy Storage

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

scholarly journals Optimization of Hybrid Energy Storage System Control Strategy for Pure Electric Vehicle Based on Typical Driving Cycle

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kanglong Ye ◽  
Peiqing Li ◽  
Hao Li
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Fuzzy Control ◽  
Control Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Fuzzy Control Strategy

Taking a hybrid energy storage system (HESS) composed of a battery and an ultracapacitor as the study object, this paper studies the energy management strategy (EMS) and optimization method of the hybrid energy storage system in the energy management and control strategy of a pure electric vehicle (EV) for typical driving cycles. The structure and component model of the HESS are constructed. According to the fuzzy control strategy, aimed at the roughness of the membership function in EMS, optimization strategies based on a genetic algorithm (GA) and particle swarm optimization (PSO) are proposed; these use energy consumption as their optimal objective function. Based on the improved EV model, the fuzzy control strategy is studied in MATLAB/Advisor, and two control strategies are obtained. Compared with the simulation results based on three driving cycles, urban dynamometer driving schedule (UDDS), new European driving cycle (NEDC), and ChinaCity, the optimum control strategy were obtained. The theoretical minimum energy consumption of HESS was reached by dynamic programming (DP) algorithm in the same simulation environment. The research shows that, compared with the PSO, the output current peak and current fluctuation of the battery optimized by the GA are lower and more stable, and the total energy consumption is reduced by 3–9% in various simulation case studies. Compared with the theoretical minimum value, the deviation of energy consumption simulated by GA-Fuzzy Control is 0.6%.

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