skip to main content


This content will become publicly available on January 4, 2025

Title: Extending Battery Life via Load SExtending Battery Life via Load Sharing in Electric Aircraft
Electric Aircraft have the potential to revolutionize short-distance air travel with lower operating costs and simplified maintenance. However, due to the long lead-time associated with procuring batteries and the maintenance challenges of replacing and repairing batteries in electric aircraft, there are still unanswered questions related to the true long-term operating costs of electric aircraft. This research examines using a load-sharing system in electric aircraft to optimally tune battery degradation in a multi-battery system such that the battery life of a single battery is extended. The active optimization of energy drawn from multiple battery packs means that each battery pack reaches its optimal replacement point at the same time; thereby simplifying the maintenance procedure and reducing cost. This work uses lithium iron phosphate batteries experimentally characterized and simulated in OpenModelica for a flight load profile. Adaptive agents control the load on the battery according to factors such as state of charge, and state of health, to respond to potential faults. The findings in this work show the potential for adaptive agents to selectively draw more power from a healthy battery to extend the lifespan of a degraded battery such that the remaining useful life of both batteries reaches zero at the same time. Simulations show that dual battery replacement can be facilitated using the proposed method when the in-service battery has a remaining useful life of greater than 0.5; assuming that the replacement battery it is paired with has a remaining useful life of 1.0. Limitations of the proposed method are discussed within this work.  more » « less
Award ID(s):
2237696
NSF-PAR ID:
10489195
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
American Institute of Aeronautics and Astronautics
Date Published:
Journal Name:
AIAA SCITECH 2024 Forum
ISBN:
978-1-62410-711-5
Format(s):
Medium: X
Location:
Orlando, FL
Sponsoring Org:
National Science Foundation
More Like this
  1. Wang, Dong (Ed.)
    Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions. 
    more » « less
  2. Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery's life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise. 
    more » « less
  3. null (Ed.)
    Fiberglass-reinforced composite materials are commonly used in engineering structures subjected to dynamic loading, such as wind turbine blades, automobiles, and aircraft, where they experience a wide range of unpredictable operating conditions. The ability to monitor these structures while in operation and predict their remaining structural life without requiring their removal from service has the potential to drastically reduce maintenance costs and improve reliability. This work exploits piezoresistive laser induced graphene (LIG) integrated into fiberglass-reinforced composites for in-situ fatigue damage monitoring and lifespan prediction. The LIG is integrated within fiberglass composites using a transfer-printing process that is scalable with the potential for automation, thus reducing barriers for widespread application. The addition of the conductive LIG within the traditionally insulating fiberglass composites enables direct in-situ damage monitoring through simple passive resistance measurements during tension-tension fatigue loading. The accumulation and propagation of structural damage are detected throughout the fatigue life of the composite through changes to the electrical resistance measurements, and the measurement trends are further used to predict the onset of catastrophic composite failure. Thus, this work results in a scalable and multifunctional composite material with self-sensing capabilities for potential use in high-performing, dynamic, and flexible composite structures. 
    more » « less
  4. null (Ed.)
    Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs’ output voltage to those of the loads’ required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance. 
    more » « less
  5. Lithium–CO2 batteries are attractive energy‐storage systems for fulfilling the demand of future large‐scale applications such as electric vehicles due to their high specific energy density. However, a major challenge with Li–CO2 batteries is to attain reversible formation and decomposition of the Li2CO3 and carbon discharge products. A fully reversible Li–CO2 battery is developed with overall carbon neutrality using MoS2 nanoflakes as a cathode catalyst combined with an ionic liquid/dimethyl sulfoxide electrolyte. This combination of materials produces a multicomponent composite (Li2CO3/C) product. The battery shows a superior long cycle life of 500 for a fixed 500 mAh g−1 capacity per cycle, far exceeding the best cycling stability reported in Li–CO2 batteries. The long cycle life demonstrates that chemical transformations, making and breaking covalent C-O bonds can be used in energy‐storage systems. Theoretical calculations are used to deduce a mechanism for the reversible discharge/charge processes and explain how the carbon interface with Li2CO3 provides the electronic conduction needed for the oxidation of Li2CO3 and carbon to generate the CO2 on charge. This achievement paves the way for the use of CO2 in advanced energy‐storage systems. 
    more » « less