Health Monitoring & Remaining Life Estimation Of Lithium-Ion Aeronautical Batteries Assignment Sample

Health Monitoring & Remaining Life Estimation Of Lithium-Ion Aeronautical Batteries Assignment by New Assignment Help

  • 54000+ Project Delivered
  • 500+ Experts 24x7 Online Help
  • No AI Generated Content
- +
35% Off
£ 6.69
Estimated Cost
£ 4.35
13 Pages 3210Words

Health Monitoring & Remaining Life Estimation Of Lithium-Ion Aeronautical Batteries 

Get free written samples by our Top-Notch subject experts and Assignment Helper team.

Introduction Of Health Monitoring & Remaining Life Estimation Of Lithium-Ion Aeronautical Batteries Assignment Sample

PHM or Prognostics & Health Management is a system maturity process related to the engineering branch and for this, a research method is not established & recognized universally. Since the last decade, the group has been studying by focusing on the reliability of the system. The general principle regarding failure with the actual purpose for understanding how the age of a complex system and then predict why the systems failed. In this regard, it can be said that the PHM project is capable of including the health monitoring components and predict the failure regarding health monitoring actions to perform the tasks for maintenance. The actual science regarding prognostics can be predicted based on four different fundamental notions. (a) The entire electromechanical system can be used as age which is based on the passage of time as well as the state of the environment. (b) Materials aging, as well as corrosion, is considered as the monotonic process which is capable of manifesting itself in the physical field can be the chemical compositions regarding the materials. (c) Recognized aging symptoms can be considered as the previous materials failures. (d) It is possible to relate a model regarding the signs of aging by assuming materials aging as well as remaining until then the individual life of the materials. MTBF or Mean Time Between Failure can be used in different applications which are considered as values for reference can be assumed and then the materials will fail. But, erosion is a complex activity that is more effective and it can manage the profile depending on profile, time as well as environmental conditions.


This whole research is going to focus on detecting the degradation or aging to accurately estimate a certain battery's life. The chosen battery kind is the one used in architecture and "electric hybrid aircraft". The battery is a lithium-ion battery. This kind of battery comprises cathode, anode, and electrolyte substances. The cathode part is produced from "Grains of Oxides' ' from metal ions, to be specific the transitive type metal ions, such as; LiCoO2. Choosing metal ions will help in providing good stability alongside "high energy density" (Hashemi et al. 2020). On the other hand, the anode ion uses lithium metal, although it can also be made of lithium or graphite alloy compounds. Also, graphite is a very ideal choice as it provides the ability to perform the anode functions. In regards to the electrolytic solution, it can be solid or liquid. Also, lithium-ion batteries possess extremely high “Initial capacity” which is very vital as an aircraft needs high capacity. Also, the lithium-ion batteries provided adequate conservation of battery charges after recapitulated cycles regarding recharges and discharges.

Also using lithium-ion batteries is an ideal choice as it is very easy to make because all kinds of lithium atoms support the reaction cell and those cells can be easily incorporated within the cathode, also they are relatively stable in respect to their function as batteries for architecture or aircraft battery application. Also, as it is very obvious from lithium's position within the periodic table that it is a very reactive metal comparatively it is very light weighted too, other preferred metals used in batteries are very heavyweight than lithium, so it is evident that this is the ideal choice for an aircraft mechanism, also due to this the batteries possess relatively way higher "gravimetric energy density". Also, Lithium-based batteries comprise a very low "self-discharge rate", as it is obvious that aircraft need batteries that can stay active with minimal attention and maintenance, and lithium-based batteries can provide that (Ahmed et al. 2019). A low "self-discharge rate" allows these batteries to stay in stock for a long time regardless of any maintenance. This research is going to adequately utilize the remote and direct aspect of a battery's components and detect the degradation of the battery and estimate the lifeline of a "Lithium-ion battery" working within an "electric-hybrid aircraft" or architecture.

Aim & Objectives

The context of the research is very simple yet vital. As long-lasting batteries are obvious preferences for all kinds of use, specifically within an "electric hybrid aircraft" or in architecture. So the research is going to take into consideration some selected models from relevant literature, and use those considerations in detecting direct and remote parameters of a certain battery working, as for this case the battery kind is "lithium-Ion battery" (Locorotondo et al. 2018). The research framework also includes some objectives, which are relevant to the context and will help to guide the research towards the aim. The research aims to prepare a model in regards to detecting the battery aging and degradation to estimate its lifeline while their usage within an "electric-hybrid aircraft" or architecture.

The objectives of the research are:

  • To utilize some relevant existing literature and semi-empirical models, to understand the parameters.
  • To prepare a model and consider the degradation or aging rate in regards to the changing parameters, such as; temperature and time, charge and discharge, and also the obvious conditions originate from the flight profile of "a regional aircraft"
  • To provide a Simulink diagram and simulation output to evaluate "battery life estimation", by using MATLAB Simulink (, 2017).


  1. Discharge & Capacity Model

Through the respiratory data regarding charges as well as discharges at VA, the actual capacity of batter can be evaluated through several cycles.

... (i)

With the proper information through applying the model, it is capable of simulating the discharge curve. For simulating the discharge curve the model can be proposed by using the software MATLAB. The model consists of two different components where one is represented as the open-circuit voltage and another one is potential voltage.

... (ii)

... (iii)

The actual voltage at the outlets of the battery (U) can be defined through the following equation:

... (iv)

In this regard the SoC or State of Charge can be defined by the following equation:

... (v)

By using the first discharge-related five separated evaluations, the new battery is capable of representing the discharge curve regarding each of the states as well as operational profiles (I, T) (Rajan et al. 2018). The discharge can be evaluated through the parameters assigned for the model related to the meaning function. In this regard, the "fminsearch” can be implemented which can provide the less square algorithm as well as a polynomial function where the actual data related to five discharge curves will be approximate.

The discharge provides a good fit for the battery model in a versatile function that can be used approximately based on the original data of the battery. In this regard, the below figure shows a comparison regarding model data with the main errors with the variants of 0.0565V as well as 0.0059V. The power model can be defined through the data regarding parameters found from the power by five life cycles of the battery. The batteries will be chosen with various discharge profiles.

Depending on the virtual characteristics related to the curve capacity vs. several cycles, it will be represented as a linear model regarding the power and also the parameter regarding the captured powered model. The main function of this model can be evaluated through the “fminsearch” (Xiong et al. 2018). In this regard the evaluation is based on differential equations which are provided in the below section:

With an appropriate capacity model, it is possible to find the actual relationship between the temperature as well as electric current deterioration by the life cycle of the battery.

Approximately a useful life cycle, the health of the battery can be monitored from the battery state or (SoH). The provider's information related to the rest of the cycle, the battery is capable of handling up t5hye marginal standard of the health by considering the prediction of failure of the materials from major limits (, 2018).

  1. Model of Health Monitoring

The actual model of health monitoring is based on several concepts which are provided in the below section:

  • State of Health

The state of health can show the health of the battery which can last, its useful battery life. While the battery is new it comes with 100% life (Ding et al. 2019). After a certain cycle, the values will be decreased based on the operating profile which can occur in the discharge cycle.

... (i)

  • Delta Health

Deterioration of the health in every battery, the discharge can be evaluated from the actual capacity values and it varies based on the model as well as discharge profile.

... (ii)

  • Relative Number of Cycles (RNC)

It is capable of representing the number where the cycle which battery should be performed at a specific time based on the operational profile for getting a certain value regarding the SoH. These parameters can be used while the battery can be worked differently based on the operating profile. In this context, the battery may get various relative numbers regarding cycle dependent on the I & T.


  • Remaining Essential Life

The remaining essential life can be evaluated by the battery state of (SH). It is capable of delivering the information regarding the rest of the cycle where the battery can handle the marginal standard in terms of health (SoH) depending on the failure of the elements from the standard limit.



Batteries are considered vital components regarding any type of aircraft electrical system. These are generally used for start-up the flying provided to the engine as well as electrical emergency power. In this context, the batteries can be displayed on most aircraft elements considering the aging and deteriorating health at the time of the operation. So, it is an appropriate assessment in terms of health battery (SH) and Rest of the Useful Life (RUL) (Jang, 2020). All of these are important regarding the operator of the aircraft. Failure to do so may result in lower usage of equipment if it is removed before the termination of the life cycle. It is also represented as the incidents of unexpected failure at the time of operation. This has consequences for decreasing the flight safety operation cost. The project is capable of providing a better understanding of lithium-ion aeronautical life cycle batteries. In this regard, a method has been proposed for generating the discharge, power as well as health monitoring models at the time of battery life cycles. The models are capable of representing how to predict the SoH of the Battery as well as the Rest of the Useful Life (RUL). The method is properly verified through the data from the national prognostics data repository. The models can be implemented through MATLAB or Simulink for simulating the general battery in various operational situations (, 2021).


Considering the summation in the MATLAB Simulink 6 different cases have been evaluated and it represents the simulation of electrical starting regarding the engines through the battery. The electrical current was in an exponential decay which has been represented through the output plots.

The threshold (SoHmin) can be evaluated through the actual number of cycles where the battery clear cannot perform any types of emergency cycles that consist of a 20-minute discharge and the temperature is nearly about 40 degreesCelsius. From the simulation, it is found that the failure occurs where SoH is nearly about 0.62.

From the outcomes of the simulation, it has been found that the failure is occurring at the cycle number of 46.7 as shown in the below figure (Meng. and Li, 2019). In this regard, the failure of the battery can be declared on cycle 46.7. Even the battery is capable of activating the initial profile up to cycle number 42.2 based on the performance needs after around 46.7 cycles.

Considering the RUL estimation method, the curves, as well as the values, have been shown as the equivalent full cycle in the below figure. It can be available in the discharge profile through ambient temperature changes throughout the outcomes of the year presented as an effective degree of accuracy as well as precision (, 2019).

An expected depreciation simulation can be performed where the battery can be powered at a normal operation of current which will be around 80 A for major conditions and temperature will be nearly around 41 degrees celsius (BIST, 2018). The actual rate of degradation has been generated through the growth in the circumference temperature and it has been shown by the yellow line in the plot.

By applying the” RUL estimation” method, the curve has been shown as the equivalent full cycle. With the growth of the rate for erosion the methods can be recognized which can change the rate of decay for this reason or context the actual number of cycles has gradually reduced where the battery can perform after 42.2 cycles (Zhang, 2018). After generating the discharge profile, it came back nominally profile where the algorithm gradually recovers the estimation of the initial value based on the number of subtraction cycles reduced while the temperature rises. The feedback from the output for this simulation approach is capable of confirming the applicability, in this case, I or T, and there are different dynamic changes in the discharge profile RUL verifies the uses of historical profiles regarding the evaluation.

Analysis & Discussion

Evaluation process with the health monitoring through the SoH data regarding the battery discharge, an actual procedure with the remaining useful life regarding the battery proposed vested. It will be consisting of different aspects:

  • A linear regression model was generated through the SoH data available for the data through the function of REGRESS.
  • Proper evaluation process regarding the number of cycles where the battery can reach the maximum threshold regarding the SoH through extrapolating the line found through the linear regression.
  • The actual combination of the model, as well as uncertainty, can be based on the operating profile or I & T in further development.

The outcome is capable of including an approximate interval depending on the RULmin & RULmax represented as a general method (Damian, 2018). It has been shown in the below figure in terms of better understanding.

Related Coefficients considering b0 and b1 as well as the BINT matrix, can be evaluated through the REGRESS function.

... (i)

... (ii)

On the other hand, the extrapolation can be calculated for the estimated life cycle of the battery in terms of failure:

... (iii)

To evaluate the RULmin & RULmax it can be determined by the following equations:

... (iv)

... (v)


Through the entire study, the paper represents a model-based approach for evaluating the state of health as well as estimating the rest of useful life related to the Aeronautical batteries. There is a public battery database that can be used for creating the models. The simulation results proved that the methods can be implemented regarding different discharge profiles as well as the dynamic changes at the time of the cycles and also throughout the life of the battery. Further investigations may be extended regarding this presented work by considering the actual effects of the discharge depth of a proposed battery model.



Ahmed, A.M., Salama, A., Ibrahim, H.A., Sayed, M.A.E. and Yacout, S., 2019, March. Prediction of battery remaining useful life on board satellites using logical analysis of data. In 2019 IEEE Aerospace Conference (pp. 1-8). IEEE.

BIST, B., 2018. A literature survey on drive system based on solar for aerial vehicles. International Journal of Pure and Applied Mathematics, 119(12), pp.4659-4666.

Damian, G.A.D., 2020. Conceptual design of hybrid battery modules for optimising electric aircraft performance (Bachelor's thesis, University of Twente).

Ding, X., Zhang, D., Cheng, J., Wang, B. and Luk, P.C.K., 2019. An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles. Applied Energy, 254, p.113615.

Hashemi, S.R., Esmaeeli, R., Nazari, A., Aliniagerdroudbari, H., Alhadri, M., Zakri, W., Mohammed, A.H., Mahajan, A. and Farhad, S., 2020. A fast diagnosis methodology for typical faults of a lithium-ion battery in electric and hybrid electric aircraft. Journal of Electrochemical Energy Conversion and Storage, 17(1).

Jang, S., 2020. An Empirical Study on Machine Learning based Smart Device Lithium-Ion Cells Capacity Estimation. The Journal of the Convergence on Culture Technology, 6(4), pp.797-802.

Locorotondo, E., Pugi, L., Berzi, L., Pierini, M. and Pretto, A., 2018, June. Online state of health estimation of lithium-ion batteries based on improved ampere-count method. In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-6). IEEE.

Meng, H. and Li, Y.F., 2019. A review on prognostics and health management (PHM) methods of lithium-ion batteries. Renewable and Sustainable Energy Reviews, 116, p.109405.

Rajan, A., Vijayaraghavan, V., Ooi, M.P.L., Garg, A. and Kuang, Y.C., 2018. A simulation-based probabilistic framework for lithium-ion battery modelling. Measurement, 115, pp.87-94.

Xiong, R., Zhang, Y., Wang, J., He, H., Peng, S. and Pecht, M., 2018. Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles. IEEE Transactions on Vehicular Technology, 68(5), pp.4110-4121.

Zhang, C., 2018, September. A Fuzzy Logic Inference System for Testing Lithium-ion Battery State of Charge. In 4th workshop on advanced research and technology in industry (WARTIA 2018). Atlantis Press, Dalian, China. https://doi. org/10.2991/warti a-18.2018 (Vol. 25).

Online Articles, 2018, Modeling and Integration of a Lithium-IonBattery Energy Storage System With theMore Electric Aircraft 270 V DC PowerDistribution Architecture, Available at: [Accessed on 17.07.2021], 2017, Fuzzy energy management for hybrid fuelcell/battery systems for more electric aircraft, Available at: [Accessed on 17.07.2021], 2021, Design and Performance Analysis of Active and Passive Cell Balancing for

Lithium-Ion Batteries, Available at: [Accessed on 17.07.2021], 2019, A Directed Acyclic Graph Network CombinedWith CNN and LSTM for Remaining UsefulLife Prediction, Available at: [Accessed on 17.07.2021]

35% OFF
Get best price for your work
  • 54000+ Project Delivered
  • 500+ Experts 24*7 Online Help

offer valid for limited time only*