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Development of battery models for on-board health estimation in hybrid vehicles
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. (Group of Micro-Modelling and Experimental Kinetics)
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Following the positive reception of electric and hybrid transport solutions in the market, manufacturers keep developing their vehicles further, while facing previously undertaken challenges. Knowing the way lithium-ion batteries behave is still one of the key factors for hybrid electric vehicles (HEVs) development, especially for the requirements of the battery management system during their operation.

Hence, this project focuses on the necessity of robust yet reasonably simple and cost-effective models of the battery for estimating the health status during the operation of the vehicles. With this aim, the procedure and models to calculate the state-of-health (SOH) indicators, internal resistance and capacity, are proposed and the results discussed. Two machine-learning based models are presented, a support vector machine (SVM) and a neural network (NN), together with one equivalent circuit model (ECM).

The data used for training and validating the models comes from testing the batteries in the laboratory with standard performance tests and real driving cycles along the battery lifespan. However, data sets measured in actual heavy-duty vehicles during their operation for three years is also analysed and compared. With respect to this matter, a study of the battery materials, behaviour and operation attributes is carried out, highlighting the main aspects and issues that affect the development of the models.

The inputs for the models are signals that can be measured on-board in the vehicles, as current, voltage or temperature, and other derived from them as the state-of-charge (SOC) calculated by the internal battery management unit. Time-series of the variables are used for simulation purposes. The management of signals and implementation of the models is done in the environment of Matlab-Simulink, using some of its in-built functions and other specifically developed.

The models are evaluated and compared by means of the normalized root mean squared error (NRMSE) of the voltage output profile compared to that of the tested batteries, but also the error of the internal resistance calculations calculated from the voltage profile for the three models, and the internal parameters in case of the ECM. While despite the difficulties faced with the data, the models can eventually perform accurate estimations of the resistance, the results of the capacity estimations are omitted in the document due to the lack of useful information derived. Nevertheless, the calculation procedure and other considerations to take into account regarding the capacity estimation and data sets are undertaken.

Finally, the conclusions about the data used, battery materials and methods evaluated are drawn, laying down recommendations as to design the performance tests following the conditions of the driving cycles, and indicating the higher general performance of the SVM respect the other two methods, while asserting the usefulness of the ECM. Moreover, the battery with NMC material composition is observed to be easier to predict by the models than LFP, also showing different evolution of its internal resistance.

Place, publisher, year, edition, pages
2017. , 79 p.
Keyword [en]
Lithium-ion battery, State-of-health, Resistance, Capacity, Materials, Model, Equivalent circuit model, Support vector machine, Neural networks
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-211680OAI: oai:DiVA.org:kth-211680DiVA: diva2:1130332
External cooperation
Scania CV AB
Subject / course
Materials Science and Engineering
Educational program
Master of Science - Industrial Engineering and Management
Presentation
2017-06-09, B117 Hörsal, Granparksvägen 10, Södertälje, 09:00 (English)
Supervisors
Examiners
Available from: 2017-08-09 Created: 2017-08-09 Last updated: 2017-08-09Bibliographically approved

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