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A Recurrent Neural Network for Battery Capacity Estimations in Electrical Vehicles Simon Corell

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A Recurrent Neural Network for Battery Capacity Estimations in Electrical Vehicles Simon Corell LIU-ITN-TEK-A-19/046--SE A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles Simon Corell 2019-09-03 Department of Science and Technology Institutionen för teknik och naturvetenskap Linköping University Linköpings universitet nedewS ,gnipökrroN 47 106-ES 47 ,gnipökrroN nedewS 106 47 gnipökrroN LIU-ITN-TEK-A-19/046--SE A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles Examensarbete utfört i Datateknik vid Tekniska högskolan vid Linköpings universitet Simon Corell Handledare Daniel Jönsson Examinator Gabriel Eilertsen Norrköping 2019-09-03 Upphovsrätt Detta dokument hålls tillgängligt på Internet – eller dess framtida ersättare – under en längre tid från publiceringsdatum under förutsättning att inga extra- ordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervisning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säkerheten och tillgängligheten finns det lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsmannens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/ Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a considerable time from the date of publication barring exceptional circumstances. The online availability of the document implies a permanent permission for anyone to read, to download, to print out single copies for your own use and to use it unchanged for any non-commercial research and educational purpose. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional on the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about the Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its WWW home page: http://www.ep.liu.se/ © Simon Corell Linköping University | Department of Science and Technology Master’s thesis, 30 ECTS | Computer Science and Engineering 202019 | LIU-ITN/LITH-EX-A--2019/001--SE A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles Simon Corell Supervisor : Daniel Jönsson Examiner : Gabriel Eilertsen Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about the Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: http://www.ep.liu.se/. © Simon Corell Abstract This study is an investigation if a recurrent long short-term memory (LSTM) based neural network can be used to estimate the battery capacity in electrical cars. There is an enormous interest in finding the underlying reasons why and how Lithium-ion batteries ages and this study is a part of this broader question. The research questions that have been answered are how well a LSTM model estimates the battery capacity, how the LSTM model is performing compared to a linear model and what parameters that are important when estimating the capacity. There have been other studies covering similar topics but only a few that has been performed on a real data set from real cars driving. With a data science approach, it was discovered that the LSTM model indeed is a powerful model to use for estimation the capacity. It had better accuracy than a linear regression model, but the linear regression model still gave good results. The parameters that implied to be important when estimating the capacity were logically related to the properties of a Lithium-ion battery. Acknowledgments This project has been the most challenging and exciting work I have ever been a part of. I am grateful to have received this opportunity in such a reputable company as Volvo cars. The idea behind the project came from Christian Fleischer at Volvo cars. Christian have supervised the project and have provided me with various pieces of advice. A huge thank you Christian for giving me this opportunity and for all the help you have provided. The initial idea was unfortunately not possible due to different circumstances but I am still satisfied with what the project resulted in. A big thank you to all other Volvo cars employees that have helped in various discussions and meetings. Specially Herman Johnsson for all the support you gave. I also want to thank my examiner Gabriel Eilertsen and supervisor Daniel Jönsson for the great feedback and guidance throughout the project. Lastly, I want to thank all other mas- ter thesis students that were at the same department for all the laughs and amusing moments. Simon Corell, 2019 iv Contents Abstract iii Acknowledgments iv Contents v List of Figures vii 1 Introduction 1 1.1 Motivation . 1 1.2 Aim............................................ 1 1.3 Research questions . 2 1.4 Delimitations . 2 2 Background description and related work 3 2.1 Big Data . 3 2.2 Time Series . 3 2.3 Machine Learning . 4 2.4 Neurons & Synapses . 4 2.5 Lithium Ion Battery Description and Battery Management System . 4 2.6 Related research . 7 3 Theory 9 3.1 Data pre-processing . 9 3.2 Linear Regression . 17 3.3 Artificial neural networks . 18 4 Method 25 4.1 Prestudy: Capacity estimation using lab data from NASA . 25 4.2 Overall Project Progression . 27 4.3 Data pre-processing . 28 4.4 Implementation of the Linear Regression model . 33 4.5 Implementation of the recurrent LSTM neural network . 33 5 Results and Analysis 35 5.1 Results From Linear Regression Model . 35 5.2 Long Short-Term Memory (LSTM) . 38 6 Discussion 40 6.1 Method . 40 6.2 Results . 43 6.3 Replicability, reliability and validity . 45 6.4 The work in a wider context . 45 v 7 Conclusion 46 7.1 Summarizing . 46 7.2 Future work . 47 A Appendix 49 Bibliography 50 vi List of Figures 2.1 A Lithium Ion battery pack for Nissan Leaf at the Tokyo Motor Show 2009 . 5 3.1 Basic idea behind Local Outlier Factor . 11 3.2 The normal distribution . 12 3.3 The typical pipeline for a filter method . 14 3.4 The typical pipeline for a Wrapper method . 16 3.5 A simple linear model . 17 3.6 A simple ANN . 19 3.7 Vanilla recurrent neural network . 23 3.8 The LSTM cell . 24 4.1 Absolute difference for every 3000 estimation . 26 4.2 Model Pipeline . 27 4.3 Capacity values over all cycles for some cars . 28 4.4 Capacity values over all cycles for some cars . 29 4.5 Top 25 Pearson correlation coefficients . 31 4.6 The Pearson correlations between the signals . 33 5.1 Error distribution for linear regression . 36 5.2 Plot showing the estimated values scattered against the real values . 36 5.3 Plot showing the estimated values and the real values for car BDCWCU5 . 37 5.4 Plot showing the estimated values against the real values for car BDCWCU5 . 37 5.5 Error distribution for LSTM . 38 5.6 Plot showing the estimated values against the real values . 38 5.7 Plot showing the estimated values against the real values for car BDCWCU5 . 39 5.8 Plot showing the estimated values against the real values for car BDCWCU5 . 39 vii 1 Introduction This research study focuses primarily on the hypothesis that a recurrent neural network can, with high accuracy, estimate the current capacity on lithium-ion batteries.
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