Neural Networks-Based Forecasting Platform for EV Battery Commodity Price Prediction

This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%.This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%

Hvordan påvirker metallprisene lønnsomheten i batterigjenvinning?

Det grønne skiftet har ført med seg en betydelig økning i etterspørsel etter metaller som brukes i elbilbatterier. En måte å øke tilbudet på, er gjennom gjenvinning av kasserte batterier, spesielt for å øke tilbudet for bilprodusenter her i Europa hvor vi har lav jomfruelig egenproduksjon. Lønnsomheten i gjenvinning er avhengig av metallprisene på verdensmarkedet, da de representerer verdien man kan få for gjenvunnet metall. Disse prisene er volatile og vanskelige å prognostisere over horisonter som er relevante for investeringer i gjenvinningsanlegg. Målet for denne oppgaven er å undersøke hvordan prisene på de viktigste batterimetallene påvirker lønnsomheten i gjenvinning av elbilbatterier. Vi forsøker ikke å lage eksakte prognoser på metallprisen, men ser isteden på lønnsomhetens følsomhet gitt ulike antakelser om prisendringene. Vi anvender standard nåverdianalyse for å måle lønnsomheten i gjenvinning av elbilbatterier i basiscaset, og analyserer prosjektets sensitivitet mot metallprisene. Metaller som gjenvinnes i modellen er kobolt, nikkel, kobber og aluminium. Vi diskuterer litium på et kvalitativt nivå utenom modellen fordi det ikke finnes gode beregninger på ekstraksjonsgrad av litium fra gjenvinning. Vi inkluderer metallet da det trolig vil påvirke lønnsomheten i batterigjenvinning i fremtiden. I basiscaset er metallprisene basert på gjennomsnittlige priser fra London Metal Exchange (LME) mellom 2018-2023. Prosjektet er lønnsomt med forutsetningene i basiscaset med en netto nåverdi på 22 millioner USD gitt en total investeringskostnad på 64,6 millioner USD. Likevel er det mange usikre momenter som kan gjøre prosjektet ulønnsomt. Volatiliteten i prisene har vært høyere de siste tre årene sammenlignet med de siste ti årene, og det er synlig positiv korrelasjon mellom enkelte av prisene. På nivå er alle prisene korrelerte med en koeffisient over 0,5, og på endringsform er kobber korrelert med aluminium og potensielt nikkel. Dette gjør at prosjektet er svært følsomt for prisendringer. Det er sannsynlig at prosjektet vil gå gjennom perioder med lav eller negativ kontantstrøm, som potensielt kan gjøre prosjektet vanskelig å gjennomføre. Prisen på de forskjellige metallene påvirker prosjektet i ulik grad. Ut ifra modellen vi benytter, finner vi at lønnsomheten er mest sensitiv for kobberprisen. Men koboltprisen har både vært mer volatil, og er mer utsatt for risikofaktorer som lav diversifisering i både tilbud og etterspørsel, og en usikker fremtid i batterisammensetningene. Dersom kobolt blir utfaset i batteriene, og koboltprisen kollapser, vil dette kunne ha store konsekvenser for batterigjenvinnere.The transition in the transport sector from fossil fuels to electric vehicles has led to a significant increase in demand for metals used in batteries. Supply can be increased through the recycling of discarded batteries, particularly for car manufacturers in Europe where virgin production of these metals is low. The profitability of recycling depends on the metal prices on the global market, as they represent the value one can obtain from recycled metal. However, these prices are volatile and difficult to forecast over timeframes relevant to investments in recycling facilities. The objective of this thesis is to examine how the prices of key battery metals impact the profitability in battery recycling. We are not attempting to forecast the metal prices. Instead, we look at the sensitivity to profitability given different assumptions about price changes. By applying net present value (NPV) analysis, we measure the profitability of recycling of batteries in Norway in a base case and analyze the project´s sensitivity to relevant metal prices. Metals used in the model are cobalt, copper, nickel, and aluminum. We discuss lithium on a qualitative level separate from the model as there are no reliable calculations on the extraction rate of lithium from recycling available. Still, we include the metal in our thesis because it is very likely to have a big impact on the profitability of battery recycling in the future. In the base case, metal prices are based on average prices from the London Metal Exchange (LME) between 2018-2023. The project is profitable under the assumptions of the base case with an NPV of 22 million USD, given a total investment cost of 65,6 million USD. However, there are many uncertain factors that can make the project to be unprofitable. Price volatility has been higher in the past three years compared to the last ten years, and there are clear positive correlations between several of the prices. At level, all prices are correlated with a coefficient above 0.5, and in terms of changes, copper show correlation with aluminium and potentially nickel. This makes the project highly sensitive to price changes, and it is likely that the project will experience periods of low or negative cash flow, potentially making it challenging to realize. The price changes of the different metals affect the project to varying degrees. Based on our model, we find that profitability is most sensitive to the copper price. Nonetheless, the cobalt price has been more volatile and is more exposed to risk factors such as low diversification in both supply and demand and has an uncertain future in battery compositions. If cobalt is phased out in batteries, and the cobalt price collapses, this could have significant consequences for battery recyclers