Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects

ABSTRACT

Governments worldwide are investing in innovative transport technologies to foster their development and widespread adoptions. Since accurate predictions are essential for evaluating public policies, great efforts have been devoted to forecast the potential demand and adoption times of these innovations. However, this proves to be challenging, and it often fails to deliver accurate predictions. Learning a lesson to guide future work is critical but difficult because forecast figures depend on modelling methods and assumptions, and exhibit a great variability in methodologies, data and contexts. This paper provides a critical review of the models and methods employed in the literature to forecast the demand for electric vehicles (EVs), with a focus on the methods for incorporating choice behaviour into diffusion modelling. The review complements and extends previous works in three ways: (1) it focuses specifically on the ways in which fuel type choice has been incorporated into diffusion models or vice-versa; (2) it includes a discussion on forecast accuracy, contrasting the predictions with the actual figures available and estimating an average root mean square error and (3) it compares models and methods in terms of their strengths and limitations, and their implications in forecasting accuracy. In doing that, it also contributes discussing the literature published between 2019 and 2021. The analysis shows that EV demand estimation requires solving the non-trivial issue of jointly modelling the factors that induce diffusion in a social network and the instrumental and psychological elements that might favour household adoption considering the available alternatives. Mixed models that integrate disaggregate micro-simulation tools to capture social interaction and discrete choice models for individual behaviour appear as an interesting approach, but like almost all methods analysed failed to deliver satisfactory results or accurate predictions even when using sophisticated modelling techniques. Further improvement in various components is still needed, in particular in the input data, which regardless of the method used, is key to the accuracy of any forecasting exercise.

KEYWORDS:

• Innovation diffusion
• electric vehicles
• agent-based models
• system dynamics
• willingness to consider
• choice and diffusion models

Acknowledgments

We would like to thank the editors and reviewers for their valuable comments and suggestions, which helped us to improve the paper. All remaining errors are our responsibility.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 This article uses market shares (i.e. the proportion of EVs in the total fleet) as indicators of market penetration, except where explicitly indicated. It must be noted that, the term market share is also interchangeably referred to as penetration rate or adoption rate in the literature.

2 In this article, the EV abbreviation generically refers to electric vehicles, and includes battery electric vehicles, hybrid vehicles and plug-in hybrids. No specific distinction is made between sub-typologies unless required.

3 Jochem et al. (2018)'s review consider 44 articles from 1995 to 2016. Our review includes 11 of these 44 articles (the ones that include both diffusion and substitution) plus 43 additional articles from between 2008 and 2021.

4 Diffusion/substitution studies outside the EV market (e.g. hydrogen or fuel-cell vehicles) and recent works involving electric share vehicles or electric/autonomous vehicles were also excluded from the review.

5 Note that, in 45% of the articles, this information is not provided. Interestingly, in some articles, data were collection concluded several years prior to article publication, which might be a slight concern in terms of parameter validity.

6 “In a deep sense, the ultimate goal of the researcher is to represent utility well enough that a logit model is appropriate (i.e. that the only remaining aspects constitute simply white noise) Seen in this way, the logit model is the ideal rather than a restriction” (Train, 2009, pp. 35–36). However, arguably noDCM is specified well enough for this to be true.

7 The cost minimisation technique is analogous to the utility maximisation expressed in monetary terms.

8 An interesting comparison of ABM and EGT can be found on Adami et al. (2016).

9 We excluded theoretical modelling exercises, forecasts without a clearly defined timeframe and/or geographic context, predictions without a reliable source for comparison, articles without detailed yearly forecasts, and recent articles for which benchmark figures are not yet available.

10 Note that four articles present detailed results for one scenario only.

11 We could not consider uncertainty in predictions of expected market shares, as suggested by one reviewer, because the necessary information to perform these analyses is generally unavailable, with only Querini and Benetto (2015) and Kangur et al. (2017) providing confidence intervals for their predictions.

12 We studied the possibility of defining an “acceptable accuracy”, as suggested by one reviewer. However, we believe this should be evaluated in a case-by-case scenario. For example, the relative error in Brown (2013) is 50%, but the author obtains an average RMSE of 0.4%, which means that he correctly predicted a small increase in adoption rates 8 years after the forecast.

13 More complex specifications were tested but the basic linear model was deemed to have the better level-of-fit and significance.

Additional information

Funding

This work was supported by The Leverhulme Trust [Doctoral Scholarship in Behaviour Informatics, grant number DS-2017-015] and the multimodal study of behaviour, and by the Newcastle University [Overseas Scholarship].