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Competition between battery switching and charging in electric vehicle: considering anticipated regret

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Abstract

Consumers may anticipate regret when making purchasing decisions on a battery-charging vehicle (CV) or a battery-switching vehicle (SV). They anticipate a revolutionary regret when purchasing SVs with an extra switching function but a higher price, and a conservative regret at a lower price but only a charging function. This study investigates whether and how anticipated regret affects competing manufacturers’ price and quality. We find that SV demand is always higher than CV demand, and anticipated regret always promotes electric vehicle (EV) adoption. Interestingly, the revolutionary regret discourages the development of the extra battery-switching function and weakens the consumer’s preference for SVs. Then, the presence of only revolutionary regret causes a lose–lose situation where SV manufacturers reduce the quality level of the additional battery-switching attribute and thus intensify price competition for both manufacturers. SV sales cannibalize CV demand when consumers anticipate revolutionary regret. Moreover, the presence of only conservative regret has no impact on SV manufacturers, but affects CV manufacturers. When EV charging is convenient for consumers, CV manufacturers benefit from conservative regret; otherwise, CV manufacturers quit the market. Finally, when two kinds of regret coexist, the profits of both manufacturers are lower than when there is no regret and only conservative regret, but they are higher than when there exists only revolutionary regret. The results not only contribute to guiding the manufacturer to invoke or mitigate consumers’ anticipated regret, but also provide advice to show or hide this regret for consumers.

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The authors confirm that the data supporting the findings of this study are available within the paper and its appendix.

Notes

  1. Tesla launched battery-switching technology in 2013, but did not commercialize it and gave up developing this technology. As of 2021, the number of CV and SV is 6.4 million and 0.25 million in China, respectively.

References

  • Abouee-Mehrizi, H., Baron, O., Berman, O., & Chen, D. (2020). Adoption of electric vehicles in car sharing market. Production and Operations Management, 30, 190–209.

    Google Scholar 

  • Ajanovic, A., & Haas, R. (2018). Electric vehicles: Solution or new problem? Environment, Development and Sustainability, 20, 7–22.

    Google Scholar 

  • Alp, O., Tan, T., & Udenio, M. (2022). Transitioning to sustainable freight transportation by integrating fleet replacement and charging infrastructure decisions. Omega, 109, 102595.

    Google Scholar 

  • AskCI, 2022. Forecasting and analysis of the number and competition pattern of China's battery switching stations of new energy vehicles in 2022.

  • Avci, B., Girotra, K. & Netessine, S. (2014). Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact. Management Science, 61(4), 772–794.

    Google Scholar 

  • Chen, X., Xing, K., Ni, F., Wu, Y.J. & Xia, Y.X. (2021). An Electric Vehicle Battery-Swapping System Concept, Architectures, and Implementations. Ieee Intelligent Transportation Systems Magazine, 14(5), 175–194.

  • Chen, Y., & Turut, Ö. (2013). Context-dependent preferences and innovation strategy. Management Science, 59, 2747–2765.

    Google Scholar 

  • Consul, S., Singh, K.V., Bansal, H.O. & Kim, K.A. (2022). Intelligent switching mechanism for power distribution in photovoltaic-fed battery electric vehicles. Environment Development and Sustainability. https://doi.org/10.1007/s10668-022-02398-0

  • Diecidue, E., Rudi, N., & Tang, W. J. (2012). Dynamic purchase decisions under regret: Price and availability. Decision Analysis, 9, 22–30.

    Google Scholar 

  • Eberle, U. & von Helmolt, R. (2010). Sustainable transportation based on electric vehicle concepts: a brief overview. Energy & Environmental Science, 3(6), 689–699.

    Google Scholar 

  • Gu, Q., Zhang, R., & Liu, B. (2023). Pricing and advertising decisions in O2O supply chain with the presence of consumers anticipated regret. Journal of Business & Industrial Marketing, 38, 1135–1149.

    Google Scholar 

  • Ha, Y. (2018). Expectations gap, anticipated regret, and behavior intention in the context of rapid technology evolvement. Industrial Management & Data Systems, 118, 606–617.

    Google Scholar 

  • Hu, T., Ma, H., Sun, H., & Liu, K. (2023). Electrochemical-theory-guided modeling of the conditional generative adversarial network for battery calendar aging forecast. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11, 67–77.

    Google Scholar 

  • IEA. (2022). Electric Vehicles. IEA.

    Google Scholar 

  • iResearch, 2022. 2022 China new energy vehicle battery switching market research report. https://report.iresearch.cn/content/2022/05/430554.shtml

  • Jiang, B. J., Narasimhan, C., & Turut, O. (2017). Anticipated regret and product innovation. Management Science, 63, 4308–4323.

    Google Scholar 

  • Jiang, J., Zhang, L., Wen, X., Valipour, E., & Nojavan, S. (2022). Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach. International Journal of Hydrogen Energy, 47, 39429–39442.

    CAS  Google Scholar 

  • Jin, Q., Zhu, M., Yang, Y., & Liu, L. (2022). Consumer search with anticipated regret. Production and Operations Management, 31, 3337–3351.

    Google Scholar 

  • Ko, H., Pack, S., & Leung, V. C. M. (2022). An optimal battery charging algorithm in electric vehicle-assisted battery swapping environments. IEEE Transactions on Intelligent Transportation Systems, 23, 3985–3994.

    Google Scholar 

  • Levinson, R. S., & West, T. H. (2018). Impact of convenient away-from-home charging infrastructure. Transportation Research Part D-Transport and Environment, 65, 288–299.

    Google Scholar 

  • Li, Y., Zhang, P., & Wu, Y. (2018). Public recharging infrastructure location strategy for promoting electric vehicles: A bi-level programming approach. Journal of Cleaner Production, 172, 2720–2734.

    Google Scholar 

  • Lin, L., Shi, J., Ma, C., Zuo, S., Zhang, J., Chen, C., & Huang, N. (2023). Non-intrusive residential electricity load decomposition via low-resource model transferring. Journal of Building Engineering, 73, 106799.

    Google Scholar 

  • Liu, Z.F., Wang, Y.J. & Feng, J. (2022). Vehicle-type strategies for manufacturer's car sharing. Kybernetes. https://doi.org/10.1108/K-11-2021-1095

  • Liu, B. Q., Pantelidis, T. P., Tam, S., & Chow, J. Y. J. (2022). An electric vehicle charging station access equilibrium model with M/D/C queueing. International Journal of Sustainable Transportation, 17, 228–244.

    Google Scholar 

  • Liu, Z., Feng, J., & Uden, L. (2023). From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction. Technological Forecasting and Social Change, 192, 122565.

    Google Scholar 

  • Mourali, M., Yang, Z. Y., Pons, F., & Hassay, D. (2018). Consumer power and choice deferral: The role of anticipated regret. International Journal of Research in Marketing, 35, 81–99.

    Google Scholar 

  • Mussa, M., & Rosen, S. (1978). Monopoly and product quality. Journal of Economic Theory, 18, 301–317.

    Google Scholar 

  • Nasiry, J., & Popescu, I. (2012). Advance selling when consumers regret. Management Science, 58, 1160–1177.

    Google Scholar 

  • NIO, 2021. The process of the battery switching only takes 3–5 minutes.

  • Niu, B. Z., Dai, Z. P., & Li, Q. Y. (2022). Sharing knowledge to an entrant for production investment confronting COVID-19: Incentive alignment and lose-lose dilemma. Risk Analysis, 42, 177–205.

    Google Scholar 

  • Ouyang, X., & Xu, M. (2022). Promoting green transportation under the belt and Road Initiative: Locating charging stations considering electric vehicle users’ travel behavior. Transport Policy, 116, 58–80.

    Google Scholar 

  • Ruan, J. G., Walker, P. D., Zhang, N., & Wu, J. L. (2017). An investigation of hybrid energy storage system in multi-speed electric vehicle. Energy, 140, 291–306.

    Google Scholar 

  • Saboori, H., & Jadid, S. (2022). Mobile battery-integrated charging station for reducing electric vehicles charging queue and cost via renewable energy curtailment recovery. International Journal of Energy Research, 46, 1077–1093.

    Google Scholar 

  • Schloter, L. (2022). Empirical analysis of the depreciation of electric vehicles compared to gasoline vehicles. Transport Policy, 126, 268–279.

    Google Scholar 

  • Schneider, F., Thonemann, U. W., & Klabjan, D. (2018). Optimization of battery charging and purchasing at electric vehicle battery swap stations. Transportation Science, 52, 1211–1234.

    Google Scholar 

  • Singh, A., & Letha, S. S. (2019). Emerging energy sources for electric vehicle charging station. Environment, Development and Sustainability, 21, 2043–2082.

    Google Scholar 

  • Sun, B., Sun, X., Tsang, D. H. K., & Whitt, W. (2019). Optimal battery purchasing and charging strategy at electric vehicle battery swap stations. European Journal of Operational Research, 279, 524–539.

    Google Scholar 

  • Tan, X. Q., Qu, G. N., Sun, B., Li, N., & Tsang, D. H. K. (2019). Optimal scheduling of battery charging station serving electric vehicles based on battery swapping. IEEE Transactions on Smart Grid, 10, 1372–1384.

    Google Scholar 

  • Tirole,. (1988). The theory of industrial organization. MIT Press.

    Google Scholar 

  • Tran, C. Q., Keyvan-Ekbatani, M., Ngoduy, D., & Watling, D. (2021). Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment. Transportation Research Part E: Logistics and Transportation Review, 154, 102460.

    Google Scholar 

  • Xiao, D., An, S., Cai, H., Wang, J., & Cai, H. M. (2020). An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length. Journal of Energy Storage, 29, 10.

    Google Scholar 

  • Yang, J., & Sun, H. (2015). Battery swap station location-routing problem with capacitated electric vehicles. Computers & Operations Research, 55, 217–232.

    Google Scholar 

  • Yang, W. H., Wang, H., Wang, Z. J., Fu, X. L., Ma, P. C., Deng, Z. C., & Yang, Z. H. (2020). Optimization strategy of electric vehicles charging path based on “traffic-price-distribution” mode. Energies, 13, 26.

    Google Scholar 

  • Zeelenberg, M. (1999). Anticipated regret, expected feedback and behavioral decision making. Journal of Behavioral Decision Making, 12, 93–106.

    Google Scholar 

  • Zeelenberg, M., Beattie, J., van der Pligt, J., & de Vries, N. K. (1996). Consequences of regret aversion: Effects of expected feedback on risky decision making. Organizational Behavior and Human Decision Processes, 65, 148–158.

    Google Scholar 

  • Zhang, X., Wang, Y., Yuan, X., Shen, Y., Lu, Z., Wang, Z. (2022). Adaptive dynamic surface control with disturbance observers for battery/supercapacitor-based hybrid energy sources in electric vehicles. IEEE Transactions on Transportation Electrification, 1–1. https://doi.org/10.1109/TTE.2022.3194034

  • Zhao, X., Peng, B., Zheng, C., & Wan, A. (2022). Closed-loop supply chain pricing strategy for electric vehicle batteries recycling in China. Environment, Development and Sustainability, 24, 7725–7752.

    Google Scholar 

  • Zheng, X. M., Menezes, F., Zheng, X. F., & Wu, C. K. (2022). An empirical assessment of the impact of subsidies on EV adoption in China: A difference-in-differences approach. Transportation Research Part a-Policy and Practice, 162, 121–136.

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge support from Shanghai Science and Technology Program (Project No. 21692109000, 22692113300).

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JF was involved in conceptualization, formal analysis; YW helped in methodology; ZL contributed to project administration; YW was involved in resources, writing—original draft; ZL and JF helped in writing—review & editing.

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Correspondence to Zhenfeng Liu.

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Liu, Z., Wu, Y. & Feng, J. Competition between battery switching and charging in electric vehicle: considering anticipated regret. Environ Dev Sustain 26, 11957–11978 (2024). https://doi.org/10.1007/s10668-023-03592-4

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