Simulating the battery price and the car-mix in key electro-mobility markets via model coupling

Figures & data

Table 1. Key features of the applied model

Model	PTTMAM	TE3
Type	SD	SD
Number of variables	1,500	575
Time horizon	1995-2050	2000-2030
	Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia,
	Denmark, Estonia, Finland, France, Germany, Greece,
Countries	Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg,	China, France, Germany,
	Malta, Netherlands, Poland, Portugal, Romania,	India, Japan, United States
	Slovakia, Slovenia, Spain, Sweden, United Kingdom
Powertrains	16	9
Sales types	Private, fleet, public	First-time sales, repeating sales
Agent groups	Users, Manufacturers, Infrastructure Providers,	Users (Innovators, Utility Maximisers, Low-Cost and
	Authorities	Habit-oriented Purchasers), Authorities
Policy inputs	CO2 targets, financial incentives, fuel taxes,	CO2 targets, purchase incentives, fuel taxes,
	investment in infrastructure	investment in infrastructure
Excluded in one model but included in the other	Battery capacity (by type of electric car) and recharging times	Car sizes (small, medium, large), geography (urban versus non-urban) and other financial incentives

Sources: based on Harrison et al. (2016) and J. J. Gómez Vilchez (2019a)

Figure 1. SD models used in this work and linkages with other models, by method

Figure 2. Soft-linking process chart

Table 2. Main assumptions

Variable	Unit	Numerical value assumed	Justification / Source
Average battery capacity (bus)	kWh	130-200	Own assumption based on Table 1 in Gómez Vilchez (2019b)
Average battery capacity (car)	kWh	30	Own estimate based on EAFO data for 2015 Battery
cost reduction fraction	dimensionless	0.155	Value within the range reported in the literature*
CO2 emission target	gram/km	80-55 (2025) / 66-22 (2030)	Own assumption based on Blanco et al. (2019) and EU (2017)
Industry margin	dimensionless	0.05	Assumed to be a reasonable proxy for profitability
Initial battery cost	dollar/kWh	1,000	Value based on EVI (2016)

Sources: own assumptions. *Such as Gerssen-Gondelach and Faaij (2012), Weiss et al. (2012), and Nykvist and Nilsson (2015).

Table 3. PHEV and BEV battery capacities

		PTTMAM			PTTMAM / TE3 / Bus mode l
		Small	Medium	Large	Average
Vehicle type	Type of EV	2000-2030			2000-2016	2017-2030
Car	PHEV	8	12	16	12	14
	BEV	24	30	88	30	40
Bus	PHEV	-	-	-	130	130
	BEV	-	-	-	200	200

Sources: own assumptions

Table 4. CO₂ emission targets [gCO₂/km], by level of policy ambition

Ambition	Re gion	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029	2030
	China	161	161	161	161	161	161	161	161	161	161	161	161	161
	EU	130	130	130	95	95	95	95	95	95	95	95	95	95
Low	India	130	130	130	130	130	130	130	130	130	130	130	130	130
	Japan	119	119	119	119	119	119	119	119	119	119	119	119	119
	US	151	151	151	151	151	151	151	151	151	151	151	151	151
	China	135	126	117	114	111	109	106	103	101	98	95	93	90
	EU	130	130	130	95	95	95	95	80	80	80	80	80	66
Medium	India	123	120	116	113	110	108	105	103	100	98	95	93	90
	Japan	116	116	115	113	110	108	105	103	100	98	95	93	90
	US	136	131	125	119	113	108	102	97	95	92	90	87	85
High	EU	130	130	130	95	95	95	95	55	55	55	55	55	22

Linear interpolation shown in italics. Sources: own policy assumptions

Figure 3. Battery cost before model linkage, data versus simulation

Sources: own work based on data from (BNEF, 2017) (EVI, 2016) and own simulations

Figure 4. Electric vehicle battery market: estimated annual supply and demand, by type

Sources: own work based on Avicenne (2018), CleanTechnica (2017), CleanTechnica (2018), and EAFO (2018) and EVI (2018)

Figure 5. Annual electric car sales before model linkage, data versus simulation

Sources: own work based on data from EAFO (2018), EVI (2018) and own simulations

Figure 6. Battery price after model linkage, data and trajectory versus simulation

Sources: own work based on BNEF (2017), and Tsiropoulos et al. (2018) and own simulations

Figure 7. Annual electric car sales after model linkage, data versus simulation

Sources: own work based on data from EAFO (2018), EVI (2018) and own simulations

Figure 8. Annual electric car sales in the EU, by simulation run

Sources: own simulations

Figure 9. Electric car stock in non-European countries, by simulation run

Sources: own simulations

Figure 10. Monte Carlo simulation of non-European electric car stock: with model coupling (left) and without (right) [M =million]

Sources: own work using Vensim®

Figure 11. Car-mix in key markets: “Alternative” [left] and sensitivity-based run [right]

Sources: data from OICA (2016) and own simulations

Figure 12. Electric car stock disaggregated by type and country, by run

Sources: (EVI, 2018) and own simulations

Figure A1. Simulated output of the “Bus” and “Norway” models

Sources: own work based on Avicenne (2018), CleanTechnica (2017), CleanTechnica (2018), EAFO (2018), and EVI (2018) and Deuten et al. (2020)

Figure A2. Annual electric car sales in France and Germany [left] and Japan [right], data versus simulation before model linkage

Sources: own work based on data from EAFO (2018), EVI (2018) and own simulations

Figure A3. Overview of the “production costs” module in the te3 model

Sources: own work using Vensim®

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