👨‍👨‍👧 🙄 🙉 When will everyone ride electric cars? 💭 🌎 🕴️

On January 11, 1914, the Henry Ford statement appeared in the New York Times:

“I hope that in a year we will begin production of the electric car. I do not like to talk about things a year in advance, but I want to tell you something about my plans. The fact is that Mr. Edison and I have been working for several years to create cheap and practical electric vehicles. They were made as an experiment, and we are satisfied that the path to success is obvious. The problem with electric vehicles has so far been to create a lightweight battery that can work over long distances without recharging. Mr. Edison has been experimenting with such a battery for some time. ”

But something went wrong ...

Thomas Edison at the Detroit Electric

This publication is a logical continuation of my previous article “Investigation of the logistic function as the law of development of the industry”.

where the parameter r influences the growth rate of the market share, since it stands in the exponent's exponent - the higher this coefficient, the faster the new technology will conquer the market, i.e. every year, technology should become interesting to more people for its convenience., K coefficient describes the growth potential of a new technology, i.e. at low K values, the technology will not be able to capture the entire market, but will only be able to win back the market segment in which it will be more interesting than the previous technology.

The statement of the problem is to find the necessary parameters for the logistic equation that allows us to predict the development of the electric car industry:

“Zero year” - a year in which half of the world's sold cars will be with an electric motor (P0 = 0.5, t = 0);
growth rate of market share ( r ) of electric vehicles.

In this case, let us assume:

electric cars will completely replace cars with an internal combustion engine (ICE) from the market (K = 1), since I do not see a line that will allow me to segment the car market.

The market of heavy vehicles and special equipment was not taken into account when drawing up the model, so there is no electric car market within this industry yet.
we now live in "negative time" (P (t) <0) and in the function we will use the offset relative to the "zero year" for our time (t-t0).

Statistics on passenger car sales are taken here .

Statistical data on electric vehicle sales is taken here .

Statistics until 2012 on electric vehicles are very scarce and will not be taken into account in the study.

As a result, we have the following data:

Zero Year and Market Growth Ratio Program

import matplotlib.pyplot as plt import numpy as np import math x = np.linspace(2012, 2019, 8) y1 = np.array([60936407, 63429200, 65708230, 66314155, 69464432, 70694834, 68690468, 64341693]) # - y2 = np.array([52605, 97507, 320713, 550297, 777495, 1227117, 2018247, 1940147]) # - y = y2/y1 # ymax=1 # Gmax=2025 # " " rmax=0.35 # k=1 # "1" , p0=0.5 # " " for j in range(10): # " " x0=2025+j r=0.35 for i in range(10): # " " r=0.25+0.02*i y4=k*p0*math.e**(r*(x-x0))/(k+p0*(math.e**(r*(x-x0))-1))-y # print(str(x0).ljust(20), str(r).ljust(20), max(abs(y4))) if max(abs(y4))<=ymax: # r ymax=max(abs(y4)) Gmax=x0 rmax=r print(str(Gmax).ljust(20), str(rmax).ljust(20), ymax) # " ", r

As a result of the program, the following values were selected:
Zero year - 2028.
Growth coefficient - 0.37 The

maximum deviation of statistical data from the function value is 0.005255.

The function graph for the section between 2012 and 2019 is as follows:

The final graph with the forecast until 2050 looks like this:

The graph shows a cut-off of 99% of the entire market, i.e. by 2040, an electric car will completely supplant cars with internal combustion engines.

Function Graphing Program

import matplotlib.pyplot as plt import numpy as np import math x = np.linspace(2012, 2019, 8) y1 = np.array([60936407, 63429200, 65708230, 66314155, 69464432, 70694834, 68690468, 64341693]) y2 = np.array([52605, 97507, 320713, 550297, 777495, 1227117, 2018247, 1940147]) y = y2/y1 k=1 p0=0.5 x0=2028 r=0.37 y1=k*p0*math.e**(r*(x-x0))/(k+p0*(math.e**(r*(x-x0))-1)) # 2012 2019 fig, ax = plt.subplots(figsize=(30, 20), facecolor="#f5f5f5") plt.grid() ax.plot(x, y, 'o', color='tab:brown') ax.plot(x, y1) # 2010 2050 x = np.linspace(2010, 2050) y2 = [k*p0*math.e**(r*(i-x0))/(k+p0*(math.e**(r*(i-x0))-1)) for i in x] y3 = 0.99+0*x fig, ax = plt.subplots(figsize=(30, 20), facecolor="#f5f5f5") ax.set_xlim([2010, 2050]) ax.set_ylim([0, 1]) plt.grid() plt.plot(x, y2, x, y3)

findings

Following the same logic as in the description of the history of the development of engines with ICEs, I tried to predict the development of the electric car industry using available statistics.

The results obtained indicate that by 2030 half of the world's sold cars will be with an electric motor, and by 2040, ICE cars will be a thing of the past.

Of course, after 2030, some people will drive gasoline cars that they bought before 2030, but they will know that their next purchase will be an electric car.
The growth rate for electric vehicles is 4 times higher than the growth rate for cars with internal combustion engines, which suggests that new technologies are increasingly entering our lives, becoming a commonplace in our everyday lives (here we recall mobile phones).

In the coming years, a problem that Edison could not solve should be solved - a sufficiently capacious battery that will increase the mileage between charging stations.

To create a network of charging stations equivalent to the existing network of gas stations, it is necessary to modernize the existing electric networks in large cities and along highways.

Also , the Jevans paradox will hinder the growth of electric vehicle sales , but it also interfered with oil amid falling demand for coal.

PS
If Edison could solve the task set for him, then the "age of oil" did not even begin ...

When will everyone ride electric cars?

findings

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