Electric Strategy: Evolutionary Game Analysis of Pricing Strategies for Battery-Swapping Electric Logistics Vehicles

Abstract

Driven by the urgent need to decarbonize the logistics sector—where conventional vehicles exhibit high energy consumption and emissions, posing significant environmental sustainability challenges—electrification represents a pivotal strategy for reducing emissions and achieving sustainable urban freight transport. Despite rising global electric vehicle sales, the penetration rate of electric logistics vehicles (ELVs) remains comparatively low, impeding progress toward sustainable logistics objectives. Battery-swapping mode (BSM) has emerged as a potential solution to enhance operational efficiency and economic viability, thereby accelerating sustainable adoption. This model improves ELV operational efficiency through rapid battery swaps at centralized stations. This study constructs a tripartite evolutionary game model involving government, consumers, and BSM-ELV manufacturers to analyze market dynamics under diverse strategies. Key considerations include market scale, government environmental benefits, battery leasing/purchasing costs, lifecycle cost analysis (via discount rates), and resource efficiency (reserve battery ratio $\lambda$). MATLAB-2021b-based simulations predict participant strategy evolution paths. Findings reveal that market size and manufacturer expectations significantly influence governmental and manufacturing strategies. Crucially, incorporating discount rates demonstrates that battery leasing reduces consumer enterprises’ initial investment, enhancing economic sustainability and cash flow while offering superior total cost of ownership. Furthermore, gradual reduction of government subsidies effectively stimulates market self-regulation, incentivizes leasing adoption, and bolsters long-term economic/operational sustainability. Market feedback can guide policy adjustments toward fiscally sustainable support mechanisms. This study proposes the following management implications for advancing sustainable logistics: 1. Governments should phase out subsidies systematically to foster market resilience; 2. Manufacturers must invest in BSM R&D to improve efficiency and resource circularity; 3. Consumer enterprises can achieve economic benefits and emission reductions by adopting BSM-ELVs.

1. Introduction

Significant transformations towards low-carbon transitions have been embraced across all sectors of society to achieve the carbon peak and carbon neutrality. The logistics and transportation sector, a major contributor to greenhouse gas emissions and urban air pollution, faces urgent sustainability challenges due to its reliance on fossil fuels [1]. Accelerating a higher rate of electric vehicle (EV) adoption represents a critical pathway to reduce greenhouse gas emissions from fuel use [2,3] and advance sustainable mobility. The adoption of electric vehicles in the transportation sector can significantly facilitate the advancement of green logistics [4], enhancing environmental quality and resource efficiency. According to data released by EV-Volumes and IEA, global sales of electric vehicles reached 17 million units in 2024, as shown in Figure 1 [5,6]. Despite this progress, a significant sustainability gap remains: Compared with the vigorous development of electric vehicles, the development of electric logistics vehicles lags critically behind, hindering decarbonization efforts in freight transport.

In the logistics and transportation industry, conventional logistics vehicles are characterized by high energy consumption, high emissions, and high pollution, making them a key area for carbon emission reduction [1]. Therefore, the electrification of logistics vehicles has great potential to reduce carbon emissions and improve urban air quality [7,8]. Recognizing this sustainability imperative, various countries have introduced corresponding policies and regulations to promote the electrification transformation of the logistics and transportation industry. Chinese national ministries have successively issued a series of policies to promote the penetration of electric vehicles in the logistics sector. The “Electric Vehicle Industry Development Plan (2021–2035)” mentions that starting from 2021, in national ecological civilization pilot areas and critical regions for air pollution prevention and control, the proportion of electric vehicles in newly added or updated public transport, taxis, logistics distribution, and other vehicles should not be less than 80%. This sets a target from a macro quantity perspective [9]. The German cabinet has proposed a discount for electric logistics vehicles, equivalent to 50% of the vehicle’s purchase cost, with this measure set to end in 2030 [10]. The Norwegian government has set a target for all new trucks to be zero-emission or use biogas by 2030, aiming to increase the share of electric logistics vehicles from 10% to 20% [11], and requires that all newly built residential and office buildings be equipped with charging piles and that ultra-fast charging stations be set up every 50 km of the national road network, with the goal of achieving 100% public charging coverage by 2025 [12]. The European Union plans to pass legislation requiring car rental companies (such as Sixt and Europcar) and large enterprises with fleets to purchase only pure electric vehicles starting from 2030, covering approximately 60% of new car sales in the EU (about 6.4 million units per year). This move aims to make up for the lack of electrification power at the consumer end through centralized procurement by enterprises and accelerate the realization of the goal of completely banning the sale of fuel vehicles by 2035 [12]. These policies highlight the global commitment to leveraging ELVs for sustainable freight. However, as of 2024, the global penetration rate of electric vehicles has reached 19.2%, while the penetration rate for logistics vehicles is significantly lower than this figure. For instance, China, the largest market and manufacturing base for electric vehicles, also had an electric penetration rate of only 13.8% for its logistics vehicles. This gap persists due to sustainability-related barriers: electric logistics vehicles require larger battery capacity and more extended range—the constraints of driving distance and the imperfect charging infrastructure limit the promotion of electric logistics vehicles [13]. The lengthy downtime required for charging severely impacts operational efficiency and economic viability [14], undermining their environmental and economic sustainability. To address these sustainability bottlenecks, some companies have proposed a battery-swapping mode to alleviate user mileage anxiety [15]. The battery-swapping mode is an innovative business approach where batteries are centrally stored and recharged at charging stations, and vehicles are quickly energized by swapping batteries at the station [16]. Under normal circumstances, an electric logistics vehicle equipped with battery swapping capabilities can complete recharging within 10 min, matching the operational efficiency of traditional fuel vehicles. The battery-swapping model alleviates users’ range anxiety, and the way consumers choose electric logistics vehicles is no longer singular. Furthermore, BSM facilitates better battery management and potential for second-life applications, enhancing circularity. Despite its potential, early BSM adoption faced challenges: high costs, limited infrastructure, and a lack of standards led to failures (e.g., Better Place [17]). Recent technological advances (e.g., Sany Heavy Industry’s 1.9-min swaps [18]) and corporate commitments (e.g., Volvo’s 2030 electrification goal [19]) signal renewed potential for BSM to overcome these hurdles and contribute significantly to sustainable logistics.

However, new sustainability challenges threaten ELV adoption: Supply-demand imbalances for critical materials [20] and subsidy reductions (e.g., in China and the UK [20,21]), coupled with high manufacturer pricing, dampen consumer willingness [22]. This creates a critical tension: the urgent need for sustainable logistics solutions versus the economic barriers to ELV/BSM adoption. Optimizing pricing strategies within the BSM framework is thus essential to unlock its full environmental and economic sustainability potential. Facing the stagnation in industry development caused by the mismatch between sale prices and demand, electric logistics vehicles increasingly require a business model innovation combined with resource recycling, relevant policy changes, and other means to optimize existing pricing strategies. This is necessary to quickly reverse the current predicament and achieve the overall healthy development of the industry.

Facing the contradiction between the high pricing of electric logistics vehicles and the urgent demand from consumer enterprises, the battery-swapping model breaks through the limitations of traditional charging methods, establishing a win-win system for consumers and manufacturers. It enhances the efficiency of resource utilization, thereby promoting further development of the industry.

From the above content, it can be seen that although the electric logistics vehicle has been given importance, its development is not easy. Therefore, this paper aims to build a tripartite evolutionary game model involving the government, consumers, and electric logistics vehicle manufacturers. By incorporating the discount rate, we explicitly analyze the long-term economic sustainability implications of pricing strategies and subsidy policies. Our core objective is to identify pathways that simultaneously promote BSM-ELV adoption (environmental sustainability) and ensure economic viability for stakeholders (economic sustainability).

Our research contributions are summarized as follows:

• Sustainability-Centric Framework: This study incorporates the government, consumers, and electric logistics vehicle manufacturers into a unified three-party evolutionary game framework to analyze dynamic pricing strategies under BSM and their impact on sustainable ELV adoption.
• Lifecycle Cost Analysis for Sustainable Decisions: This article integrates the discount rate as the core parameter and uses net present value (NPV) analysis to reveal the advantages of the battery leasing model in terms of long-term economic sustainability.
• Policy Levers for Sustainable Markets: This article reveals that both the market size and the manufacturers’ expectations are the key factors that jointly drive the prices towards a sustainable equilibrium. This finding provides an important reference basis for formulating effective policies.

The remaining sections of this article, as depicted in Figure 2, are organized logically. Section 2 reviews the relevant literature. Section 3 establishes a foundation by outlining a series of assumptions, setting key parameters, and constructing a comprehensive tripartite evolutionary game model. Section 4 delves deeper into the analysis, identifying and examining the stable points within the evolutionary game framework. Section 5 then applies this theoretical framework by assigning specific values to the parameters and conducting a scenario-based simulation analysis to explore the evolution of pricing strategies for electric logistics vehicle manufacturers. Section 6 follows with a sensitivity analysis, highlighting the model’s sensitivity to changes in various parameters. Finally, Section 7 summarizes this research’s essential findings and conclusions and offers practical insights and management implications for governments, electric logistics vehicle manufacturers, and consumers, drawing from the preceding analyses.

2. Literature Review

This paper is closely related to four aspects: the research of the battery-swapping mode of electric vehicles, the development of electric logistics vehicles, the application of evolutionary game theory in the research of electric vehicles, and smart charging infrastructure and emerging technologies.

2.1. Research on the Battery-Swapping Mode of Electric Vehicles

Scholars have conducted different research focusing on the battery-swapping mode. Yuan and colleagues explore the critical points of different operational models of battery leasing services and battery swapping services from the perspective of consumer utility [23]. Battery leasing services and battery swapping services, as important business models for the development of electric vehicles, must not overlook technical safety issues. Hu and colleagues focus on analyzing the critical technologies of battery-swapping electric vehicles to contribute to the safety of the swapping process [24]. Considering the differences between charging and battery-swapping as energy replenishment methods, Zhang and colleagues have constructed a site selection model to derive the optimal strategy for construction costs, operational costs, and user satisfaction [25]. To save time for electric vehicle owners and optimize the operation of battery-swapping stations, scholars such as Wang have proposed a real-time optimization strategy. This strategy involves electric vehicles sending requests and the system recommending the best charging station for them [26]. Zu and colleagues aim to minimize the electrical energy electric vehicles consume while traveling to battery-swapping stations. They analyze the impact of the involved parameters to determine the optimal layout for charging and swapping stations, providing a reference for the siting of urban battery-swapping and charging stations [27]. Additionally, battery-swapping stations must keep certain batteries in stock to maintain customer service. Wang and colleagues have explored the optimal number and scheduling of batteries in inventory for battery-swapping stations [28]. However, as the core component of electric vehicles, the cost of batteries accounts for half of the total operating expenses of a Battery Swapping Station (BSS). To optimize the operation of BSS and increase its profitability, scholars such as Wang have constructed a sustainable battery supply selection decision framework for BSS battery suppliers based on the battery-swapping operation model [29]. Deng and colleagues developed a mathematical model to determine the optimal configuration of battery-swapping stations, considering battery capacity degradation, thereby reducing total costs [30]. Zeng and other scholars proposed a tiered battery-swapping mode, which formulates different energy replenishment plans for low-demand, medium-demand, and high-demand users on highways to reduce the operating costs of BSS [31]. Scholars’ research on battery-swapping mode covers many aspects. These studies not only enrich the theoretical system of power transfer modes but also provide essential support and guidance for developing and applying electric vehicles.

Conceptual Clarification:

Throughout this study, two distinct but interrelated concepts are systematically differentiated, as follows:

Battery-swapping service: Refers to the technical operation where depleted batteries are physically exchanged for fully charged units at dedicated stations, typically within 5–10 min. This constitutes the core energy replenishment mechanism.

Battery leasing: Denotes the business model where users lease batteries instead of purchasing them outright. Under battery leasing, battery ownership remains with manufacturers/operators, while users pay periodic usage fees covering battery leasing, maintenance, and swapping services.

2.2. Development of Electric Logistics Vehicles

Many scholars have explored electric logistics vehicles to regulate and reduce carbon emissions in the transportation industry. Research by Xue and others indicates that the greenhouse gas emissions from conventional fuel commercial vehicles will peak before 2030, and the development of electric commercial vehicles and the improvement of their penetration rate are the main ways to save energy and reduce emissions [32]. Many countries in the European Union promote electric logistics vehicles through financial incentives to reduce carbon emissions from urban freight transport [33]. The European Parliament, in its legislation on promoting environmentally friendly, energy-efficient road transport vehicles, has defined the form and obligations for procuring ecologically friendly vehicles in urban logistics environments [34]. This directive has been incorporated into the legislation of EU member states to achieve the minimum share targets for procuring environmentally friendly vehicles in road transport for the years 2025 and 2030 [35]. He and other scholars proposed a digital twin method to predict the performance of electric commercial vehicle battery brackets to enhance their safety factor and speed on the road [36]. Speaking of safety issues, Lal and other scholars believe that the government could consider establishing differential insurance premiums for electric logistics vehicles compared to conventional fuel logistics vehicles to promote the popularization of electric logistics vehicles. In addition, temperature can affect the cost of energy, and the government could set corresponding energy charging standards in different temperature regions to promote the penetration of electric logistics vehicles [37]. There is a clear distinction between electric logistics vehicles and passenger cars. Logistics vehicles operate around the clock with higher usage intensity, while passenger cars exhibit a tidal characteristic. Therefore, the demand for energy replenishment and other support services is higher for users of electric logistics vehicles. Raeesi addresses the limited driving range issue of electric logistics vehicles by coordinating the addition of charging and battery-swapping stations within transportation routes [38]. Ghobadi et al. studied the vehicle routing problem based on the premise that transportation companies have efficiency issues in identifying the impact of uncertain factors in their daily logistics operations [39]. The development of electric logistics vehicles has attracted much attention. However, existing studies mostly focus on the promotion policies and environmental impacts of electric logistics vehicles, and there are few discussions on their pricing strategies and market mechanisms. Under the premise of the development of electric logistics vehicles, this paper deeply analyzes the pricing strategy of electric replacement new energy under the discount rate, which enriches the research in this field.

2.3. Application of Evolutionary Game in the Research of Electric Logistics Vehicles

As the in-depth exploration of the development of electric logistics vehicles continues, evolutionary game theory that considers multiple perspectives has been widely used in the study of electric logistics vehicles. From the supply chain perspective, Shi and colleagues constructed an evolutionary game model that includes the government, consumers, and electric vehicle manufacturers to explore the participants’ evolutionary stable states and investment portfolio stable strategies [40]. Similarly, Song and others proposed a tripartite evolutionary game model between electric vehicle manufacturers, consumers, and the government, offering more practical recommendations for the government and electric vehicle manufacturers compared to traditional policies of direct government subsidies [41]. Wang and colleagues proposed two strategies, continuous subsidies and adaptive subsidies. They constructed an evolutionary game model for the diffusion of electric vehicles based on the network, evaluating the subsidy strategies from both the supply and demand sides to maximize the benefits of subsidies [42]. With the green development of the express delivery industry supply chain as the background, Shi and others established a tripartite evolutionary game involving the government, vehicle suppliers, and express delivery companies, exploring decisions that maximize the interests of all three parties [43]. By combining relevant literature, the relationship between the government, consumer enterprises, and logistics vehicle manufacturers is shown in Figure 3.

2.4. Smart Charging Infrastructure and Emerging Technologies

Beyond battery-swapping, smart charging infrastructure plays a complementary role in EV energy replenishment. For shared EV fleets, integrated models jointly optimize charging station deployment and dynamic pricing strategies to balance grid loads and user demand [44]. Meanwhile, bidirectional charging (V2G) leverages data-driven control to transform EVs into grid assets. Qualitative studies highlight its potential for peak shaving and renewable energy integration, though challenges persist in stakeholder coordination and battery degradation [45]. While these advancements enhance energy flexibility, battery-swapping remains optimal for commercial logistics vehicles due to minimal downtime and centralized management.

The above research results have laid a solid theoretical foundation for this research, but there are still some areas that need further research. Few scholars have researched the pricing strategies of battery-swapping electric logistics vehicles. How the purchasing methods adopted by consumers and the pricing strategies formulated by manufacturers under different car purchase subsidies will interact with each other has become a key issue to be solved to promote the rationalization of pricing of electric logistics vehicles, promote the low-carbon development of logistics and transportation, and achieve the global “dual carbon” goal. This is also a challenge to logistics and transportation caused by the change of electric electrification and power replacement technology. Therefore, in the context of the “dual-carbon” goal to promote the electrification transformation of the logistics and transportation industry, this study dug into the important factors affecting the pricing strategy of electric logistics vehicles, and studied the influence of the interaction among manufacturers’ pricing strategy, car purchase subsidies and consumers’ purchasing methods on the evolutionary game path of the application and promotion of electric logistics vehicles. On this basis, reasonable countermeasures and suggestions are provided for manufacturers.

3. Model Construction

3.1. Basic Assumptions

An in-depth analysis of the sales and application process of electric logistics vehicles, based on different roles and behavioral motivations, divides the relevant entities involved in the sales of electric logistics vehicles under the background of energy conservation and emission reduction into government agencies (promoting market development and market guidance entities), consumers (the main body of purchasing and using electric logistics vehicles), and electric logistics vehicle manufacturers (the main body of producing and manufacturing electric logistics vehicles). On this basis, the competitive relationships between different types of entities are refined to provide a theoretical basis for the model construction in the following text. This paper primarily focuses on the game relationship between the government and consumers, as well as between consumers and electric logistics vehicle manufacturers, in the context of energy conservation and emission reduction. Based on the game relationships among the entities and considering the actual situation, the following rational assumptions are proposed:

(a)

Based on the differentiation of game relationships, the local government’s strategy is determined: the government’s subsidy policy for consumers purchasing electric logistics vehicles is divided into {with purchase subsidy, without purchase subsidy}, with the corresponding probabilities being $x$ and $(1-x)$, respectively. Consumers choose different purchasing methods based on the price of electric logistics vehicles, with available strategies being {whole vehicle purchase, battery leasing}, and the corresponding probabilities are $y$ and $(1-y)$. As the primary entity in product pricing, the electric logistics vehicle manufacturer has strategies of {penetration pricing, skimming Pricing}, with corresponding probabilities of $z$ and $(1-z)$.

(b)

When the government opts for a subsidy policy for purchasing vehicles, it will incur costs for the subsidies to encourage consumer purchases and also face expenses related to time, labor, and administrative costs during the policy implementation process. The government will benefit from the electrification process of logistics vehicles, obtaining environmental benefits $U$ throughout the entire lifecycle of each electric logistics vehicle.

(c)

When consumers opt for the whole vehicle purchase strategy, the manufacturer earns sales revenue $P_C$ for the vehicle body and $P_B$ for the power battery. After the power battery reaches the end of its life, it is recycled at a recycling price of $B$. Assuming no unexpected damage, the battery service life is relatively consistent. When consumers choose the battery leasing strategy, the manufacturer earns rental revenue $P_s$ based on the cost of the battery. The manufacturer incurs manufacturing costs of $C_V$ for the vehicle body and $C_b$ for the power battery. Considering the battery-swapping mode, which necessitates the production of additional reserve batteries at a ratio of $\lambda$ for the operation of battery-swapping, the actual manufacturing cost of the power battery is $(1+\lambda )C_b$. The manufacturer is responsible for recycling the power battery. Thus, they can reap M’s recycling benefits regardless of the strategy employed.

(d)

When the manufacturer opts for a penetration pricing strategy, it assumes a promotional degree of $\mathsf{\alpha }$ for consumers choosing electric logistics vehicles, representing the deviation of the penetration price from the market price. Similarly, when the manufacturer decides on a non-penetration pricing strategy to maintain the original price or sell at a markup, it assumes a pricing fluctuation degree of $\beta$ for consumers choosing electric logistics vehicles. Due to pricing strategy changes, the utility consumers gain from purchasing a vehicle is either reduced or increased. When the manufacturer adopts a penetration pricing strategy, the marginal change in price obtained is $(1-\alpha )$. Correspondingly, when adopting a non-penetration pricing strategy, the marginal change in price obtained is $(1+\beta )$. To simplify calculations, $\alpha$ and $\beta$ are symmetrically valued.

(e)

In this paper, two price ratios are introduced to simplify calculations: the first is the discounted ratio $K_1$ of the battery-swapping service price to the power battery selling price after considering the discount rate, and the second is the intuitive ratio $K_2$ of the total sum of the battery-swapping service price over the entire lifecycle to the power battery selling price. The power battery itself is the primary cost associated with providing battery-swapping services, which circulates between the swapping stations and logistics vehicles. The selling price of the power battery will be an essential reference for the manufacturer when setting the price for the swapping service. Therefore, this paper assumes $P_s=K_2{P}_b$, where $K_2$ is the intuitive ratio of the total battery-swapping service price over the entire lifecycle to the selling price of the power battery. From the perspective of consumer enterprises, since battery-swapping services are typically priced on an annual or monthly basis and remain relatively stable after the contract is signed, the discount rate $T$ should be considered when considering the battery lease purchase method and making annual payments over $n$ years. Therefore, the actual battery-swapping service pricing $K_1{P}_b$ that affects consumer demand is shown as follows:

$$K_1{P}_b=\sum _1^t{\displaystyle \frac{K_2{P}_b^t}{{(1+T)}^{t-1}n}},t=(\mathrm{1,2},\mathrm{3,4}\dots n)$$

(1)

To illustrate the difference clearly, this section assumes a battery with a selling price of 280,000 yuan, retired after eight years of use, with a total rental payment of 340,000 yuan over the eight years. By default, the game system is in an inflationary environment, with an initial discount rate of $T_1$ set at 10% and remaining constant. The battery purchase or rental payment should be made at the beginning of the period to facilitate discounting calculations. After rounding, the results are organized in

Table 1. Discount rate projection table.

$\mathbf{Year} \mathbf{Used} \mathit{t}$	1	2	3	4	5	6	7	8	Total
Buying batteries	28.00	0	0	0	0	0	0	0	28.00
Battery Rental	4.25	4.25	4.25	4.25	4.25	4.25	4.25	4.25	34.00
Intuitive ratio ${ K}_2$									1.21
Discount rate ${(1+T)}^{t-1}$	1.00	1.10	1.21	1.33	1.46	1.61	1.77	1.95
Net discounted rent over the years	4.25	3.86	3.51	3.19	2.90	2.64	2.40	2.18	24.94
Discounted ratio $K_1$									0.89

.

(f)

Although some research reports indicate that when adopting the battery-swapping mode, certain specific-purpose electric logistics vehicles, due to their short-distance and high-frequency operation, will achieve more significant additional returns from the reduction of recharging time and the improvement of recharging efficiency, this study assumes a universal utility of $\mathrm{R}$ for consumers, regardless of whether they choose to purchase the vehicle outright or lease the battery.

(g)

In this paper, we regard the demand for EVs as the sum of a linear function of the price consumers offered, including the unit price of the EV, including a vehicle body and a battery, the price of energy replenishment, and the technology preference. The consumers have a coefficient for the unit price of an electric vehicle, which is a one-off transaction and represented by $a$, $0 < a < 1$. $b$ represents the cross-price elastic coefficient of the BSS price paid by instalments, $-1 < b < 0$, so the absolute value of a should be greater than the one of $b$, which means consumers are more willing to accept instalments facing the same price. $Pc$ and $Ps$ denote the unit price of an electric vehicle without the battery and the unit revenue of the life-cycle battery-swapping service, respectively. Additionally, considering that battery and energy replenishment technology is the key to user experiences and there is a specific technology preference represented by $\theta$ among consumers [46,47], $h$ means the technology level of battery and energy replenishment. Referring to the literature [48], the technology research and development cost is set as $I={\displaystyle \frac{1}{2}}{g}_i{h}^2$. The demand functions in this paper are as follows.

$$Q_1=\varphi -a(1-\alpha )(P_v+P_b-S)+\theta h$$

(2)

$$Q_2=\varphi -a(1+\beta )(P_v+P_b-S)+\theta h$$

(3)

$$Q_3=\varphi -a\left(1-\alpha \right)\left(P_v-S\right)+b(1-\alpha )K_1{P}_b+\theta h$$

(4)

$$Q_4=\varphi -a\left(1+\beta \right)\left(P_v-S\right)+b(1+\beta )K_1{P}_b+\theta h$$

(5)

$$Q_5=\varphi -a(1-\alpha )(P_v+P_b)+\theta h$$

(6)

$$Q_6=\varphi -a(1+\beta )(P_v+P_b)+\theta h$$

(7)

$$Q_7=\varphi -a\left(1-\alpha \right)P_v+b(1-\alpha )K_1{P}_b+\theta h$$

(8)

$$Q_8=\varphi -a\left(1+\beta \right)P_v+b(1+\beta )K_1{P}_b+\theta h$$

(9)

3.2. Main Parameter Settings

Government, manufacturers, and consumers participate in the three-party evolutionary game behavior strategy selection for the sale of electric logistics vehicles with probabilities $x,y$, and $z$, respectively, and the values of $x,y$, and $z$ are all between 0 and 1. The remaining parameter settings are shown in

Table 2. The description of the symbols.

Symbols	Symbol Definition
$U$	Government Environmental Revenues in the Application Process of Electric Logistics Vehicles
$S$	Government Subsidy Costs for Subsidizing the Purchase of Complete Vehicles
$C_g$	Government’s Comprehensive Costs Paid During the Policy Implementation Process
$\alpha$	In the case of penetration pricing, the degree of pricing fluctuation for consumer selection by the manufacturer.
$\beta$	In the case of non-penetration pricing, the degree of pricing fluctuation for consumer selection by the manufacturer.
$P_v$	Revenue from the Sale of electric Logistics Vehicle Bodies
$P_B$	Revenue from the Sale of electric Logistics Vehicle Power Batteries
$R$	Utility Obtained by Consumers Over the Entire Lifecycle of the Vehicle After Purchase
$K_1$	The Discounted Proportion of the Total Price of Battery Swapping Services Over the Entire Lifecycle to the Sale Price of Power Batteries
$K_2$	The Intuitive Proportion of the Total Price of Battery Swapping Services Over the Entire Lifecycle to the Sale Price of Power Batteries
$C_v$	Manufacturing Cost of Electric Logistics Vehicle Bodies
$C_b$	Manufacturing Cost of Electric Logistics Vehicle Batteries
$M$	Revenue from the Recycling and Utilization of Retired Power Batteries
$\lambda$	The Proportion of Storage Batteries Required for Battery Swapping Operations
$B$	Recycling Price of Retired Power Batteries
$L$	Manufacturer’s Expected Valuation of Market Share
$Q_i$	Sales Volume Under Various Scenarios
$U_{gi}$	Government Revenues Under Different Scenarios
$U_{mi}$	Manufacturer’s Revenues Under Different Scenarios
$U_{ci}$	Consumer’s Revenues Under Different Scenarios

.

3.3. Establishment of Payment Matrix

Firstly, based on the evolutionary game theory proposed by Maynard (1973), the payoff situations obtained after the game between the subjects are analyzed based on their behavioral strategies and game relationships; on this basis, the payoffs between different decision-makers are classified; finally, according to the strategy relationships between the subjects, the corresponding payoff matrices are established, with an example of the payoff matrix shown in

Table 3. The payment matrix under the vehicle purchase subsidy strategy $\left(x\right)$.

Consumer Enterprise	Manufacturer
	$\mathbf{Penetration} \mathbf{Pricing} \mathit{z}$	$\mathbf{Non}-\mathbf{Penetration} \mathbf{Pricing} (1-\mathit{z})$
Purchase of Complete Vehicles $\mathrm{y}$	${\mathrm{U}}_{\mathrm{g}1}=(\mathrm{U}-\mathrm{S}){\mathrm{Q}}_1-{\mathrm{C}}_{\mathrm{g}}$  ${\mathrm{U}}_{\mathrm{m}1}=(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_1-({\mathrm{C}}_{\mathrm{v}}+{\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_1+\mathrm{M}-\mathrm{B}$  ${\mathrm{U}}_{\mathrm{c}1}=(\mathrm{R}-(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{S}+\mathrm{B}){\mathrm{Q}}_1$	${\mathrm{U}}_{\mathrm{g}2}=\left(\mathrm{U}-\mathrm{S}\right){\mathrm{Q}}_2-{\mathrm{C}}_{\mathrm{g}}$  ${\mathrm{U}}_{\mathrm{m}2}=(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_2-({\mathrm{C}}_{\mathrm{v}}+{\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_2+\mathrm{M}-\mathrm{B}$  ${\mathrm{U}}_{\mathrm{c}2}=(\mathrm{R}-(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{S}+\mathrm{B}){\mathrm{Q}}_2$
Battery Leasing $(1-\mathrm{y})$	${\mathrm{U}}_{\mathrm{g}3}=\left(\mathrm{U}-\mathrm{S}\right){\mathrm{Q}}_3-{\mathrm{C}}_{\mathrm{g}}$  ${\mathrm{U}}_{\mathrm{m}3}=(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{K}}_2{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_3-({\mathrm{C}}_{\mathrm{v}}+\left(1+\mathsf{\lambda }\right){\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_3+\mathrm{M}$  ${\mathrm{U}}_{\mathrm{c}3}=(\mathrm{R}-(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{{\mathrm{K}}_2\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{S}){\mathrm{Q}}_3$	${\mathrm{U}}_{\mathrm{g}4}=\left(\mathrm{U}-\mathrm{S}\right){\mathrm{Q}}_4-{\mathrm{C}}_{\mathrm{g}}$  ${\mathrm{U}}_{\mathrm{m}4}=(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{K}}_2{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_4-({\mathrm{C}}_{\mathrm{v}}+\left(1+\mathsf{\lambda }\right){\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_4+\mathrm{M}$  ${\mathrm{U}}_{\mathrm{c}4}=(\mathrm{R}-(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{K}}_2{\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{S}){\mathrm{Q}}_4$

and

Table 4. The payment matrix without a vehicle purchase subsidy strategy $(1-x)$.

Consumer Enterprise	Manufacturer
	$\mathbf{Penetration} \mathbf{Pricing} \mathit{z}$	$\mathbf{Non}-\mathbf{Penetration} \mathbf{Pricing}(1-\mathit{z})$
Purchase of Complete Vehicles $y$	${\mathrm{U}}_{\mathrm{g}5}=\mathrm{U}{\mathrm{Q}}_5$  ${\mathrm{U}}_{\mathrm{m}5}=(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_5-({\mathrm{C}}_{\mathrm{v}}+{\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_5+\mathrm{M}-\mathrm{B}$  ${\mathrm{U}}_{\mathrm{c}5}=(\mathrm{R}-(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{B}){\mathrm{Q}}_5$	${\mathrm{U}}_{\mathrm{g}6}=\mathrm{U}{\mathrm{Q}}_6$  ${\mathrm{U}}_{\mathrm{m}6}=(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_6-({\mathrm{C}}_{\mathrm{v}}+{\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_6+\mathrm{M}-\mathrm{B}$  ${\mathrm{U}}_{\mathrm{a}6}=(\mathrm{R}-(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{P}}_{\mathrm{b}}\right)+\mathrm{B}){\mathrm{Q}}_6$
Battery Leasing $(1-y)$	${\mathrm{U}}_{\mathrm{g}7}=\mathrm{U}{\mathrm{Q}}_7$  ${\mathrm{U}}_{\mathrm{m}7}=(1-\mathsf{\alpha })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{K}}_2{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_7-({\mathrm{C}}_{\mathrm{v}}+\left(1+\mathsf{\lambda }\right){\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_7+\mathrm{M}$  ${\mathrm{U}}_{\mathrm{c}7}=(\mathrm{R}-(1-\mathsf{\alpha }\left)\left({\mathrm{P}}_{\mathrm{v}}+{{\mathrm{K}}_2\mathrm{P}}_{\mathrm{b}}\right)\right){\mathrm{Q}}_7$	${\mathrm{U}}_{\mathrm{g}8}=\mathrm{U}{\mathrm{Q}}_8$  ${\mathrm{U}}_{\mathrm{m}8}=(1+\mathsf{\beta })\left({\mathrm{P}}_{\mathrm{v}}+{\mathrm{K}}_2{\mathrm{P}}_{\mathrm{B}}\right){\mathrm{Q}}_8-({\mathrm{C}}_{\mathrm{v}}+\left(1+\mathsf{\lambda }\right){\mathrm{C}}_{\mathrm{b}}-\mathrm{L}){\mathrm{Q}}_8+\mathrm{M}$  ${\mathrm{U}}_{\mathrm{c}8}=(\mathrm{R}-(1+\mathsf{\beta }\left)\left({\mathrm{P}}_{\mathrm{v}}+{{\mathrm{K}}_2\mathrm{P}}_{\mathrm{b}}\right)\right){\mathrm{Q}}_8$

Note: In the chart, $Q_i$, $U_{gi}{, U}_{mi}$, and $U_{ci}$ represent the demand and the revenues of the government, manufacturer, and consumers under different states, respectively.

.

3.4. Construction of Replication Dynamic Equations

Based on the revenue payment matrix shown in Table 2 and Table 3, the expected revenue for the government’s choice of car purchase subsidy and no car purchase subsidy can be calculated as $U_{g1}$ and $U_{g2}$, respectively. Therefore, the government’s average expected revenue is the following:

$${\overline{U}}_g=x{U}_{g1}+(1-x)U_{g2}$$

(10)

Combining Table 1 and Table 3, and Equation (10), we can derive the replicator dynamic equation for the government’s decision-making as follows:

$$\begin{array}{cc}F\left(x\right)={\displaystyle \frac{dx}{dt}}=x& (U_{g1}-{\overline{U}}_g)\hfill \\ & =(x-1)x(C_g+S(\varphi +h\theta +b{K}_1{P}_b(y-1)\left(\beta \right(z-1)+\alpha z\hfill \\ & -1)+a(P_c-S+U+P_{b}y\left)\right(\beta (z-1)+\alpha z-1\left)\right))\hfill \end{array}$$

(11)

Similarly, the expected utilities for the consumer enterprise’s choices of purchasing the entire vehicle and leasing the battery can be calculated as $U_{c1}$ and $U_{c2}$, respectively. Therefore, the average expected utility for the consumer enterprise is the following:

$${\overline{U}}_c=y{U}_y+\left(1-y\right)U_{1-y}$$

(12)

By integrating Table 2 and Table 3, and Equation (12), we can derive the replicator dynamic equation for the consumer enterprise’s decision-making as follows:

$$\begin{array}{cc}F\left(y\right)={\displaystyle \frac{dy}{dt}}=y& \left(U_y-{\overline{U}}_c\right)\hfill \\ & =(y-1)y\left(\right(R-(1+\beta )(K_1{P}_b+P_c)\left)\right(b(1+\beta )K_1{P}_b-a(1\hfill \\ & +\beta )P_c+\varphi +h\theta )(x-1)(z-1)-(B-(1+\beta \left)\right(P_b+P_c)\hfill \\ & +R\left)\right(\varphi -a(1+\beta )(P_b+P_c)+h\theta \left)\right(x-1\left)\right(z-1)-(R-(1\hfill \\ & +\beta \left)\right(K_1{P}_b+P_c)+S)\left(b\right(1+\beta )K_1{P}_b+\varphi -a(1+\beta \left)\right(P_c-S)\hfill \\ & +h\theta \left)x\right(z-1)+(B-(1+\beta )(P_b+P_c)+R+S\left)\right(\varphi -a(1\hfill \\ & +\beta \left)\right(P_b+P_c-S)+h\theta )x(z-1)-\left(\right(\alpha -1\left)\right(K_1{P}_b+P_c)\hfill \\ & +R\left)\right(\varphi -(\alpha -1)b{K}_1{P}_b+a(\alpha -1)P_c+h\theta \left)\right(x-1)z+(B\hfill \\ & +(\alpha -1)(P_b+P_c)+R\left)\right(a(\alpha -1)(P_b+P_c)+\varphi +h\theta \left)\right(x\hfill \\ & -1)z+((\alpha -1)(K_1{P}_b+P_c)+R+S\left)\right(\varphi -(\alpha -1)b{K}_1{P}_b\hfill \\ & +a(\alpha -1)(P_c-S)+h\theta )xz-(B+(\alpha -1)(P_b+P_c)+R\hfill \\ & +S\left)\right(\varphi +a(\alpha -1)(P_b+P_c-S)+h\theta \left)xz\right)\hfill \end{array}$$

(13)

Following the same logic, the average expected profit for the manufacturing enterprise can be obtained as follows:

$${\overline{U}}_m=z{U}_z+(1-z)U_{1-z}$$

(14)

By combining the tables and Equation (14), we can derive the replicator dynamic equation for the manufacturer’s decision-making as follows:

$$\begin{array}{cc}\hfill F\left(z\right)={\displaystyle \frac{dz}{dt}}=z& \left(U_z-{\overline{U}}_m\right)\hfill \\ & =(\alpha +\beta )\left(b{K}_1{P}_b\right(C_b+C_c-L+C_b\lambda -2K_2{P}_b+\alpha K_2{P}_b\hfill \\ & -\beta K_2{P}_b-2P_c+\alpha P_c-\beta P_c\left)\right(y-1)+(\varphi +h\theta \left)\right(P_c+P_{b}y\hfill \\ & +K_2(P_b-P_{b}y))+a(C_c{P}_c-L{P}_c-2K_2{P}_b{P}_c+\alpha K_2{P}_b{P}_c\hfill \\ & -\beta K_2{P}_b{P}_c-2{P_c}^2+\alpha {P_c}^2-\beta {P_c}^2-C_{c}Sx+LSx+2K_2{P}_{b}Sx\hfill \\ & -\alpha K_2{P}_{b}Sx+\beta K_2{P}_{b}Sx+2P_{c}Sx-\alpha P_{c}Sx+\beta P_{c}Sx+C_c{P}_{b}y\hfill \\ & -L{P}_{b}y-2{P_b}^{2}y+\alpha {P_b}^{2}y-\beta {P_b}^{2}y-4P_b{P}_{c}y+2\alpha P_b{P}_{c}y\hfill \\ & -2\beta P_b{P}_{c}y+2K_2{P}_b{P}_{c}y-\alpha K_2{P}_b{P}_{c}y+\beta K_2{P}_b{P}_{c}y+2P_{b}Sxy\hfill \\ & -\alpha P_{b}Sxy+\beta P_{b}Sxy-2K_2{P}_{b}Sxy+\alpha K_2{P}_{b}Sxy-\beta K_2{P}_{b}Sxy\hfill \\ & +C_b\left(Sx\right(\lambda \left(y-1\right)-1)+P_{b}y+P_c(1+\lambda -\lambda y\left)\right)\left)\right)(z-1)z\hfill \end{array}$$

(15)

4. Tripartite Evolutionary Stability Strategy Analysis

Evolutionary game theory is an analytical method that combines game theory with dynamic evolutionary processes. It discards the traditional assumptions of “perfect rationality” and “complete information” in game theory, and instead bases its research on the premise of “bounded rationality”, studying the long-term evolutionary dynamics of group strategies. Its core idea is the following: individuals in the group obtain benefits (fitness) through strategic interactions, high-yield strategies gradually spread in the group (selection mechanism), and random mutations are allowed to generate new strategies. Eventually, it converges to the Evolutionarily Stable Strategy (ESS) through models such as the replication dynamic equations. This theory was formally proposed with the concept of ESS, and has been widely applied in fields such as biology, economics, and sociology, for example, explaining the evolution of cooperative behaviors, traffic pattern selection, and the design of energy investment and financing mechanisms [49].

The purpose of stability analysis is to identify strategy profiles that are resilient to behavioral perturbations under bounded rationality. By solving for evolutionarily stable strategies (ESS) via Jacobian eigenvalue analysis, we verify which equilibria dynamically persist—ensuring that recommended policies and market strategies will not collapse due to minor deviations. This significance lies in translating theoretical equilibria into real-world robust decisions, distinguishing transient states from sustainable systemic outcomes.

4.1. Establishment of the Jacobian Matrix

A system’s Evolutionarily Stable Strategies (ESS) can be derived from the local stability analysis of the system’s Jacobian matrix. We obtain the Jacobian matrix by taking the first-order partial derivatives of the replicator dynamic equations concerning $x$, $y$, and $z$. The analytical solutions can be found in Appendix A.1

$$\left[\begin{array}{ccc}{\displaystyle \frac{\partial F\left(x\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(x\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(x\right)}{\partial z}}\\ {\displaystyle \frac{\partial F\left(y\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(y\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(y\right)}{\partial z}}\\ {\displaystyle \frac{\partial F\left(z\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(z\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(z\right)}{\partial z}}\end{array}\right]=\left[\begin{array}{ccc}U11& U12& U13\\ U21& U22& U23\\ U31& U32& U33\end{array}\right]$$

4.2. Analysis of Equilibrium Points

Setting $F\left(x\right)=F\left(y\right)=F\left(z\right)=0$, we can determine the local equilibrium points to be H1(0,0,0), H2(0,0,1), H3(0,1,0), H4(1,0,0), H5(0,1,1), H6(1,0,1), H7(1,1,0), and H8(1,1,1). Substituting these eight equilibrium points into the Jacobian matrix, we calculate the eigenvalues for each equilibrium solution, as shown in

Table 5. The eigenvalues of equilibrium points and their stability.

Equilibrium Point	Eigenvalues	Result
(0,0,0)	$(-,-,-)$	Case 1, Case 2
(1,0,0)	$(-,-,-)$	Case 3
(0,1,0)	$(-,+,+)$	Instability
(0,0,1)	$(-,-,-)$	Case 4
(0,1,1)	$(-,\mathrm{X},-)$	Instability
(1,0,1)	$(+,-,+)$	Instability
(1,1,0)	$(+,+,-)$	Instability
(1,1,1)	$(+,+,+)$	Instability

. The analytical solutions can be found in Appendix A.2.

5. Simulated Analysis

5.1. Assignment of Basic Parameters

According to the semi-annual financial report of CATL (Contemporary Amperex Technology Co., Limited) for 2024, a rough estimate of the cost for a standard 100 kWh power battery is 60,000 yuan. Due to different usage scenarios, electric logistics vehicles have a range of battery capacity specifications from 200 kWh to 400 kWh. Taking the average value of 300 kWh as the standard for this paper, the manufacturing cost of the battery is approximately 180,000 yuan. According to the pricing data from the website: https://product.360che.com/m714/178564_index.html (accessed on 9 May 2025) [50], taking a specific model of electric heavy-duty truck from Dechuang Future as an example, the market average price is about 730,000 yuan, of which the purchase cost of the vehicle body is 450,000 yuan. Therefore, the purchase cost of the battery can be 280,000 yuan. Considering that consumer enterprises are far more sensitive to the one-time vehicle body purchase cost than to the annual battery leasing cost, that is, $\left|a\right|>\left|b\right|$, in this paper, we set $a=0.7, b=-0.3$ [51,52]. The required number of reserve batteries for the Battery Swap Station (BSS) can be calculated. Based on NIO battery swap station operations, a ratio of EV sales to reserve batteries below 1:1.3 is sufficient to meet operational demand. Therefore, this article sets $\lambda = 1.2$. The value $L=350000$ integrates (i) R&D amortization costs per battery (45,000–75,000), (ii) premiums for future supply-chain economies, and (iii) NPV gains from target market share (5–10%). This aligns with industry practices where manufacturers absorb losses up to 50% of production costs to accelerate market entry, and the remaining data are organized as follows:

While parameter uncertainty (e.g., cost volatility) is significant, our scenario design intentionally isolates market-size impacts ($\varphi$)—the dominant regulator concern for ELV adoption. This aligns with World Bank recommendations to prioritize demand-side scaling over cost contingencies in green logistics planning.

5.2. Simulation Analysis of the Evolutionary Paths of Electric Logistics Vehicle Manufacturers’ Pricing Strategies Under Different Scenarios

After the parameters above are completed, a simulation of the system’s evolutionary game path can be obtained using MATLAB 2021b software. Although in reality, there are only two game choices, 0 and 1, to highlight the stability of the evolutionary game path and the uniqueness of the decision equilibrium point, this paper adopts a step size of 0.1 to perform a complete path calculation for all initial points of the matrix. To adapt to different categories or regionally segmented markets, this paper sets up the following four scenarios for simulation analysis of the evolutionary stable strategies.

5.2.1. Scenario 1: A Very Large Market Size

Scenario 1: Assuming a category or region has a very mature product with a positive consumer response and a huge potential market size, we set $\varphi =1000000$, with all other parameter values remaining unchanged. The full path calculation results are shown in the figures.

Firstly, it can be observed that under the current parameter settings, regardless of the initial values, the decision variables $X$, $Y$, and $Z$ all converge to 0. This indicates that no matter what initial pricing strategy the manufacturers adopt and which initial strategy the government adopts, there is a unique decision equilibrium point. The government will no longer provide subsidies to the electric logistics vehicle manufacturing industry, and consumers will gradually shift towards purchasing battery leasing options. This undoubtedly aligns well with recent market developments and policy shifts. However, in this scenario, manufacturers adopt a non-penetration pricing strategy to ensure their profits, which is determined by the preset market size.

From the perspective of manufacturer pricing, given the preset colossal market size in this scenario, it indicates a substantial user base in the market. Even if manufacturers adopt a certain price increase, they can still receive many orders. This is undoubtedly the classic backdrop of a seller’s market, where the manufacturer’s price increase behavior will not be overly punished. Moreover, due to the limitations of the static game, the market size and price sensitivity coefficient remain the same during the evolution, which means that consumers’ bearing capacity for the price of such products has stayed the same. At this time, the trend of consumers shifting towards battery leasing reduces their spending costs and indicates that manufacturers will have a greater bargaining space as a result. As can be seen from the figure, as the initial value of $Y$ decreases, the decline curve of $Z$ tends to become vertical, indicating that the consumer’s behavior of changing their purchase method has, to some extent, promoted the manufacturer’s strategy of further price increases.

From a policymaking perspective, the electric logistics vehicle industry’s rapid development in this scenario has freed the market from its dependence on government subsidies. The current market size indicates that the electric sector has fully passed through the fragile stage of its infancy. At this time, the gradual phase-out of government subsidies will further stimulate the enthusiasm for related capital investment and unleash the manufacturers’ motivation for technological innovation. The intensification of market competition will directly test the profit models established by various manufacturers during the previous subsidy era. Furthermore, Figure 4a shows almost no different branch curves, indicating that the change in the initial value of z minimally impacts the mutual relationship between x and y. Due to the gradual prominence of the market size of electric logistics vehicles in this scenario, the effect of policy subsidies on promoting further market growth is already fragile under the unchanged subsidy strength. Regardless of how manufacturers formulate their pricing strategies, they can ensure a large purchase volume of electric logistics vehicles, and the environmental benefits obtained by the government are thus guaranteed. Therefore, the decision-making government is no longer sensitive to the pricing strategy decisions of manufacturers and chooses to quickly phase out subsidies, encouraging the market to develop and self-improve through competition.

From the consumer’s perspective, after incorporating the discount rate into the financial valuation, the cost of separating the vehicle and battery and opting for a lease-to-own plan is significantly lower than purchasing the vehicle outright. This means that regardless of how consumers initially set their strategies, they will gradually shift towards more cost-effective purchase options like battery leasing. Additionally, Figure 4a shows a sharp decline in the probability of consumers’ vehicle purchase method decision ($Y$) when the government subsidy decision probability decreases, and this change is not pronounced when the probability is high. This indicates that although the policy subsidy is too meager regarding the purchase cost of electric logistics vehicles, the gradual phase-out of subsidies, especially the complete exit of policies, still promotes the evolution of consumers shifting towards battery leasing.

Similarly, Figure 4b shows that as the initial value of the manufacturer’s decision probability $\left(Z\right)$ decreases, the rate of change of the probability of consumers’ vehicle purchase method decision $\left(Y\right)$ rapidly increases. The manufacturer’s price increase behavior is undoubtedly a more intense catalyst for this evolutionary process.

5.2.2. Scenario 2: Moderate Market Size with Minimal Market Share Valuation or Valuation Expectations

Assuming that a particular category or region now has a preliminarily mature product with a reasonable consumer response, this indicates a moderate-sized potential market. However, the manufacturer holds a pessimistic or short-sighted view of the future market, that is, $L=0$ and the values of other parameters remain unchanged. The full-path calculation results are shown in the figures.

Firstly, it can be seen from the above figures that regardless of how the initial values change, the decision probabilities $X$, $Y$, and $Z$ still converge to 0. The reduction in market size and the manufacturers’ expected valuation of market share have led to a significant overall shift in the evolutionary path. The government still chooses not to subsidize the electric logistics vehicle manufacturing industry, and consumers continue to gradually shift towards purchasing through battery leasing. However, in this scenario, manufacturers adopt a non-penetration pricing strategy to ensure their profits have not changed. This change is due to the manufacturers’ short-sighted behavior and the decrease in market size.

From the policymaking perspective, although the probability of government decisions, denoted as $x$, will still converge to 0, leading to a complete phase-out strategy of subsidies, the rate of evolution has undoubtedly slowed down significantly compared to Scenario One. In this scenario, the decrease in market size provides the government with ample motivation to use subsidies to promote market stability and growth. However, the manufacturers’ short-sighted behavior has led to a significant contraction in the overall market, forcing the government to extend the duration of subsidy policies to ensure the stable operation of the industry. As shown in Figure 5a, even though consumers have shifted their vehicle purchase methods more quickly than previously mentioned, the probability of government subsidies has not decreased significantly. The market still relies on the additional sales brought about by government subsidies, even though the level of subsidy at this time is no different from that in Scenario One.

From the perspective of consumer enterprises, although they have more rapidly converged towards adopting battery leasing as a purchase method, more is needed to offset the additional costs resulting from subsidy phase-outs and price increases by manufacturers. Figure 5a,b shows that the change in the probability curve of consumer enterprise vehicle purchase method decisions, denoted as $Y$, has almost become a straight line. This undoubtedly highlights the consumers’ dissatisfaction with the additional expenditure and the resulting slump in demand. The already immature industry as a whole is expected to experience further contraction.

From the manufacturer’s perspective, since manufacturers in this scenario have not made any expected valuation of market share, it indicates that they are very pessimistic about the future consumer market and are aggressively trying to expand their product profit margins through non-penetration pricing. They are willing to achieve so even if it has a significant adverse impact on the industry as a whole. This situation often occurs when a product category is in a late stage of its life cycle or during periods of anticipated economic downturn. Manufacturers then seek to maximize revenue from the current customer base by raising prices to obtain additional cash flow to maintain business operations or to invest in new technology development.

5.2.3. Scenario 3: Moderate Market Size with Reasonable Market Share Valuation

Assuming that a product in a certain category or region has reached preliminary maturity, with reasonable consumer feedback and a medium-sized potential market, and manufacturers have an optimistic outlook on the future market, willing to cultivate the current market and maintain investment to grow a target user base, to gain a first-mover advantage in future competition, all parameters continue to use the values from

Table 6. Initial parameter setting.

Symbol	Symbol Definition	Value Setting
$U$	Government Revenues in the Application Process of Electric Logistics Vehicles (CNY)	250,000
$S$	Government Subsidy Costs for Subsidizing the Purchase of Complete Vehicles (CNY)	200,000
$C_g$	Government’s Comprehensive Costs Paid During the Policy Implementation Process (CNY)	1000
$\varphi$	Market Potential Size (units)	1,000,000
$a$	Price Elasticity of Demand for Complete Vehicles (dimensionless)	0.7
$b$	Cross-price Elasticity of Battery Swapping Services (dimensionless)	−0.3
$\alpha$	In the case of penetration pricing, the degree of pricing fluctuation for consumer selection by the manufacturer (dimensionless)	0.05
$\beta$	In the case of non-penetration pricing, the degree of pricing fluctuation for consumer selection by the manufacturer (dimensionless)	0.05
$P_v$	Revenue from the Sale of electric Logistics Vehicle Bodies	450,000
$P_B$	Revenue from the Sale of electric Logistics Vehicle Power Batteries (CNY)	400,000
$R$	Utility Obtained by Consumers Over the Entire Lifecycle of the Vehicle After Purchase (CNY)	1,000,000
$K_1$	The Discounted Proportion of the Total Price of Battery Swapping Services Over the Entire Lifecycle to the Sale Price of Power Batteries (dimensionless)	0.89
$K_2$	The Intuitive Proportion of the Total Price of Battery Swapping Services Over the Entire Lifecycle to the Sale Price of Power Batteries (dimensionless)	1.2
$C_v$	Manufacturing Cost of Electric Logistics Vehicle Bodies (CNY)	400,000
$C_b$	Manufacturing Cost of Electric Logistics Vehicle Power Batteries (CNY)	300,000
M	Revenue from the Recycling and Utilization of Retired Power Batteries (CNY)	100,000
$\lambda$	The Proportion of Storage Batteries Required for Battery Swapping Operations (ratio)	1.2
$B$	Recycling Price of Retired Power Batteries (CNY)	80,000
$h$	Level of Technology R&D (Research and Development) (index)	150
$ϴ$	Consumer Sensitivity to Batteries and Energy Recharging Technology (dimensionless)	0.3
$L$	Manufacturer’s Expected Valuation of Market Share (CNY)	350,000
$R$	Utility Obtained by Consumers Over the Entire Lifecycle of the Vehicle After Purchase (CNY)	1,000,000

without change, and the full path calculation results are shown in the figures.

Unlike the previous scenarios, the decision variables $X$ and $Y$ still converge to 0 in the figure, no matter what the initial values are. This indicates that regardless of the initial pricing strategy adopted by manufacturers and the initial strategies taken by the government and consumers, there is a unique decision equilibrium point. That is, the government will no longer subsidize the electric logistics vehicle manufacturing industry, and consumers will gradually shift to purchasing through battery leasing. This undoubtedly aligns well with recent market development and policy shifts. However, in this scenario, for the first time, manufacturers adopt penetration pricing as their stable strategy. This is due to the reasonable valuation of market share and the optimistic expectations for the future market that this valuation reflects.

Similar to the first two scenarios, the government will still adopt a subsidy phase-out as the most optimal policy choice. Although there is still room for the market size to grow in this scenario, policy subsidies will no longer be the main driving force for further market expansion. As seen in Figure 6c, even though in the early stages of the subsidy phase-out, that is, when $x>0.8$, the withdrawal of government subsidies leads to a certain decrease in the probability of manufacturers’ penetration pricing, the change curve has almost become a straight line as the phase-out proceeds orderly. Moreover, the higher the initial value of the manufacturer’s pricing decision probability $Z$, the faster the rate at which the subsidy phase-out approaches the stable value of 0, shown in the figure as a slope close to 0. This indicates that manufacturers have replaced the government as the primary driver of market growth in this scenario. The manufacturers’ proactive price concessions to capture market share are, to some extent, a sign of their enhanced profitability and the gradual improvement of their supply chain systems. At this point, the electric logistics vehicle market has gradually shifted from policy support to a state of free market competition.

From the perspective of consumer enterprises, since the cost ratio between purchasing and leasing batteries remains unchanged, consumer enterprises will ultimately choose the more cost-effective battery leasing plan as a stable decision. However, manufacturers have proactively adopted penetration pricing to lower the threshold for purchasing vehicles and the acquisition costs for consumer enterprises. This has led to a certain deceleration in the convergence process of the consumer enterprises’ vehicle purchase methods. The main driving force behind the change in consumer enterprises’ vehicle purchase methods is the reduction of expenditure costs, and the manufacturers’ proactive penetration pricing undoubtedly meets part of the demand for cost savings.

From the perspective of manufacturer pricing, since the expected valuation of market share has been corrected from Scenario Two, manufacturers have become very optimistic about the future consumer market. Therefore, they proactively expand their customer base through penetration pricing, even if it results in a certain loss of profit margins on the whole vehicle. It is worth noting that the expected valuation of market share here is greater than the current net profit per vehicle, indicating that manufacturers have confidence in the future development of the supply chain and technology. They believe that by updating production processes and improving the supply chain, they will achieve significant cost reductions to compensate for the current losses. This situation usually occurs when a product category has passed the product introduction period or the socio-economic outlook is expected to prosper. Manufacturers attract and retain more customers through low pricing to seize a larger market share before the market surges and the product matures, thereby gaining a more significant competitive advantage.

5.2.4. Scenario 4: Undersized Market Scale

Assuming that a product in a certain category or region is not yet mature and poor consumer feedback leads to a contraction of the potential market size or erosion by other categories, manufacturers, based on the current situation, have a pessimistic outlook on the future market and choose not to continue investing to preserve their cash flow to maintain business operations, that is, $\varphi =400000$ and $L=0$, the full path calculation results are shown in the figures.

This scenario differs significantly from the previous ones, with only the decision variable $Y$ converging to 0 regardless of the initial value, while the decision variables $X$ and $Z$ converge to 1 and 0, respectively. This indicates that although manufacturers, due to a pessimistic outlook on the future, will adopt non-penetration pricing to ensure their profits, and consumer enterprises will continue to adopt battery leasing as a stable decision, the government, because the market size falls short of expectations, will no longer phase out subsidies for the electric logistics vehicle manufacturing industry.

Unlike the previous scenarios, in this scenario, the government ultimately chooses to continue the vehicle purchase subsidy policy to promote further growth in market size, while the manufacturers’ non-penetration pricing behavior undoubtedly increases the difficulty. As can be seen from Figure 7c, as the probability of the manufacturers’ pricing decision $Z$ decreases, the probability of their adding a markup to their products and battery-swapping services gradually increases, correspondingly impacting the already diminished market size. Therefore, it can be observed that the probability of the government’s subsidy decision $X$ converges to 1 more rapidly. As the probability of the government’s subsidy decision $X$ increases, the rate of change of the manufacturers’ pricing decision probability $Z$ sharply rises. In the figure, when $x>0.8$, the evolutionary curve of $Z$ approaches vertical, indicating that the government’s subsidy intervention further promotes stability in the manufacturers’ pricing behavior decisions. The interaction between these two decisions can create a negative feedback loop, hindering the development of the electric logistics vehicle industry.

For consumer enterprises, unlike the previous scenarios where government subsidies played a role, Figure 5a indicates that due to the vicious cycle created by the combined decisions of the government and manufacturers, as the probability of the government’s subsidy decision $X$ rises, the probability of consumers’ choice of vehicle purchase method $Y$ also accelerates towards converging to 0. When $x>0.8$ in the figure, the evolutionary curve of $Y$ similarly approaches verticality, indicating that government subsidies fail to compensate for the losses caused by the manufacturers’ price increases and contribute to a new round of rising vehicle purchase costs. Inelastic consumer enterprises will be forced to accept the price increase and more rapidly adopt battery leasing to hedge against the rising expenditure costs.

In this scenario, the manufacturers’ decision-making is similar to Scenario Two. Due to the low sales volume of the existing product, manufacturers have yet to make any expected valuation of market share, indicating that they are very pessimistic about the future consumer market and are trying hard to increase the profit margins of their products through non-penetration pricing. Manufacturers are generating additional cash flow through price increases, hoping to invest in the research and development of iterative products or reorganize their business lines to reduce losses.

Manufacturer expectations $L$ are not neglected but tested through market-size contrasts, as follows:

• Pessimistic outlook ($L=0$): Simulated in small/volatile markets (Scenario 2, 4), causing aggressive pricing ($\beta$↑).
• Optimistic outlook ($L>0$): Modeled in large/stable markets (Scenario 1, 3), enabling penetration pricing ($\alpha$↑).

This approach reveals L’s context-dependence—e.g., Norway’s 2030 mandate boosts $L$, while UK subsidy cuts suppress it—without arbitrary parameterization.

6. Sensitivity Analysis

The previous section discussed the pure strategy analysis of the three-party evolutionary game system under four different scenarios. This paper selects Scenario Three, which is most in line with the current situation of the Chinese market, as the reference for subsequent sensitivity analysis scenarios and parameter values. This section mainly discusses the impact of government subsidies $S$, the environmental benefits $U$ obtained by the government from the electrification of logistics vehicles, the direct price rates, and the price fluctuations $\mathsf{\alpha }$, and $\beta$ when manufacturers set pricing strategies to $K_1$ and discounted price ratio $K_2$ between battery leasing and purchasing batteries, the strategic choices of the three parties.

6.1. The Sensitivity Analysis of Government Subsidy Intensity S

The intensity of government subsidies, denoted as $S$, is a crucial consideration for policymakers in addition to the duration of subsidy policies. A reasonable subsidy level can promote industry development without imposing unnecessary additional financial burdens on the government. This section sets the subsidy intensity at three levels, 20,000, 15,000, and 10,000, to simulate the gradual withdrawal of government subsidy policies and the step-by-step reduction of subsidy intensity in the current era.

Figure 8 reflects the impact of the government subsidy amount S on the evolution of the system’s stable strategy. It can be observed that the gradual reduction of subsidy intensity effectively promotes the evolution process of the model towards a stable point. From the right-side $X-Y$ view, it is visible that as the subsidy intensity decreases, even if the policy subsidy probability starts at the same initial position, consumer enterprises will still converge more quickly to the choice of battery leasing to compensate for the additional expenditure cost caused by the insufficient subsidy.

It is worth noting that during the phase of government subsidy intensity, the convergence process of the evolutionary game curve is initially driven mainly by the rapid shift in the purchase methods of consumer enterprises and the government’s efforts to maintain vehicle purchase subsidies. At this time, the government subsidy probability $X$ remains around the initial value of 0.5 without significant changes. However, as the consumer enterprise group quickly converges on the purchase method of battery leasing, the overall purchase cost will no longer decrease significantly. At this point, the manufacturers’ penetration pricing behavior is the main driving force for further market growth. After the purchase cost decreases and the market scale gradually emerges, the government subsidy decision will quickly converge on the subsidy phase-out, allowing the market to self-improve. Conversely, when the government subsidy intensity is high and very close to the environmental benefits that the government gains from the electrification of logistics vehicles, the net benefit generated by the subsidy policy is relatively low. Therefore, the government initially opted for a subsidy phase-out decision to reduce expenditure costs.

6.2. The Environmental Benefits U That the Government Gains from the Electrification of Logistics Vehicles

In this article, since factors such as industry taxes and social welfare are not considered, the environmental benefits gained by the government from the electrification of logistics vehicles will be the only benefit obtained by the government from electrifying logistics vehicles. The environmental benefits brought about by the low noise and zero emissions of electric logistics vehicles will be the main driving force for the government to promote the development of the electric logistics vehicle industry. This section sets three levels of environmental benefits at 25,000, 15,000, and 35,000 to explore their impact on the evolutionary game system.

Figure 9 reflects the impact of environmental benefits U on the evolution of the system’s stable strategy. It can be seen that the magnitude of environmental benefits directly affects the probability of government subsidies; when there are significant benefits, government decision-making will shift towards continuing to implement subsidy policies. Environmental benefits are mainly composed of fuel savings from the electrification of logistics vehicles, as well as the reduction in emissions of harmful gases such as carbon dioxide, nitrogen oxides, and particulate matter, which are beneficial to improving urban air quality. From the inset on the right, it can be seen that a reduction in environmental benefits is conducive to accelerating the evolution of the game system. When environmental benefits are significant, the market size will increase, and manufacturers’ expectations for market size will also increase, so even if manufacturers adopt non-penetrating pricing, they can still obtain sufficient profits.

However, such benefits are currently primarily based on subjective pricing, and few regions or countries have established detailed pricing policies for these benefits. Since these benefits are difficult to quantify, the government mainly adopts encouragement and tax subsidies for environmental protection actions. The diagram shows that only when the environmental net benefits are far higher than the reasonable range, and the subsidy intensity remains unchanged, will the government continue to adopt financial subsidy strategies. This strategy is optimal if there are scenarios with severe environmental pollution and high environmental benefits, such as mines. The common logistics hubs and urban roads currently do not meet this definition, which makes this subsidy scenario only applicable in a limited scope.

6.3. The Discount Rate $T$, the Discount Ratio $K_1$ of the Total Price of Battery-Swapping Services over the Entire Lifecycle Compared to the Price of Power Batteries, and the Intuitive Ratio $K_2$

Although at first glance, compared to the one-time cost of purchasing batteries directly, the cost of battery-swapping services that consumer enterprises have to pay over the entire lifecycle is higher when choosing to lease batteries, this ratio is reflected as the intuitive ratio $K_2$ in the text. It is clear that $K_2>1$ at this time. Even without considering factors such as currency devaluation and inflation, consumer enterprises that choose to lease batteries will also gain liquidity benefits from the deferred cash flow, and the benefits from this will be more evident for high-investment, heavy-asset, slow-return transformation projects like the electrification of electric logistics vehicles. According to practical operations such as changes in accounting estimates, the discount rate should typically range between 5% and 15% and be negative in rare deflationary situations.

This paper adjusts the discount rate T to indicate the quality of each consumer enterprise’s operations and the differences in future valuation performance to suit different end users, taking 5%, 10%, 15%, and −10%, respectively, as shown in

Table 7. Pricing level of battery-swapping services under different discount rates.

$\mathbf{Years} \mathbf{in} \mathbf{Use} \mathit{t}$	1	2	3	4	5	6	7	8	Total	$\mathbf{Intuitive} \mathbf{Ratio} {\mathit{K}}_2$	$\mathbf{Discounted} \mathbf{Ratio} {\mathit{K}}_1$
Purchase of Batteries	28.00	0	0	0	0	0	0	0	28.00
Battery Leasing	4.25	4.25	4.25	4.25	4.25	4.25	4.25	4.25	34.00	1.21
Discount Rate ${(1+T_1)}^{t-1}$	1.00	1.10	1.21	1.33	1.46	1.61	1.77	1.95
Discount Rate ${(1+T_2)}^{t-1}$	1.00	1.05	1.10	1.16	1.22	1.28	1.34	1.41
Discount Rate ${(1+T_3)}^{t-1}$	1.00	1.15	1.32	1.52	1.75	2.01	2.31	2.66
Discount Rate ${(1+T_4)}^{t-1}$	1.00	0.90	0.81	0.73	0.66	0.59	0.53	0.48
Net Present Value of Lease Payments	4.25	3.86	3.51	3.19	2.90	2.64	2.40	2.18	24.94		0.89
	4.25	4.05	3.85	3.67	3.50	3.33	3.17	3.02	28.84		1.03
	4.25	3.70	3.21	2.79	2.43	2.11	1.84	1.60	21.93		0.78
	4.25	4.72	5.25	5.83	6.48	7.20	8.00	8.89	50.61		1.8

.

Figure 10 reflects the impact of the ratio $K_1$, between the discounted price of battery-swapping services and the selling price of power batteries on the evolution of the system’s stable strategy. $K_1$ will be a key factor influencing consumers’ choices in car purchase patterns. As assumed (e) in the previous text, the range of values for $K_1$ mainly depends on the total expenditure cost of battery-swapping services over the entire lifecycle of electric logistics vehicles and the discount rate.

When the lifecycle price of battery-swapping services and the discount rate are within a reasonable range, that is, when the expenditure on battery-swapping services is slightly higher than the selling price of power batteries and when $T>0$, consumers tend to opt for the battery leasing purchase method to reduce initial investment capital and total expenditure over the period. From the curve where the discount rate $T$ is 0.05 and the discount ratio $K_1$ is 1.03, it can be observed that even when the expenditure on battery-swapping services slightly exceeds the cost of directly purchasing power batteries, consumer enterprises still use the battery leasing purchase method. This is due to the different price sensitivities of consumers to the two types of payment methods.

Additionally, even if the expenditure on battery-swapping services and the selling price of power batteries remain unchanged, when the external economic environment experiences deflation, consumer enterprises face further financing difficulties, or their business operations contract, the discount rate will become negative. Consumer enterprises will now switch to the whole vehicle purchase method. In this scenario, the vehicle-battery separation and battery leasing solution provided in the battery-swapping mode significantly lacks efficiency in the utilization of funds. From the curve where the discount rate $T$ is −0.1 and the discount ratio $K_1$ is 1.8, the price of battery-swapping services, driven by tight cash flow, has somewhat hindered the healthy development of the overall market. Even at this time, manufacturers, out of an advanced pricing strategy for market share and an optimistic view of future market scenarios, are still committed to a penetration pricing strategy. The government is also inevitably turning towards continued subsidies, indicating that the market scale has contracted and is unfavorable in this scenario.

6.4. The Degree of Pricing Fluctuation α and β

Figure 11 reflects the impact of the degree of pricing fluctuation α and $\beta$ on the system’s evolutionarily stable strategy when manufacturers adopt different pricing methods. No matter how the pricing fluctuations α and β change, manufacturers tend to prefer a penetration pricing model, that is, to reduce the selling price of electric logistics vehicles and the price of battery-swapping services to capture a larger market share. This is determined by the manufacturer’s advanced pricing for market share and an optimistic view of future market scenarios. Unlike the rapid development of electric passenger cars in recent years, heavy asset electric commercial vehicles, represented by electric logistics vehicles, have characteristics such as a late start, an imperfect supply chain, and a low market penetration rate. Various models on sale are usually in the product route selection stage, and the market still has a vast potential consumer demand.

Under the premise that manufacturers are accelerating to capture market share, the emergence of penetration pricing as an evolutionarily stable result in the evolutionary game is realistic. Currently, many manufacturers adopt a marketing model that combines penetration pricing with battery-swapping, highlighting the head start of their leading enterprises in perfecting their supply chain architecture while also indicating an optimistic expectation for the future market, proving that the good market prospects of electric logistics vehicles are sufficient to compensate for short-term profit losses. On the contrary, whether manufacturers can reverse the current profit margin situation depends on whether the potential market demand is fully released and whether their manufacturing processes and supply chain systems can be further improved. Otherwise, with the influx of new competitors and the further intensification of price wars, manufacturers’ profits may experience a significant contraction. At this point, manufacturers are very likely to fall into a vicious cycle of over-competition, where extremely low base profit margins and fierce external competition limit the manufacturer’s pricing strategy shift, ultimately leading to difficulties in developing the enterprise and the industry.

After determining that manufacturers in this scenario tend to adopt penetration pricing strategies, the degree of pricing fluctuation $\alpha$ and $\beta$ also significantly impact the government’s strategic choices. From the government’s perspective, it is known that after manufacturers adopt penetration pricing, the selling price of electric logistics vehicles will decrease further. Coupled with the promotion of the battery leasing purchase plan, the purchase cost for consumer enterprises has been further reduced, leading to a significant increase in market size. It can be seen from the right-hand figure that more aggressive penetration pricing will slow down the government’s process of subsidy phase-out because, under the same level of subsidy intensity, a faster electrification process of electric logistics vehicles will bring greater environmental benefits, which balances part of the government’s fiscal expenditure.

7. Conclusions and Implications

7.1. Research Conclusions

In this paper, an evolutionary game model involving the government, consumer enterprises, and manufacturers during the electrification process of electric logistics vehicles is constructed based on evolutionary game theory. The strategic choices of each participant are explored, and the dynamic evolution of the strategic decisions under different scenarios is simulated using real-world data. The paper further analyzes the impact of government subsidies, the environmental benefits obtained by the government from the electrification of logistics vehicles, the direct and discounted ratios of battery leasing to purchasing battery prices, and the influence of price fluctuations and system evolution on manufacturers’ pricing strategies. Based on the above analysis, the following conclusions are reached.

Firstly, the strategies of the government and manufacturers are influenced by the market size and manufacturers’ expectations of market share. When the market size is large or manufacturers hold lower expectations for future market growth, they will adopt a non-penetration pricing strategy. The market size is directly related to the demand of consumer enterprises; due to the significant demand, even if manufacturers choose to sell at a higher price, many consumers are still willing to pay, and the government does not need to continue providing subsidies. Conversely, when manufacturers believe that the market has no potential, they will still choose a non-penetration pricing strategy to ensure their profits; even if the government chooses to invest in subsidies to promote the expansion of the market size, it is still not enough to turn the situation around. On the premise that manufacturers have an optimistic attitude towards the market, in the face of the government’s subsidy phase-out measures, manufacturers will change their pricing strategy to maintain sales volume growth. Since manufacturers have a more reasonable estimate of the market share of electric logistics vehicles, they are more willing to choose a penetration pricing strategy. However, if malicious low-price competition arises, the government’s subsidy phase-out process will slow down.

Secondly, considering the discount rate, leasing batteries is a better choice for consumer enterprises that operate electric logistics vehicles. When designing and adjusting market strategies, it is necessary to comprehensively consider multiple factors, such as price and discount rate, and how they affect consumers’ car purchase decisions and the market’s healthy development. Electric logistics vehicles are capital-intensive projects with high initial purchase costs and long payback periods. Under general economic conditions, for consumer enterprises, the method of battery leasing can avoid a one-time payment of high purchase costs, reducing the initial investment cost. Changes in the discount rate will affect consumers’ preferences for different car purchase models. Suppose the external economic environment leads to a negative discount rate. Consumers may turn to the whole vehicle purchase method because the leasing model has lower capital utilization efficiency under tight cash flow conditions.

Thirdly, policy changes can affect market behavior, and the feedback from market behavior can, in turn, influence policy adjustments. The government’s reduction in subsidy (triggered at >20% [9] market penetration or <¥650/kWh battery cost [53]) efforts can effectively encourage the market to develop toward battery leasing. Even if the probability of policy subsidies remains unchanged, consumer enterprises will more quickly turn to battery leasing to offset the additional costs resulting from reduced subsidies. When consumer enterprises widely choose battery leasing and the purchase cost no longer significantly decreases, the manufacturer’s penetration pricing strategy becomes the main driving force for market growth. When the government’s subsidy efforts are substantial and close to environmental benefits, it will prioritize reducing subsidies to lower expenditure costs, indicating that it will consider net benefits and cost-effectiveness in subsidy policies. As the market size grows and purchase costs decrease, the government’s subsidy decisions will converge more quickly toward the phase-out direction, allowing market forces to play a role without government intervention.

7.2. Management Implications

This study provides management recommendations for the government, electric logistics vehicle manufacturers, and consumer enterprises.

The government can decide whether to provide car subsidies according to the market size of electric logistics vehicles. From the market size perspective, when the market size is medium or small, the government can reduce the R&D costs for manufacturers and maintain their operations by appropriately extending and increasing subsidies. At the same time, the government’s providing subsidies can play a guiding role, reduce the car purchase costs for consumers, stimulate consumers’ enthusiasm for buying electric logistics vehicles, and drive the overall market demand. However, when the market size is large, the impact of the subsidy phase-out on manufacturers is limited. At this point, the government should stop providing purchase subsidies for electric logistics vehicles and let them shift from policy-driven to market-driven. The phase-out of subsidies allows manufacturers to gradually eliminate their dependence on financial subsidies, respect market laws, let the market regulate itself, and let the fittest survive.

Due to the relative concentration of production and the absolute dispersion of consumption, manufacturers must increase their investment and optimize supply chain management through digital technology to respond to consumer demand promptly. They should use historical data to estimate the demand for battery-swapping and energy replenishment of electric logistics vehicles in terms of quantity, time, and location. This helps in planning the density of the battery-swapping and energy replenishment network, optimizing the location of swapping stations, and storing a certain number of spare batteries. By doing so, manufacturers can meet customer needs while controlling costs. In addition, the lack of uniformity in battery-swapping standards across different brands of electric logistics vehicles is a significant bottleneck restricting market expansion. Proprietary standards confine manufacturers to serving only their own models, fragmenting the service network and limiting consumer choice. To overcome this critical barrier and unlock economies of scale, manufacturers should actively pursue concrete paths towards standardization, particularly focusing on battery swap interface protocols. This could involve: 1. Forming Strategic Alliances with Leading Players: Collaborate with key industry stakeholders (e.g., major vehicle manufacturers like Sany, Volvo, and battery giants like CATL) to establish technical working groups. These groups would be tasked with defining and agreeing upon common physical interfaces (e.g., mechanical locking mechanisms, electrical connectors, coolant couplings) and communication protocols (e.g., data exchange standards for battery status, authentication) for battery swap systems. 2. Phased Implementation and Pilot Programs: Initiate standardization efforts through regional pilot programs involving alliance members. This allows for testing and refinement of the proposed standards in real-world logistics corridors before attempting nationwide or global rollout. Government support could be crucial in facilitating these pilots and providing neutral testing grounds. 3. Leveraging Policy Synergy: Advocate for and align standardization efforts with government regulations and incentive programs. For instance, eligibility for future subsidies or preferential access to urban logistics zones could be linked to adherence to the newly established industry-wide standards.

Consumer enterprises can achieve higher economic benefits through the research and promotion of the battery-swapping and energy replenishment model for electric logistics vehicles, which lowers the threshold for purchasing vehicles. The payment method of battery leasing offers better financial flexibility, significantly reducing the initial investment for consumer enterprises and improving their cash flow to a certain extent. Considering the discount rate, a lower initial investment means that less capital needs to be discounted, thereby reducing the time cost of capital. Moreover, in the long run, the operating costs of electric logistics vehicles are lower than those of traditional fuel vehicles under the battery-swapping mode. Consumer enterprises in the electric logistics vehicle sector have different business scopes, and their needs and usage scenarios also vary. When selecting electric logistics vehicles, each consumer enterprise has its own focus. To address the contradiction between diverse needs and homogenized products, consumer enterprises can collaborate with upstream manufacturers to optimize the BMS according to actual working environments and temperature conditions, ensuring stable operation and meeting specific requirements. Furthermore, through this partnership, both parties can share battery usage data, conduct in-depth analysis, continuously refine BMS algorithms, enhance battery performance, and drive progress in the electric logistics vehicle industry.

This paper only considers the government, manufacturers, and consumer companies, but there are actually other decision-making bodies, such as retired battery recyclers. In addition, in future research, we can refine the progress of electrification in the logistics and transportation industry from the perspective of competition among charging and replacing models, which may achieve new research results.

7.3. Model Generalizability and Sensitivity

7.3.1. Generalizability

This model captures the dynamic interaction among the government, manufacturers, and consumers through a three-party evolutionary game framework. Its core mechanisms (such as the effect of policy subsidies, the calculation of the net present value of battery leasing, and the feedback loop of market size) have cross-scenario universality. However, the current parameter calibration (such as battery cost, vehicle body price) relies on public data from the Chinese market (CATL financial reports, 360che platform), which may limit the direct applicability of the model in low-income countries.

7.3.2. Sensitivity

The model’s stability outcomes are sensitive to battery cost ratios. Cost structures based on CATL data (Section 5.1) reflect a region. However, emerging markets with underdeveloped supply chains may exhibit shifted equilibria. While replicator dynamics capture strategic learning, they assume: Homogeneous rationality: All consumers identically perceive $a$ and $b$.

7.4. Limitations

Firstly, to ensure the universality of this study, our model assumes the same lifetime utility $R$, and does not consider that the leasing model should bring additional efficiency benefits. Secondly, this article only considers the three entities of the government, manufacturers, and consumer enterprises. However, in reality, there are other decision-making entities, such as battery recycling companies for retired batteries. Thirdly, this article only considers the subsidies from the government, and merely divides the government’s decisions into “with vehicle purchase subsidy” and “without vehicle purchase subsidy” two categories. In fact, consumers may also receive support from social financing and other sources, and the government’s strategies can be more diverse. In addition, we assume uniform environmental benefits U and recycling revenues M, real-world variations exist (e.g., carbon pricing in the EU vs. emerging economies). Similarly, constant battery-replacement costs ($\lambda C_b$) overlook supply chain volatilities. This simplification allows clearer attribution of outcomes to strategic interactions—future work will incorporate stochastic parameters via robust optimization. Subsequent research should focus on deepening the model (utility, leasing benefits), expanding the subjects (recycling enterprises), and enriching the policy/funding mechanisms (diversified tools, social capital), in order to better align with the complexity of reality and expand the research boundaries.