Research on Battery Aging and User Revenue of Electric Vehicles in Vehicle-to-Grid (V2G) Scenarios

Abstract

With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this study, aging models for power batteries in electric passenger vehicles and electric trucks are established. A time-of-use electricity price model and an economic model considering battery aging costs are constructed. Two scenarios were established for daily use and V2G operation. The impacts of different scenarios and charging/discharging patterns on battery life and user profit are analyzed. The results indicate that the additional V2G discharging process increases the cyclic aging rate of EV batteries. Within the studied parameter ranges, the cyclic aging rate increased by 5.89% for electric passenger vehicles and 3.72% for electric trucks, respectively. Additionally, the initial V2G revenue may struggle to cover early-stage battery aging costs, but the subsequent slowdown in degradation may eventually offset these costs. With appropriate charging and discharging strategies, the maximum revenue per year reaches 18,200 CNY for electric trucks and 5600 CNY for electric passenger vehicles. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios.

1. Introduction

The global energy structure is experiencing a rapid green transformation, with transportation electrification serving as a key path for low-carbon development [1]. In recent years, the electric vehicle (EV) industry has experienced explosive growth, accompanied by a continuous rise in the global vehicle stock [2,3]. While this trend contributes to reducing transportation emissions, it also introduces new challenges and opportunities for the stable operation of power systems [4]. As a new type of load, the uncoordinated charging behavior of EVs may widen the peak-to-valley load difference in the grid [5]. However, if the vast number of EV batteries can be utilized as distributed energy storage resources, they could provide flexibility for the power system [6]. In this context, vehicle-to-grid (V2G) technology has emerged and is regarded as a key enabling technology for building novel power systems and enhancing the grid’s capacity to integrate a high share of renewable energy [7].

The core of V2G technology lies in establishing bidirectional energy flow between EVs and the power grid [8]. Through advanced communication and control technologies, distributed EV batteries can be aggregated into a large-scale virtual power plant or distributed energy storage system [9]. This enables EVs to move beyond their conventional role as transportation tools and function as flexible distributed assets that actively support grid services. These services include peak shaving, frequency regulation, voltage support, and renewable energy integration [10]. This participation provides grid operators with low-cost, highly adaptable regulation resources that enhance grid flexibility and resilience. Simultaneously, it creates new revenue opportunities for EV users, thereby improving the overall lifecycle value and economic attractiveness of electric vehicles [11].

The large-scale promotion of V2G technology faces the key issue of battery durability. Compared to the relatively regular and shallow charge–discharge cycles of daily commuting, the frequent, deep, and high-rate V2G operations required to meet grid demands can accelerate battery aging [12]. From a mechanistic perspective, these additional discharge conditions intensify irreversible electrochemical side reactions within the battery, ultimately leading to accelerated capacity decay and increased internal resistance [13,14]. Several studies have investigated the impact of V2G technology on EV battery aging. Mosammam et al. [15] found that increasing V2G intensity can increase battery aging to 7.83%, and an additional 12.23% aging is caused by an increase in ambient temperature from 10 °C to 30 °C. Sagaria et al. [16] conducted simulation studies showing that V2G increases battery aging by 9–14% over ten years and raises the proportion of cyclic aging from 10–15% to 20–25%, providing a basis for estimating economic compensation standards. Casals et al. [17] proposed from the perspective of user behavior that introducing V2G active accelerated aging is a reasonable strategy to match the functional lifespan of batteries and vehicles. Movahedi et al. [18] introduced a new metric of “throughput gained versus days lost”, suggesting that batteries dominated by calendar aging are more suitable for V2G, with decision-making relying on estimating the proportion of static aging. A review by Lehtola [19] further suggests that V2G shortens battery life through increased charge transfer, though the actual impact depends on usage balance. Li et al. [20] proposed an integrated power and thermal management strategy based on a multi-horizon model predictive control framework, which improved battery longevity under driving conditions and demonstrated potential for charging and discharging scheduling. However, the battery performance degradation and potential premature replacement costs caused by V2G constitute core economic concerns for user participation. If these costs exceed V2G service revenues, user participation willingness will be undermined.

In user benefit assessment, existing studies such as De Caro et al. [21] focus on optimizing charging and discharging strategies under different electricity pricing mechanisms to maximize user revenue. These studies note that the profitability of V2G depends heavily on multi-stakeholder satisfaction. However, these analyses generally assume that battery degradation costs during V2G services are negligible or can be roughly estimated. Few studies adequately incorporate battery aging costs into user economic evaluations, making it difficult to quantify how additional V2G cycling affects net benefits through battery lifespan reduction. This limitation may lead to inaccurate assessment of true user benefits. To address this gap, studies such as Sagaria et al. [16] have begun applying co-simulation to quantify V2G-induced battery aging. Their results show a 9–14% increase in degradation over 10 years, enabling estimation of corresponding economic compensation benchmarks and providing reference for more accurate benefit evaluation. Furthermore, survey research by Bakhuis et al. [22] reveals that although financial incentives remain the primary motivation for user participation, battery degradation and loss of flexibility are major concerns. This shows the importance of precise net benefit assessment for improving participation willingness. Additionally, current research concentrates predominantly on electric passenger vehicles, with limited investigation into commercial vehicles like electric trucks. Given their substantially larger battery capacity, higher usage intensity, and greater V2G potential compared to electric passenger vehicles, the unique lifecycle economic models and benefit-risk profiles of these vehicles require urgent assessment.

In summary, as shown in

Table 1. Comparison of focus and methodology in current V2G studies.

Study	Primary Focus	Battery Aging Modeling	Vehicle Type	Consider Battery Aging Cost
Mosammam et al. [15]	Multi-objective optimization of charging and V2G	Calendar and cycle aging models	Plug-in hybrid electric vehicle	No
Sagaria et al. [16]	Quantifying V2G aging for economic compensation	Calendar and cycle aging models	Passenger vehicle	Yes
Casals et al. [17]	EV behavior, battery lifespan, and grid impact	Semi-empirical model	Passenger vehicle	No
Movahedi et al. [18]	Throughput vs. days lost metric for V2G	Degradation mechanism model	Passenger vehicle	No
De Caro et al. [21]	EV aggregator profit from flexibility services	Considered as a constraint	Passenger vehicle	No
This study	Integrated battery aging and user economic revenue analysis	Multi factor semi-empirical calendar and cycle aging models	Passenger vehicle and electric truck	Yes

, current research needs to be further explored in the following areas. First, few studies have examined how V2G technology affects battery aging in EVs. The relationship between user benefits and battery aging costs remains unclear, making it difficult to accurately measure how V2G profits relate to battery life loss. Second, existing research focuses mainly on electric passenger vehicles, with little attention to the potential of large-battery vehicles like electric trucks in V2G applications. There is also a lack of comparative studies across different vehicle types. Furthermore, the influence of different V2G scenarios and their charging–discharging patterns on battery degradation has not been fully explored. This may limit the practical usefulness of current strategies.

To address these research gaps, this study develops an integrated analytical framework that dynamically couples high-precision battery aging models with a comprehensive economic model. This framework evaluates the cost of battery degradation and directly converts the simulated degradation cost under V2G conditions into a portion of the time-varying economic cost in net revenue calculation. Furthermore, this study compared the differences between electric passenger vehicles and electric trucks in participating in V2G and quantified the battery aging and user benefit differences in different vehicle models participating in V2G. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios.

The remainder of this paper is structured as follows. Section 2 details the battery aging model, user economic model, and the constructed V2G scenarios. Section 3 analyzes the patterns of battery aging and revenue changes for both electric passenger vehicles and electric trucks under different V2G charging and discharging modes. Section 4 provides a summary of the full paper.

2. Model Development

To accurately quantify battery performance degradation in V2G scenarios, this study established high-precision battery aging models for two typical vehicle types: electric passenger vehicles and electric trucks. Considering the differences in battery specifications, usage scenarios, and operating conditions between these two vehicle types. Small-capacity cylindrical batteries representing electric passenger vehicle applications and large-capacity prismatic batteries representing electric truck applications were selected as research subjects. Separate calendar aging and cycle aging models were developed and validated for each battery type. The technical specification parameters of the studied battery cells are shown in

Table 2. Technical specification parameters of the cylindrical battery for electric passenger vehicles [23].

Parameter	Positive Electrode Material	Negative Electrode Material	Nominal Capacity	Upper Cut-Off Voltage	Lower Cut-Off Voltage	Operating Temperature Range	Dimensions	Weight
Value	NCM	Si/Graphite	3.2 Ah	3.6 V	2.5 V	−20 °C~60 °C	65 × 18 mm	0.045 kg

and

Table 3. Technical specification parameters of the prismatic battery for electric trucks [24].

Parameter	Positive Electrode Material	Negative Electrode Material	Nominal Capacity	Upper Cut-Off Voltage	Lower Cut-Off Voltage	Operating Temperature Range	Dimensions	Weight
Value	NCM	Si/Graphite	144 Ah	4.25 V	2.8 V	−30 °C~55 °C	56.3 × 148.0 × 103 mm	1.97 kg

.

Given the substantial time span involved in actual EV usage, the impact of calendar aging cannot be neglected. Therefore, both calendar aging and cycle aging models were established for the two battery types. All parameters involved in the model were determined through nonlinear fitting using MATLAB 2025a software on the experimental data provided in the corresponding references [23,24]. The fitting process aims to minimize the error between model prediction and experimental data, with the optimization goal of minimizing the average absolute error between experimental data points and simulated values. The final parameters obtained ensure the accuracy of the model’s prediction of battery aging under various operating conditions.

$$\min {\displaystyle \frac{1}{N}}{\displaystyle \sum _{i=1}^N\left|SO{H}_{\exp ,i}-SO{H}_{sim,i}\right|}$$

(1)

2.1. Modeling and Validation of Aging Model for Power Batteries

2.1.1. Aging Model for Electric Passenger Vehicle Power Batteries

The calendar aging model employs a coupled framework integrating Arrhenius kinetics and the Tafel equation. The capacity fade rate is expressed as a function of storage time (t) and two acceleration parameters [23]:

$$C_{loss,cal}=k_{cal,0}{\theta }_T{\theta }_V{t}^{n_{cal}}$$

(2)

where k_cal,0 and n_cal are fitting parameters. The temperature-dependent Arrhenius acceleration factor (${\theta }_T$) and the State-of-Charge (SOC)-dependent Tafel acceleration factor (${\theta }_V$) are calculated by Equations (2) and (3), respectively [23]:

$${\theta }_T=\exp \left({\displaystyle \frac{-E_{a,cal}}{R}}\left({\displaystyle \frac{1}{T}}-{\displaystyle \frac{1}{T_{ref}}}\right)\right)$$

(3)

$${\theta }_V=\exp \left({\displaystyle \frac{-a_{1}F}{R}}\left({\displaystyle \frac{1+a_{2}SOC+a_{3}SO{C}^2}{T}}-{\displaystyle \frac{1+a_{2}SO{C}_{ref}+a_{3}SO{C}_{ref}^2}{T_{ref}}}\right)\right)$$

(4)

where t is the storage time, F is Faraday’s constant, and a₁, a₂, a₃ are fitting parameters. The model parameters are listed in

Table 4. Parameters of electric passenger vehicle battery calendar aging model.

Parameter	ncal	kcal,0	Ea,cal	a1	a2	a3
Value	0.6562	0.02986	54,054	0.0054	6.5858	−3.2929

.

For modeling cycle aging, an aging model based on Arrhenius kinetics is used. The capacity loss caused by cycling can be modeled by the following equation [25]:

$$C_{loss,cyc}=B\exp \left({\displaystyle \frac{-E_{a,cyc}+\lambda \cdot C_{rate}}{RT}}\right)H^z$$

(5)

where T is the battery temperature during cycling, H is the charge throughput, R is the gas constant, and C_rate is the average current rate. The parameters E_a, B, λ, and z are obtained by fitting experimental data. The equation above neglects the influence of Depth of Discharge (DOD) on cycle aging. To develop a more comprehensive aging model, an acceleration term accounting for the DOD effect is further incorporated [23]:

$$C_{loss,cyc}=B\exp \left({\displaystyle \frac{-E_{a,cyc}+\lambda \cdot C_{rate}}{RT}}\right)H^z{\left({\displaystyle \frac{DOD}{DO{D}_{ref}}}\right)}^{\alpha }$$

(6)

where α is another fitting parameter. The capacity loss is expressed as a percentage. Cycle aging is a function of battery temperature, DOD, charge/discharge current, and the number of cycles. The reference values for T_ref, DOD_ref, and SOC are set to 25 °C, 0.5, and 0.5, respectively. The cycle aging model parameters for the electric passenger vehicle battery are listed in

Table 5. Parameters of electric passenger vehicle battery cycle aging model.

Parameter	z	B	λ	Ea,cyc	α
Value	0.575	0.3239	481.85	33,040	0.1765

.

To validate the model’s accuracy, the simulation results were compared against multiple sets of experimental data from the literature [23]. The experimental conditions covered different temperatures (25 °C and 40 °C), discharge depths (40% and 80%), and charge/discharge rates C_rates (1C and 1.9C). The validation results show that under different ambient temperatures and charge/discharge conditions, the average absolute error between the model-predicted battery State of Health (SOH) and the experimental values is only 1.75%, as shown in Figure 1. This confirms the model’s reliability in predicting the aging behavior of electric passenger vehicle batteries under complex V2G operating conditions.

2.1.2. Modeling and Validation of Aging Model for Electric Truck Power Batteries

For the large-capacity prismatic battery used in electric trucks, the calendar aging model adopts a hybrid form combining a high-order polynomial and the Arrhenius equation. The capacity loss is expressed as [24]:

$$C_{loss,cal}=\exp \left(-k{t}^z\right)\times 100\%\times C_{nom}$$

(7)

$$\begin{array}{ll}k=& a_{cal}+b_{cal}\cdot T+c_{cal}\cdot SOC+d_{cal}\cdot T^2+e_{cal}\cdot T\cdot SOC\\ & +f_{cal}\cdot SO{C}^2+g_{cal}\cdot T^2\cdot SOC+h_{cal}\cdot T\cdot SO{C}^2+i_{cal}\cdot SO{C}^3\end{array}$$

(8)

where C_loss,cal is the capacity loss due to calendar aging, k represents the pre-exponential factor, R is the universal gas constant, and t is the storage time in seconds. The parameters z, a_cal, b_cal, c_cal, d_cal, e_cal, f_cal, g_cal, h_cal, and i_cal are used for model development. The model parameters are listed in

Table 6. Parameters of electric truck battery calendar aging model.

Parameter	acal	bcal	ccal	dcal	ecal
Value	1.10 × 10−3	−9.29 × 10−6	5.63 × 10−3	1.77 × 10−8	−2.93 × 10−5
Parameter	fcal	gcal	hcal	ical
Value	−1.54 × 10−3	4.10 × 10−8	3.08 × 10−6	2.05 × 10−4

.

To characterize the cycle aging phenomenon, the effects of temperature, DOD, and C_rate are considered in the aging model. The equation describing cycle aging is given by [24]:

$$\begin{array}{ll}{C}_{loss,cyc}& =C_{nom}\times \exp \left(a_{cyc}\right)\cdot \exp \left(b_{cyc}\right)\cdot \exp \left(c_{cyc}\right)\cdot \exp \left(-{\displaystyle \frac{E_{cyc}}{RT}}\right)\\ & \cdot \exp \left(\exp \left(d_{cyc}\right)E_{0,pl}\exp \left({\displaystyle \frac{E_{1,pl}}{RT}}+E_{2,pl}\right)\right)\cdot kA{h}^{z_{cyc}}\left({\displaystyle \frac{\%}{100}}\right)\end{array}$$

(9)

$$a_{cyc}=a_{0,cyc}\cdot \left(SO{C}_0-SOC\right)$$

(10)

$$b_{cyc}=b_{0,cyc}\cdot \left(DO{D}_0-DOD\right)$$

(11)

$$c_{cyc}=c_{0,cyc}\cdot \left(DC{H}_0-DCH\right)$$

(12)

$$d_{cyc}=d_{0,cyc}\cdot \left(CH{G}_0-CHG\right)$$

(13)

$$z_{cyc}=\max \left(0,z_{0,cyc}+z_{1,cyc}\cdot DOD+z_{2,cyc}\cdot DO{D}^2\right)$$

(14)

where C_loss,cyc represents the capacity loss due to cycle aging, with the parameters as listed in

Table 7. Parameters of electric truck battery cycle aging model.

Parameter	a0,cyc	b0,cyc	c0,cyc	d0,cyc	E0,pl	E1,pl
Value	0	1.43676	0.78804	−1.57333	309.48	18.975
Parameter	E2,pl	z0,cyc	z1,cyc	z2,cyc	Ecyc
Value	−48.2515	0.669	0	0	−590.1155 + 3.97095 × T − 0.0065 × T2

.

The validation results are shown in Figure 2. The model achieves a total average absolute error as low as 0.36% in predicting calendar aging under different storage SOC levels (30%, 50%, 70%, 95%, and 100%) and temperatures (25 °C, 45 °C, and 60 °C). Furthermore, as shown in Figure 3 and Figure 4, the cycle aging experiment conditions include different temperatures (25 °C, 35 °C, and 45 °C), charging rates (0.5C and 2.0C), discharging rates (0.3C and 1.0C), and discharge depths (55%, 75%, and 90%). The average absolute error in predicting aging under various cycling conditions is 0.30%. These results demonstrate that the developed model exhibits excellent predictive accuracy for the aging of electric truck batteries across various potential V2G scenarios, laying a solid foundation for subsequent lifecycle and benefit analysis.

2.1.3. Definition of Battery Aging Rate

To evaluate the impact of different operating conditions on battery lifespan due to calendar aging, the battery calendar aging rate is defined as:

$$Q_{loss,cal}={\displaystyle \frac{C_{loss,cal}}{C_{nom}}}\times 100\%$$

(15)

where Q_loss,cal is the battery calendar aging rate, and C_nom is the battery nominal capacity. The battery cycle aging rate is defined as:

$$Q_{loss,cyc}={\displaystyle \frac{C_{loss,cyc}}{C_{nom}}}\times 100\%$$

(16)

where Q_loss,cyc is the battery cycle aging rate. The total capacity loss can be expressed as:

$$Q_{loss}={\displaystyle \frac{C_{loss,cal}+C_{loss,cyc}}{C_{nom}}}\times 100\%$$

(17)

where Q_loss is the total capacity loss rate. The SOH of the battery can be calculated as:

$$SOH=\left(1-{\displaystyle \frac{C_{loss,cal}+C_{loss,cyc}}{C_{nom}}}\right)\times 100\%$$

(18)

2.2. Development of Time-of-Use Electricity Pricing Model

The time-of-use electricity pricing model describes the variation in electricity rates throughout different periods of a day, typically adjusted based on grid load demand. In the V2G bidirectional charging and discharging process of electric vehicles, utilizing the TOU pricing model helps users select optimal charging and discharging timings, thereby reducing costs and improving economic benefits. The pricing periods are generally divided into four categories: (1) Peak-peak period (P_ppeak): periods with extremely high grid load where electricity prices are typically higher than regular peak periods, usually occurring during specific hours in summer or winter (e.g., evening); (2) Peak period (P_peak): periods with the highest grid load where electricity prices are relatively high, usually occurring during daytime and evening hours; (3) Normal period (P_normal): periods with moderate grid load where electricity prices are at intermediate levels; 4) Valley period (P_valley): periods with the lowest grid load where electricity prices are relatively low, usually occurring during nighttime or early morning hours. Based on the 2023 annual electricity price data from the Jibei Grid in China, the pricing model is defined as follows. Let P(t) represent the electricity price at time t during a day, where t denotes the time (1 ≤ t ≤ 24). The specific electricity prices vary according to the period categories:

Summer period (June, July, and August) electricity price:

$$P_{\mathrm{summer}}(t)=\left\{\begin{array}{ll}{P}_{\mathrm{ppeak}}\hfill & \mathrm{if} (10\le t<11) or (17\le t<18) or (20\le t<21)\hfill \\ P_{\mathrm{peak}}\hfill & \mathrm{if} (11\le t<12) or (14\le t<17) or (19\le t<20)\hfill \\ P_{\mathrm{normal}}\hfill & \mathrm{if} (7\le t<10) or (12\le t<14) or (18\le t<19) or (21\le t<23)\hfill \\ P_{\mathrm{valley}}\hfill & \mathrm{if} (0\le t<7) or (23\le t<24) \hfill \end{array}\right.$$

(19)

Winter period (November, December and January of the following year) electricity price:

$$P_{\mathrm{winter}}(t)=\left\{\begin{array}{ll}{P}_{\mathrm{ppeak}}\hfill & \mathrm{if} 17\le t<19\hfill \\ P_{\mathrm{peak}}\hfill & \mathrm{if} (8\le t<9) or (10\le t<11) or (14\le t<17) or (19\le t<20)\hfill \\ P_{\mathrm{normal}}\hfill & \mathrm{if} (7\le t<8) or (9\le t<10) or (11\le t<14) or (20\le t<23)\hfill \\ P_{\mathrm{valley}}\hfill & \mathrm{if} (0\le t<7) or (23\le t<24) \hfill \end{array}\right.$$

(20)

Other seasons (February to May and September to October) electricity price:

$$P_{\mathrm{other}}(t)=\left\{\begin{array}{ll}{P}_{\mathrm{peak}}\hfill & \mathrm{if} (9\le t<12) or (15\le t<18) or (19\le t<21)\hfill \\ P_{\mathrm{normal}}\hfill & \mathrm{if} (7\le t<9) or (12\le t<15) or (18\le t<19) or (21\le t<23)\hfill \\ P_{\mathrm{valley}}\hfill & \mathrm{if} (0\le t<7) or (23\le t<24) \hfill \end{array}\right.$$

(21)

Therefore, the annual electricity price curve for the Jibei Grid in China for 2023 is shown in Figure 5.

2.3. Development of a V2G User Economic Model Considering Battery Aging Cost

To accurately assess the economic benefits for users participating in V2G, considering battery aging costs, this study constructs an economic model that incorporates grid interaction revenue and battery degradation costs. By dynamically coupling the battery aging model with revenue calculation, a detailed assessment of the net profit over the entire V2G lifecycle is achieved. The model primarily consists of three components: discharge revenue, charging cost, and battery retirement cost.

(1) Discharge Revenue:

When an EV discharges in response to grid demand, the user gains revenue from energy export. This revenue depends on the electricity price during discharge hours and the amount of energy discharged, calculated as follows:

$$Re{v}_{discharge}={\displaystyle \sum _{i=1}^n{P}_{discharge }}(i)\cdot E_{discharge }(i)$$

(22)

where P_discharge(i) is the discharge electricity price in the i-th hour, E_discharge(i) is the discharge energy in the i-th hour, and $n$ is the total number of discharge hours.

(2) Charging Cost:

To maintain the energy required for daily travel and V2G discharge, users need to charge during periods with lower electricity prices. The charging cost is calculated as:

$$Cos{t}_{charge}={\displaystyle \sum _{i=1}^n{P}_{charge }}(i)\cdot E_{charge }(i)$$

(23)

where P_charge(i) is the charging electricity price in the i-th hour, E_charge(i) is the charging energy in the i-th hour, and n is the total number of charging hours.

(3) Battery Retirement Cost:

The additional charge–discharge cycles during V2G operation accelerate battery capacity degradation. This study directly converts the capacity fade output from the battery aging model into an economic cost, calculated as:

$$Cos{t}_{Retirement}=C_{nom }\cdot P_{battery}\cdot \left(1-SOH\right)/0.2$$

(24)

where P_battery is the battery cost per unit capacity.

(4) Total Net Profit:

The total net profit of the V2G system is calculated by subtracting the charging cost and the retirement cost from the total discharge revenue. The formula for net profit is:

$${Rev}_{total}={Rev}_{discharge} -{Cost}_{charging} -{ Cost}_{Retirement}$$

(25)

2.4. Establishment of V2G Scenarios and Model Parameters

Figure 6 illustrates two computational scenarios developed for EV: the daily use scenario and the V2G scenario, designed to analyze battery aging under different conditions. Daily usage scenarios simulate EVs used solely for commuting needs, with input variables including driving mileage, environmental conditions, and the charging modes required to meet demands. The V2G scenario builds upon daily use by additionally incorporating charge and discharge behaviors that serve grid dispatch needs, simulating how electric vehicles function as distributed storage units to provide auxiliary services such as peak shaving in response to grid demands.

Both scenarios are computed using the EV battery aging model and the economic model. The computation process increments day by day until the battery SOH drops to 80% or below, which serves as the termination condition. Ultimately, both scenarios output data on battery health status and user revenue. In this study, the battery service life is the quantitative output obtained through the aforementioned simulation process. Under specific charge–discharge patterns and environmental conditions, starting from a battery SOH of 100%, the total time elapsed until the SOH drops to 80% is determined by accumulating daily simulated calendar and cycle aging amounts.

The model calculation parameters are shown in

Table 8. Model calculation parameters.

Parameter	Value
Start charge/discharge time	0:00/17:00
Energy consumption	Electric truck: 1.6 kWh/km, electric passenger vehicle: 0.2 kWh/km
Battery pack capacity	Electric truck: 513 kWh, electric passenger vehicle: 85 kWh
Battery cost	500 CNY/kWh
Daily driving range	Electric truck: 100 km, electric passenger vehicle: 50 km
Charge–discharge pattern parameter range	Electric truck: DOD = 20–70%, Crate = 0.5–3.0C, T = 10–40 °C; electric passenger vehicle: DOD = 20–80%, Crate = 0.5–3.0C, T = 10–40 °C

. The daily driving ranges for electric trucks and electric passenger vehicles reference the typical daily travel distances of urban logistics vehicles and private cars, respectively. Energy consumption and battery pack capacity are determined based on publicly available technical specifications of current mainstream models. Battery costs refer to recent industry market analysis reports. The start times for charging and discharging are set to typical valley and peak grid periods to maximize revenue or minimize costs. The parameter ranges for charge–discharge patterns cover the main scenarios that batteries might encounter in practical V2G applications, ensuring the broad applicability of the research conclusions.

3. Result and Discussion

3.1. Calendar Aging Patterns of Electric Truck and Electric Passenger Vehicle Batteries

Based on the established aging models for electric passenger vehicle and electric truck batteries, the effects of different storage SOCs and temperatures on calendar aging were analyzed. Figure 7 shows the changes in calendar aging of electric truck batteries over time under different influencing factors. For the prismatic batteries used in electric trucks, the calendar aging rate initially decreases and then increases with rising temperature at lower storage SOC, with the optimal storage temperature observed around 25 °C, resulting in minimal battery aging under the same storage duration. Additionally, calendar aging in prismatic batteries first increases and then decreases as the storage SOC decreases. In contrast, cylindrical batteries used in electric passenger vehicles show a consistent increase in calendar aging rate with rising temperature, while it decreases with reduced SOC.

3.2. Cycle Aging Patterns of Electric Truck and Electric Passenger Vehicle Batteries

Using the established battery aging models for both vehicle types, the impact of different charging and discharging patterns on cycle aging was analyzed. Figure 8 shows the effects of different temperatures, charge/discharge rates, and discharge depths on the cyclic aging rate of electric truck batteries. When the operating temperature of prismatic batteries increases from 10 °C to 40 °C, the cycle aging rate progressively increases by 20.5%. The cycle aging rate also rises with increasing C_rate, showing an 18.9% increase when the C_rate increases from 0.5C to 2.5C. Furthermore, when the depth of discharge increases from 10% to 100%, the cycle aging rate increases by 7.7%.

Figure 9 shows the effect of different charging and discharging modes on the cycle aging rate of electric passenger vehicle batteries. For cylindrical batteries, when the operating temperature rises from 10 °C to 40 °C, the cycle aging rate increases by 5.4%. Increasing the C_rate from 0.5C to 2.5C results in a 7.0% increase in cycle aging rate. When the depth of discharge increases from 10% to 100%, the cycle aging rate increases by 5.4%. Therefore, under identical conditions, the large-capacity prismatic batteries exhibit higher cycle aging rates compared to the small-capacity cylindrical batteries in this study.

3.3. Impact of Charging Patterns on Battery Life and Usage Cost in Daily Use Scenarios

Figure 10 illustrates the influence of different temperatures and C_rates on the service life of electric trucks under daily use conditions. The data points and box plots in Figure 10a represent battery service life under various conditions. Within the 20–40 °C range, battery service life initially increases and then decreases with rising temperature. The maximum service life reaches 14 years at 25 °C, while at a higher ambient temperature of 40 °C, the maximum service life is only 9 years. Figure 10b shows that battery service life decreases significantly with increasing C_rate. When the C_rate increases from 0.5C to 3C, the service life is reduced by 80.14%, indicating that C_rate has a significant negative impact on battery longevity.

Regarding user usage cost, it initially decreases and then increases with rising ambient temperature. When the C_rate increases from 0.5C to 3C, the usage cost decreases by 52.84%. This suggests that under daily use conditions, when batteries reach their end of life, longer service periods result in higher usage costs for EV users, as these costs include battery aging costs and charging costs.

Figure 11 shows the influence of different factors on electric passenger vehicle battery life and user costs. The service life of electric passenger vehicle batteries decreases with increasing temperature, showing a 69.53% reduction when operating temperature rises from 20 °C to 40 °C. Additionally, service life decreases with increasing C_rate, with an average reduction of 31.10% when C_rate increases from 0.5C to 3.0C. In terms of usage cost, higher operating temperatures lead to lower usage costs, indicating that under normal driving conditions, shorter service periods until battery end of life result in lower costs for EV users.

3.4. Impact of Charging/Discharging Patterns on Battery Life and Revenue in V2G Scenarios

In V2G scenarios, the additional charging and discharging behaviors of EV batteries are primarily driven by grid demands, leading to usage patterns that differ significantly from those in daily travel-only scenarios. Figure 12 illustrates the influence of various factors on the service life of electric truck batteries under V2G operation.

The study reveals that battery service life in V2G scenarios is substantially reduced compared to daily use scenarios, primarily due to the additional charge–discharge cycles introduced to support grid interactions. These cycles not only increase the total number of cycles but also feature deep charging and discharging characteristics required for power regulation and energy transfer, further accelerating battery performance degradation.

For electric truck batteries, service life consistently decreases with increasing temperature. When temperature rises from 20 °C to 40 °C, service life is reduced by an average of 55.73%. Similarly, service life decreases with higher C_rates, showing an average reduction of 86.74% when C_rate increases from 0.5C to 3.0C. Service life also declines with increasing DOD, with an average reduction of 56.08% when DOD increases from 20% to 70%.

User net revenue decreases with rising ambient temperature and increasing C_rate, but increases with deeper discharge depths. This occurs because the large battery capacity of electric trucks means that the additional revenue from deeper discharge can better offset the associated battery costs, ultimately balance usage expenses, and generate net benefits for users.

Electric passenger vehicle batteries exhibit similar patterns in V2G scenarios, with service life declining as temperature, C_rate, and discharge depth increase, as shown in Figure 13. When temperature rises from 20 °C to 40 °C, C_rate increases from 0.5C to 3.0C, and discharge depth increases from 20% to 80%, service life decreases by 51.19%, 42.82%, and 62.43%, respectively. User net revenue decreases with higher ambient temperatures and increased C_rates, but increases with deeper discharge depths. This reflects that greater energy discharge to the grid generates higher revenue, potentially resulting in net positive benefits for users.

Overall, as shown in Figure 14, the research findings enable the identification of distinct operational strategies for electric trucks and passenger vehicles to optimize the trade-off between revenue and battery aging in V2G scenarios. For electric trucks, a combination of moderate discharge depths (50–60% DOD) and low C_rates is recommended to leverage their large battery capacity for substantial energy arbitrage without inducing excessive degradation. In contrast, electric passenger vehicles, being more sensitive to cyclic aging, should adopt a more conservative strategy with shallower discharge depths (40–50% DOD) and similarly low C_rates. By operating within these parameter ranges during V2G discharge, an optimal balance can be achieved where the marginal revenue gain is effectively counterbalanced by the marginal cost arising from battery degradation, thereby maximizing the user’s annualized net revenue over the economic lifespan of the vehicle battery.

From a grid dispatch perspective, different V2G charging and discharging patterns correspond to distinct battery aging profiles. High-frequency, fast-response frequency regulation services typically require higher C_rates, while long-duration, large-capacity energy services often involve deeper discharge depths. Therefore, in practical dispatch operations, differentiated strategies should be developed based on service types. For power-oriented services, EVs with excellent rate capability should be prioritized, with reasonable limits on single-event duration. For energy-oriented services, the impact of discharge depth on battery cycle life should be comprehensively considered, with appropriate upper limits set for energy throughput. From an industrial and policy perspective, differentiated compensation mechanisms should be established to account for the varying battery degradation costs associated with different V2G services. Furthermore, EV manufacturers and battery suppliers can leverage these findings to define service-specific, warranty-compliant V2G operating envelopes, thereby safeguarding battery health while facilitating vehicle-grid integration. Establishing such guidelines is crucial for building stakeholder confidence and ensuring the sustainable scalability of V2G technologies.

3.5. Comparison of Two Scenarios

To compare battery aging and user benefits between daily use and V2G scenarios, operating conditions with an ambient temperature of 25 °C, a C_rate of 0.5C, and a depth of discharge of 50% were selected. Figure 15 illustrates the composition of battery aging and changes in user revenue for both electric trucks and electric passenger vehicle batteries under the two scenarios.

The introduction of V2G discharge into the daily driving scenario alters the final aging composition. The additional charging and discharging cycles increase the proportion of cycle aging, with cycle aging rates rising by 3.72% for electric trucks and 5.89% for electric passenger vehicles after incorporating V2G operation.

Regarding user benefits, net revenue remains consistently negative under daily use only, as users must cover both battery costs and charging expenses. Additionally, the observation that user net revenue remains negative during the initial phase of V2G participation before turning positive stems from the inherent interplay between revenue accumulation and battery degradation costs. Due to the higher capacity fade rate in the early stage of battery life, the economic cost of battery degradation, calculated via Equation (24), is front-loaded. Consequently, during the initial phase, the cost induced by rapid capacity loss exceeds the cumulative revenue from energy arbitrage. As the degradation rate slows, the persistent revenue generation eventually offsets this cost, leading to net positive returns. This pattern reveals the potential of user benefits over the entire lifecycle of EV batteries under V2G operation.

Furthermore, in the studied cases, the average service life of electric vehicles is shortened by 4.7 years. These results show that the additional charge–discharge cycles introduced by V2G are a key factor accelerating battery aging and reducing service life, which must be carefully considered when formulating charging and discharging strategies.

Further analysis of key economic and behavioral parameters reveals that a higher battery price raises the aging cost, thereby increasing the economic expense for users participating in V2G. This may prolong the initial period of negative net revenue for both vehicle types, as the higher upfront cost takes longer to be offset by V2G earnings. However, due to their larger battery capacity, electric trucks are expected to show greater resilience to rising aging costs compared to electric passenger vehicles, thus maintaining their relative economic advantage. Conversely, a larger peak-valley electricity price difference directly increases user income from V2G arbitrage. This enables EVs to sell more energy during high-price periods, particularly benefiting electric trucks. Regarding user behavior, an increase in daily driving distance consumes more battery energy for travel needs, reducing the remaining capacity available for V2G discharge. Consequently, this diminishes the potential V2G revenue for electric vehicles but may extend battery service life due to reduced cycling.

4. Conclusions

This study investigates the impact of different charging and discharging patterns on battery lifespan and user benefits when V2G discharge is incorporated into daily electric vehicle usage. Calendar and cycle aging models were developed for both electric truck and electric passenger vehicle batteries, alongside electricity pricing and user economic models. The differences in battery lifespan and user revenue under various charging and discharging patterns in both daily use and V2G scenarios were analyzed. The main conclusions are as follows:

• The introduction of V2G discharge alters the battery aging pattern, increasing the proportion of cyclic aging in total capacity degradation. Within the parameter ranges studied, V2G participation increased the cyclic aging rate by 3.72% for electric trucks and 5.89% for passenger vehicles, shortening the battery lifespan compared to daily use scenarios.
• Different battery types exhibit varying responses to the V2G scenarios. Compared to the large-capacity prismatic batteries used in electric trucks, the small-capacity cylindrical batteries in passenger cars showed a greater increase in cyclic aging rate under frequent V2G charging and discharging, indicating their aging process may be more sensitive to the additional discharge introduced by V2G.
• To minimize the additional degradation caused by V2G, grid dispatch of EVs through V2G requires differentiated charging and discharging strategies based on battery aging characteristics, achieving a balance between grid peak shaving demands and prolonging battery lifespan. The results show that employing moderate discharge depths and lower C_rates can strike an optimal balance between extending battery service life and enhancing user economic benefits.
• For EVs participating in V2G, the rapid initial battery degradation means early-stage revenues cannot cover aging costs. As degradation slows in later stages and cumulative revenues increase, costs may eventually be covered. This highlights the importance of appropriate pricing mechanisms and incentive programs to ensure user benefits, emphasizing the need for sustained user participation and policy support.

Although the aging-economic coupling framework developed in this study reveals the variation patterns between battery aging and user revenue under different V2G charging and discharging patterns, it does not fully encompass diverse real-world electricity market services such as frequency regulation and demand response. Future research should focus on developing collaborative optimization strategies for multi-service markets to maximize users’ comprehensive benefits. Additionally, emphasis should be placed on validating these models with data from actual V2G demonstration projects to more thoroughly investigate the dynamic and complex behaviors of electric vehicles under real grid interaction conditions.