Life Cycle Prediction Assessment of Battery Electrical Vehicles with Special Focus on Different Lithium-Ion Power Batteries in China

Abstract

The incentive policies of new energy vehicles substantially promoted the development of the electrical vehicles technology and industry in China. However, the environmental impact of the key technology parameters progress on the battery electrical vehicles (BEV) is uncertain, and the BEV matching different lithium-ion power batteries shows different environmental burdens. This study conducts a life cycle assessment (LCA) of a BEV matching four different power batteries of lithium-ion phosphate (LFP), lithium-ion nickel-cobalt-manganese (NCM), lithium manganese oxide (LMO), and lithium titanate oxide (LTO) batteries. In addition, the 2025 and 2030 prediction analyses of the batteries production and life cycle BEV are conducted with the specially considered change and progress of the power battery energy density, battery manufacturing energy consumption, electricity structure, battery charge efficiency, and vehicle lightweight level. In addition, sensitivity analyses of power battery energy density, battery manufacturing energy consumption, electricity structure, and battery charge efficiency are conducted. The results show that the LFP battery is more environmentally friendly in the global warming potential (GWP) and acidification potential (AP), and the NCM battery is more environmentally friendly in abiotic depletion (fossil) (ADP(f)) and human toxicity potential (HTP). However, the LTO battery shows the highest environmental impact among the four environmental impact categories due to the lower energy density. For life cycle BEV, GWP and ADP(f) of BEV based on LFP, NCM, and LMO are lower than those of internal combustion engine vehicles (ICEV), while AP and HTP of BEV based on the four batteries are higher than those of ICEV. The grave-to-cradle (GTC) phase of vehicle has substantial environmental benefit to reduce the human toxicity emission. With the improvement of the battery density, battery charge efficiency, electricity structure, and glider lightweight level, life cycle BEVs based on the four different batteries show substantial environmental benefits for four environmental impact categories.

1. Introduction

The latest data show that China’s dependence on foreign oil consumption reached 72% in 2021, and energy security has become an important issue to be solved urgently. Meanwhile, to address global warming problems, the Chinese government committed to reaching a carbon peak by 2030 and achieving carbon neutrality by 2060. Facing a carbon-neutral future prospect, the China State Council issued “the Notice on The Action Plan for Carbon Peak by 2030” on 24 October 2021, which took green and low-carbon transport action as one of the important tasks. It showed that the proportion of new and clean energy-powered transport vehicles would reach about 40% and the carbon emission intensity per unit of converted turnover of operating vehicles would be reduced by about 9.5% by 2030 compared with 2020. Meanwhile, the State Council issued “Made in China 2025” in 2015, which made energy-saving and new energy vehicles as one of the ten essential development areas. Moreover, the “New Energy Vehicle Industry Development Plan (2021–2035)” proposed that new energy vehicle sales volume of new car sales would achieve about 20% by 2025. The incentive policies substantially promoted the development of new energy vehicles market.

China has become the largest market of electric vehicles (EVs) globally in recent years. In 2021, there have been over 3545 thousand units of EVs (including plug-in hybrid ones) sold in China [1]. In 2021, China’s total power battery capacity produced and assembled in those EVs is 154.5 GWh, increasing by 142.8% when compared with 2020. Due to the fast-growing number of EVs and the soaring capacity of lithium-ion power batteries, the potential environmental impacts have drawn much attention. Electric vehicles have obvious environmental benefits during operation. However, it is uncertain whether the electrical vehicles have environmental benefits compared with internal combustion engine vehicles (ICEV) when considering the source of electricity energy and the environmental burden of the vehicle manufacturing process. Therefore, domestic and foreign scholars have systematically quantified the environmental impact of electric vehicles by using the life cycle assessment method.

1.1. Literature Review

Life cycle assessment (LCA) is a method of objectively evaluating the environmental impacts of a product, process, or activity through quantitative analysis on the process of energy transfer and conversion, material consumption, and the discharge of environmental pollutants [2]. To date, there have been several LCA studies of battery electrical vehicles (BEV) and other power fuel vehicles [3,4,5,6,7,8,9,10]. In detail, Notter et al. compiled a rough LCA of BEV and provided a detailed life cycle inventory of a Li-ion battery [11]. Wu et al. calculated and compared the life cycle greenhouse gas emissions of BEV and ICEV in 2010, 2014, and 2020. It was found that with the optimization of electricity structure, the progress of electricity generation technology, and combined heat and power scales, the life cycle greenhouse gas emissions of BEV will be reduced by 13.4% in 2020 when compared to ICEV [12]. Shafique et al. presented a comparative cradle-to-grave life cycle environmental footprint analysis of BEV in 10 selected countries using the current and future electricity mix scenarios [13]. Qiao et al. analyzed the greenhouse gas emissions of the cradle-to-gate phase, well-to-wheel phase, and grave-to-cradle phase for BEV and ICEV in different times. It was found that the greenhouse gas emissions of producing a BEV are 15.0–15.2 t CO₂-eq, which are 50% higher than the 10.0 t CO₂-eq of ICEV. However, the life cycle greenhouse gas emissions of a BEV are about 41.0 t CO₂-eq, which are 18% lower than those of an ICEV [14,15]. Tagliaferri et al. presented and compared the LCA of BEV and ICEV for Europe. Two manufacturing inventories were analyzed, and different vehicle disposal pathways were considered for BEV [16]. Burchart et al. presented a LCA of BEV in Poland and the Czech Republic, considering the production of electricity structure. It was found that greenhouse gas emissions and fossil energy consumption of BEV would be lower than those of ICEVs both current and future scenarios, while the acidification, eutrophication, human toxicity, and particulate formation caused by BEVs are higher than ICEV [17].

Some scholars evaluated and compared the life cycle assessment of different power batteries applied for BEV [18,19,20]. In detail, Ellingsen et al. provided a transparent inventory for a lithium-ion nickel-cobalt-manganese (NCM) battery and quantitively assessed its cradle-to-gate environmental impacts [21]. Marques et al. presented and compared a life cycle primary fossil energy, global warming potential, acidification potential, and eutrophication potential of lithium manganese oxide (LMO) and lithium-ion phosphate (LFP) battery, addressing real-life operational conditions and battery capacity fade [22]. Shu et al. conducted the life cycle environmental impact of LFP and NCM battery and found that LFP battery is more environmentally friendly in the phase of production, while NCM battery is more environmentally friendly in the phase of the application and transportation [23]. Sun et al. collected primary inventory data from two Chinese leading lithium-ion battery suppliers, two leading cathode material producers, and two battery recycling corporations from 2017 to 2019 to quantitively evaluate the environmental impact of NCM battery. It was found that the material preparation stage is the largest contributor to life cycle environmental impact of the lithium-ion battery, with the cathode active material, wrought aluminum and electrolytes as the predominant contributors [24]. Hao et al. estimated the greenhouse gas emissions from the production of LFP, NCM, and LMO batteries by establishing a life cycle assessment framework. It was found that the greenhouse gas emission from the production of a 28 kWh LFP, NCM, and LMO battery are 3061 kg CO₂-eq, 2912 kg CO₂-eq, and 2705 kg CO₂-eq, respectively [25]. Yin et al. studied and constructed a life cycle assessment of lithium titanate oxide (LTO) batteries for battery electrical buses, including the resetting and reusing phase, and calculated that the life cycle greenhouse gas emission of each kWh LTO battery is 1860 kg CO₂-eq [26].

When the capacity of lithium-ion battery is reduced by 20–30%, it can no longer satisfy the demands of BEV. The disposal methods of retired battery mainly include remanufacturing, cascade utilization, recycling, and direct scrapping [27]. Considering the cascade utilization application scenarios, the retired batteries have been reused on the residential energy storage with rooftop photovoltaic [28,29], utility-level photovoltaic firming [30], and utility-level peak-shaving [31,32]. Considering the recycling methods of retired battery, different scholars researched pyrometallurgical, hydrometallurgical, bio-hydrometallurgical, and direct recycling technologies [33,34].

In summary, life cycle assessment of different type vehicles and power batteries were compared, and cascade utilization and recovery of batteries were researched and conducted by different scholars. Meanwhile, the vehicle inventories, calculation model, important influence factors were established and analyzed by scholars. However, few studies quantitatively evaluate life cycle environment impact of battery electrical vehicles when matching different power batteries, especially considering the LTO batteries used for battery electrical passenger vehicles. Meanwhile, the green technology of vehicle, battery, and fuel will rapidly develop and advance with the motivation of the Chinese government incentive policy and the carbon reduction pressure of transportation industry. The impact of the key technology parameters progress on the life cycle BEV becomes uncertain. Moreover, limited studies have conducted a sensitivity analysis of power battery energy density, battery manufacturing energy consumption, electricity structure, and battery charge efficiency according to different assumption scenarios.

1.2. Contribution of This Work

To bridge the research gap, this study conducts a life cycle assessment of a battery electrical passenger vehicles matching four different power batteries of LFP, NCM, LMO, and LTO batteries. The 2025 and 2030 prediction analyses of the batteries production and life cycle BEV are conducted especially considering the change and progress of the power battery energy density, battery manufacturing energy consumption, electricity structure, battery charge efficiency, and vehicle lightweight level. The entire life cycle BEV includes cradle-to-gate (CTG), well-to-wheel (WTW), and grave-to-cradle (GTC) in this study. The inventories of four different power batteries are summarized and the life cycle assessment calculation models are established. Potential environmental impacts are further explored based on the CML 2001 method.

In addition, sensitivity analyses of power battery energy density, battery manufacturing energy consumption, electricity structure, and battery charge efficiency are conducted according to different assumption scenarios. Quantitative research on production, use, and disposal of battery electrical vehicles and sensitivity analysis of key components can provide reference for sustainable development of battery electrical vehicle industry and policy-making.

The remainder of this study is organized as follows. Section 2 illustrates the methodology for the life cycle assessment of the BEV. In particular, the assessment objects and function unit are presented in Section 2.1. The system boundary is shown in Section 2.2. The calculation models including CTG, WTW, and GTC calculation model are introduced in Section 2.3. Life cycle inventories including power battery, glider, ICEV, vehicle assembly, and prediction scenarios are summarized in Section 2.4. Section 3 presents research results including the environmental impact of CTG phase of different power battery in Section 3.1 and the environmental impact of life cycle BEV based on different batteries in Section 3.2. Section 4 presents the sensitivity analysis results including the power battery energy density in Section 4.1, battery manufacturing energy consumption in Section 4.2, electricity structure in Section 4.3, and battery charge efficiency in Section 4.4. Section 5 provides the concluding remarks.

2. Methodology

The LCA method is now widely applied in many industry sectors and playing a more and more important role in the eco-design, green manufacturing, and other sustainability-related fields.

2.1. Assessment Object and Functional Unit

Vehicle model specification and assessment object was the basic assumption for quantitative analysis and substantially influences the assessment results. The vehicle model matched the popular BEV market, because the goal of this study is to reveal the real situation in China. The BYD qin BEV and Chang’an CS55 ICEV were chosen as the research object, whose vehicle model specification is presented as Table 1. The LFP battery was used in the baseline scenario, whose capacity is 57 kWh. This study assumes that both BEV and ICEV can drive 150,000 km in China road conditions.

Function unit can quantify and compare selected product performance characteristics. To compare different lithium-ion power batteries production, 1 kWh was chosen as the function unit. To compare life cycle BEV based on different batteries, 1 km was chosen as the function unit.

2.2. System Boundary

The goal of this study is to quantitatively assess and predict the environmental impacts of BEV based on different lithium-ion power batteries and to quantitatively identity the effect of different influence factors on environment benefits for BEV. As Figure 1 shows, the entire life cycle BEV includes CTG, WTW, and GTC. The CTG phase is the BEV production phase, including power battery production, glider production, and vehicle assembly. The process includes material extraction, material transformation, component manufacturing, and vehicle assembly. Component manufacturing includes different batteries manufacturing and glider manufacturing. Different power batteries include LFP battery, NCM battery, LMO battery, and LTO battery.

The WTW phase is the BEV use phase. The fuel economy of BEV based on LFP battery is 12.3 kWh∙(100 km)⁻¹, which comes from official enterprise data. BEV will generate different electricity consumption when matching different power batteries. Therefore, some parameters such as fuel economy are calculated linearly based on the curb weight of BEV matching four different batteries. When the curb weight of BEV increases 1 kg, the electricity consumption will increase 0.0051 kWh [37]. Meanwhile, battery charge efficiency can significantly influence life cycle electricity consumption, which is assumed as 90% for the baseline scenario.

The GTC phase is the BEV recycling phase. The beginning of BEV recycling is vehicle dismantling, which is divided into recyclable parts and unrecyclable parts [38]. Unrecyclable parts are incinerated and landfilled. The vehicle recycling technology has nothing special and is commonly adopted by both BEVs and ICEV [14]. Pyrometallurgical and hydrometallurgical technologies are two major vehicle recycling technologies worldwide [33,34]. This study assumes that the hydrometallurgical technology will be widely used in future China, and only steel, aluminum, copper, and iron recycling are considered.

The key parameters prediction includes electricity structure, battery energy density, battery manufacturing energy consumption, glider lightweight level, and battery charge efficiency. The key parameters change according to the literature, development tendency, and industry policy.

2.3. Calculation Model

Life cycle assessment calculation model for BEV is established based on the previous work of the authors’ research team [36,39,40]. The calculation model includes CTG calculation model, WTW calculation model, and GTC calculation model.

2.3.1. CTG Calculation Model

The CTG calculation model includes an energy consumption model and a pollution emission model.

• (1). Calculation model of energy consumption

Energy consumption matrix of CTG phase for BEV is

(1)${\mathit{E}}_{\mathrm{CTG}}={\displaystyle \sum _k[{(m_{ij})}_{k\times n}\cdot {(e_{1ij})}_{n\times u}+}{(e_{2ij})}_{k\times r}\cdot {(e_{0ij})}_{r\times u}]+{(e_{3j})}_{1\times r}\cdot {(e_{0ij})}_{r\times u}$

where m_ij denotes the mass of j vehicle material contained in i vehicle component/kg, k denotes the quantity of vehicle component, n denotes the quantity of vehicle material; where e_0ij denotes the quantity of j primary energy consumption (crude coal, crude oil, natural gas, etc.) in producing i per unit secondary energy (electricity, thermal energy, etc.)/MJ∙MJ⁻¹, r denotes the quantity of secondary energy, u denotes the quantity of primary energy; where e_1ij denotes the quantity of j primary energy consumption in producing i per unit vehicle material/MJ∙kg⁻¹; where e_2ij denotes the quantity of j secondary energy consumption in manufacturing i vehicle component/MJ∙kg⁻¹; and where e_3j denotes the quantity of j secondary energy consumption in vehicle assembly/MJ.

• (2). Calculation model of pollution emission

The pollution emission matrix of CTG phase for BEV is

(2)${\mathit{P}}_{\mathrm{CTG}}={\displaystyle \sum _k[{(m_{ij})}_{k\times n}\cdot {(p_{0ij})}_{n\times s}+{(e_{2ij})}_{k\times r}\cdot {(p_{1ij})}_{r\times s}]}+{(e_{3j})}_{1\times r}\cdot {(p_{1ij})}_{r\times s}$

where p_0ij denotes the pollution emission intensity of j pollutant in producing i per unit vehicle material/kg∙kg⁻¹, s denotes the quantity of pollutant; p_1ij denotes the pollution emission intensity of j pollutant in producing i per unit secondary energy/kg∙MJ⁻¹.

2.3.2. WTW Calculation Model

The WTW calculation model includes energy consumption model and pollution emission model.

• (1). Calculation model of energy consumption

Energy consumption matrix of WTW phase for BEV is

(3)${\mathit{E}}_{\mathrm{WTW}}=Q_1\times l/100\times 3.6/\eta \cdot {(e_{0j})}_{1\times u}$

where Q₁ denotes fuel economy of vehicle/kWh∙(100 km)⁻¹, l denotes the life time mileage/km, η denotes battery charge efficiency, e_0j denotes the quantity of j primary energy consumption in producing per unit electricity energy/MJ∙MJ⁻¹.

The pollution emission matrix of WTW phase for BEV is

(4)${\mathit{P}}_{\mathrm{WTW}}=Q_1\times l/100\times 3.6/\eta \cdot {(p_{1j})}_{1\times s}$

where p_1j denotes the pollution emission intensity of j pollutant in producing per unit electricity energy/kg∙MJ⁻¹.

2.3.3. GTC Calculation Model

The GTC calculation model includes energy consumption model and pollution emission model. This study mainly considers the recovery of steel, cast iron, aluminum, and copper in metal material. The cobalt, nickel, and lithium are considered for battery recycling. It is assumed that the recovery rates of steel, cast iron, aluminum, copper, cobalt, nickel, and lithium are ξ₁, ξ₂, ξ₃, ξ₄, ξ_5, ξ_6, and ξ₇ respectively.

• (1). Calculation model of energy consumption

The energy consumption matrix of GTC phase for BEV is

(5)${\mathit{E}}_{\mathrm{GTC}}={\displaystyle \sum _k{(m_{ij}\cdot {\xi }_j)}_{k\times 7}}\cdot ({(e_{3ij})}_{7\times r}\cdot {(e_{0ij})}_{r\times u}-{(e_{1ij})}_{7\times u})$

where e_3ij denotes the quantity of j secondary energy consumption in recycling i per unit vehicle metal material/MJ∙kg⁻¹.

• (2). Calculation model of pollution emission

The pollution emission matrix of GTC phase for BEV is

(6)${\mathit{P}}_{\mathrm{GTC}}={\displaystyle \sum _k{(m_{ij}\cdot {\xi }_j)}_{k\times 7}}\cdot ({(e_{3ij})}_{7\times r}\cdot {(p_{1ij})}_{r\times s}-{(p_{0ij})}_{7\times s})$

The CML2001 assessment method developed by the Institute of Environmental Sciences of Leiden University was selected as the based method [5,36]. Four impact categories, including abiotic depletion (fossil) (ADP(f)), global warming potential (GWP), acidification potential (AP), and human toxicity potential (HTP) were chosen to assess the impact characterization results.

Global warming potential (GWP) represents potential impacts on global warming due to the emissions of CO₂, CH₄, and N₂O, with the potentials of 1, 25, and 298 units CO₂-eq, respectively.

Abiotic depletion (fossil) (ADP(f)) indicates the fossil energy consumption as a result of the consumption of hard coal, crude oil, and natural gas.

Acidification potential (AP) indicates potential impacts on the environment as a result of NO_x and SO₂ emissions.

Human toxicity potential (HTP) represents potential impacts on human health due to emissions of toxic substances such as heavy metals and hydrocarbons.

2.4. Life Cycle Inventory

2.4.1. Power Battery

In 2020, the output of lithium iron phosphate power batteries for new energy vehicles in China was 34.6 GWh, accounting for 32.4% of the total output of power batteries. According to the enterprise investigation, LFP battery of capacity density is assumed as 170 W∙h∙kg⁻¹ and NCM battery of capacity density is assumed as 175 W·h∙kg⁻¹. According to literature investigation [25,26], LMO battery of capacity density is 133 W·h∙kg⁻¹ and LTO battery of capacity density is assumed as 60 W·h∙kg⁻¹. This study assumes that battery capacity of LFP, NCM, LMO, and LTO battery is 57 kWh. Different power battery inventories are shown in Table 2. Power batteries include cathode, anode, electrolyte, diaphragm, shell, cooling liquid, and battery management system (BMS).

The battery manufacturing energy consumption shows differences when the battery manufacturing process, electricity structure, and battery type are different. Battery manufacturing inventories are presented in detail in the literature [11,21]. Two different battery manufacturing inventories were analyzed by the literature [16], whose battery manufacturing electricity energy consumptions are 27 and 100.8 MJ∙kg⁻¹, respectively. According to the Argonne National Laboratory in the United States and literature [41], energy consumption in battery assembly is proportional to mass, which is about 2.67 MJ∙kg⁻¹. Based on enterprise research and literature data [11,16,21,41], this paper assumes that the electricity energy and natural gas consumption are 18 MJ∙kg⁻¹ and 8.8 MJ∙kg⁻¹ for battery manufacturing based on enterprise research and literature data for the baseline scenario. Table 2

Different power batteries inventories.

LFP Battery/kg			NCM Battery/kg		LMO Battery/kg		LTO Battery/kg
Total		335	Total	326	Total	429	Total	950
Cathode	LFP (1)	82	NCM (2)	92	LMO (3)	144	NCM	228
	Aluminum	68	Aluminum	64	Aluminum	79	Aluminum	112
	Polyvinylidene fluoride (PVDF)	4	PVDF	4	PVDF	5	PVDF	10
Anode	Graphite (4)	51	Graphite	60	Graphite	63	LTO (5)	170
	Copper	41	Copper	37	Copper	47	Carbon black	24
	PVDF	4	PVDF	4	PVDF	5	PVDF	10
Electrolyte	LiPF6	9	LiPF6	6	LiPF6	8	LiPF6	18
	Ethylene carbonate (6)	26	Ethylene carbonate	18	Ethylene carbonate	23	Ethylene carbonate	51
	Dimethyl carbonate (7)	26	Dimethyl carbonate	18	Dimethyl carbonate	23	Dimethyl carbonate	51
Diaphragm	Polypropylene (PE)	6	PE	6	PE	8	PE	35
	Polyethylene (PP)	1	PP	1	PP	1
Shell	PP	4	PP	4	PP	6	Aluminum	177
	Steel	5	Steel	5	Steel	6		0
	Glass fiber	1	Glass fiber	1	Glass fiber	1		0
Cooling liquid	Ethylene glycol	3	Ethylene glycol	3	Ethylene glycol	4	Ethylene glycol	35
BMS	Transistor	2	Transistor	2	Transistor	2	Transistor	14
	Resistor	2	Resistor	2	Resistor	2	Resistor	15
References	[25,41,42,43]		[25,41,42]		[25,41,42]		[18,26]

Note: ⁽¹⁾ Processing 1 kg LFP can produce 26 kg CO₂-eq and 0.06 kg SO₂-eq [25,42]; ⁽²⁾ Processing 1 kg NCM can produce 36.8 kg CO₂-eq and 0.08 kg SO₂-eq [25,42]; ⁽³⁾ Processing 1 kg LMO can produce 19.6 kg CO₂-eq and 0.045 kg SO₂-eq [25,42]; ⁽⁴⁾ Processing 1 kg graphite can produce 1.12 kg CO₂-eq and 0.0026 kg SO₂-eq [41]; ⁽⁵⁾ Processing 1 kg LTO can produce 18.3 kg CO₂-eq [18]; ⁽⁶⁾ Ethylene as a proxy for ethylene carbonate; ⁽⁷⁾ Dimethyl ketone as a proxy for dimethyl carbonate.

2.4.2. Glider

The glider of BEV mainly includes motor, electronic controller, final drive, body, chassis, and fluid [35]. Permanent magnet synchronous motor is installed on battery electric vehicles in this study, whose total power and torque are 100 kW and 180 N∙m, respectively. The motor inventories refer to literature [44]. The production process of body mainly consists of stamping, welding, painting, final assembly [36]. Material inventories and energy consumption for BEV glider are shown in Table 3.

2.4.3. ICEV

The Chang’an CS55 internal combustion engine vehicle was chosen as representative vehicle type based on the comprehensive consideration of the curb quality, market sales, and fuel economy. The fuel consumption of ICEV is 8.5 L∙(100 km)⁻¹, the curb weight of ICEV is 1490 kg. The material inventories and energy consumption of ICEV are shown in Table 3 [8,15,36]. Table 3

Material inventories and energy consumption for ICEV and BEV (exclude battery) [8,15,36].

Component	Vehicle Type	Material Proportion	Mass/kg	Electricity Energy Consumption/MJ	Thermal Energy Consumption/MJ
Engine	ICEV	35.7% steel, 42% aluminum, 12.3% iron, 1% copper, 4.5% rubber and 4.5% plastic	150	1653	/
Transmission	ICEV	30% steel, 30% aluminum, 30% iron, 5% rubber and 5% plastic	125	2246.25	1337.5
Lead-acid battery	ICEV	69% lead, 7.9% sulfuric acid, 6.1% polypropylene, 2.1% glass fiber, 14.1% water and 0.8% others	19	51.68	/
Motor	BEV	31.5% steel, 39.5% aluminum, 15.8% copper, 12.3% neodymium iron boron	35	184.8	66.5
Electronic controller	BEV	5% steel, 47.5% aluminum, 8.3% copper, 23% plastic, 3.8% rubber and 12.4% organics	10	13.8	/
Final drive	BEV	60.6% steel, 20% aluminum, 19% copper, 0.2% plastic and 0.2% organics	35	630	352.45
Body	ICEV	68.7% steel, 0.8% aluminum, 0.1% magnesium, 6.6% glass fiber, 17.4% plastic, 1.9% copper and 4.5% others	626	2147.18	/
	BEV		690	2366.7	/
Chassis	ICEV	82.3% steel, 6.3% iron, 1.0% aluminum, 2.3% copper, 3.3% plastic, 4.2% rubber and 0.6% others	541	751.99	232.63
	BEV		516	717.24	221.88
Fluid	ICEV	3.2% lubricating oil, 3.6% braking liquid, 28.6% cooling liquid, 10.7% wiper liquid and 53.9% additive	29	2102.5	/
	BEV		29	2102.5	/

2.4.4. Vehicle Assembly

The main energy consumption in an automobile assembly plant comes from processes or equipment, including painting, air conditioning and lighting, heating, material handling, welding, and workshop compressed air utilization [45]. The energy consumption data of the vehicle assembly stage are shown in Table 4.

2.4.5. Prediction Scenarios

The Chinese government incentive policy including “New Energy Vehicle Industry Development Plan (2021–2035)” and “Technology Roadmap for Energy Saving and New Energy Vehicles 2.0” will promote the technology development of battery electrical vehicles and battery. The future development tendency includes the improvement of battery energy density, the reduction of battery manufacturing energy consumption, the reduction of cathode and anode carbon emission factors, the cleanliness of electricity structure, the reduction of glider mass, and the improvement of battery charge efficiency. Therefore, the prediction scenarios of key parameters for BEV are shown in Table 5.

3. Results

The following LCA results were generated from life cycle inventories of BEV using calculation model and CML2001 method. The first results were evaluated on the CTG phase of different power battery to identify the environmental impact difference of the different battery type and different component, and to predict the environmental impact of the key technology parameters change on different power batteries. The second results were evaluated on the life cycle BEV to identify the environmental impact difference when matching different power batteries, and to predict the environmental impact of the development of vehicle lightweight, electricity structure, and fuel economy on BEV.

3.1. CTG Phase of Different Power Battery

Characterization results of the CTG environment impact of different power batteries are shown in Table 6. The function unit for the CTG phase of different power battery is per kWh. GWP, ADP(f), AP, and HTP of CTG phase of different batteries are shown in Figure 2. It can be found that the LTO has the highest GWP, ADP(f), AP, and HTP among the four batteries because of the lowest battery energy density and the highest battery quality.

As shown in Figure 2a, the GWP of LFP, NCM, LMO, and LTO in the CTG phase is 93.7, 113.0, 118.4, and 383.6 kg∙kWh⁻¹, respectively. LFP battery shows the lowest carbon emission among four different batteries, and the cathode accounts for 66.3% among different component parts in 2020 scenario. In terms of component contribution, the cathode has the highest contribution among the four different batteries and the battery manufacturing has the second contribution. With the progress and development of battery energy density, battery manufacturing energy, and electricity structure, the GWP of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 26.7%, 26.1%, 24.1%, and 41.5%, respectively, in the 2025 scenario, and the GWP of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 43.8%, 42.9%, 44.0%, and 53.8%, respectively, in the 2030 scenario.

As shown in Figure 2b, the ADP(f) of NCM battery shows the lowest fossil energy consumption among four different batteries, and the battery manufacturing accounts for 46.8% among different component parts in 2020 scenario. With the progress and development of battery energy density, battery manufacturing energy, and electricity structure, the ADP(f) of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 29.2%, 29.3%, 26.9%, and 43.7%, respectively, in the 2025 scenario, and the ADP(f) of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 46.1%, 46.2%, 46.9%, and 56.0%, respectively, in the 2030 scenario.

As shown in Figure 2c, the AP of LFP, NCM, LMO, and LTO in the CTG phase is 0.336, 0.374, 0.367, and 1.060 kg∙kWh⁻¹ respectively. LFP battery shows the lowest acidification gas emission among four different batteries, and the cathode accounts for 66.4% among different component parts in the 2020 scenario. With the progress and development of battery energy density, battery manufacturing energy, and electricity structure, the AP of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 19.5%, 19.4%, 17.9%, and 36.8%, respectively, in the 2025 scenario, and the AP of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 32.1%, 31.1%, 33.6%, and 45.7%, respectively, in the 2030 scenario.

As shown in Figure 2d, the HTP of NCM battery shows the lowest human toxicity emission among four different batteries, and the cathode accounts for 52.3% among different component parts in 2020 scenario. With the progress and development of battery energy density, battery manufacturing energy, and electricity structure, the HTP of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 15.9%, 15.7%, 12.5%, and 34.3%, respectively, in the 2025 scenario, and the ADP(f) of the CTG phase of LFP, NMC, LMO, and LTO will decrease by 26.0%, 25.7%, 25.7%, and 41.5%, respectively, in the 2030 scenario.

For the production of four different power batteries, the LFP battery is more environmentally friendly in the GWP and AP, and the NCM battery is more environmentally friendly in ADP(f) and HTP. However, the LTO battery shows the highest environmental impact for four environmental impact categories due to the lower energy density.

3.2. Life Cycle BEV Based on Different Batteries

Characterization results of the life cycle environment impact of BEV based on different power batteries are shown in Table 7. The function unit for life cycle BEV based on different batteries is g∙km⁻¹. The GWP, ADP(f), AP, and HTP of life cycle BEV based on different batteries are shown in Figure 3. It was found that the GWP and ADP(f) of life cycle BEVs based on LFP, NCM, LMO, and LTO are lower than those of ICEV, while the AP of life cycle BEVs is higher than that of ICEV.

As shown in Figure 3a, the GWP of life cycle BEVs based on LFP, NCM, LMO, and LTO are 162.0, 169.1, 175.1, and 281.1 g CO₂∙km⁻¹ in the 2020 scenario. Compared with ICEV, BEVs based on LFP, NCM, and LMO have substantial carbon emission reduction benefits. BEV based on LTO battery has similar carbon emission with ICEV. In terms of different phases, the WTW phase of BEV has the highest carbon emissions due to the high coal electricity proportion in China electricity structure, and the WTW phase of ICEV has the highest carbon emissions due to the high carbon emission of gasoline combustion. Meanwhile, the CTG power batteries have higher carbon emission compared with CTG engine. With the development of vehicle lightweight, battery charge efficiency, and electricity structure, the GWP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 28.7%, 28.5%, 28.2%, and 37.8%, respectively, in the 2025 scenario. The GWP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 49.7%, 49.0%, 49.7%, and 55.1%, respectively, in the 2030 scenario. The GTC phase of ICEV and BEV contributes to the carbon emission reduction.

As shown in Figure 3b, the ADP(f) of the life cycle BEVs based on LFP, NCM, LMO, and LTO are 1627, 1616, 1721, and 2277 kJ∙km⁻¹ in the 2020 scenario. Compared to ICEV, the ADP(f) results of BEV based on different power batteries are similar to the results of GWP. With the development of vehicle lightweight, battery charge efficiency, and electricity structure, the ADP(f) of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 28.8%, 28.8%, 19.4%, and 36.7%, respectively, in the 2025 scenario. The ADP(f) of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 49.5%, 49.6%, 49.8%, and 55.3%, respectively, in 2030 scenario. The GTC phase of ICEV and BEV contributes to the energy consumption reduction.

As shown in Figure 3c, the AP of the life cycle BEVs based on LFP, NCM, LMO, and LTO are 0.364, 0.382, 0.380, and 0.604 g SO₂∙km⁻¹ in the 2020 scenario. Compared with ICEV, BEVs based on LFP, NCM, LMO, and LTO have higher acidification gas emission. In terms of different phases, the WTW phase and CTG power battery are the main contributors for acidification gas emissions. With the development of vehicle lightweight, battery charge efficiency, and electricity structure, the AP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 27.4%, 27.0%, 27.3%, and 36.1%, respectively, in the 2025 scenario. The GWP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 47.3%, 46.5%, 48.3%, and 52.0%, respectively, in the 2030 scenario. The GTC phase of ICEV and BEV contributes to the acidification gas emission reduction.

As shown in Figure 3d, the HTP of the life cycle HTP of BEV based on LFP, NCM, LMO, and LTO are higher than ICEV when the GTC phase of vehicle is not considered. The GTC phase of vehicle has substantial environmental benefit to reduce the human toxicity emission. The CTG power battery phase accounts for the largest proportion in human toxicity emission. With the development of vehicle lightweight, battery charge efficiency, and electricity structure, the HTP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 20.9%, 21.6%, 18.6%, and 34.1%, respectively, in the 2025 scenario. The HTP of life cycle BEV based on LFP, NCM, LMO, and LTO batteries will decrease by 35.4%, 36.3%, 34.7%, and 47.3%, respectively, in the 2030 scenario.

For life cycle BEV based on different batteries, the GWP and ADP(f) of BEV based on LFP, NCM, and LMO are lower than those of ICEV, while the AP and HTP of BEV based on the four batteries are higher than those of ICEV.

4. Sensitivity Analysis

4.1. Power Battery Energy Density

The above results indicate that the difference of battery energy density has an important impact on the environment burdens of the CTG phase of different power batteries. Meanwhile, the value of power battery energy density is uncertain due to the difference of enterprise development level and the development of battery technology. In order to quantitively evaluate the impact of the power battery energy density on the environmental burdens of the CTG phase of BEVs based on different batteries, the power battery energy density was chosen as a sensitive factor in this study. Different power battery energy density scenarios are shown in Table 8. The results of sensitivity analysis of power battery energy are shown in Figure 4.

As Figure 4 shows, with the power battery energy density increases by 10%, the GWP of CTG phase of BEV based on LFP, NMC, LMO, and LTO decreases by 4.75%, 5.17%, 5.27%, and 7.43%, respectively; the ADP(f) decreases by 3.77%, 3.65%, 4.20%, and 6.20% respectively; the AP decreases by 5.02%, 5.26%, 5.22%, and 7.24%, respectively; the HTP decreases by 5.42%, 5.25%, 5.78%, and 7.05%, respectively. The improvement of power battery energy density has the highest environmental benefit on the CTG phase of BEV based on the LTO. The improvement of power battery energy density has substantial impact on GWP and AP.

4.2. Battery Manufacturing Energy Consumption

The battery manufacturing energy consumption shows differences when the battery manufacturing process, electricity structure, and battery type are different. Therefore, the value of battery manufacturing energy consumption is uncertain due to the difference of enterprise manufacturing process and battery technology development. The electricity energy and natural gas consumption are 18 MJ∙kg⁻¹ and 8.8 MJ∙kg⁻¹ for battery manufacturing based on enterprise research and literature data for baseline scenario in this study. The battery manufacturing energy consumption decreases by 10%, 20%, and 30%, respectively, for three different scenarios. The sensitivity analysis of battery manufacturing energy consumption is shown in Figure 5.

As Figure 5 shows, with the battery manufacturing energy consumption decreases by 10%, the GWP of CTG phase of BEV based on LFP, NMC, LMO, and LTO decreases by 1.43%, 1.21%, 1.55%, and 1.49%, respectively, the ADP(f) decreases by 1.89%, 1.88%, 2.22%, and 2.91%, respectively, the AP decreased by 0.88%, 0.80%, 1.07%, and 1.13%, respectively, the HTP decreases by 0.21%, 0.21%, 0.24%, and 0.33%, respectively. The reduction of the battery manufacturing energy consumption has the highest impact on ADP(f) while it has the lowest impact on HTP.

4.3. Electricity Structure

The above results indicate that electricity structure has an important impact on the environment burdens of the life cycle BEV. In order to quantitatively evaluate the impact of the electricity structure on the environmental burdens of the life cycle BEVs based on different batteries, three electricity structure scenarios were constructed, as shown in Table 9. The baseline electricity structure scenario is based on Chinese realistic development state, and scenario 1 and scenario 2 are the hypothetical scenarios. The results of sensitivity analysis of electricity structure are shown in Figure 6.

As Figure 6 shows, the optimization of electricity structure has some environmental benefit on the life cycle BEV. Compared to the baseline scenario, the GWP of the life cycle BEV based on LFP, NMC, LMO, and LTO will decrease by 14.0%, 13.5%, 13.8%, and 11.3% in scenario 1; the ADP(f) of those will decrease by 14.6%, 14.6%, 14.4%, and 13.7% in scenario 1; the AP of those will decrease by 13.8%, 13.3%, 14.0%, and 11.3% in scenario 1; and the HTP of those will decrease by 6.2%, 6.7%, 5.7%, and 6.8% in scenario 1. Compared to the baseline scenario, the GWP of the life cycle BEV based on LFP, NMC, LMO, and LTO will decrease by 22.9–29.4% in scenario 2; the ADP(f) of those will decrease by 29.1–30.4% in scenario 2; the AP of those will decrease by 23.4–29.0% in scenario 2; and the HTP of those will decrease by 12.2–13.7% in scenario 2. The optimization of electricity structure has an obvious impact on GWP, ADP(f), and AP while it has a lower impact on HTP.

4.4. Battery Charge Efficiency

The battery charge efficiency is assumed as 90% in the baseline scenario, which has an impact on the electricity energy consumption. Three different battery charge efficiency sensitivity analysis scenarios were established. The battery charge efficiencies were 90%, 94%, and 98%. The results of sensitivity analysis of battery charge efficiency are shown in Figure 7.

As Figure 7 shows, when the battery charge efficiency increases from 90% to 98%, the GWP of the life cycle BEV based on LFP, NMC, LMO, and LTO decreases by 5.1%, 4.9%, 4.9%, and 3.7%; the ADP(f) of the life cycle BEV based on LFP, NMC, LMO, and LTO decreases by 5.4%, 5.4%, 5.3%, and 4.8%; the AP of the life cycle BEV based on LFP, NMC, LMO, and LTO decreases by 4.9%, 4.7%, 4.9%, and 3.7%; the HTP of the life cycle BEV based on LFP, NMC, LMO, and LTO decreases by 3.8%, 3.9%, 3.5%, and 3.8%. The improvement of battery charge efficiency has minor environmental benefit on the life cycle BEV based on the LTO.

5. Conclusions

In this study, the environmental impacts of battery electrical passenger vehicles matching four different power batteries (LFP, NCM, LMO, and LTO) in China are conducted throughout the life cycle assessment approach. The future prediction analysis of batteries production and life cycle BEV are conducted to evaluate the environmental benefits of the development of key technology parameters. Inventories of four different power batteries are collected from the literature investigation and enterprise investigation. The study results can be concluded as follows.

For the production of four different power batteries, the LFP battery is more environmentally friendly in the GWP and AP, and the NCM battery is more environmentally friendly in ADP(f) and HTP. However, the LTO battery shows the highest environmental impact for four environmental impact categories due to the lower energy density. In detail, the cathode is the largest contributor to GWP, AP, and HTP due to the production of cathode active material and aluminum. The battery manufacturing energy consumption is the largest contributor to ADP(f). Meanwhile, with the improvement of the battery density, the reduction of battery manufacturing energy consumption, the clean of electricity energy, four different batteries show substantial environmental benefits for four environmental impact categories, which are decreased by 26.0% to 56% in 2030 when compared with 2020.

For life cycle BEV based on different batteries, GWP and ADP(f) of BEV based on LFP, NCM, and LMO are lower than those of ICEV, while AP and HTP of BEV based on the four batteries are higher than those of ICEV. In terms of different phases, the WTW phase of BEV has the highest GWP and ADP(f); the CTG phase power battery has higher contributors to AP and HTP; the GTC phase of the vehicle has substantial environmental benefit to reduce the human toxicity emission. With the improvement of battery charge efficiency and glider lightweight level, the BEV based on the four different batteries shows substantial environmental benefits for four environmental impact categories, which are decreased by 35.4% to 55.3% when compared with 2020.

A sensitivity analysis of power battery energy density, battery manufacturing energy consumption, electricity structure, and battery charge efficiency was conducted according to different assumption scenarios. The improvement of power battery energy density has a substantial impact on GWP and AP. Reduction of battery manufacturing energy consumption has the highest impact on ADP(f) while it has the lowest impact on HTP. The optimization of electricity structure has an obvious impact on GWP, ADP(f), and AP while it has lower impact on HTP. The improvement of battery charge efficiency has minor environmental benefit on the life cycle BEV based on the LTO.

The inventories of power batteries and battery electrical vehicles remain uncertain. The sensitivity analysis of different important factors can improve the accuracy of research results. The assessment model used is static and ignores the underlying dynamics within the system [50]. The accuracy of battery inventories will be improved in the future [51].

Author Contributions

Conceptualization, Y.Y.; data curation, G.L.; formal analysis, Y.C.; investigation, L.L.; methodology, Z.H.; software, J.Z.; writing—original draft, L.L.; writing—review and editing, P.F. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ADP(f)	Abiotic depletion (fossil)
AP	Acidification potential
BEV	Battery electrical vehicle
BMS	Battery management system
CTG	Cradle-to-gate
GTC	Grave-to-cradle
GWP	Global warming potential
HTP	Human toxicity potential
ICEV	Internal combustion engine vehicles
LCA	Life cycle assessment
LFP	Lithium-ion phosphate
LMO	Lithium manganese oxide
NCM	Lithium-ion nickel-cobalt-manganese
PE	Polyethylene
PP	Polypropylene
PVDF	Polyvinylidene fluoride
LTO	Lithium titanate oxide
WTW	Well-to-wheel

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. System boundary of BEV.

Figure 2. GWP, ADP(f), AP and HTP of CTG phase of different batteries.

Figure 3. GWP, ADP(f), AP, and HTP of life cycle BEVs based on different batteries.

Figure 4. Sensitivity analysis of power battery energy density.

Figure 5. Sensitivity analysis of battery manufacturing energy consumption.

Figure 6. Sensitivity analysis of electricity structure.

Figure 6. Sensitivity analysis of electricity structure.

Figure 7. Sensitivity analysis of battery charge efficiency.

Table 1

Vehicle model specification [15,35,36].

Vehicle Type	BEV	ICEV
Body size/mm	4765 × 1837 × 1515	4500 × 1855 × 1690
Life time mileage/km	150,000	150,000
Battery capacity/kWh	57	/
Fuel economy/kWh or L∙(100 km)−1	12.3	8.5
Battery type	LiFePO4	Lead-acid
Curb weight/kg	1650	1490
Lithium-ion power battery (mass fraction)	20.3%	/
Lead-acid battery	/	1.3%
Engine	/	10.1%
Transmission	/	8.4%
Motor	2.1%	/
Electronic controller	0.6%	/
Final drive	2.1%	/
Body	41.8%	42.0%
Chassis	31.3%	36.3%
Fluid	1.8%	1.9%

Table 4

Energy consumption data of vehicle assembly stage [45,46].

	Painting	Air Conditioning and Lighting	Heating	Material Handling	Welding	Workshop Compressed Air Utilization
Electricity/MJ∙kg−1	2.72	2.18	/	0.45	0.61	0.9
Thermal energy/MJ∙kg−1	/	/	2.03	/	/	/

Table 5

Prediction scenarios of key parameters for BEV.

Key Parameters	Object	2020	2025	2030	References
Battery energy density/W∙h∙kg−1	LFP	170	200	225	[25,26,47]
	NCM	175	205	230
	LMO	133	150	175
	LTO	60	90	100
Battery manufacturing energy consumption/MJ∙kg−1	Electricity consumption	20	15	10	[11,16,21,41]
	Natural gas consumption	8.8	6	5
Cathode carbon emission factors/kg CO2∙kg−1	LFP	26	23.4	20.8	[26,41,42]
	NCM	36.8	33.1	29.4
	LMO	19.6	17.6	15.7
Anode carbon emission factors/kg CO2∙kg−1	Graphite	1.12	1.0	0.9
	LTO	18.3	16.5	14.6
Electricity structure	Coal electricity	71%	60%	48%	[48,49]
	Wind electricity	4%	10%	14%
	Nuclear electricity	3%	4%	8%
	Hydroelectric electricity	15%	17%	18%
	Photovoltaic electricity	2%	6%	11%
	Other electricity	5%	3%	1%
Glider mass reduction proportion	/	0%	15%	25%	[47]
Battery charge efficiency	/	90%	95%	99%	[14,47]

Table 6

Characterization results of the CTG environment impact of different power batteries.

Assessment Indictors		GWP/kg CO2-eq				ADP(f)/MJ				AP/kg SO2-eq				HTP/kg DCB-eq
Year	Component	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO
2020	Cathode	3540.0	4710.0	4496.0	10,730.0	13,200.0	12,300.0	15,570.0	21,874.0	12.7	15.3	13.1	33.1	903.0	853.0	1064.5	1489.0
	Anode	262.0	252.0	304.0	3190.0	3970.0	4050.0	4720.0	1980.0	3.0	2.7	3.4	0.1	326.0	292.0	367.0	2.8
	Electrolyte	86.3	66.5	87.5	194.0	2850.0	2200.0	2890.0	6400.0	0.3	0.2	0.3	0.7	10.9	8.4	11.1	24.6
	Diaphragm	19.1	15.6	20.5	78.5	631.0	513.0	675.0	2690.0	0.03	0.02	0.03	0.1	0.9	0.8	1.0	3.8
	Shell	28.2	25.1	34.2	3655.0	551.0	491.0	669.0	33,700.0	0.1	0.1	0.2	17.7	423.0	384.0	523.8	2343.0
	Cooling liquid	2.9	2.8	3.7	30.0	120.0	117.0	153.0	1256.0	0.01	0.01	0.01	0.1	0.2	0.2	0.2	1.9
	BMS	0.9	0.9	1.2	8.3	10.0	10.0	13.0	86.6	0.001	0.001	0.003	0.003	0.04	0.04	0.05	0.4
	Manufacturing	1400.0	1370.0	1800.0	3980.0	17,800.0	17,300.0	22,800.0	50,400.0	3.0	3.0	3.9	8.6	60.9	59.3	78.0	173.0
	All	5340	6440	6740	21,900	39,130.0	36,980.0	47,400.0	118,000	19.1	21.3	20.9	60.5	1725.0	1597.7	2045.7	4038.38
2025	Cathode	2830	3728	3730	6587	11,200	10,464	13,800	14,600	10.8	13.01	11.6	22.1	768.00	727.00	943.00	992.00
	Anode	218	209	263	1920	3380	3450	4180	1320	2.54	2.32	3	0.1	277.00	249.00	325.00	1.86
	Electrolyte	73.5	56.7	77.5	129	2430	1870	2560	4270	0.257	0.198	0.271	0.451	9.31	7.19	9.83	16.40
	Diaphragm	16.2	13.3	18.1	52.3	537	437	598	1790	0.025	0.0206	0.0282	0.08	0.81	0.66	0.91	2.52
	Shell	24	21.4	30.3	2436	469	419	593	22,500	0.108	0.096	0.135	11.8	360.00	327.70	464.00	1561.60
	Cooling liquid	2.43	2.4	3.24	20	102	99	136	837	0.005	0.0048	0.0066	0.04	0.15	0.15	0.20	1.23
	BMS	0.67	0.65	0.89	4.6	7	7	9	48.3	0.001	0.001	0.002	0.01	0.03	0.03	0.04	0.20
	Manufacturing	750	731	999	1660	9640	9400	12,900	21,400	1.62	1.58	2.16	3.6	35.20	34.30	46.90	78.20
	All	3914	4760	5120	12,800	27,700	26,150	34,700	66,700	15.4	17.2	17.2	38.2	1450.50	1346.03	1789.88	2654.01
2030	Cathode	2350	3070	2990	5430	9930	9340	11,830	13,100	9.6	11.6	9.98	19.9	682	648	809	893
	Anode	190	182	221	1540	3000	3080	3580	1190	2.26	2.07	2.57	0.088	246	222	279	1.68
	Electrolyte	65.2	50.6	66.5	116	2150	1670	2200	3840	0.228	0.177	0.232	0.406	8.27	6.42	8.43	14.7
	Diaphragm	14.4	11.8	15.6	47.1	476	390	513	1610	0.0224	0.0184	0.0242	0.074	0.717	0.591	0.776	2.26
	Shell	21.3	19.1	26	2190	416	374	508	20,200	0.0959	0.0853	0.116	10.6	320	292	398	1406
	Cooling liquid	2.16	2.12	2.78	18	90.4	88.6	116	754	0.00439	0.00431	0.0056	0.366	0.133	0.13	0.171	1.1
	BMS	0.47	0.46	0.6	3.27	4.91	4.82	6.32	34.4	0.001	0.001	0.001	0.007	0.02	0.02	0.03	0.17
	Manufacturing	353	346	455	795	5020	4920	6470	11,300	0.761	0.746	0.98	1.71	18.5	18.2	23.9	41.8
	All	3000	3680	3780	10,100	21,100	19,900	25,200	52,100	13	14.7	13.9	32.8	1275.64	1187.361	1519.307	2360.71

Table 7

Characterization results of the life cycle environment impact of BEVs based on different power batteries.

Assessment Indictor		GWP/kg CO2-eq				ADP(f)/MJ				AP/kg SO2-eq				HTP/kg DCB-eq
Year	Phase	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO	LFP	NMC	LMO	LTO
2020	CTG Power battery	5340	6440	6740	21,900	39,130	36,980	47,400	118,000	19.1	21.3	20.9	60.5	1725	1597.7	2045.7	4038.4
	CTG Glider	4880	4880	4880	4880	55,100	55,100	55,100	55,100	15.5	15.5	15.5	15.5	1170	1170	1170	1170
	Vehicle assembly	2680	2670	2840	3690	28,200	28,000	29,800	38,700	5.8	5.77	6.13	7.96	113	113	120	156
	WTW phase	15,300	15,200	15,900	19,200	160,000	160,000	166,000	201,000	33.1	33	34.4	41.5	656	654	682	823
	GTC phase	−3900	−3820	−4090	−7510	−38,400	−37,700	−40,200	−71,200	−18.9	−18.3	−20	−34.8	−2260	−2190	−2410	−4400
	Life cycle	24,300	25,370	26,270	42,160	244,030	242,380	258,100	341,600	54.6	57.27	56.93	90.66	1404	1344.7	1607.7	1787.4
2025	CTG Power battery	3914	4760	5120	12,800	27,700	26,150	34,700	66,700	15.4	17.2	17.2	38.2	1450.50	1346.03	1789.88	2654.01
	CTG Glider	3980	3980	3980	3980	45,100	45,100	45,100	45,100	12.8	12.8	12.8	12.8	988	988	988	988
	Vehicle assembly	1960	1950	2090	2440	20,600	20,500	45,100	25,700	4.22	4.19	4.5	5.26	88.1	87.7	94.1	110
	WTW phase	10,900	10,800	11,300	12,600	114,000	114,000	119,000	132,000	23.5	23.4	24.5	27.2	504	503	527	585
	GTC phase	−3420	−3360	−3640	−5590	−33,700	−33,200	−35,800	−53,400	−16.3	−15.8	−17.6	−25.5	−1920	−1870	−2090	−3160
	Life cycle	17,334	18,130	18,850	26,230	173,700	172,550	208,100	216,100	39.62	41.79	41.4	57.96	1110.60	1054.73	1308.98	1177.01
2030	CTG Power battery	3000	3680	3780	10,100	21,100	19,900	25,200	52,100	13	14.7	13.9	32.8	1275.64	1187.361	1519.307	2360.71
	CTG Glider	3340	3340	3340	3340	38,000	38,000	38,000	38,000	10.9	10.9	10.9	10.9	867	867	867	867
	Vehicle assembly	1420	1410	1500	1780	14,900	14,900	15,800	18,700	3.05	3.03	3.23	3.82	69.7	69.4	73.8	87.5
	WTW phase	7600	7580	7880	8810	80,000	79,800	82,900	92,600	16.4	16.3	17	19	394	393	409	457
	GTC phase	−3130	−3070	−3280	−5090	−30,900	−30,400	−32,300	−48,700	−14.6	−14.3	−15.6	−23	−1700	−1660	−1820	−2830
	Life cycle	12,230	12,940	13,220	18,940	123,100	122,200	129,600	152,700	28.75	30.63	29.43	43.52	906.34	856.761	1049.107	942.21

Table 8

Different power battery energy density scenarios/W∙h∙kg⁻¹.

Scenarios	LFP	NCM	LMO	LTO
Baseline	170	175	133	60
Battery energy density + 10%	187	192.5	146.3	66
Battery energy density + 20%	204	210	159.6	72
Battery energy density + 30%	221	227.5	172.9	78

Table 9

Different electricity structure scenarios.

Electricity Structure	Baseline	Scenario 1	Scenario 2
Coal electricity	71%	60%	48%
Wind electricity	4%	10%	14%
Nuclear electricity	3%	4%	8%
Hydroelectric electricity	15%	17%	18%
Photovoltaic electricity	2%	6%	11%
Other electricity	5%	3%	1%

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