Reliable renewable electricity generation system on small animal farms for rural electric vehicle charging

Abstract

The adoption of electric vehicles (EVs) in rural areas is constrained by limited charging infrastructure, while small-scale dairy farms remain significant sources of greenhouse gas (GHG) emissions from manure management. This study addresses both challenges by demonstrating a dispatchable renewable electricity generation system that integrates anaerobic digestion (AD) with an external-combustion, Stirling-type combined heat and power (CHP) unit to support rural EV charging. A trailer-based system incorporating a 2.25 m³ AD reactor and a 5.6 kW CHP unit was constructed and operated using 28 kg/day of dairy manure. Results from a 50-day demonstration informed scaling and modeling for a representative 30-cow dairy farm. The AD process achieved a methane productivity of 232 L/kg volatile solids, with electrical and thermal conversion efficiencies of 15.05% and 37.11%, respectively. When scaled to a 30-cow farm, the system produced 46.46 kWh/day of net electricity and 10.43 kWh/day of recoverable heat, sufficient to meet realistic rural EV charging demands. Techno-economic analysis estimated a total capital investment of $99,000, annual operating costs of $2,000, and combined energy revenues of $7,797/year, corresponding to a 28-year payback period. Sensitivity analysis indicated that system economics are most strongly influenced by revenue generation. Life cycle assessment showed substantial environmental benefits relative to conventional manure management, including annual reductions of 324.44 tons CO₂-eq in global warming potential. Compared with stand-alone photovoltaic EV charging systems that require large battery storage to ensure winter reliability in northern climates, the biogas-based system leverages biogas storage as a low-cost energy buffer, enabling on-demand electricity generation with lower capital intensity. Overall, this work demonstrates a compact and scalable pathway for integrating manure-derived biogas with EV charging to advance rural electrification and circular bioeconomy goals.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-026-02745-7.

Introduction

Greenhouse gas (GHG) emissions, including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), are widely recognized as the primary drivers of global warming. Among all sectors, transportation is currently the largest emitter [1]. To reduce fossil fuel consumption and CO₂ emissions from vehicles, governments have enacted increasingly stringent regulations on internal combustion engine vehicles (ICEVs), while electric vehicles (EVs) have received growing policy support and consumer interest [2]. The U.S. aims for half of all new vehicle sales to be zero-emission models by 2030, including battery electric, plug-in hybrid, or fuel-cell vehicles. Despite rapid technological advances and industry investment, EVs represented only 5.8% of new-vehicle sales in 2022 [3], far from the 2030 target. Key barriers remain: limited charging infrastructure, long charging times, and range anxiety [4].

According to the Alternative Fuels Data Center, the U.S. had 56,256 EV charging stations as of November 2022 (52,375 public; 3,816 private). However, installation rates must triple over the next 5–8 years to meet projected EV adoption targets. Moreover, charging infrastructure is unevenly distributed: California (15,706), New York (3,594), and Florida (3,033) together account for nearly 25% of all stations [5]. Rural regions remain particularly underserved, significantly constraining EV adoption among residents and small businesses. One promising strategy to expand charging access is to generate renewable electricity locally through technologies that are modular, scalable, and compatible with rural energy resources.

Recent research on renewable-based EV-charging infrastructure has primarily focused on solar- and wind-integrated smart-charging systems, emphasizing grid interaction, energy management, and techno-economic optimization [6]. These studies have demonstrated the potential of renewable integration to reduce the carbon intensity of charging and enhance system resilience. However, such solutions remain limited to grid-connected or urban contexts. In contrast, biogas-to-electricity systems provide a dispatchable, carbon-neutral power source suitable for off-grid or rural regions, offering continuous energy generation independent of weather conditions.

Dairy farms represent another major source of GHG emissions. Key emission pathways include enteric CH₄ from ruminant digestion, CH₄ and N₂O from long-term manure storage, and N₂O from nitrification and denitrification during land application [7]. Anaerobic digestion (AD) offers a practical and well-established solution by converting organic carbon in manure into biogas, thus reducing methane emissions while recovering renewable energy [8]. AD can reduce manure-related GHG emissions by more than 50%, primarily through CH₄ abatement during storage [9]. When biogas is converted to electricity, it can further offset on-farm fossil fuel use, decreasing the overall carbon footprint of agricultural operations [10].

However, current AD technologies and business models (e.g., biogas upgrading to renewable natural gas or centralized CHP applications) primarily favor large-scale operations, limiting adoption among small and medium dairy farms (less than 1,000 cows), which constitute approximately 45% of all U.S. dairies [11]. From the farm economic perspective, 98% of U.S. farms are family owned. Small family farms with an annual gross cash farm income (GCFI) less than $350,000 make up 89% of all U.S. farms and hold 59% of total farm assets [12]. These statistics underscore the need for cost-effective, decentralized, and low-maintenance technologies that can meet the energy and waste management needs of smaller farms. Recently, small-scale anaerobic digestion (SSAD) systems also known as on-farm ADs, have gained attention for their simplicity, modularity, and ability to deliver both environmental and economic benefits in rural communities worldwide [13].

Transportation and livestock production represent two major yet traditionally separate sources of GHG emissions; transportation contributes approximately 29% of total U.S. emissions, while livestock manure management accounts for about 9% of agricultural GHG emissions [7]. This study explores the intersection of these two sectors by developing a small-farm system that converts dairy manure into renewable electricity, thereby reducing methane emissions from manure storage while displacing gasoline consumption through rural EV charging. This dual-benefit framework connects agricultural waste management with rural electrification and demonstrates a practical pathway for integrated GHG mitigation across both the agriculture and transportation sectors.

The scarcity of rural EV charging combined with the concentration of small livestock farms provides the motivation of this study, to develop an integrated approach that utilizes on-farm resources to produce electricity in situ. This study designed and demonstrated a compact, trailer-mounted system that converts dairy manure to biogas through AD and then utilizes the biogas in a Stirling-type external combustion engine (ECE) combining heat and power (CHP) unit to generate renewable heat and electricity. The objectives of this study are to (i) demonstrate the technical feasibility and operating performance of the integrated system at a small scale; (ii) conduct a techno-economic analysis (TEA) and a life-cycle assessment (LCA) to evaluate performance; cost, and environmental impact; and (iii) compare the studied system with traditional manure lagoon storage practices. To our knowledge, this is the first study integrating manure-based AD with a raw-biogas ECE-CHP unit designed for small dairy farms and evaluating it through a combined TEA and LCA framework. The results provide a roadmap for zero-emission farm operations and a replicable model for sustainable, circular bioenergy systems in rural areas.

Materials and methods

The trailer-based demonstration unit

A trailer-based system was designed and developed to demonstrate the concept of the biogas EV charging solution for small-scale dairy operations and to generate data for TEA and LCA. The system comprises four primary components: (1) a 2.25 m³ anaerobic digestion (AD) reactor with a feed tank (Built by the MSU team); (2) a 10 m³ biogas collection bag (Ready Containment LLC, FL, U.S.A.); (3) a 5.6 kW biogas external combustion engine (ECE) combined heat and power (CHP) unit (PowerGen, Qnergy, Co. UT, U.S.A.), and (4) a Grizzle-E Classic Level 2 EV charger to distribute electricity (United Chargers Inc. Canada) (Fig. 1).

Fig. 1.

Schematic diagram (a) and real-world depiction (b) of the trailer-based demonstration unit

The Deluxe 2990 GVWR 3 m × 18 m Utility Trailer (Jackson, MI Model No. 49201) functioned as the platform for most pieces of equipment. Within the system, a 208-L feeding vessel was connected to the AD reactor with an effective volume of 1,500 L. A type E thermocouple (Eisenmann Co. Germany) was installed to monitor and control the temperature of the AD reactor. A grinding pump (PRG101M, Liberty Pumps, NY, U.S.A.) was integrated into the feeding vessel to facilitate the grinding and pumping of feedstock into the AD reactor. The biogas was collected and stored in the biogas bag. The trailer unit is controlled and the data are recorded by a Unitronics PLC + HMI (USP-156-B10, Unitronics Inc., MA U.S.A.). The biogas, consisting of approximately 60% CH₄, was utilized by the CHP unit to simultaneously generate heat and electricity. The electricity was then used for charging EVs and other electrical needs. The trailer-based system was operated and demonstrated at the Anaerobic Digestion Research and Education Center (ADREC, MSU), Lansing, MI.

The feedstock and feeding operation

Fresh dairy manure was collected weekly from the MSU dairy farm in Lansing, MI. Upon collection, the dairy manure was analyzed for parameters such as pH, total solids (TS), volatile solids (VS), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₃-N) (Table 1). As part of the daily feeding operation, 28 kg of fresh dairy manure was pumped into the AD reactor as feedstock. The organic loading rate (OLR) of the AD reactor was calculated by Eq. 1 as follows:

$$Organicloadingrate\left(\frac{\frac{\mathit{gVS}}{L}}{d}\right)=\frac{Totalvolatilesolidsadded\left(g\right)}{ADeffectivevolume\left(L\right)\bullet Operationtime\left(d\right)}.$$ 1

Table 1.

Measured parameters of dairy manure

	Dairy manure
pH	7.99 ± 0.91
Total solids (g/kg)	147.63 ± 11.67
Volatile solids (g/kg)	121.42 ± 16.86
Chemical oxygen demand (g/kg)	114.98 ± 21.20
Soluble chemical oxygen demand (g/kg)	38.07 ± 0.12
Total nitrogen (g/kg)	3.53 ± 0.03
Total phosphorus (g/kg)	3.46 ± 0.021
Ammonia nitrogen (mg/kg)	732 ± 23

Values from seven dairy manure collections are presented as mean ± standard deviation

On each feeding day, 150 L of digestate was transferred to the feeding tank to measure the temperature of digestate in the reactor using a thermometer installed in the feeding tank. Fresh dairy manure was added to the feeding tank and thoroughly mixed with the digestate by an agitator. An electric heater was used to increase the temperature of 28 kg dairy manure and 150 L digestate (feedstock) mixture per day to a temperature of 55–60℃ to ensure the mesophilic conditions (30–35 °C) in the AD reactor. Once the target temperature was reached, the feedstock was pumped into the AD reactor using the grinding pump. After the feeding, the temperature in the AD reactor was recorded as the post-feeding temperature (T₁). Temperature readings were displayed on the control panel. The feedstock heating time (h), the grinding pump working time (min), and the flushing water used (kg) were also recorded during the feeding operation. These data were critical for calculating the heat demand to maintain digestion temperature and the electricity demand for the feeding, which are two key parameters of AD TEA analysis. The effluent from AD, digestate, was analyzed weekly to assess AD performance by measurement of the parameters such as pH, soluble COD, TN, TP, and NH₃-N concentrations using Hach methods. Methane and other components of the biogas, including CO₂ and hydrogen sulfide (H₂S), were also analyzed weekly using gas chromatography (SRI 8610C, SRI Instruments, Torrance, CA) in the samples withdrawn from the gas bag. The standard methods used to measure these parameters are described in our previous studies [14–16].

External combustion engine (ECE) CHP operation

The biogas was used as the fuel for the Stirling engine CHP to generate both heat and electricity. During each 30-min engine operation test, the volume of combusted biogas was recorded to determine the biogas consumption rate (L at ambient temperature/h), while the power output (W) was recorded every five minutes to calculate the electricity produced. The electricity generation rate was determined using Eq. 2:

$$\text{Electricity}\text{generation}\text{rate}\left(\text{Wh}\left./\phantom{\text{Wh}\text{L}\text{biogas}}\right)\text{L}\text{biogas}\right)=\frac{\text{Electricity produced}\left(\text{kWh}\right)}{\text{Biogas combusted}\left(\text{L}\right)}\times 1,000\frac{\text{W}}{\text{KW}}.$$ 2

The consumed biogas energy was calculated using Eq. 3, based on the lower heating value (LHV) of CH₄ and the CH₄ consumption rate:

$$Biogasenergy\left(k,W,h,-,\frac{e}{h}\right)=\frac{Methaneconsumptionrate\left(\frac{m^3}{h}\right)\times 33.33\frac{\mathit{MJ}}{m^3}}{3.6}.$$ 3

In Eq. 3, 33.33 MJ/m³ is the lower heating value (LHV) of CH₄ (see Sect. "Performance of the external combustion engine on raw biogas"), and 3.6 is the conversion factor from MJ to kWh-e. During each 30-min engine test, the heat energy produced was diverted to heat the anaerobic digestate via the global heat transfer (GHT). The utilized heat energy was calculated using Eq. 4:

$$\text{Heat}\text{utilized}\left(KWh-e/h\right)=\frac{m(kg)\times C\left(J\left./\phantom{Jkg_{}^{\circ }C}\right)kg_{}^{\circ }C\right)\times \mathrm{\Delta }T(_{}^{\circ }C)/1000/Time(h)}{3.6},$$ 4

where m is the mass of heated digestate (150 kg), C is the specific heat capacity of the digestate (4 J/kg·℃), ΔT is the temperature change during each heating period (℃), Time represents the GHT operation time (0.5 h), and 3.6 is the conversion factor from MJ to kWh-e.

Electrical and heat generation efficiencies are critical indicators for evaluating CHP system performance. These efficiencies were calculated using Eqs. 5 and 6, respectively:

$$Electricalefficiency\left(\%\right)=\frac{Powerproduced\left(k,W\right)}{Biogasenergy\left(k,W,h,-,e,/,h\right)}\times 100\%,$$ 5

$$GHTefficiency\left(\%\right)=\frac{Heatutilized\left(k,W,h,\_,e\right)}{Biogasenergy\left(k,W,h,-,e,/,h\right)\times GHTtime(h)}\times 100\%.$$ 6

Finally, the overall energy efficiency of the external combustion engine was determined by Eq. 7:

$$Totalenergyefficiency\left(\%\right)=\frac{Heatutilized\left(k,W,h,-,e\right)+electricitygenerated(kWh)}{Biogasenergy\left(k,W,h,-,e,/,h\right)\times GHTtime(h)}\times 100\%.$$ 7

Details on the performance of the AD operation and CHP system are provided in Sects. "Digestion performance and biogas production" and "Performance of the external combustion engine on raw biogas", respectively.

Techno-economic analysis (TEA)

The experimental data obtained from the trailer-based unit handling the manure from a single cow were scaled up to 30 cow farm operation and used to conduct the TEA of the EV charging station integrated with the AD process. In other words, the TEA analysis was made based on a small-scale, family-owned dairy farm with 30 cows producing 1,800 kg of manure for daily treatment. Mass and energy balance analyses were first conducted to determine the mass flow and energy demand of the system. The mass flow analysis of the AD process included dairy manure and water as inputs and biogas and digestate as outputs. The parameters for dairy manure and digestate were derived from the operational data collected from the demonstration unit. The energy balance analysis centered on the electricity and heat energy inputs and outputs for the daily operation of the integrated system considering the AD and the ECE-CHP unit data.

Data from the mass and energy balance analysis were further used to conduct the TEA to investigate the feasibility of the EV charging station powered by the biogas produced from manure in a small farm setting. Two key parameters were considered: capital expenditure (CapEx) and operational expenditure (OpEx). The lifespan of this system was set at 30 years. The Modified Accelerated Cost Recovery System (MACRS) was used to calculate the annual depreciation of CapEx. In addition, an annual inflation of 3% was used for OpEx calculations based on the five-year average inflation rate in Michigan. The net cash flow, accounting for depreciated CapEx, inflated OpEx, and the system’s lifespan, was analyzed to determine the discounted payback period of the EV charging solution. The annual depreciation rates are set to 0.100, 0.188, 0.144, 0.115, 0.092, 0.074, 0.066, 0.066, 0.065, 0.065, 0.033, and 0.033 (after 10 years) according to the data from MACRS [17].

Meanwhile, a stand-alone photovoltaic (PV) system with equivalent electricity generation as a control was analyzed to illustrate differences between the studied biogas EV charging solution and the PV-battery EV charging solution. The stand-alone PV system includes three key components of PVs, batteries, and a charger. The detailed methods were described in the supplemental TEA in the Supporting Information document.

Life cycle assessment (LCA)

Based on the detailed mass and energy balance analysis, an LCA was conducted to evaluate the environmental impacts of the EV charging system compared to conventional lagoon storage of manure. The assessment followed an attributional LCA approach using the EPA’s Tool for the Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI 2.1) methodology [18]. The analysis adopted a one-year operational time frame, consistent with the functional unit used in the techno-economic and mass–energy balance analyses, representing a full annual manure management cycle for a 30-cow dairy farm.

The system boundary extended from dairy manure management to the production of AD effluent, electricity, heat energy, and fertilizer for land application (Fig. S1). The LCA focused on operational-phase impacts, as the dominant emissions and environmental burdens are associated with manure management and energy conversion. The embodied impacts from equipment fabrication (e.g., digester tank, ECE-CHP unit, pumps, and biogas storage bag) were not included because they represent a minor contribution to annualized emissions at this scale.

Three impact categories related to carbon emission, air quality, and water quality were selected for the LCA: global warming potential (GWP), smog potential, and water eutrophication potential (WEP). GWP is used to quantify GHG emissions and fossil fuel displacement. Smog potential is for capturing regional ozone-precursor emissions (NO_x, VOCs, CH₄) associated with combustion and displaced gasoline use, and to allow consistent comparison with related studies. WEP is to evaluate the risk of nutrient enrichment in surface and groundwater systems resulting from nitrogen and phosphorus in the digestate and manure effluent, which can lead to algal blooms and water-quality degradation.

CO₂ emissions from dairy manure were considered biogenic and thus excluded from GWP calculation [19]. N₂O emissions were analyzed based on the TN concentrations in the dairy manure and AD digestate. Data generated from the mass and energy balance are reported in Table 2, together with the emission and characterization factors applied for each compound.

Table 2.

Inventory for the life cycle assessment ^a

	Value	Unit	Source
Raw materials
Manure	657	Metric ton/year	MSU farm data
Total solids of dairy manure	14.76	%	Operational data
Volatile solids of dairy manure	12.14	%	Operational data
TN of manure	3.53	g/kg	Operational data
TP of manure	3.46	g/kg	Operational data
SCOD of manure	38.07	g/kg	Operational data
Anaerobic digestion and energy production inventory
Biogas production	30,856	m3/year	Operational data
CH4 content in biogas	60	% (v/v)	Operational data
CO2 content in biogas	40	% (v/v)	Operational data
Electricity production from biogas	16,677	kWh-e/year	Operational data
Heat production from biogas	3,807	kWh-e/year	Operational data
Effluent	748.25	Metric ton/year	Operational data
TS of effluent	4.16	% (w/w)	Operational data
TN of effluent	2.58	g/kg	Operational data
TP of effluent	1.32	g/kg	Operational data
SCOD of effluent	7.803	g/kg	Operational data
N2O emission from the effluent	0.005	g N2O/g TN in the effluent	[20]
GWP of N2O	298	g CO2-e/g N2O	[20]
CH4 emission from the effluent	3.08 × 10–4	Metric ton CO2-e/metric ton TS in the effluent	[21]
Smog formation of biogas electricity	0.035	kg O3-e/kWh	[18]
Smog formation of gasoline	1.5	kg O3-e/kg gasoline	[18]
Smog formation of N2O	24.8	kg O3-e/kg N2O	[18]
Smog formation of CH4	0.0144	kg O3-e/kg CH4	[18]
WEP of TN	0.9864	g N-e/kg TN in the effluent	[20]
WEP of TP	7.29	g N-e/kg TP in the effluent	[20]
WEP of COD	0.05	g N-e/kg COD in the effluent	[20]
Animal wastes lagoon storage and land application inventory
CH4 emission	0.127	kg CH4/kg VS	[22]
N2O emission	0.005	g N2O/g TN in the waste	[20]
Smog formation of N2O	24.8	kg O3-e/kg N2O	[18]
Smog formation of CH4	0.0144	kg O3-e/kg CH4	[18]
WEP of TN	0.9864	g N-e/kg TN in the waste	[20]
WEP of TP	7.29	g N-e/kg TP in the waste	[20]
WEP of COD	0.05	g N-e/kg COD in the waste	[20]

a: CO₂ emissions from manure waste are not counted in the calculation of greenhouse gas emissions because the CO₂ is considered biogenic, meaning it is assumed to be offset by the CO₂ captured during the regrowth of plants

Results and discussion

System performance of the trailer-based demonstration unit

Digestion performance and biogas production

The data used in this section are summarized in Table 3. The integrated system with AD, CHP engine, and EV charger was operated for a total of 50 days (7 weeks). During this period, 1,400 kg of dairy manure (1 kg manure/L manure) was fed into the AD reactor over 32 days except weekend days. The average daily amount of manure fed was 28 kg, equivalent to ~ 40% of manure produced by a lactating cow each day [23]. To summarize, 1,400 kg manure containing 170 kg of volatile solids (VS) was fed in the AD reactor with an organic loading rate (OLR) of 2.27 g VS/L/day (calculated by Eq. 1). The total biogas produced was 65,750 L over the 50-day operation, with an average of 1,315 L of biogas produced per day. The average CH₄ content was 59.67 ± 0.76% based on weekly biogas composition analysis. A total of 39,450 L of CH₄ with an average CH₄ productivity of 232.11 L/kg VS added was achieved in this demonstration trailer system.

Table 3.

The primary anaerobic digestion performance data

Items	Value
Testing perioda	Aug 03-Sep 21
Total testing daysb	50
Digester effective volume (L)	1,500
Total dairy manure fed (kg) c	1,400
Total solids of dairy manure input (kg)d	206.64
Total volatile solids of dairy manure input (kg)d	169.96
Average daily dairy manure fed (kg)e	28
Organic loading rate (g VS/L/day)f	2.27
Total biogas produced (L)	65,750
Average biogas produced per operation day (L/d)	1,315
Average methane content (%)g	59.67 ± 0.76
Total methane produced (L)h	39,450
Average methane production (L/kg VS added)i	232.11
Digestion temperature (℃) j	32 ± 0.96
Heat demand to maintain the digestion temperature (kWh-e/kg wet dairy manure as feed)k	0.091
Electricity demand for the feeding pump (Wh-e/kg wet dairy manure as feed)l	7.071
Power of the heat exchanger pump (kW)m	0.3

a: This demonstration research was conducted in 2023. b: The 50-day testing period included anaerobic digestion feeding and external combustion engine operation, dairy manure was fed for 32 days, with no feeding on weekends. c: A total of 370 gallons of dairy manure, equivalent to 1,400 kg, was fed into the AD reactor, based on weighing and calculation. d: The total solids (TS) content of 14.76% and volatile solids (VS) content of 12.14% were used to calculate the TS and VS of dairy manure input (see Table 1). e: The average daily manure feed was calculated by dividing the total manure fed (1,400 kg) by the total testing days (50). This value was key for the later energy balance calculation. f: The organic loading rate is defined as the gram of volatile solids per liter of reactor volume per day. g: The average methane content (%) with standard deviation was calculated weekly based on gas chromatography (GC) testing. h: For simplicity, the average methane content of 60% was used to calculate the total methane production and for the later energy balance process. i: The average methane productivity was calculated by dividing the total methane produced by the total volatile solids (VS) of the dairy manure fed. j: The digestion temperature was calculated as the average AD reactor temperature before feedstock heating, with standard deviation (see Fig. S2, Table S1). k: The average daily electricity consumption for heating 28 kg of wet dairy manure was 2.54 kWh, and the average heat demand to maintain the digestion temperature for 1 kg of manure was 0.091 kWh. l: The pump had a power rating of 0.75 kW, with an average daily operation time of 15 min. The control panel consumed a fixed 0.01 kWh/day. This results in a total electricity input of 0.198 kWh/day for 28 kg of wet dairy manure, which is equal to 7.071 Wh-e/kg wet dairy manure fed. m: The heat exchanger pump had a power rating of 0.3 kW

The recorded temperatures for each feeding day, both before (T₀) and after heating (T₁), are provided in Table S1 and Fig. S2. The average digestion temperature was 32 ± 0.96 ℃, within the range of a typical mesophilic anaerobic digestion temperature. Based on the calculations of the electric heating power and time, the average daily energy consumption was 2.54 kWh-e/day for heating, corresponding to a heat demand of 0.091 kWh-e per kilogram of wet dairy manure brought to the digestion temperature. In addition to the heat demand, the feeding pump and control panel consumed electricity. The pump, with a power of 0.75 kW and a 15-min operating time per feeding day, consumed 0.188 kWh. The electricity consumption of the control panel was 0.01 kWh per day. Consequently, the additional electricity demand for each kilogram of wet dairy manure feedstock was 7.071 Wh-e. The heat exchanger pump had a power rating of 0.3 kW.

Biochemical parameters of digestate

Throughout the testing period, anaerobic digestate was collected for biochemical parameter measurements, including pH, TS, VS, TN, NH₃-N, sCOD, and TP on a weekly basis. The average values of the parameters analyzed are summarized in Table 4. The pH value ranged from 7.60 to 7.89, with an average of 7.76 ± 0.11 for the digestate. All pH results fell within the optimal range of 6.5 to 8.0 for anaerobic digestion, and particularly within the ideal range for methanogenesis, which is typically around 7.0 to 8.0 [24]. The biogas composition analysis showed CH₄ content ranging from 58.82 to 60.41%(v/v), with an average of 59.67 ± 0.76%(v/v) over the seven weeks. The consistency in both pH and CH₄ content throughout the whole dairy manure feeding period indicated that a stable anaerobic digestion process was successfully maintained. The average sCOD concentration in the digestate was 7.80 ± 1.33 g/L, representing a significant reduction from the initial sCOD concentration of 38.07 g/L in the raw dairy manure (Table 1). In addition to sCOD, the decrease in total solids (TS) and, more specifically, volatile solids (VS) indicated the degradation of organic compounds in the dairy manure. The average TS and VS concentrations in the digestate were 41.56 ± 2.39 and 29.02 ± 4.46 g/kg, respectively.

Table 4.

Characteristics of the anaerobic digestate

Items	Value
pH	7.76 ± 0.11
Total solids (g/kg)	41.56 ± 2.39
Volatile solids (g/kg)	29.02 ± 4.46
Total nitrogen (g/kg)	2.58 ± 0.19
Ammonia nitrogen (g/kg)	1.35 ± 0.15
sCOD (g/kg)	7.80 ± 1.33
Total phosphorus (g/kg)	1.32 ± 0.13

Values are presented on a weekly basis as mean ± standard deviation

Total nitrogen (TN), ammonia nitrogen (NH₃-N), and total phosphorous (TP) in the digestate were measured to evaluate its nutrient value. As shown in Table 4, the average TN, NH₃-N, and TP in the digestate were 2,581 ± 188, 1,352 ± 151 mg/L, and 1,320 ± 130 mg/L, respectively, which suggested the digestate is a fertilizer source, contributing to the sustainability of the integrated system.

Performance of the external combustion engine on raw biogas

External combustion engine operation data were crucial to calculate the mass and energy balance, conduct a TEA, and perform an LCA for a 30-cow dairy farm scenario. To obtain comprehensive engine operation data, five independent, parallel engine tests were conducted under a consistent air–fuel ratio of 1.05 and a testing duration of 30 min each. Instantaneous power output was recorded every 5 min, resulting in seven data points per test at 0, 5, 10, 15, 20, 25, and 30 min. Table 5 summarizes the key data discussed in this section.

Table 5.

Performance of the Stirling combined heat and power (CHP) on raw biogas

Parameters	Value
Power output (kW)a	2.015 ± 0.045
Biogas consumption rate (L/h)b	2,420 ± 165
Electricity generation rate (Wh/L biogas)c	0.836 ± 0.06
Electricity generation efficiency (%)d	15.05 ± 1.09
Heat generation rate (Wh-e/L biogas)e	2.061 ± 0.07
Heat generation efficiency (%)f	37.11 ± 1.18
Overall energy efficiency (%)g	52.15 ± 1.55

Values in this table are presented as mean ± standard deviation from five engine tests. Raw engine testing data can be found in Table S2. a: Each engine test lasted 30 min, with instantaneous energy production recorded at 0, 5, 10, 10, 15, 20, 25, and 30 min, resulting in a total of 35 power (W) values. b: The biogas consumption during each 30-min engine efficiency test was 1,120, 1,340, 1,164, 1,216, and 1,209 L, with an average biogas consumption rate of 2,420 L/h for the engine. c: The electricity generation rate was calculated by dividing the generated electricity (kWh) by the biogas consumed (L) to produce that electricity. d: The energy of the consumed biogas (60% methane) in MJ/h was calculated by multiplying the methane consumption rate (m³/h) by the methane lower heating value (LHV) of 33.33 MJ/m³. The electricity generation efficiency was then calculated by dividing the generated electricity (kW) by biogas energy (kWh-e/h), with 1 kWh equivalent to 3.6 MJ. e. The heat generation rate was calculated by dividing the utilized heat for heating the digestate (Wh-e) by the volume of biogas combusted to produce that heat (L). f: The heat generation efficiency (%) was calculated by dividing the utilized heat (kWh-e) by the total energy content of the biogas produced (kWh). g: The overall energy efficiency (%) was calculated by summing the utilized heat (kWh-e) and the electricity generated (kWh), the dividing by the total energy content of the biogas (kWh)

The consumed biogas for each engine test was 1,120, 1,340, 1,164, 1,216, and 1,209 L, respectively, with an average consumption rate of 2,420 ± 165 L/h (Table S2). The average power output was 2.015 ± 0.05 kW, corresponding to an average electricity generation of 1.007 kWh. Given that the CH₄ content is 60%, this corresponds to an average CH₄ consumption rate of 1,452 L/h. The electricity generation rate was 0.836 ± 0.06 Wh/L biogas, calculated using Eq. 2 with the lower heating value (LHV) of CH₄ of 50 MJ/kg (33.33 MJ/m³), with a CH₄ density of 0.667 kg/m³ at standard temperature and pressure (STP). Like the calculation for the electricity generation rate, the heat generation rate was calculated by dividing the heat generated (Wh-e) by the corresponding volume of biogas combusted (L) to produce heat energy. The average heat energy generation rate was 2.061 ± 0.07 Wh-e/L biogas, which was 2.47 times the average electricity generation rate. The CHP achieved an electricity generation efficiency of 15.05 ± 1.09% and a heat generation efficiency of 37.11 ± 1.18% based on Eqs. 5 and 6, respectively. The overall energy efficiency of the external combustion engine in this demonstration unit was 52.15 ± 1.55% calculated by Eq. 7.

Mass and energy balance analysis of the biogas EV charging station in a 30-cow dairy farm

According to the anaerobic digester and engine performance data from the trailer-based demonstration unit, a mass and energy balance analysis was conducted using a small dairy farm with 30 cows.

Mass balance

Figure 2 illustrates the mass balance for the daily operation of a biogas EV charging solution on a 30-cow dairy farm. The daily manure from 30 cows is 1,800 kg with TS and VS contents of 14.76% and 12.14%, respectively. Additionally, approximately 321 kg of water is added to mix with the manure before it is pumped into the anaerobic digestion reactor. This brings the total daily feedstock to 2,121 kg. The effective volume of the digester is 96,400 L. The corresponding organic loading rate is 2.27 g VS/L/day. The manure is then converted into biogas and anaerobic digestate (Fig. 2). As mentioned earlier, the biogas production rate was 1,315 L/day with a daily manure feeding rate of 28 kg. For a 30-cow dairy farm scenario, with 1,800 kg of manure fed daily, the biogas output can be estimated at 84,536 L/day.

Fig. 2.

Mass balance of biogas production on a 30-cow dairy farm

The CH₄ and CO₂ contents of biogas were 60% (v/v) and 40% (v/v), respectively, based on the measurements done in the gas samples. The anaerobic digestate mass flow rate is calculated as 2,050 kg/day, with TS of 41.56 g/kg and VS of 29.02 g/kg, also based on data from the demonstration unit. These values are used in the LCA section.

Energy balance

The energy balance analysis provides a detailed assessment of energy generation and consumption for the EV charging station for a 30-cow dairy farm scenario (Table 6). As discussed in Sect. "Digestion performance and biogas production", the electricity inputs for heating and pumping each kilogram of wet dairy manure are 91 Wh-e and 7.07 Wh-e, respectively. Consequently, for 1,800 kg of dairy manure, the daily heat input is 163.80 kWh-e, and the electricity demand for pumping is 12.73 kWh. Based on the engine’s biogas combustion rate of 2,420 L/h, the daily biogas production of 84,536 L requires the engine to run for 34.93 h, necessitating the use of two engines. The additional electricity input for the heat exchanger pump in the CHP unit is 10.48 kWh/day (with a fixed pump power of 0.3 kW and a running time of 34.93 h). The control panel also requires 1 kWh/day of electricity. In total, the electricity input is 24.21 kWh/day for the 30-cow EV charging system.

Table 6.

Energy balance of the biogas EV charging station on a 30-cow dairy farm^a

	The biogas EV charging solution
Energy consumed
Heat input (kWh-e/day)b	−163.80
Electricity input (kWh/day)c	−24.21
Energy generated
Heat output (kWh-e/day)d	174.23
Electricity output (kWh/day)e	70.67
Net energyf
Net heat (kWh-e/day)	10.43
Net electricity (kWh/day)	46.46

a: The energy balance analysis was based on data from the operation of the trailer-based demonstration unit. Energy input is considered negative, while energy output is positive. b: Heat was used to maintain the AD reactor temperature by heating the feedstock in the feeding tank. c: The electricity input includes the demand from the feeding pump, heat exchanger recirculation pump, and the control panel. For a 30-cow dairy farm, the electricity demand of the feeding pump is 12.73 kWh/day, calculated based on 7.071 Wh-e/kg of wet dairy manure as feed (see Table 3). The heat exchanger pump requires 10.48 kWh/day, based on the Stirling engines operating for 34.93 h/day (two engines used). The control panel consumes 1 kWh/day. d: The heat output is derived from the heat exchanger in the engine cooling system. The engine's heat generation rate is 2.061 Wh-e/L biogas (see Table 5). e: The electricity output is based on the electricity generation rate of the Stirling engine, which is 0.836 kWh/L biogas (see Table 5). f: The net energy was calculated by subtracting the energy consumed from the energy generated

Additionally, as presented in Sect. "Performance of the external combustion engine on raw biogas", the electricity and heat generation rates are 0.836 and 2.061 Wh-e/L biogas, respectively. With a biogas production of 84,536 L/day, the daily energy generation rates are 70.67 kWh of electricity and 174.23 kWh-e of heat. In summary, the net daily energy outputs are 10.43 kWh-e of heat and 46.46 kWh of electricity, resulting in a positive energy balance for both heat and electricity.

Techno-economic analysis (TEA) of the biogas EV charging solution for the small dairy operation

Economic feasibility is another key factor that determines the commercial applicability of a biogas EV charging solution for small dairy operations. Based on the mass and energy balance of a 30-cow dairy farm, a TEA was conducted to evaluate economic performance. The analysis considered capital expenditure (CapEx), operational expenditures (OpEx), and revenue streams from electricity and heat recovery. As shown in Table 7, the total CapEx to implement the AD-EV charging solution is $99,000 (not including land purchase or rental costs). The CHP unit represents the largest single investment ($35,000), reflecting its central role in converting biogas into usable electricity and thermal energy. The anaerobic digester, including heat exchanger insulation, is the second most expensive component at $30,000. Additional costs include the feeding vessel ($3,000), feeding pump ($1,000), digester mixer ($10,000), biogas storage bag ($10,000), EV charger and control panel ($5,000), and miscellaneous installation and permitting fees ($5,000). All cost values are derived from the trailer demonstration unit and scaled to the 30-cow scenario.

Table 7.

Economic analysis of the renewable EV charging solution on a 30-cow dairy farm

	The system
Capital expenditure (CapEx)a
Feeding vessel	$3,000
Feeding pump	$1,000
Anaerobic digester with heat exchanger insulation	$30,000
Digester mixer	$10,000
Biogas storage bag (100 m3)	$10,000
Stirling engine	$35,000
EV charger and control panel	$5,000
Others	$5,000
Total CapEx cost	$99,000
Revenue
EV chargingb	$5,935/year
Heat energy savingc	$1,862/year
Total revenue	$7,797/year
Operational expenditure (OpEx)
Maintenanced	$2,000/year
Net Revenue
Net revenue	$5,797/year
Payback time
Payback time (year) e	28

a: The installation cost is included in the capital cost of each unit. All costs are based on projects that the authors have carried out. b: The electricity revenue was calculated using the price of Level 2 EV charging at $0.35/kWh. c: The current price of natural gas in Michigan is $8.82 per 1,000 cubic feet, which corresponds to a heat price of $0.03/kWh-e. This was used to calculate heat energy savings. d: The maintenance cost includes labor expenses. e: The cashflow chart can be seen in the supporting information section (Fig. S3)

Revenue generation is based on electricity production for EV charging and savings from recovered heat. The system produces 16,958 kWh/year, corresponding to a net daily output of 46.46 kWh (Table 6). Two electricity utilization pathways are considered: i) on-farm self-consumption (e.g., lighting, pumping, refrigeration, or tractor charging); and (ii) EV charging provided as a service to farm or community vehicles. To maintain consistency across scenarios, a Level 2 EV charging price of $0.35/kWh was adopted as a benchmark electricity value [25]. At this rate, annual electricity revenue is estimated at $5,935, representing a theoretical upper bound that depends on actual utilization patterns and local electricity tariffs. In addition to electricity, thermal energy recovered from the CHP unit can offset on-farm fuel consumption. Using a Michigan natural gas price of $8.82 per 1,000 cubic feet in 2025 (equivalent to $0.03/kWh-e), annual heat energy savings are estimated at $1,862. Together, electricity and heat recovery yield a total potential energy value of $7,797/year.

Annual OpEx for the biogas EV charging system is estimated at $2,000/year, covering routine maintenance, repairs, and labor. After accounting for operating costs, the system generates a net annual revenue of $5,797, corresponding to a discounted payback period of 28 years (Fig. S3). The TEA assumes a family-farm ownership model in which land is already available, and therefore land acquisition costs are excluded. Importantly, this base case analysis does not include potential financial incentives, such as renewable-energy tax credits, cost-share programs, or cooperative ownership structures, all of which could substantially improve economic performance and reduce payback time.

Beyond direct economic metrics, the biogas EV charging system offers an important functional advantage: that biogas storage inherently provides energy buffering, enabling electricity generation on demand without the need for large and costly battery banks. This feature allows flexible scheduling of EV charging, particularly for overnight or low-power charging scenarios common in rural settings. A daily energy supply of 46 kWh is sufficient to charge a typical 60 kWh EV battery to roughly 75% capacity overnight or to support multiple partial charges for light-duty farm or community vehicles. This operational flexibility enhances system practicality and aligns well with real-world rural EV charging behavior.

When compared with a stand-alone photovoltaic (PV) system charging designed to deliver the same 46 kWh/day of continuous electricity (S-TEA2.1 in the supplemental materials), the biogas EV solution demonstrates distinct techno-economic advantages. As shown in the supplemental TEA (Table S6), achieving year-round reliability with PV alone in Michigan requires substantial oversizing of both the PV array and battery storage, resulting in a total installed cost of approximately $162,000, which is more than 60% higher than the biogas EV system. The large battery bank required to ensure multi-day autonomy under winter low-insolation conditions is the dominant cost driver in the PV-only configuration. In contrast, the biogas EV charging system relies on biogas storage as a low-cost energy buffer, significantly reducing capital requirements while maintaining dispatchable power availability. Although the PV-only system offers lower operational complexity and zero fuel handling, its long payback period (47 years) highlights the economic challenge of achieving high reliability without complementary energy storage or dispatchable generation. Overall, the comparison underscores the economic and operational benefits of integrating biogas-based energy conversion for EV charging in small dairy farms, particularly in northern climates with strong seasonal variability in solar resources.

The sensitivity analysis (Fig. 3) evaluates the impact of ± 25% variations in key techno-economic parameters on the payback period of the biogas-based EV charging system and is presented as a tornado chart to highlight the relative importance of each factor. Among the parameters evaluated, revenue is by far the most influential factor, with a 25% decrease in revenue extending the payback period by approximately 8.5 years, while a 25% increase shortens the payback by 5 years. This strong sensitivity reflects the cumulative effect of annual cash flow on long-term system economics. In contrast, variations in maintenance, engine capital, and digester capital produce comparatively smaller and nearly symmetric changes in payback period, each on the order of ± 1.5–2 years. These results indicate that while cost reductions remain beneficial, enhancing and diversifying revenue streams is the most effective pathway to improving economic viability. From an economic standpoint of view, carbon credit monetization represents a particularly important opportunity for small dairy operations, as biogas EV charging simultaneously displaces fossil electricity, mitigates methane emissions, and supports vehicle electrification. However, current carbon credit mechanisms are often structured for large-scale projects and remain difficult for small farms to access due to high transaction costs and complex verification requirements. The sensitivity analysis underscores the need for simplified, aggregated, or cooperative carbon credit frameworks that enable small farms to capture the climate value of their emission reductions, thereby increasing revenue, reducing payback time, and accelerating adoption of biogas EV charging systems in rural agricultural settings.

Fig. 3.

Sensitivity analysis of economic performance of the biogas EV charging solution

Life cycle assessment of the renewable EV charging solution on the small dairy operation

The LCA was also conducted to evaluate the environmental impact of the EV charging solution compared with the conventional lagoon storage scenarios. Global warming potential (GWP), smog potential, and water eutrophication potential (WEP) are the three impact factors evaluated in this study. The life cycle inventory for the LCA is presented in Table 2.

Global warming potential (GWP)

In the renewable EV charging solution scenario, CO₂ emissions from the anaerobic digestion process, including those from biogas combustion, are biogenic and assumed to be offset by the CO₂ captured during plant regrowth [19]. The biogas-derived electricity used for EV charging can reduce GWP by decreasing gasoline consumption. The total electricity generated can reach 16,958 kWh-e per year, based on a net daily production of 46.46 kWh, as discussed in the energy balance section. This amount of electricity can power a moderately efficient electric vehicle for approximately 50,874 miles, assuming an efficiency of 3 miles per kWh [26], replacing the need for 1,696 gallons of gasoline (based on a fuel efficiency of 30 miles per gallon for a gasoline-powered vehicle). Consequently, the EV charging solution has a negative GWP of -15.07 tons of CO₂ per year, considering the GWP of gasoline is 8,887 g CO₂-eq per gallon [27]. Based on the mass balance analysis, the yearly AD digestate is estimated to be 748.25 Mg, with a total nitrogen concentration of 2,581 g/Mg. This results in N₂O emissions of approximately 0.015 Mg per year. Using the GWP factor for N₂O of 298 [20], the N₂O emissions from the AD effluent contribute 4.52 Mg of CO₂-eq GWP annually (Table 2). Additionally, CH₄ emissions from the AD effluent contribute approximately 0.0096 Mg CO₂-eq per year. In total, the GWP for land application of the AD effluent is assumed to be 4.53 tons CO₂-eq per year (Fig. 4a, Table S3). The yearly dairy manure production is estimated at 657 Mg, with 1,800 kg of manure generated daily. The TN and VS concentrations in the manure are 3,530 g/Mg and 121.42 g/Mg, respectively, as detailed in the mass balance analysis section. Annual N₂O and CH₄ emissions from VS in the manure are calculated to be 0.018 Mg and 10.13 Mg, respectively (Table S3). Combined with the GWP factors of these two gases, listed in Table 2 [20, 22], the yearly N₂O and CH₄ emissions from manure lagoon storage contribute 5.43 and 303.93 tons CO₂-eq GWP, respectively. It is assumed that no emissions occur during the land application process following lagoon storage in scenario 0. Therefore, the total GWP in this scenario is 309.37 tons CO₂-eq per year, which is significantly higher than that of the renewable EV charging solution. Besides the negative GWP, the EV charging solution can reduce 324.44 tons CO₂-eq per year compared to the conventional manure management practice.

Fig. 4.

Life cycle impact assessment (a) GWP; (b) SMOG; (c) WEP

Smog formation potential

The smog formation potential assessment illustrates the environmental trade-offs between the renewable EV charging solution and the conventional manure management practice with lagoon storage (Fig. 4b, Table S4). The results indicate a notable reduction in smog formation for the renewable EV charging solution compared to traditional manure handling methods. In the renewable EV charging solution scenario, smog formation is influenced by two primary components: a significant reduction due to EV fuel substitution (–6.96 metric tons O₃ per year) and a small increase (0.38 metric tons O₃ per year) from the land application of anaerobic digestion (AD) effluent. The substitution of gasoline with biogas electricity significantly reduces smog formation, as gasoline combustion contributes a high smog potential of 1.5 kg O₃-e per kg of gasoline consumed. Given that the renewable electricity system generates 16,958 kWh-e annually, and assuming the smog formation potential of biogas electricity is 0.035 kg O₃-e per kWh, the total smog formation from the renewable electricity use is relatively low compared to the avoided gasoline combustion emissions. The land application of AD effluent results in a small increase in smog potential (0.38 metric tons O₃ per year), primarily due to N₂O and CH₄ emissions. The N₂O contribution is significant given its high smog formation potential of 24.8 kg O₃-e per kg N₂O emitted. Additionally, methane (CH₄) emissions from effluent application contribute to smog formation, although at a much lower rate of 0.0144 kg O₃-e per kg CH₄. In contrast, the conventional manure management scenario with lagoon storage contributes a positive smog formation potential of 0.60 metric tons O₃ per year. This impact is primarily from the uncontrolled emissions of nitrogen oxides (NO_x), ammonia (NH₃), and volatile organic compounds (VOCs) from manure storage and application. Lagoons facilitate anaerobic decomposition, leading to increased emissions of methane and ammonia, both of which contribute to photochemical smog formation when released into the atmosphere.

Water eutrophication potential (WEP)

WEP was assessed to quantify the potential contribution of nutrient-rich effluents to surface and groundwater eutrophication. The analysis considered total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) as the primary contributors. The corresponding TRACI 2.1 characterization factors were 0.9864 g N-eq kg⁻¹ TN, 7.29 g N-eq kg⁻¹ TP, and 0.05 g N-eq kg⁻¹ COD (Table 2) [20]. In the renewable EV charging scenario, the anaerobic digestion (AD) effluent contributed 1.905, 7.200, and 0.292 kg N-eq yr⁻¹ from TN, TP, and COD, respectively, resulting in a total WEP of 9.40 kg N-eq yr⁻¹ (Fig. 4c; Table S5). By contrast, in the conventional land application scenario, untreated manure released substantially higher nutrient loads, contributing 2.288, 16.586, and 3.777 kg N-eq yr⁻¹ from TN, TP, and COD, respectively. The total WEP for lagoon storage was 22.65 kg N-eq yr⁻¹, approximately 2.4 times higher than that of the renewable system. The reduction in WEP achieved by the AD–ECE system reflects the improved nutrient stabilization and organic matter degradation inherent to anaerobic digestion, which converts a portion of reactive nitrogen and carbon compounds into stable biomass and biogas. Consequently, the digestate exhibits lower eutrophication potential per unit of manure treated and can be managed more effectively as a fertilizer with reduced nutrient runoff risk.

The comparative LCA results clearly demonstrate that the renewable EV charging system provides substantial environmental advantages over conventional lagoon manure management across all three assessed impact categories. The system achieves a major reduction in GWP through methane abatement from manure storage and displacement of gasoline combustion via biogas-derived electricity. It also significantly lowers smog formation potential, reflecting decreased emissions of ozone precursors such as NOx, VOCs, and CH₄. Additionally, the system reduces WEP by stabilizing organic matter and nutrients during anaerobic digestion, thereby mitigating nitrogen and phosphorus losses that contribute to water pollution. Although minor GHG and nutrient emissions occur during the land application of AD effluent, these are small relative to the avoided emissions and pollutant load reductions achieved. Overall, the integrated AD–ECE renewable electricity system yields complementary climate, air quality, and water-quality benefits, underscoring its potential as a sustainable, circular solution for small-scale dairy farms that simultaneously supports rural electrification, GHG mitigation, and nutrient management.

Conclusions

This study demonstrates a practical and scalable solution for reducing greenhouse gas emissions from manure management while enabling renewable EV charging on small dairy farms. By integrating anaerobic digestion with an external-combustion CHP unit, the system provides dispatchable renewable electricity suitable for rural EV charging. For a representative 30-cow dairy farm, the system generated 46.46 kWh/day of electricity and 10.43 kWh-e/day of thermal energy, achieving a positive net energy balance. Techno-economic and sensitivity analyses indicate that system economics are most sensitive to revenue generation, while variations in capital and maintenance costs have smaller impacts on payback period. These results highlight the importance of increasing energy utilization and developing additional revenue streams, particularly carbon credit mechanisms, to improve financial viability for small farms. Life cycle assessment further confirmed substantial environmental benefits compared to conventional manure land application, including significant reductions in global warming potential, smog formation, and eutrophication. The compact, trailer-based design supports decentralized deployment and enhances rural energy resilience. Unlike stand-alone photovoltaic EV charging systems that require oversized battery storage to ensure winter reliability in northern climates, the biogas-based system leverages biogas storage as a low-cost energy buffer, enabling on-demand electricity generation without large batteries. Overall, the studied system provides a field-tested pathway for integrating manure-derived biogas with EV charging, supporting circular bioeconomy and sustainable rural electrification.

Abbreviations

AD

Anaerobic digestion

CapEx

Capital expenditure

CHP

Combined heat and power

DM

Dairy manure

EVs

Electric vehicles

ECE

External combustion engine

GHG

Greenhouse gas

GWP

Global warming potential

GHT

Global heat transfer

ICEVs

Internal combustion engine vehicles

LCA

Life cycle assessment

LHV

Lower heating value

Mg

Megagram

NH₃-N

Ammonia nitrogen

OLR

Organic loading rate

OpEx

Operational expenditure

SCOD

Soluble chemical oxygen demand

STP

Standard temperature and pressure

TEA

Techno-economic analysis

TN

Total nitrogen

TP

Total phosphorus

TS

Total solids

VS

Volatile solids

Author contributions

Meicai Xu: methodology, investigation, data curation, and writing—original draft. Carter Monson: methodology, investigation, data curation. Jacob Willsea: conceptualization, methodology. Sibel Uludag-Demirer: conceptualization, methodology, writing—review and editing. April Leytem: conceptualization, writing—review and editing. Barry Bradford: conceptualization, writing—review and editing. Wei Liao: conceptualization, methodology, investigation, data curation, writing—review and editing.

Funding

This study was funded by the U.S. Department of Agriculture’s (USDA) Agriculture Research Services (ARS) supporting this research (Agreement No.: 58–2054-2–002), Michigan Economic Development Corporation (MEDC), and MSU AgBioResearch.

Data availability

The datasets generated and/or analyzed during this study are available from the corresponding author upon request.

Declarations

Competing interests

The authors declare no competing interests.