Vehicle-Integrated Photovoltaics (VIPV) in Electrified Mobility: A Structured Systematic Review of Technical Performance, System Integration, and Strategic Deployment

Abstract

The rapid electrification of road transport has increased interest in distributed energy strategies that reduce grid demand and support decarbonization. Vehicle-integrated photovoltaics (VIPV), including vehicle-applied photovoltaic configurations (VAPV), can generate electricity directly on the vehicle. This systematic review examines peer-reviewed VIPV literature published between 2015 and 2026, focusing on the distinction between theoretical photovoltaic generation and practically usable energy. A Scopus search conducted on 2 May 2026 identified 196 records, of which 88 studies were included after screening against predefined criteria. Due to heterogeneity in vehicle types, climates, technologies, modeling assumptions, and reported metrics, no meta-analysis was performed. Instead, the review applies a multi-layered framework covering climate, geometry, thermal effects, electrical mismatch, battery state-of-charge interactions, fleet-scale modeling, economics, and life-cycle implications. The evidence shows that VIPV is technically feasible and can deliver measurable energy yields, especially in high-irradiance regions and vehicles with favorable daytime parking exposure. However, useful contribution depends strongly on curvature losses, dynamic shading, electrical configuration, SOC limits, charging behavior, seasonality, and vehicle energy demand. Therefore, VIPV is best understood as a context-dependent supplementary energy strategy rather than a transformative standalone solution. Its strongest value lies in specific vehicle classes, climates, and usage patterns where on-board generation can reduce charging demand, support operational resilience, or improve distributed self-consumption. The review also proposes minimum reporting requirements for future studies, including annual energy yield, Wh/km contribution, PV area or capacity, mileage assumptions, SOC modeling, and curtailment treatment. The review was not formally registered, and no formal risk-of-bias or certainty assessment was applied.

1. Introduction and Conceptual Context

1.1. Background and Motivation

The rapid electrification of road transport is central to climate mitigation strategies [1,2]. Current vehicles are still predominantly powered by internal combustion engines using petroleum-derived fuels, which emit substantial greenhouse gases (GHGs) and harmful air pollutants such as nitrogen oxides and particulate matter, causing damage to both human health and the environment [3]. A transition away from conventional transport systems is necessary to meet decarbonization targets. Battery-electric vehicles (BEVs) and hydrogen fuel-cell electric vehicles (FCEVs) are leading zero-tailpipe-emission options, but both remain dependent on upstream energy sources [4].

Solar energy provides a key renewable pathway to support both systems, either directly for BEV charging or indirectly via hydrogen production. However, large-scale photovoltaic (PV) deployment faces land-use constraints, which has driven interest in alternative configurations such as agrivoltaics (APV) and floating photovoltaics (FPV) ([5,6,7]. While these approaches improve land-use efficiency, they remain centralized. These photovoltaic integration pathways can be broadly categorized into static and dynamic systems, as shown in Figure 1.

This has motivated interest in distributed solutions, including vehicle-integrated photovoltaics (VIPV), which enable direct solar energy generation at the point of use.

Given these constraints, generating energy closer to the point of use becomes increasingly attractive. Battery-electric vehicles already reduce tailpipe emissions and facilitate the integration of transport with renewable electricity generation. Nevertheless, large-scale EV adoption also increases electricity demand and may place additional strain on the grid, particularly during peak charging periods such as evenings [8,9].

These limitations have driven interest in distributed, point-of-use energy solutions that can reduce reliance on grid-based charging. Vehicle-integrated photovoltaics (VIPV) represents one such approach, in which photovoltaic modules are embedded directly into the vehicle body, enabling on-vehicle electricity generation, as shown in Figure 2 [10,11]. Unlike stationary PV systems, VIPV operates within a mobile system where generation, storage, and consumption are co-located. As a result, energy generated during parking or driving can be utilized directly by the vehicle, reducing grid charging demand under favorable conditions [12,13].

In addition to VIPV, vehicle-applied photovoltaics (VAPV) refers to systems in which PV modules are externally mounted onto the vehicle rather than structurally integrated. Although terminology is not always consistent, the literature generally distinguishes between integrated and attached configurations, which differ in design, thermal behavior, and system performance [9,15]. This distinction is analogous to building-integrated and building-applied PV systems.

The level of integration influences operating conditions: fully integrated systems typically experience higher thermal buildup due to limited rear ventilation, whereas externally mounted configurations allow improved airflow and greater flexibility [16]. Consequently, VIPV and VAPV represent distinct design approaches to on-board solar generation.

Table 1. Comparison between vehicle-integrated photovoltaics (VIPV) and vehicle-applied photovoltaics (VAPV) in terms of integration level, structural role, thermal behavior, and system implications.

Feature	VIPV	VAPV	Supporting References
Integration level	Fully embedded into vehicle body panel	Externally mounted onto vehicle surface	[9,15]
Structural role	PV laminate is part of the vehicle structure	Attached as add-on; no structural function	[9]
Thermal behavior	Higher operating temperature; limited rear ventilation	Better rear-surface airflow; lower operating temperature	[16]
Aerodynamic impact	Flush-mounted; minimal additional drag	Potential drag increase if poorly integrated	[9]
Flexibility/retrofitting	Designed during manufacturing	Can be retrofitted to existing vehicles	[15]
Weight penalty	Lower (integrated into existing panel)	Higher (additional mounting hardware)	[9,15]
Design complexity	High (curvature, certification, durability)	Moderate	[9,15]
Analogous building concept	BIPV (building-integrated PV)	BAPV (building-applied PV)	[16]

summarizes their key differences.

1.2. Development and Current State of VIPV Research

Although photovoltaic integration into vehicles is not new, early applications were largely confined to solar racing competitions and experimental prototypes using lightweight construction and high-efficiency cells under controlled conditions, as shown in Figure 3 [11,17]. These demonstrations did not reflect mainstream automotive constraints such as durability requirements, curved body panels, dynamic shading, and varied real-world usage patterns. From the early 2010s onward, research shifted toward integration within commercial electric vehicle platforms, supported by improvements in crystalline silicon efficiency, lightweight encapsulation, and power electronics [18,19].

As EV adoption accelerated, VIPV research moved from proof-of-concept demonstrations to system-level modeling and techno-economic assessment [21,22]. Studies began quantifying annual energy yield under realistic climatic conditions, estimating contribution per kilometer traveled, and modeling interactions with battery state-of-charge (SOC) dynamics [8,23]. More recent work has incorporated curvature-aware irradiance modeling, electrical mismatch analysis, and life-cycle assessment [24,25].

1.3. Performance Variability and System Constraints

Despite these advances, reported outcomes vary substantially. Annual yield estimates for passenger vehicles range from several hundred to over 1000 kWh, depending on climate, integration area, and modeling assumptions [13,21,26]. Proportional energy offsets range from modest single-digit percentages to higher values in favorable low-mileage scenarios [8,17]. Fleet-scale estimates also diverge, reflecting sensitivity to behavioral assumptions, EV adoption trajectories, and grid carbon intensity [9,13].

This variability arises from the interaction of environmental, geometric, electrical, and system-level factors rather than from any single dominant mechanism. Environmental conditions determine the available solar resource, while vehicle geometry introduces projection losses, curvature effects, and self-shadowing that reduce effective irradiance capture relative to flat, stationary systems [18,24]. These effects are compounded by electrical mismatch and MPPT behavior under dynamic irradiance [19,27]. At the system level, realized energy utilization depends on SOC constraints, charging behavior, and vehicle availability, which determine the fraction of generated energy that is effectively used [8,23].

In addition to these modeling-dependent factors, several structural constraints define the upper bound of VIPV performance. Installed capacity is inherently limited by available vehicle surface area and integration geometry [18,24]. Mobility prevents consistent optimal alignment and introduces orientation variability relative to stationary PV systems [28,29]. Seasonal asymmetry further reduces winter generation in temperate climates, while high-energy-intensity vehicle classes limit proportional contribution regardless of modeling detail [11,30]. These constraints establish practical limits on achievable VIPV performance.

Beyond technical performance, system-level evaluation introduces opportunity-cost considerations. The same photovoltaic capacity deployed on rooftops or at utility scale may achieve higher capacity factors and more predictable grid interaction due to optimal orientation and reduced variability [8,9]. Consequently, VIPV should be assessed not only in terms of technical feasibility but also relative to alternative photovoltaic deployment pathways within broader decarbonization strategies.

Existing VIPV studies tend to focus on isolated aspects of system performance, including photovoltaic materials, environmental exposure, or vehicle-level integration, without consistently linking these across system scales, as summarized in Table 2. As a result, three key gaps emerge:

(i)

limited analysis of practically usable energy under SOC constraints, with many studies reporting theoretical generation without modeling curtailment or charging behavior;

(ii)

the lack of integrated geometric–electrical modeling that directly links curvature-induced irradiance gradients to mismatch and MPPT losses; and

(iii)

insufficient consideration of opportunity cost, where VIPV performance is rarely evaluated against equivalent stationary PV deployment in terms of energy yield, cost, or emissions.

These limitations highlight the need for a structured, multi-layered synthesis that connects module-level behavior, vehicle-level performance, and system-level energy utilization under real operating constraints. Addressing this gap is essential for distinguishing between theoretical generation and practically usable energy, and for enabling consistent cross-study comparison.

Table 2. Comparison of key VIPV-related review studies.

Scope of Work	Areas Covered	Limitations/Gaps	Study
Conceptual review of VIPV systems	Applications, PV technologies, deployment challenges	Limited system-level modeling and SOC interaction	[9]
System-level review	Powertrain integration, energy management, storage	Limited linkage to real-world performance metrics	[31]
Passenger vehicle VIPV	PV tech, layouts, route-based yield	Focus on environmental factors without SOC/system coupling	[32]
Environmental and design drivers	Irradiance, temperature, shading, geometry	Factors analyzed in isolation, with limited system-level integration	[10]
Solar mobility systems	PV efficiency, integration, and cost challenges	VIPV is treated as a sub-component, with limited detailed analysis	[15]
Large-scale review (270+ studies)	Vehicle design, control, PV systems, environment	Broad coverage, limited depth on usable energy and SOC	[33]

Table 2. Comparison of key VIPV-related review studies.

Scope of Work	Areas Covered	Limitations/Gaps	Study
Conceptual review of VIPV systems	Applications, PV technologies, deployment challenges	Limited system-level modeling and SOC interaction	[9]
System-level review	Powertrain integration, energy management, storage	Limited linkage to real-world performance metrics	[31]
Passenger vehicle VIPV	PV tech, layouts, route-based yield	Focus on environmental factors without SOC/system coupling	[32]
Environmental and design drivers	Irradiance, temperature, shading, geometry	Factors analyzed in isolation, with limited system-level integration	[10]
Solar mobility systems	PV efficiency, integration, and cost challenges	VIPV is treated as a sub-component, with limited detailed analysis	[15]
Large-scale review (270+ studies)	Vehicle design, control, PV systems, environment	Broad coverage, limited depth on usable energy and SOC	[33]

1.4. Aims and Contributions

In response to the identified gap, this study undertakes a structured review of VIPV research published between 2015 and 2025, focusing on technical performance, system integration, and strategic deployment. Unlike existing VIPV reviews, which commonly treat photovoltaic technologies, environmental drivers, and vehicle integration as largely separate themes, this review adopts a unified, multi-layered analytical framework that links environmental exposure, geometry, electrical behavior, interactions between battery state-of-charge (SOC) and the vehicle, vehicle-level contributions, and fleet-scale implications. This approach reflects the coupled nature of VIPV systems, where generation, storage, and consumption occur simultaneously within a mobile platform.

The first contribution of this review is to distinguish between theoretical photovoltaic generation and practically usable energy. While many studies report annual yield or nominal energy contribution, the realized value of VIPV depends on temporal alignment with demand, SOC constraints, and charging behavior. The second contribution is to separate methodological influences from intrinsic physical limits. Some variability in reported performance arises from modeling assumptions, whereas other limitations, such as restricted surface area and mobility-induced variability, are inherent to the technology. The third contribution is to position VIPV within a broader system-level context by considering opportunity cost, alternative photovoltaic deployment pathways, and interaction with electricity demand.

Overall, this review provides an integrated perspective that supports more consistent cross-study interpretation and a clearer assessment of where VIPV offers meaningful value within electrified mobility systems. Taken together, these contributions position the review not simply as a summary of existing VIPV studies, but as a framework for interpreting how different modeling choices and operating conditions shape the practical value of vehicle-integrated solar generation.

The remainder of this paper is organized as follows. Section 2 outlines the review methodology, including the literature search, study selection, and analytical framework used in this study. Section 3 examines the technical factors governing VIPV performance, covering environmental exposure, geometry, electrical behavior, and system-level energy flow. Section 4 evaluates the resulting energy contribution of VIPV at both vehicle and fleet scales. Section 5 considers economic and environmental performance, including life-cycle implications and the comparative value of VIPV against other photovoltaic deployment pathways. Section 6 discusses the broader significance of the findings by distinguishing between modeling assumptions and intrinsic constraints, identifying suitable deployment contexts, and highlighting key research priorities. Finally, Section 7 concludes the paper by summarizing the main insights and clarifying the strategic role of VIPV in electrified mobility systems.

2. Methodology

This section describes the methodological framework adopted for the structured systematic review of VIPV literature. It outlines the review scope, search strategy, study selection process, eligibility criteria, data extraction approach, and analytical framework used to evaluate and compare the included studies.

2.1. Review Scope

The review focused on studies published between 2015 and 2026, reflecting the rapid development of VIPV alongside modern electrified mobility systems. The scope includes passenger cars, commercial vehicles, buses, and other mobile platforms where photovoltaic systems are deployed to directly support onboard energy demand.

In this review, VIPV is interpreted in a functional and system-oriented sense, encompassing both structurally integrated photovoltaic systems and VAPV, provided that the photovoltaic system contributes directly to the vehicle’s electrical energy balance. This definition is adopted to capture the full spectrum of on-board solar energy utilization relevant to electrified mobility, including cases where integration may be partial but operationally significant.

Studies were considered relevant if they addressed on-board photovoltaic integration in vehicles, including VIPV, VAPV, solar-assisted electric vehicles, solar buses, solar trucks, and related applications. Studies focusing solely on externally mounted or non-functional photovoltaic systems without a measurable contribution to vehicle energy performance were excluded.

The main outcomes of interest included annual energy yield, driving-range extension, photovoltaic conversion efficiency, module area or installed capacity, electrical architecture, maximum power point tracking (MPPT) performance, battery state-of-charge interaction, carbon-emission reduction, cost, payback period, and life-cycle performance.

2.2. Literature Search and Selection

A structured literature search was conducted using the Scopus database, selected for its broad coverage of peer-reviewed engineering and energy research. The search strategy was designed to capture studies related to vehicle-integrated photovoltaics (VIPV) and closely related on-board photovoltaic applications in the context of electrified mobility. The Scopus search was last conducted on 2 May 2026.

The final search query applied in Scopus was:

(“vehicle-integrated photovoltaics” OR “VIPV” OR “vehicle applied photovoltaics” OR “VAPV” OR “automotive photovoltaics” OR “solar vehicle”) AND (“energy yield” OR “performance” OR “Wh/km” OR “efficiency”), with publication years restricted to 2015–2026.

The search returned 196 records, of which one duplicate was removed, resulting in 195 unique records for screening.

Following title and abstract screening, 107 records were excluded, and 88 reports were retained for full-text assessment. All retained studies satisfied the eligibility criteria and were included in the final dataset. The study selection process followed the PRISMA 2020 guidelines [34] and is illustrated in Figure 4.

To supplement database retrieval, reference lists of included studies and recent review articles were screened. Studies meeting the eligibility criteria through the screening process were retained in the final dataset (n = 88). Additional studies identified through this process were used for contextual support only and are not part of the systematically screened dataset and are therefore not represented in the PRISMA flow diagram. Consequently, the total number of cited references exceeds the number of systematically included studies.

2.3. Selection Process

Screening was conducted based on relevance to on-board photovoltaic applications in vehicles and the presence of technically meaningful performance information. Studies were excluded if they fell outside the scope of vehicle-integrated or vehicle-applied photovoltaics, addressed unrelated application domains, or lacked sufficient technical depth for comparative analysis. Screening and eligibility assessment were conducted by a single reviewer using predefined inclusion and exclusion criteria to ensure consistency.

To ensure consistency and transparency, exclusion decisions were categorized using predefined criteria:

• E1—Absence of quantitative performance metrics;
• E2—Lack of reproducible or clearly defined methodology;
• E3—Irrelevant application domain (not related to VIPV or vehicle photovoltaics);
• E4—Conceptual or review-only study without original analysis;
• E5—Redundant modeling or limited novelty;
• E6—Insufficient data for extraction.

No additional exclusions were required during the full-text stage, indicating that the screening criteria were sufficiently selective.

A complete list of excluded records with corresponding exclusion reasons, as well as the full set of included studies (n = 88), is provided in the Supplementary Material to ensure transparency and reproducibility of the selection process.

2.4. Eligibility Criteria

Studies were considered eligible if they were peer-reviewed journal articles or conference papers addressing on-board photovoltaic integration in vehicles, including both VIPV and VAPV. Inclusion required the presence of quantitative results or technically extractable information related to performance, energy contribution, system design, or integration.

Studies were excluded if they focused exclusively on stationary photovoltaic systems, non-vehicle applications, unrelated energy or thermal-fluid problems, or general electric vehicle charging infrastructure without vehicle-mounted photovoltaics. Conceptual discussions without technically usable or reproducible evidence were also excluded.

2.5. Data Collection and Data Items

Relevant information was systematically extracted from each included study, including bibliographic details, study type, vehicle category, photovoltaic integration approach, and geographic or climatic context. Technical parameters such as photovoltaic technology, module area, or installed capacity (where available), and key performance indicators were recorded. Data extraction was performed by a single reviewer using a structured template to ensure consistency across studies.

Where reported, additional data were collected on annual energy yield, driving-range extension, MPPT strategy, shading effects, battery state-of-charge interaction, economic indicators, and carbon-emission impacts. Due to differences in reporting methods and metrics, data were standardized where possible to enable consistent comparison.

Given the heterogeneity of the dataset, a quantitative meta-analysis was not performed. Instead, the review employed structured comparison and narrative synthesis.

2.6. Analytical Framework

The included studies were evaluated using a multi-layer analytical framework designed to capture the principal dimensions of VIPV system performance and integration. The framework considers

• Environmental exposure, including irradiance variability, temperature effects, and shading;
• Geometric representation, including surface curvature, orientation, and projection losses;
• Electrical behavior, including interconnection topology, mismatch effects, and MPPT operation;
• System-level integration, including interaction with battery state-of-charge, charging dynamics, and energy management;
• Vehicle-level contribution, including annual energy yield, energy consumption (Wh/km), and driving-range extension;
• Fleet-level implications, including aggregate energy savings and potential grid interaction;
• Economic and life-cycle performance, including cost, payback, and emissions.

This framework supports consistent comparison across studies and enables identification of differences arising from physical constraints, modeling assumptions, and system design choices. It also provides a basis for assessing strategic deployment potential across vehicle types, climates, and usage conditions.

2.7. Review Limitations

This review is subject to limitations primarily related to the heterogeneity of the included studies. Variations in vehicle types, climatic conditions, photovoltaic technologies, driving cycles, and system architectures result in a wide range of reported performance metrics. Some studies report annual energy yield, while others focus on range extension, percentage energy contribution, emissions reduction, or system efficiency. Consequently, findings were synthesized qualitatively rather than through statistical meta-analysis.

The review was not formally registered, and no separate review protocol was prepared. No standardized risk-of-bias, reporting bias, or certainty-of-evidence assessment tools were applied. This reflects the nature of the included literature, which consists primarily of heterogeneous engineering simulations, experimental studies, and techno-economic analyses rather than comparable intervention-based studies. As such, conventional bias assessment frameworks (e.g., those used in clinical systematic reviews) were not considered directly applicable.

As described in Section 2.2, additional studies were used for contextual support but were not included in the systematic dataset.

A further limitation concerns search sensitivity. The review relied on a single database (Scopus) with a fixed query vocabulary. Studies indexed under alternative terminology, such as ‘transient irradiance’, ‘solar highway’, or ‘highway photovoltaics’, may not have been retrieved despite meeting the eligibility criteria. This introduces a potential risk of incomplete coverage across adjacent research domains. Consequently, the review should not be interpreted as an exhaustive census of the literature, but rather as a structured synthesis of the identified evidence base.

3. Physical Determinants of VIPV Performance

This section examines the key technical factors governing VIPV performance. These factors are analyzed systematically to distinguish intrinsic physical drivers from modeling-dependent assumptions. Building on the methodological framework defined in Section 2, the analysis evaluates how environmental conditions, geometric constraints, electrical behavior, and system-level interactions influence the amount of usable energy generated. Rather than treating these factors independently, the section emphasizes their coupled effects within a mobile photovoltaic system.

VIPV performance depends on several interacting factors. A useful way to describe this is to express the net electrical energy available for battery charging as the incident solar resource multiplied by a set of loss and correction terms (adapted from [35]):

$${\mathrm{E}}_{\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{l}}\propto \mathrm{G}\times f_{PR}\times f_{sh}\times f_{spec}\times f_{temp}\times f_{geo}$$

where

• $G (\mathrm{W}/{\mathrm{m}}^2)$ = incident irradiance;
• $f_{PR} (-)$ = performance-ratio-related losses;
• $f_{sh} (-)$ = shading factor;
• $f_{temp} (-)$ = temperature factor;
• $f_{spec} (-)$ = spectral factor;
• $f_{geo} (-)$ = geometry factor.

It should be noted that this expression characterizes generation-side losses and does not incorporate a battery SOC utilization factor. In a VIPV system, a proportion of generated energy may be curtailed if SOC is at or near its upper limit at the time of generation. The fraction of generated energy that is effectively stored and used ($f_{SOC}$) therefore constitutes an additional multiplicative term in a full system-level energy balance: ${\mathrm{E}}_{\mathrm{u}\mathrm{s}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}}$ = ${\mathrm{E}}_{\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{f}\mathrm{u}\mathrm{l}}$ × $f_{SOC}$, where $f_{SOC}$ depends on SOC headroom, charging strategy, and temporal alignment between generation and demand. The base formulation is adapted from stationary PV performance models and is extended here to reflect VIPV system-level utilization.

Irradiance represents incident solar resource. $f_{PR}$ (performance ratio) captures conversion and balance-of-system losses; Shading reflects external obstruction; Temperature and Spectral terms represent efficiency deviations from standard test conditions; and the Shape factor represents geometric effects including projection losses, curvature, orientation, and self-shadowing. Environmental shading should be distinguished from geometric effects to avoid double-counting in modeling studies [23,36].

3.1. Environment Modifier

Environmental conditions are a primary determinant of VIPV performance, as they directly influence irradiance availability, operating temperature, and exposure variability under real-world driving and parking conditions [37].

3.1.1. Solar Resource and External Shading

Solar resource is the primary driver of annual VIPV yield. Studies across multiple regions consistently show higher VIPV output in high-irradiance climates and strong seasonal variability at higher latitudes, with effective irradiance further influenced by parking exposure and urban surroundings [12,21]. This variability is particularly pronounced for commercial vehicle applications, where high-resolution simulations show annual energy yields of 3–7 MWh per truck depending on technology, with additional gains of 20–75 kWh/year attributable to motion-induced cooling effects (headwind) [38]. While such results are derived from simulation-based approaches, field observations further confirm that solar vehicle performance is highly sensitive to environmental variability; comparisons between telemetry and simulation data show that irradiance intensity and temperature fluctuations are the dominant drivers of deviations in real-world energy output [39].

Urban shading from buildings and vegetation is consistently identified as a major loss factor. In addition to macroscopic shading, irradiation across vehicle surfaces is inherently non-uniform; simulations show that lower parts of the vehicle body can receive up to 35% less irradiance than upper surfaces due to topography and surrounding structures [40]. Reported annual shading losses in dense urban contexts are dependent on whether vehicles spend daytime hours in exposed parking or shaded streets and structures [22,25,29]. Shading also varies over time: brief obstruction during driving may have less impact than persistent shading during long parking periods, particularly where electrical control mitigates transient mismatch effects [41]. Scenarios assuming continuous unshaded parking may overestimate performance relative to mixed real-world conditions (garages, multi-stories, shaded kerbside), while persistently shaded assumptions may underestimate workplace or park-and-ride settings with exposed daytime parking [8,21]. This distinction between parking environments is further supported by GIS-based analyses, which demonstrate that solar energy availability on roads and parking areas varies significantly with surrounding buildings and vegetation, requiring high-resolution modeling (≈0.5 m spatial scale) to accurately capture shading and seasonal effects [42]. Beyond static exposure conditions, solar energy capture during motion is also trajectory-dependent, as irradiance varies with vehicle speed, road topography, and geographic location, highlighting the coupled influence of environmental and mobility factors on VIPV performance [43].

3.1.2. Soiling

Soiling generally negatively impacts photovoltaic system performance, though the magnitude of the losses depends strongly on location and environmental conditions. Dust accumulation on PV surfaces reduces the transmittance of incident irradiance through scattering and absorption, directly leading to a reduction in power output [44,45]. Depending on the location, the losses can vary significantly. In relatively clean environments such as Europe and the UK, losses are typically around 5–10%, whereas in dry, arid regions, losses are much higher, reaching above 50% under prolonged exposure without cleaning [44]. In extreme conditions, studies have reported even higher values, depending on the duration of dust accumulation and environmental severity. Power production losses from PV have been shown to have an approximately linear relationship with the level of soiling deposition, particularly at moderate dust accumulation levels [45]. However, this relationship may not remain strictly linear at very high dust densities, where saturation effects can occur.

Dust particle characteristics also play an important role. Airborne particulate matter (PM), especially PM2.5, tends to be more detrimental than larger particles such as PM10 due to its smaller size and greater adhesion to PV surfaces [45,46]. The particle composition, morphology, and interaction with surface materials also influence the degree of optical loss and difficulty of removal. Environmental conditions further affect soiling behavior. Factors such as wind speed, rainfall, humidity, and surface material determine the rate of dust accumulation and natural cleaning processes [44,45]. For example, heavy rain may remove accumulated particles, while light rain and humidity can increase adhesion and lead to more persistent soiling [47].

In VIPV systems, soiling behavior can be more complex than in conventional static application-based PV. VIPV may experience increased exposure to road particulates and splash back, especially in urban environments. However, an additional mechanism may also exist due to the dynamic nature of vehicles; wind flow during motion may remove loosely attached particles, depending on wind speed and particle size. This self-cleaning effect is still not well quantified in the literature and remains uncertain.

In addition, different vehicle surfaces may experience different levels of soiling. Roof-mounted VIPVs, which are typically more horizontal, are likely to experience higher dust accumulation than more inclined or vertical body-integrated surfaces. This is consistent with findings from PV studies showing that tilt angle significantly influences dust deposition rates [48].

Despite its importance, soiling is often simplified in VIPV modeling studies. Many simulations either neglect soiling effects or apply constant correction factors, without accounting for temporal variation, particle properties, or interactions with environmental conditions [8,10,49]. As a result, the impact of soiling on real-world VIPV performance may be either underestimated or oversimplified depending on modeling assumptions.

3.1.3. Thermal Behavior

Module temperature is a critical determinant of photovoltaic performance and acts as a key environmental modifier in VIPV systems. In general, increasing operating temperature reduces power output, primarily due to a decrease in open-circuit voltage and, consequently, maximum power [50]. However, in vehicle-integrated applications, temperature behavior is not governed by material properties alone, but by the interaction between photovoltaic technology, integration design, and dynamic operating conditions.

Photovoltaic technologies exhibit distinct temperature sensitivities. Crystalline silicon, which dominates VIPV applications due to its maturity and efficiency, typically shows a relatively high negative temperature coefficient of approximately −0.3 to −0.5% per °C. This results in a near-linear reduction in power output with increasing temperature [51,52]. In contrast, thin-film technologies such as CdTe with coefficients around −0.24 to −0.26% per °C and CIGS with −0.31 to −0.52% per °C generally exhibit lower temperature sensitivity, allowing them to retain a higher fraction of rated output under elevated temperatures [53]. Emerging third-generation technologies may offer favorable thermal characteristics under specific conditions, although they remain less mature for widespread deployment [16,54]. As a result, while crystalline silicon remains the dominant technology, its higher thermal sensitivity introduces a performance penalty under real operating conditions.

Beyond material properties, integration design plays a decisive role in thermal behavior. In VIPV systems, modules are typically bonded directly to the vehicle body, which limits rear-surface ventilation and promotes conductive heat transfer into the underlying structure. This configuration is analogous to poorly ventilated building-integrated photovoltaics, where restricted airflow leads to elevated operating temperatures and increased efficiency losses [16]. In contrast, vehicle-applied photovoltaics incorporate a physical air gap between the module and the vehicle surface, enabling convective heat removal from both the rear surface of the module and the vehicle body, thereby reducing thermal accumulation.

VIPV thermal behavior is governed by the contrast between stationary and dynamic operating states. During parking, limited airflow leads to heat accumulation and elevated module temperatures, whereas motion induces forced convection that significantly enhances heat dissipation. This transition between thermally penalized stationary conditions and dynamically cooled operation is a defining characteristic of VIPV systems [55,56,57].

This behavior is supported by both modeling and experimental evidence. Field-based analysis shows that temperature increases of 20 to 30 °C during stationary exposure can result in efficiency losses of approximately 9 to 13% for crystalline silicon modules [55]. Vehicle-scale thermal simulations indicate that airflow corresponding to typical driving speeds can reduce photovoltaic cell temperature by up to approximately 40 °C under certain configurations [56]. Similarly, full-scale experimental studies demonstrate that convective heat transfer increases strongly with vehicle speed and varies with geometry, with observed temperature change rates reaching up to 16.5 °C per minute. This is an order of magnitude higher than standard thermal cycling conditions for stationary photovoltaic systems [57].

Real-world demonstrator studies further support this behavior, showing that thermal conditions are strongly linked to operational patterns, particularly the relative duration of stationary exposure and vehicle motion [58].

The underlying heat-transfer mechanisms are analogous to ventilated building-integrated photovoltaics, where the presence of an air gap facilitates convective heat removal and prevents thermal build-up [59,60]. Consequently, assumptions of uniform or well-ventilated conditions in VIPV modeling may underestimate operating temperatures in integrated configurations and lead to overestimation of real-world energy yield.

Despite these well-established effects, many VIPV studies continue to rely on constant temperature coefficients or simplified steady-state models. Validation work comparing commonly used formulations, such as the Ross and Faiman models, with measured onboard temperatures shows that steady-state approaches can exhibit large instantaneous errors and only achieve accuracy when averaged over extended time periods. This limitation arises primarily from the neglect of thermal capacitance and explicit wind-speed dependence [61].

Overall, thermal behavior in VIPV systems is governed by the combined effects of material properties, integration design, and dynamic operating conditions. Accurately capturing these interactions requires transient, speed-dependent modeling approaches that account for heat accumulation during stationary periods and convective cooling during motion. Incorporating these effects is essential for realistic estimation of energy yield, efficiency losses, and long-term system performance.

3.2. Geometry: Shape Factor, Projection Losses, and Self-Shadowing

Geometry strongly influences VIPV performance because curved body panels create varying surface orientations, leading to effective irradiance that scales with the incidence angle. Early work on solar vehicle design already demonstrated that geometric layout and shadow analysis are inseparable, as even internally generated shadows from vehicle components (e.g., canopy, curvature transitions) can significantly reduce net energy collection and must be accounted for during array design optimization [62]. Models assuming flat-horizontal integration can therefore overestimate annual yield by neglecting projection losses and realistic solar alignment over daily and seasonal cycles [28,63,64]. This limitation has been explicitly demonstrated for nonplanar PV systems, where conventional flat-surface formulations overestimate performance by neglecting local cosine losses, non-uniform irradiance distribution, and the ambiguity between projected and active surface area in curved geometries [65].

These effects have been formalized through the concept of a curve-correction factor, which quantifies the deviation in energy yield between curved and flat PV surfaces by accounting for cosine losses, self-shading, and irradiance distribution across the module [66].

Curvature also produces an uneven irradiance distribution, leading to an electrical mismatch when cell strings receive different irradiance levels [67]. Experimental evidence confirms that curvature-induced deformation significantly impacts electrical performance; measured power output reductions increase sharply with bending angle, with losses ranging from ~11.6% to 47.6% for curvature angles between 0° and 20°, identifying bending as a dominant driver of performance degradation [68].

Self-shadowing between vehicle surfaces (e.g., roof edges, pillars, or adjacent panels) can further reduce output, particularly at low solar elevations. Such internal shading effects are strongly geometry-dependent and may vary dynamically with vehicle orientation and solar position, reinforcing the need to distinguish them from external environmental shading in modeling frameworks. This internal shading should be treated separately from environmental shading to avoid double-counting [28].

Geometric modeling is a key source of variation in reported yields. The experimental verification of curvature effects also requires specialized measurement techniques; for example, collimated solar simulators have been developed to reproduce realistic solar incidence on curved modules, reducing artificial irradiance non-uniformity from up to ~20% in conventional simulators to below 0.5%, thereby enabling accurate characterization of curvature-induced losses [69]. Geometry also interacts with shading: curvature-aware mapping may show that some regions contribute little even without external obstruction, reducing the benefit of additional module area or higher cell efficiency [67].

Table 3. Selected curvature-aware modeling and experimental studies illustrating geometric loss mechanisms in VIPV systems. The table includes both systematically included studies and contextual technical references used to support the interpretation of curvature-induced losses.

Study	Geometry Treatment	Method	Curvature-Induced Impact	Key Mechanism
[70]	Real curved vehicle roof (Chambéry, France)	Experimental (5 × 5 cell + temperature sensor matrix)	~17% loss (sunny); ~6% (overcast) vs. flat	Irradiance and temperature non-uniformity; strong dependence on sky conditions
[71]	3D curved roof and hood across vehicle types	Numerical energy yield simulation	Up to ~25% loss (series configuration); reduced to ~3–6% with optimized topology	Curvature → irradiance inhomogeneity → electrical mismatch; mitigated by parallel subgroup design
[64]	Curved roof (1-year measurement)	Analytical correction factor derived from measurements	~8% loss (shape factor ≈ 0.92)	Geometric projection reduces effective irradiance
[24]	Full curved vehicle geometry (Toyota Prius PHEV: hood, roof, rear)	Experimental + high-resolution curved-surface modeling (differential geometry)	Flat assumption overestimates yield by ~49% (hood); ~7–11% (roof); corrected shape factor 0.93–0.97 (~3–7% residual loss)	Cosine losses and self-shading; the strongest impact on highly curved/obstructed surfaces (e.g., hood)
[68]	Flexible PV under controlled curvature (0–20° bending)	Experimental (outdoor measurement + regression modeling)	~11.6–47.6% loss depending on bending angle	Mechanical bending alters projected area and irradiance distribution, inducing electrical mismatch and efficiency loss

PHEV: plug-in hybrid electric vehicle. Note: Loss figures represent the percentage reduction in energy yield or power output relative to an equivalent flat-mounted module under identical irradiance conditions. The shape factor is defined as the ratio of the curved module output to that of an equivalent flat module. However, reported values are not directly comparable due to differences in the definition of curvature-induced loss (e.g., flat reference comparison, shape factor, or configuration-dependent mismatch losses), as well as variations in geometry, electrical configuration, and environmental conditions.

demonstrates that curvature-induced losses are highly dependent on geometry, electrical configuration, and modeling approach, with reported values ranging from ~3% to over 25%, and significantly higher discrepancies when flat-surface assumptions are used.

These results highlight that curvature-induced losses are not solely a geometric projection effect but arise from coupled optical and electrical mechanisms, including reduced projected area, non-uniform irradiance distribution, and mismatch losses within interconnected cell strings. Importantly, experimental studies indicate that real-world losses can exceed simplified theoretical predictions, particularly under combined bending and environmental conditions [68].

In addition to geometric effects, module design constraints play a critical role in enabling curvature integration. Lightweight and flexible module concepts are increasingly required to conform to vehicle body shapes while maintaining structural integrity and electrical performance. Recent developments in fiber-reinforced polymer (FRP) front-sheets demonstrate weight reductions of 44–74% compared to conventional glass modules, while maintaining mechanical robustness and low power degradation (−0.9% to −1.1% under thermal cycling) [72].

Similarly, lightweight epoxy–fiberglass module designs for VIPV applications report cell-to-module efficiency losses of up to ~3%, while maintaining ≥90% electrical performance after simulated vehicular vibration testing, highlighting the importance of mechanical resilience under dynamic operating conditions [73].

Furthermore, geometric effects interact strongly with urban spatial variability, as city-scale modeling shows that surrounding buildings and street configurations can reduce VIPV yield by up to ~70% compared to open-road conditions, depending on urban density and orientation [74].

3.3. Electrical Behavior

Non-uniform irradiance is a fundamental source of electrical loss in photovoltaic systems and is particularly significant in VIPV due to curvature, orientation variability, and transient shading. Under such conditions, series-connected cells operate at the current of the lowest-performing element, resulting in mismatch losses and reduced system output [75]. This effect is amplified in VIPV systems, where spatial and temporal irradiance variability is inherent.

Partial shading introduces strongly nonlinear behavior, where even small shaded regions can cause disproportionate reductions in output due to current limitation and bypass diode activation [76]. Reported losses range from approximately 10% to over 70%, with severe conditions often exceeding 50–60% [77]. In addition to magnitude losses, partial shading produces multiple local maxima in the power–voltage characteristic, complicating maximum power point tracking (MPPT) and reducing effective energy extraction [78].

These mismatch effects are strongly configuration-dependent. Controlled VIPV experiments show that total cross-tied (TCT) configurations can deliver up to 21%, 8%, and 14% higher output than series-parallel, bridge-link, and honeycomb topologies, respectively [79], with outdoor evaluations under driving conditions further confirming that TCT architectures reduce shading-induced losses (e.g., ~18.8% vs. ~31.6% power loss under single-cell shading) compared to conventional series-parallel modules [80]. Similarly, Macías et al. [81] and Moruno et al. [27] demonstrate that interconnection topology strongly influences yield under curvature-induced irradiance gradients, while Karoui et al. [82] show that electrical design choices significantly alter performance under realistic VIPV conditions. Series-dominated configurations are consistently more susceptible to mismatch losses, whereas optimized subgrouping and alternative architectures can partially mitigate these effects. This behavior is consistent with analytical and probabilistic modeling, which shows that mismatch losses decrease with increasing parallelization, with efficiency approaching approximately (1 − 1/N) as the number of strings increases [83].

Mismatch losses are also sensitive to modeling resolution. High-resolution simulations indicate that simplified shading representations can significantly underestimate losses under spatially heterogeneous irradiance [84], a limitation particularly relevant for VIPV systems. Mitigation strategies such as distributed MPPT and module-level power electronics can recover approximately 10–30% of mismatch losses [85], although these introduce additional cost, weight, and system complexity.

In addition to spatial mismatch, VIPV systems experience rapid temporal variations in irradiance due to vehicle motion and changing orientation. These dynamics challenge conventional MPPT algorithms, which are typically designed for steady or slowly varying conditions [86]. Under such conditions, the power–voltage curve evolves continuously and may exhibit multiple local maxima, increasing the risk of suboptimal tracking [87]. Real-world route-based measurements further show that most irradiance fluctuations occur at relatively low frequencies (0–24 Hz), with only short dynamic intervals causing abrupt power drops, indicating that typical driving conditions are dominated by smoother shading variations rather than extreme transients [88].

Advanced MPPT strategies have been developed to address these challenges. Hybrid approaches combining artificial neural networks with hill-climbing techniques improve tracking accuracy under fast-changing conditions [89], while adaptive and global optimization methods further enhance performance under partial shading [90]. Tracking efficiencies exceeding 99% have been reported under complex conditions [91], with additional improvements achieved through real-time adaptive control in urban environments [92].

Quantitative evidence indicates that MPPT-related losses are moderate but non-negligible. High-resolution measurements show that tracking systems operating at 0.1–1 Hz retain approximately 90–97% of convertible energy, corresponding to losses of around 3–10% under realistic conditions [93]. Partial shading introduces an additional 5–10% reduction relative to idealized operation [93]. System-level optimization further shows that improved control strategies can increase energy extraction by 16–25%, enabling 2–7 km daily range extension and reducing grid charging frequency by up to ~19% [94].

At the system level, electrical efficiency is further constrained by power electronics. Advanced DC–DC converter topologies achieve efficiencies exceeding 98.4% while maintaining stable operation at low irradiance (~70 W/m²) [95]. Multiport converter architectures achieve efficiencies of 95–98.9% and enable driving range improvements of 15–26% depending on configuration [96], with integrated optimization studies indicating that VIPV systems can supply up to ~60% of auxiliary electrical demand under favorable conditions [97].

Overall, electrical performance in VIPV systems is dominated by mismatch losses arising from non-uniform irradiance, with partial shading capable of reducing output by 10–70% or more, depending on configuration. While optimized architectures and module-level power electronics can recover approximately 10–30% of these losses, their effectiveness depends strongly on system design. In parallel, dynamic irradiance introduces MPPT-related losses of around 3–10%, which, although smaller than mismatch losses, remain relevant under real-world conditions.

Consequently, VIPV electrical performance is inherently scenario-dependent and governed by the interaction between electrical configuration, control strategy, and system integration, rather than a single dominant loss mechanism.

3.4. Quantitative Performance Comparison of VIPV Systems

Although 88 studies were included in the review, only a subset is presented in Table 2. This table focuses exclusively on studies that report quantitative, vehicle-level performance metrics, including annual energy yield (kWh/year), solar-derived driving range, or contribution to vehicle energy demand. Many studies identified in the review focused on irradiance characterization, material performance, or conceptual system design without providing comparable system-level outputs. Therefore, only studies with extractable and comparable performance data are included to ensure consistency and enable meaningful cross-study comparison.

Table 4. Comparative summary of system-level VIPV performance metrics across selected studies, including annual energy yield (kWh/year), solar-derived driving range, energy contribution, and modeling assumptions.

Study	Location	Vehicle Class	PV Area (m2)	Module Type	Eff. (%)	Energy Yield (kWh/yr)	Solar Contribution	Mileage Assumed	SOC Modeled
[13]	Multi-climate	Car, van	2.0/2.86	c-Si	~22.7	NR	1800–5100 km/year	NR	No
[26]	Cologne	Utility EV	4.8	SHJ c-Si	19.7	479	NR	NR	No
[8]	Amsterdam/Madrid	Multi-vehicle	~2–25 (incl. sides)	c-Si	21 (assumed)	scenario-based	15–80% contribution	~5000–140,000 km/year (archetypes)	Yes
[82]	Paris/Malaga	Passenger car	1.44 (base)	c-Si	system-level	~300–510 *	293–1444 km/year	~12,250 km/year	Yes
[23]	Palermo, Italy	Minibus	multi-surface	c-Si/CdTe/CIGS	15–21	3100–4300	up to 60% demand	NR (route-based)	No
[67]	Melbourne, Australia	Passenger EV (Model 3)	2.5 (bonnet + roof)	CIGS	23.4 (cell)	930	4366–6838 km/year	NR	Yes
[98]	India (highway)	Passenger car	~4	Thin-film/mono-Si	NR	~0.5–0.7 kWh (3 h)	~5.25% (~8 km per trip)	150 km trip	No
[99]	Japan	PHEV (Toyota Prius)	~2.8	III-V triple-junction	~34	NR	~6211 km/year	~10,000 km/year	Yes
[100]	Germany	Light commercial vehicle	~10–15	c-Si heterojunction	NR	NR	~36 km/day	NR (test route)	Yes
[94]	Turkey (simulated)	Passenger EV	~1–2	c-Si	NR	NR	2–7 km/day	NR	Yes
[96]	China (simulated)	Car/van/bus	NR	c-Si	NR	NR	15–26% range increase	NR	Yes
[97]	USA (multi-climate)	Passenger EV	NR	c-Si	NR	NR	up to ~60.5% aux load	NR	Yes
[101]	China	Passenger EV	multi-surface	c-Si	24.4–28	NR	up to ~78–96% grid reduction	TMY-based	Yes
[102]	Korea (sim./exp.)	LEV	~0.5–1	c-Si	NR	NR	up to ~100% daily demand	NR	Yes
[103]	Seoul	Electric bus	~8–10	c-Si	NR	~1000 kWh/year	auxiliary load support	route-based	No
[104]	Graz, Austria	Passenger EVs (Fiat 500e, VW ID.3, ID.Buzz)	~4.6–10.9	c-Si and thin-film (CZTSSe)	12–20	~2000–5000 **	~16–25% monthly; up to ~25–35% annual	12,000 km/year	Yes
[58]	Germany (Hannover)	Light commercial EV	~15 (active ~11.6)	c-Si (SHJ, flat modules)	~18 (module), ~60–65 system	~390–420 ***	~30% distance (case-specific)	1750 km (measured period)	Yes
[105]	EU + Middle East airports	Bus/Minibus/Service vehicles	3–20	c-Si	21–21.6	~1000–12,000 (city and scenario dependent)	1700–5500 km/yr (bus); 650–5000 (minibus); 840–6180 (service)	Not explicitly fixed (range derived from energy)	Partial (not dynamically modeled)

Abbreviations: NR = not reported; SOC = state of charge; VIPV = vehicle-integrated photovoltaics; EV = electric vehicle; HDV = heavy-duty vehicle; c-Si = crystalline silicon; SHJ = silicon heterojunction; CIGS = copper indium gallium selenide; CdTe = cadmium telluride. Note: Performance metrics vary across studies (e.g., energy yield, driving range, or percentage contribution) and are not directly comparable due to differences in vehicle class, system configuration, climate, and modeling assumptions. * Karoui et al. (2023) [82]: energy yield reported in kWh/kWp; values shown represent estimated gross annual production, while lower values reflect net usable energy after system constraints (e.g., SOC limits). ** Jalkh et al. (2024) [104]: energy yield not explicitly reported; values derived from reported mileage contribution and typical vehicle energy consumption, representing effective usable energy. *** Patel et al. (2025) [58]: annual values estimated from a 4-month measurement (~129 kWh) and scaled; results are scenario-dependent and influenced by parking behavior and seasonality.

highlights the significant variability in reported VIPV performance across studies, driven primarily by differences in vehicle type, installed PV area, climatic conditions, and modeling assumptions. Annual energy yields range from less than 500 kWh/year for passenger vehicles to over 4000 kWh/year for larger multi-surface applications such as minibusses.

A key distinction emerges between studies that assume full energy utilization and those incorporating system-level constraints such as battery state-of-charge and charging behavior. Studies lacking SOC modeling tend to report higher theoretical yields, while system-level models show reduced but more realistic contributions.

Furthermore, the reporting of performance metrics is highly inconsistent, with some studies presenting energy yield, others reporting driving range, and several expressing results as percentage contribution to demand. This lack of standardization limits direct comparability and highlights the need for a unified reporting framework.

To address this limitation, future VIPV studies should adopt a minimum reporting framework comprising (i) absolute annual yield (kWh/year); (ii) energy contribution normalized to distance (Wh/km); (iii) installed PV area (m²) and/or rated capacity (Wp); (iv) module efficiency at standard test conditions (%); (v) annual mileage assumption (km/year); (vi) location and irradiance data source; (vii) SOC modeling approach (none, static, or dynamic); and (viii) explicit treatment of curtailment and charging constraints.

Without consistent reporting of these parameters, percentage-based or range-extension metrics cannot be independently verified or meaningfully compared across studies, vehicle classes, or climatic conditions.

4. Energy Contribution and Utilization of VIPV

This section evaluates the energy contribution of VIPV systems at both vehicle and fleet scales. The technical dependencies outlined in Section 3 collectively determine the extent to which photovoltaic generation translates into usable energy under real-world conditions. In particular, environmental variability, geometric constraints, electrical losses, and system-level interactions limit the fraction of theoretically available energy that can be effectively utilized.

Building on this framework, the section assesses how generated photovoltaic energy translates into practical outcomes such as range extension, reduced grid electricity demand, and system-level impacts. Particular attention is given to the distinction between theoretical generation and realized energy utilization.

4.1. Energy Contribution and Range Extension

VIPV performance is commonly evaluated in terms of annual energy yield (kWh/year) and distance contribution (Wh/km), often expressed as ‘range extension’. Across the literature, reported yields for passenger vehicles typically range from several hundred to over 1000 kWh/year, depending on installed area, module efficiency, climate, and exposure conditions [13,26,106]. Under favorable conditions, this can represent a meaningful share of annual driving energy for low- to moderate-mileage users. However, these values reflect upper-bound potential, and realized performance is consistently lower when dynamic operating conditions are considered.

A key finding across both experimental and modeling studies is the systematic gap between nominal and real-world performance. Field measurements indicate practical module efficiencies of approximately 13% compared to rated values of around 18%, with overall PV-to-wheel efficiencies of approximately 9% under real driving conditions [107]. This gap arises from a combination of non-optimal orientation, temperature effects, partial shading, and system-level losses that are often simplified in modeling studies.

This discrepancy is further supported by simulation–measurement approaches that explicitly incorporate driving behavior, parking conditions, and spatial irradiance variability. Under such realistic assumptions, VIPV’s contribution is typically more modest but remains non-negligible. For example, integrated modeling under Central European conditions indicates that VIPV can supply approximately 16–25% of monthly driving demand, with outcomes highly sensitive to shading, vehicle geometry, and usage patterns [104]. Real-world demonstrator studies reinforce this variability, showing that performance is strongly governed by operational factors: a monitored light commercial EV equipped with a 2.18 kWp system delivered approximately 129 kWh over four months, corresponding to around 530 km of additional range and roughly 30% of total driven distance, with parking phases accounting for the majority of energy generation [58].

Direct experimental measurements further clarify the underlying mechanisms driving this gap. Although motion-induced airflow reduces module temperature, performance ratios decrease during driving due to dynamic partial shading from surrounding infrastructure, falling from approximately 1.03 in parking conditions to 0.99 during driving and as low as 0.87 in urban environments [108]. Taken together, these findings demonstrate that VIPV performance is inherently governed by real-world operating conditions, particularly shading and usage patterns, rather than by module efficiency alone.

Vehicle class introduces a second major source of variation. Passenger vehicles generally achieve the highest proportional benefit due to lower baseline energy demand. In contrast, SUVs and light commercial vehicles exhibit similar absolute generation but reduced relative contribution, while heavy-duty vehicles show low proportional impact because energy demand exceeds the roof-area-limited generation potential [11,82,109]. Nevertheless, VIPV can provide indirect system-level benefits in these applications, including reduced battery capacity requirements and lower depth-of-discharge cycling in buses and fleet operations [110]. Hybrid configurations reinforce this interpretation, with PV acting as an auxiliary energy source that reduces fuel consumption but does not fundamentally alter propulsion demand [111].

System design and solar geometry further shape the achievable energy contribution. Even limited surface coverage (~1–3 m²) can yield measurable gains, including daily range extensions of several miles and lifetime energy savings of 4.5–21 MWh under favorable conditions [112]. More detailed modeling shows that location-specific solar geometry and surface orientation are critical: local-centric approaches estimate daily yields of approximately 1.66–2.09 kWh under high-irradiance conditions when diurnal sun-path variation and surface-specific exposure are considered [113]. Empirical urban driving studies provide consistent evidence under dynamic conditions, reporting approximately 1.03 kWh/day generation, ~9.4% energy offset, and ~11 km range extension in a tropical city environment, despite significant losses from transient shading and traffic conditions [114].

Emerging analyses highlight the importance of vehicle type and duty cycle. Structured, scenario-based frameworks applied to operational fleets indicate that VIPV can provide annual solar driving ranges of approximately 1700–5500 km for buses, 650–5000 km for minibusses, and 840–6180 km for service vehicles, depending on irradiance and usage patterns [105]. Light electric vehicles (LEVs) can achieve substantially higher proportional contribution due to lower baseline consumption, with PV generation in some cases exceeding daily energy demand [102]. In contrast, buses and larger vehicles tend to use VIPV primarily for auxiliary loads, with typical contributions around 2.7 kWh/day supporting non-propulsion systems [103]. These differences reinforce that absolute generation alone is insufficient to assess system value; proportional contribution depends strongly on demand profile.

Climate remains a dominant external driver of performance. High-irradiance regions consistently show higher yields and greater proportional offsets than temperate climates [8,13,106], while lower-irradiance regions exhibit stronger seasonal variability and reduced annual contribution [21]. However, climate effects extend beyond irradiance alone. Temperature-dependent auxiliary loads, particularly air conditioning, significantly influence effective driving range. Global analyses show that while high-irradiance regions can theoretically enable annual solar driving ranges exceeding 15,000 km, cooling demand can reduce this by 48–57% in hot climates, compared to 13–17% in milder conditions [115]. In parallel, climate-dependent modeling across Asian contexts reports grid charging reductions of up to 70–90% under favorable assumptions, although such results are highly sensitive to driving behavior and system utilization and should be interpreted as upper-bound estimates [101].

A recurring issue in the literature is the interpretation of percentage-based metrics. High proportional contributions often occur in low-mileage scenarios even when absolute generation remains limited. Studies that report percentage contribution without corresponding kWh output and mileage assumptions may therefore overstate practical significance [8,17]. Robust comparison requires consistent reporting of absolute energy yield, Wh/km contribution, and usage assumptions.

Finally, VIPV must be evaluated relative to competing vehicle efficiency strategies. At higher speeds, aerodynamic drag dominates energy demand, accounting for approximately 20–50% of passenger vehicle consumption and up to around 60% in heavy-duty vehicles [116], with propulsion power increasing with the cube of vehicle speed [117]. In contrast, VIPV provides a constrained and quasi-steady energy input limited by surface area and irradiance. As a result, increases in drag-related demand can exceed photovoltaic contribution, and modest aerodynamic improvements may yield energy savings comparable to or greater than rooftop PV generation [11,19,118].

Overall, the literature consistently shows that VIPV provides a measurable but bounded energy contribution. Its effectiveness is governed by the interaction between climate, vehicle class, system design, and usage patterns. Rather than a dominant energy source, VIPV functions as a context-dependent supplementary system whose value depends on how closely real-world operating conditions align with favorable exposure scenarios.

4.2. System-Level Energy Utilization

Vehicle-level photovoltaic generation does not automatically translate into grid electricity displacement. The proportion of generated energy effectively utilized depends on system-level interactions, particularly battery state of charge (SOC), charging behaviors, and temporal alignment between generation and demand. System modeling determines what share of generated energy is stored within SOC limits and later used to reduce grid charging. If PV output occurs when SOC is near its upper limit, surplus energy is curtailed unless alternative sinks exist (auxiliary loads or export) [8,67,119]. SOC modeling shows that usable SOC windows, buffer strategies, and charging rules can substantially affect realized PV utilization [23].

Quantitative evidence from Rotas et al. [120] supports this sensitivity: in a comparative Berlin–Los Angeles analysis, PV utilization varied substantially with SOC strategy. A ‘charge-right-away’ approach with no SOC headroom led to frequent curtailment, whereas maintaining a mid-SOC operating window increased the fraction of PV energy absorbed by the battery. Their results show that solar driving share ranged from 5.2 to 7.1% in Berlin and 8.3–11.2% in Los Angeles, depending solely on SOC management and charging behavior.

Charging behavior is equally important. Daytime PV reduces grid import only if it lowers subsequent plug-in demand or shifts charging away from the grid supply. Studies linking PV generation with realistic plug-in timing and parking exposure report stronger sensitivity than those using annual averages [15,23,121]. Rotas et al. [120] further demonstrate that plug-in timing interacts strongly with SOC headroom: vehicles arriving with high SOC and charging immediately absorb little subsequent PV generation, whereas delayed or PV-aligned charging strategies substantially reduce curtailment and increase effective PV utilization. Because vehicle consumption varies with driving cycle and auxiliary loads, more robust approaches couple irradiance modeling with consumption rather than relying solely on average Wh/km values [8,17].

Energy management strategies play a central role in determining how photovoltaic energy is distributed between propulsion and storage. Early studies demonstrate that hybrid PV–battery systems rely on control strategies to balance power flows and meet dynamic load requirements, with PV acting as the primary source and batteries providing support during transient demand [122]. More advanced architectures extend this approach using mode-selection algorithms based on driving conditions and SOC, enabling reductions in fuel consumption of up to ~30% in hybrid configurations, although the direct PV contribution remains relatively small [123,124].

Curtailment may also arise from charge power limits, temperature-dependent constraints, and vehicle availability. In addition, VIPV integration can influence battery operating conditions. Simulation-based analysis shows that solar-assisted operation reduces battery current amplitude by up to ~7.3% and current fluctuation by ~2.5%, indicating reduced electrochemical stress [125]. Beyond electrical constraints, behavioral and thermal factors also influence effective energy utilization. The “parking dilemma” highlights a trade-off between solar charging and increased thermal loads: parking in direct sunlight increases PV generation but raises cabin temperature, leading to higher air-conditioning demand and potential offset of range gains [126].

Experimental and simulation studies indicate that net energy benefit depends on system size and parking duration, with short exposure periods potentially yielding negligible or even negative net gains due to increased auxiliary loads, particularly from air-conditioning demand [126]. Where SOC headroom and charging strategy are not explicitly represented, reported potential grid displacement may exceed what is achievable under real operating conditions [8,23].

Taken together, these factors show that the usable share of photovoltaic energy is strongly constrained by system-level interactions rather than generation alone. In practice, battery dynamics, charging behavior, and operational conditions determine how much of the available solar energy can be effectively captured and utilized. As a result, theoretical generation values must be interpreted alongside utilization constraints to provide a realistic assessment of VIPV performance.

4.3. Fleet-Scale and Grid Implications

Fleet-scale modeling extends vehicle-level VIPV performance to system-wide electricity demand and grid interaction. By aggregating per-vehicle generation and utilization, these analyses estimate the potential contribution of VIPV to reducing overall electricity demand and associated emissions [8,13,19]. Under high-irradiance and favorable behavior assumptions, some European scenarios estimate significant reductions in annual electricity demand; however, these estimates depend strongly on adoption rates, exposure assumptions, and the share of PV energy that is usable [8,13,118].

Charging patterns influence both total displacement and peak demand. If charging is concentrated in the evening, on-board PV may reduce annual grid consumption but have a limited effect on peak load unless it changes charging timing or quantity. In contrast, workplace charging or extended daytime parking with SOC headroom increases direct daytime displacement [8,13]. Sensitivity analysis shows that relatively small changes in plug-in timing and parking exposure can significantly affect results [8,23].

Adoption assumptions also shape outcomes. Many scenarios assume high penetration of VIPV-equipped vehicles, yet manufacturing cost, design constraints, and consumer acceptance may limit uptake [9].

Opportunity cost remains an important consideration. Several studies compare mobile PV with stationary deployment and note that identical module capacity may achieve higher lifetime output on roofs or facades due to controllable orientation and lower shading variability [8,9].

Overall, the literature indicates that VIPV can provide measurable energy contributions under favorable conditions, particularly at the vehicle level. However, translating these contributions into fleet-scale electricity demand reduction remains highly sensitive to behavioral assumptions, adoption rates, and system integration factors. This synthesis shows that energy yield alone is an insufficient indicator of VIPV value, and that meaningful assessment must consider how the electricity generated is actually utilized within wider mobility and grid contexts. These issues are further examined in Section 5.

5. Economic and Environmental Performance

This section assesses the economic and environmental performance of VIPV systems, extending the analysis beyond technical feasibility and energy contribution. It evaluates life-cycle emissions, cost-effectiveness, and opportunity costs relative to alternative photovoltaic deployment pathways. This perspective is essential for determining whether VIPV represents an efficient use of resources within wider decarbonization strategies.

5.1. Life-Cycle Assessment and Emissions

Life-cycle assessment (LCA) provides a critical framework for evaluating whether the additional material and integration impacts of VIPV are justified by operational energy savings. Early LCA studies of solar vehicle conversion provide important context for evaluating VIPV sustainability. Conversion-based analyses show that hybrid solar vehicles can reduce total energy consumption and greenhouse gas emissions compared to conventional vehicles, particularly when applied to existing fleets [127].

Broader LCA frameworks further suggest that integrating photovoltaic systems into existing vehicles may offer a viable intermediate strategy for reducing emissions and energy consumption while avoiding the full lifecycle impacts associated with new vehicle production [128].

Results indicate that VIPV can lower life-cycle emissions relative to grid-based charging, but outcomes depend strongly on climate, utilization, grid carbon intensity, and methodological choices [15,19,129]. Early system-level analyses incorporating real-world usage patterns further support this variability, showing that integrating approximately 1 kW of onboard PV with modest battery storage (~4 kWh) can reduce well-to-wheel GHG emissions by up to ~30%, although outcomes remain highly sensitive to regional solar exposure and user behavior [130].

This system-level perspective is further supported by integrated energy management studies, which show that combining VIPV with forecasting and vehicle-to-grid (V2G) strategies can enable near-zero or zero-carbon operation under certain usage conditions, particularly for low daily travel distances and favorable solar resource profiles [129].

Comparability across studies is strongly influenced by differences in system boundaries and allocation rules. Some LCAs treat the VIPV laminate as an additional component with dedicated embodied impacts, whereas others allocate impacts differently between the photovoltaic layer, vehicle structure, and associated electronics. Similarly, studies vary in whether they include only manufacturing and use-phase effects or also account for degradation, replacement, and end-of-life treatment. These differences can substantially affect reported life-cycle intensities, particularly when annual VIPV electricity generation is modest.

Functional units also vary across the literature, including electricity delivered, distance traveled, and vehicle-level environmental impact, further complicating direct comparisons.

To illustrate this variability, a limited number of studies provide detailed life-cycle assessments of VIPV systems, and those that do differ substantially in scope and methodological choices. Kanz et al. [26] present the most complete vehicle-level LCA to date, evaluating a 930 Wp VIPV system integrated into the StreetScooter Work L electric van. Their analysis uses 1 kWh of on-board PV electricity as the functional unit and applies a cradle-to-grave boundary including module manufacturing, integration, use-phase performance, and shading losses. They report a life-cycle intensity of 0.357 kg CO₂-eq/kWh, which can decrease to 0.221 kg CO₂-eq/kWh under a 12-year lifetime assumption, highlighting the sensitivity of results to service life and shading conditions.

Clemente et al. [121] adopt a different approach, focusing on the embodied emissions of VIPV laminates rather than full vehicle-level impacts. Their functional unit is m² of VIPV panel, and they apply a cradle-to-gate boundary covering raw material extraction, cell processing, lamination, and assembly. They report 118 kg CO₂-eq/m² for the VIPV laminate and show that net climate benefits depend strongly on local irradiance and grid carbon intensity during the use phase. Because the study does not model vehicle-level operation, its results are not directly comparable to kWh-based LCAs.

Yamaguchi et al. [19] provide a broad review of solar-powered vehicles and VIPV concepts, synthesizing results from multiple studies that use different functional units, including vehicle-kilometers, annual CO₂ reduction, and PV electricity delivered. Although not a standalone LCA, the review highlights that reported CO₂ reductions vary widely (typically 50–75% relative to grid-charged BEVs) depending on climate, driving patterns, and grid mix.

Oluwalana and Grzesik [15] examine methodological variability across EV LCAs and discuss VIPV as an emerging subsystem. While not a dedicated VIPV LCA, their analysis emphasizes how choices in allocation, degradation modeling, and end-of-life treatment can substantially shift results. Their findings reinforce the need for harmonized reporting if VIPV is to be compared meaningfully with stationary PV or other vehicle-efficiency measures.

Taken together, these studies show that VIPV LCA results are highly sensitive to functional unit selection, system boundary definition, assumed service life, shading conditions, and local grid carbon intensity. Reported carbon intensities range from approximately 0.22–0.36 kg CO₂-eq/kWh for vehicle-level LCAs to ≈118 kg CO₂-eq/m² for cradle-to-gate laminate assessments. This heterogeneity underscores the importance of consistent reporting of system boundaries, functional units, degradation assumptions, and end-of-life treatment to enable robust comparison with alternative photovoltaic deployment pathways.

5.2. Economic Performance and Opportunity Cost

Economic viability depends on the delivered cost per kWh rather than the installed cost per Watt. VIPV systems incur integration premiums, including curved laminates, certification requirements, and distributed power electronics, alongside uncertainty in long-term performance.

Battery lifetime is an additional economic factor often overlooked. Building on the system-level effects discussed in Section 4.2, the smoothing of current profiles and reduction in peak loads associated with VIPV operation can extend battery lifespan and reduce replacement frequency, thereby improving overall system economics [125].

Early techno-economic design studies highlight the sensitivity of VIPV performance to system-level parameters. Abdelhamid et al. [131] demonstrate that factors such as PV technology selection, installation area, geographical location, mounting configuration, and vehicle aerodynamics jointly determine both energy contribution and economic viability. Their analysis shows that even modest PV coverage (~1–3 m²) can provide measurable range extension (≈2–7 miles/day), while system performance remains highly dependent on operating conditions and vehicle characteristics.

Recent techno-economic studies indicate that payback period and levelized cost of electricity (LCOE) are highly sensitive to electricity price, annual mileage, parking exposure, and assumptions regarding the fraction of photovoltaic energy effectively utilized [15,67,132]. Reported payback periods of around 8–9 years suggest potential viability, but these outcomes remain strongly scenario-dependent.

Fundamentally, VIPV operates under constrained conditions compared to stationary PV. Limited surface area, non-optimal and time-varying orientation, curvature-induced mismatch, and SOC-constrained utilization reduce the effective capacity factor and increase cost per delivered kWh. While stationary PV systems typically achieve LCOE values of $0.03–0.12/kWh in developed regions under optimized conditions [133], VIPV systems are inherently less efficient due to these operational constraints.

In some vehicle classes, alternative efficiency measures, such as aerodynamic optimization, may reduce energy demand at lower cost and complexity, providing comparable or greater benefits than onboard photovoltaic generation.

At the system scale, opportunity cost becomes a critical consideration. Photovoltaic manufacturing capacity and capital investment are finite, and identical module capacity may achieve higher lifetime energy yield when deployed in stationary configurations with optimized orientation and reduced variability. Therefore, several studies emphasize that the value of VIPV must be assessed not only in terms of its direct contribution but also relative to alternative deployment pathways [8,9]. This comparison suggests that VIPV is most justified in contexts where distributed, on-vehicle generation provides specific operational or infrastructural advantages, rather than as a universally optimal strategy.

Overall, the economic value of VIPV depends strongly on deployment context, particularly climate, electricity prices, integration costs, and system utilization. While VIPV can provide benefits under favorable scenarios, its relevance lies less in direct cost competitiveness and more in system-level value, including autonomy and distributed generation.

6. Discussion and Perspective

The preceding analysis shows that VIPV systems are technically feasible and capable of generating measurable energy under real-world conditions. However, their performance depends on environmental exposure, geometric constraints, electrical behavior, and system-level integration. This section synthesizes these findings to clarify the realistic role of VIPV within electrified mobility systems. It distinguishes between modeling assumptions and intrinsic structural constraints, evaluates where VIPV delivers meaningful benefit, and identifies suitable deployment contexts. It also highlights key research priorities and positions VIPV within broader system-level optimization strategies.

6.1. Realistic Contribution

For passenger vehicles with moderate annual mileage, VIPV can represent a meaningful share of energy demand, particularly under favorable daytime parking exposure [8,12,13].

Under these conditions, VIPV may

• Reduce annual grid electricity import [8,13,23];
• Extend effective driving range [12,26,106];
• Reduce charging frequency [8,23];
• Provide limited resilience in off-grid or constrained-grid scenarios [9,13].

This resilience perspective is further supported by system-level analyses incorporating physical and behavioral variability. Monte Carlo-based modeling shows that distributed VIPV-equipped vehicles can provide emergency energy support under certain conditions, with required fleet densities dependent on climate, shading environment, and demand scenarios [134]. Complementary scenario-based assessments indicate that even modest daily VIPV generation can support critical loads in disaster contexts, with applications such as ambulances capable of powering medical devices for several hours per day and contributing up to ~2 MWh annually under favorable conditions, highlighting the role of VIPV as a mobile and flexible energy source beyond conventional transport use [135].

At scale, modest per-vehicle contributions can translate into measurable electricity savings across fleets [8,13,26], with proportionally greater emissions reductions in carbon-intensive grids [15,136,137]. However, the magnitude of these benefits remains strongly context-dependent, as discussed in Section 6.2.

6.2. Sources of Performance Variability: Modeling Assumptions and Structural Constraints

To explain variability in reported outcomes, VIPV performance must be understood in terms of both modeling assumptions and intrinsic structural constraints. While modeling approaches influence how system behavior is represented, several limitations arise directly from the physical and operational nature of vehicle-integrated systems. Distinguishing between these sources is essential for interpreting results and assessing realistic contributions.

Recent studies show that differences in modeling assumptions—particularly in geometry, shading representation, electrical behavior, temporal resolution, and battery state-of-charge (SOC) are a major source of variation in reported results [8]. In many cases, these assumptions lead to overestimation of energy yield and grid displacement compared to real-world operation. VIPV modeling typically combines irradiance modeling, electrical simulation, and system-level energy flow. However, model structure and parameterization strongly influence outputs, with circuit-based and analytical PV models producing different results under nonlinear operating conditions [138]. Studies incorporating real-world driving data further show that time-dependent effects, such as shading sequence and motion, introduce variability that is not captured in simplified or steady-state approaches [41].

6.2.1. Modeling Assumptions

Modeling choices strongly influence reported outcomes, particularly in geometry, shading representation, electrical behavior, temporal resolution, and SOC integration [8]. Simplified assumptions frequently lead to overestimation of energy yield and grid displacement.

Geometric simplification: Geometric representation remains one of the most influential modeling assumptions in VIPV systems. Simplified approaches often assume flat or uniformly oriented surfaces, which neglect curvature, projection losses, and orientation variability. However, three-dimensional irradiance modeling demonstrates that curved vehicle surfaces significantly alter irradiance distribution and reduce effective energy capture [28].

Recent VIPV studies using route-based simulations further show that shading patterns vary strongly with environment and motion, with urban shading identified as the dominant driver of performance loss [27]. Similarly, image-based shading analysis confirms that realistic solar resource estimation must account for spatial and temporal variability rather than assuming uniform conditions [22]. This is further supported by city-scale validation studies, which demonstrate that incorporating high-resolution spatio-temporal data, such as building geometry, road orientation, and trajectory-dependent exposure, enables model predictions within approximately 5–10% of measured VIPV performance, highlighting the importance of urban-aware modeling frameworks [139].

As a result, models that assume simplified geometry tend to overestimate irradiance capture and energy yield, particularly when exposure conditions are idealized.

Idealized SOC utilization: Some analyses assume continuous battery headroom for PV absorption. In practice, SOC limits, charge acceptance constraints, and driver behavior lead to partial curtailment [8,23,67]. Modeling shows that usable energy can be significantly lower than theoretical generation, where daytime parking coincides with high SOC or restricted charging windows [23,119,121]. Assumptions of full PV utilization may therefore overstate grid displacement, particularly at fleet scale [8,13,23].

Dynamic electrical losses: Fleet-scale projections often assume ideal MPPT and neglect mismatch under dynamic irradiance, and this omission introduces bias in aggregate scenarios, especially where partial shading is frequent [81,137,140]. Studies explicitly modeling interconnection topology show that electrical configuration can significantly affect output under non-uniform conditions [41,81,137].

6.2.2. Structural Constraints

Beyond modeling assumptions, several constraints are inherent to VIPV deployment and define the practical limits of system performance.

Surface area limitation: Vehicle integration surfaces are constrained by geometry. Even with high-efficiency modules, installed capacity remains limited relative to rooftop or utility-scale systems, so annual generation is capped by available area [18,19,24].

Mobility-induced variability: Unlike fixed arrays, vehicles experience continuously changing orientation and intermittent exposure. Optimal tilt and azimuth cannot be maintained, and irradiance capture depends on driving patterns, parking behavior, and urban context [8,25,28]. Therefore, mobility imposes a structural penalty relative to optimally sited stationary PV [9].

Seasonal Asymmetry: At higher altitudes, winter solar resource declines significantly, limiting VIPV contribution when auxiliary loads and seasonal demand may increase. Contribution is therefore seasonally misaligned with peak electricity demand in temperate climates [12,21,26].

Energy Intensity Scaling: For heavy-duty and long-distance vehicles, energy demand far exceeds VIPV generation potential. Improvements in module efficiency do not substantially alter the proportional contribution in these applications [11,82,109].

Behavioral variability: Parking exposure, charging timing, and annual mileage vary widely across users and regions. Although modeling can approximate averages, real-world diversity reduces predictability and increases sensitivity to behavioral assumptions [8,15,23].

These constraints define the practical limits of VIPV. Technological refinement may improve performance within those limits, but cannot remove the underlying physical and behavioral constraints [8,9].

6.3. Opportunity Cost and System-Level Optimization

VIPV must be evaluated within the broader context of photovoltaic resource allocation. Given finite materials, manufacturing capacity, and capital, equivalent PV capacity deployed in stationary configurations typically achieves higher capacity factors due to optimal orientation, reduced shading, and simpler integration [8,9,26].

In addition to system-level allocation, VIPV must also be considered within whole-vehicle energy optimization. The addition of photovoltaic modules introduces integration complexity and small mass penalties, while vehicle energy demand is strongly influenced by aerodynamic drag, particularly at higher speeds. In addition, the mass of integrated PV systems introduces a non-negligible trade-off. Although PV modules are relatively lightweight, their contribution to vehicle mass increases propulsion energy demand, and the net energy benefit depends on the balance between PV generation and weight-induced consumption [141]. VIPV should therefore be evaluated as one component of integrated vehicle efficiency strategies rather than an isolated energy technology [82,136,137]. This trade-off is explicitly demonstrated in recent system-level optimization studies, where adjusting PV tilt improves net “effective power” only when aerodynamic penalties are accounted for, with gains of up to ~66.7% (≈15.6% on average) under realistic operating conditions [142].

In addition to hardware optimization, route-level strategies can further enhance VIPV performance. Energy-aware routing approaches that maximize solar exposure during driving have been shown to reduce overall energy consumption by selecting paths with higher irradiance availability, accounting for shading, weather conditions, and time of day [143]. At the fleet scale, multi-objective routing frameworks extend this concept by jointly optimizing energy generation, charging behavior, and operational constraints. For example, route optimization for photovoltaic-assisted waste collection vehicles demonstrates that integrating solar-aware routing with charging strategies can improve overall system efficiency under realistic urban conditions [144].

Therefore, the relative value of VIPV depends on context, including

• Climate [12,21,26];
• Grid carbon intensity [15,136,137];
• Integration cost and manufacturing differentials [15,67];
• Exposure patterns and shading [8,25,29];
• Durability and integration complexity [10,11];
• The value of distributed self-consumption and charging interaction [8,13,23].

VIPV is most effective in high-irradiance regions, carbon-intensive grids, and applications with predictable daytime exposure. In contrast, where stationary deployment is abundant and grid carbon intensity is low, opportunity cost becomes more significant [9,13].

6.4. Research Priorities

The review identifies several targeted research gaps necessary to refine the evaluation of VIPV deployment:

• Long-term field validation remains limited. Most existing studies rely on short-term experiments or simulation-based analyses, with insufficient multi-year datasets capturing real parking behavior, soil dynamics, and degradation under operational conditions. This limits confidence in long-term performance projections and lifecycle assessments [8,10].
• Thermal behavior is not yet fully resolved. Current models often rely on simplified or steady-state assumptions, which do not adequately capture the transient interaction between radiative heating, convective cooling, and vehicle-specific airflow. High-resolution, time-dependent thermal models are required to improve accuracy in energy yield estimation [21].
• Standardized testing and reporting methodologies are lacking. Variations in curvature treatment, mismatch representation, and dynamic irradiance modeling hinder cross-study comparability. The absence of unified testing protocols and reporting frameworks limits the ability to benchmark performance across technologies and deployment scenarios [24].
• Usable energy is inconsistently defined and reported. Many studies focus on theoretical generation without accounting for system-level constraints such as state-of-charge limits, curtailment, and charging behavior. Standardized SOC-integrated metrics are required to distinguish between generated, stored, and grid-displacing energy, which is critical for realistic system evaluation [8,23].
• Urban solar resource modeling remains underdeveloped. Existing approaches often lack sufficient spatial and temporal resolution to capture dynamic shading effects in complex urban environments. Integration of high-resolution datasets, including global positioning system (GPS) trajectories and street-level imagery, is necessary to accurately represent vehicle-specific irradiance exposure [145].
• Advanced PV architectures require validation under VIPV conditions. While emerging technologies such as tandem PV modules and PV–battery integrated systems demonstrate high efficiencies under controlled conditions, their performance under vehicle-specific constraints—including curvature, vibration, thermal cycling, and intermittent exposure—remains insufficiently understood. In addition, their techno-economic viability in mobile applications has yet to be fully assessed [146,147].
• Orientation constraints remain largely unmitigated. Although active tracking and orientation strategies have been proposed, their practical implementation in vehicle applications remains uncertain due to added complexity, energy consumption, and integration challenges. Further work is needed to evaluate their net system benefit under realistic operating conditions [148].
• Computational scalability remains a limiting factor. High-fidelity VIPV simulations are computationally intensive, restricting their application in large-scale or multi-scenario analyses. While surrogate and machine learning-based models show promise, further development is required to ensure accuracy, generalizability, and integration with physical models [149].

Addressing these gaps would improve cross-study comparability and reduce methodological divergence.

6.5. Strategic Deployment

The evidence indicates that VIPV should not be framed as a universal decarbonization solution. Rather, it is a context-dependent distributed generation strategy whose effectiveness depends on climatic, behavioral, and infrastructural conditions [8,9,21].

Favorable deployment contexts include

• Urban commuter passenger vehicles in high-irradiance regions [8,13,26];
• Fleet vehicles with predictable daytime parking exposure [8,121];
• Regions with high grid carbon intensity [15,136,137];
• Applications where reduced charging frequency provides operational value [8,9,13].

By contrast, high-mileage heavy-duty transport platforms and low-irradiance regions exhibit structurally limited proportional benefit [11,21,82].

VIPV therefore occupies a supplementary position within transport decarbonization pathways. Its contribution is incremental rather than transformative and should be assessed alongside stationary deployment and vehicle efficiency measures within system-level optimization [8,9,13]. Framed appropriately, VIPV can enhance resilience and reduce marginal grid demand in specific niches; framed as universal, it risks overstating its systemic impact [8,9].

Overall, the discussion shows that the value of VIPV lies not in its universal applicability, but in its selective, context-sensitive deployment. The interactions among physical constraints, modeling assumptions, user behavior, and broader system conditions shape its contribution. This synthesis reinforces the central argument of the review: VIPV should be assessed not as an isolated photovoltaic technology, but as a coupled mobility-energy system whose relevance depends on where, how, and for whom it is deployed. These insights are summarized in Section 7.

7. Conclusions

This review evaluated VIPV as a distributed generation strategy within electrified mobility systems. The literature shows that VIPV is technically feasible and capable of generating measurable electrical energy under real-world conditions. In favorable climatic and behavioral contexts, it can reduce annual grid demand, extend effective range, and contribute to transport decarbonization.

However, impact is context-dependent. Annual generation does not automatically translate into grid displacement; usable contribution depends on parking exposure, battery state-of-charge constraints, and charging behavior. Modeling simplifications can overstate performance. Accurate representation of geometry, electrical mismatch, and system integration is therefore essential.

Several intrinsic constraints limit achievable contribution, including restricted module area, mobility-induced orientation variability, intermittent shading, and seasonal resource asymmetry. In addition, photovoltaic integration must be considered alongside other vehicle-efficiency measures, such as aerodynamics optimization and mass reduction, which can significantly influence energy demand.

Fleet-scale modeling indicates that contributions may reduce electricity demand under high adoption scenarios, but outcomes depend on penetration rates, behavioral alignment, and the fraction of energy that is usable. Opportunity cost is also central: equivalent PV capacity deployed in a stationary configuration may achieve higher lifetime yield and more predictable grid interaction. VIPV must therefore be assessed within the broader context of system optimization.

VIPV is not a universal solution to transport electrification. Its value lies in targeted deployment, particularly urban commuter passenger vehicles in high-irradiance regions, fleets with predictable daytime parking exposure, and regions with carbon-intensive grids. In heavy-duty and low-irradiance contexts, proportional benefit remains limited.

VIPV therefore plays a supplementary role in transport decarbonization strategies. Under defined conditions, it can deliver grid reduction and resilience benefits; beyond those conditions, expectations should remain proportionate. The key question is not whether VIPV can generate useful energy, but under which conditions its deployment represents an efficient allocation of photovoltaic resources within the broader energy system.