INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Determinants of Optimizing Solar Photovoltaic Systems for Home  
Electric Vehicle Charging: Evidence from Malaysian Households  
Puteri Afiqah Syamimi Mohd Zuki1, Adilah Mohd Din1*, Nadia Nurnajihah Mohamad Nasir2  
1Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka,  
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia  
2Department of Mechanical and Manufacturing Engineering, Faculty of Engineering & Built  
Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia  
*Corresponding Author  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100133  
Received: 10 November 2025; Accepted: 20 November 2025; Published: 02 December 2025  
ABSTRACT  
The integration of solar photovoltaic (PV) systems with home electric-vehicle (EV) charging represents a key  
opportunity to enhance residential energy efficiency and support Malaysia’s low-carbon mobility agenda.  
However, empirical evidence identifying the determinants that influence the optimisation of solar-powered EV  
charging remains limited. This study examines four critical factors—EV ownership, energy trading, charging  
variables, and battery storage—to determine their influence on the optimisation of home EV charging using solar  
PV systems. A quantitative approach was employed, and data were collected from 384 Malaysian households  
with experience or interest in solar electric vehicle (EV) usage. The dataset was analysed using SPSS Version  
29, incorporating descriptive statistics, Pearson correlation, and multiple regression analysis. The findings  
indicate that charging variables and battery storage are significant predictors of optimisation, demonstrating  
strong positive effects. In contrast, EV ownership and energy trading were not statistically significant in the final  
model. These results highlight the dominant role of technological determinants, particularly charging  
configuration and storage capacity, in enabling optimal utilisation of solar energy for residential EV charging.  
This study contributes new empirical insights to the renewable-energy and electromobility literature by  
clarifying the technological factors that most strongly influence solar–EV optimisation at the household level.  
The findings offer practical implications for policymakers, industry practitioners, and homeowners aiming to  
strengthen Malaysia’s transition toward efficient, solar-powered EV charging systems.  
Keywords: Solar Photovoltaic Systems, Electric Vehicle Charging, Battery Storage, Energy Trading,  
Renewable Energy, Malaysia  
INTRODUCTION  
The global transition toward low-carbon energy systems has intensified interest in integrating renewable  
technologies, particularly solar photovoltaic (PV) systems and electric vehicles (EVs). Solar PV has gained  
significant momentum due to declining installation costs, improved system reliability, and favourable national  
incentives (Tanoto, 2023). In Malaysia, programmes such as Solar BOLEH! and the Net Energy Metering  
(NEM) scheme have encouraged broader household participation in solar adoption by offering tax benefits,  
rebates, and streamlined applications (Bernama, 2024). These policies reflect the government's broader  
commitment to accelerate the uptake of clean energy and position solar PV as a key driver of national  
sustainability.  
At the same time, EV adoption is increasing globally and locally. The International Energy Agency (IEA, 2023)  
reported strong growth in the EV market between 2023 and 2024. Meanwhile, Malaysia registered over 13,000  
EVs in 2023 alone, supported by tax exemptions, improved charging infrastructure, and growing consumer  
acceptance. Despite this progress, households continue to express concerns regarding the reliability, cost, and  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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convenience of home-charging systems, particularly the rising electricity tariffs and dependency on grid supply  
(Umair et al., 2024). As a result, integrating solar PV with home EV charging has emerged as an attractive  
solution for reducing energy costs, improving charging efficiency, and enhancing household energy resilience.  
Recent studies emphasise the potential benefits of solar-powered EV charging. Ayoade and Longe (2024) and  
Albaba et al. (2025) highlight that coupling PV systems with EV charging can significantly reduce household  
electricity expenses and contribute to carbon-emission reductions. Moreover, advancements in battery storage  
technology enable homeowners to store surplus solar energy for evening charging, thereby increasing self-  
consumption and reducing reliance on the grid (Barman et al., 2023). However, despite the substantial potential  
of solar–EV integration, several technical and behavioural challenges persist. These include variability in solar  
generation, misalignment between charging schedules and peak solar output, differing capabilities of home  
chargers, and uncertainty around the role of energy trading under NEM (Sarker et al., 2024; Rotas et al., 2024).  
Although previous research provides valuable insights into solar adoption and EV-charging behaviour  
independently, there is still limited empirical evidence on the key determinants that influence the optimisation  
of solar PV systems for home EV charging, particularly within Malaysia’s residential context. Existing studies  
rarely examine how technological factors (charging features and storage capacity), organisational aspects (EV  
ownership), and environmental conditions (energy trading schemes) collectively contribute to optimisation  
outcomes. This gap highlights the need for an integrated empirical investigation that identifies the most  
influential drivers of solar–EV system performance.  
Despite growing interest in renewable-energy integration, there is insufficient empirical evidence identifying the  
determinants that significantly influence the optimisation of solar PV systems for home EV charging in Malaysia,  
especially with respect to technological, organisational, and environmental factors.  
To address this gap, the present study investigates four determinants—EV ownership, energy trading, charging  
variables, and battery storage—to examine their relationships with optimisation outcomes in Malaysian  
households. The study employs the Technology–Organization–Environment (TOE) framework to conceptualise  
the influence of technological readiness, organisational conditions, and environmental support mechanisms. The  
scope focuses on households with existing or potential interest in solar–EV adoption, and the methods include  
descriptive analysis, correlation, and multiple regression techniques.  
Therefore, this study empirically examines the factors that most strongly influence the optimisation of solar PV  
systems for home EV charging and provides evidence-based insights for policymakers, industry practitioners,  
and homeowners seeking to enhance Malaysia’s renewable energy transition.  
LITERATURE REVIEW  
The rapid integration of solar photovoltaic (PV) systems and electric vehicles (EVs) in residential settings has  
attracted increasing scholarly attention due to their ability to transform household energy consumption patterns.  
As EV adoption accelerates globally and PV costs decline, optimizing solar-powered EV charging emerges as a  
critical research priority (Albaba et al., 2025; Ayoade & Longe, 2024). In Malaysia, rising electricity tariffs,  
strong solar irradiance, and supportive policies such as NEM further accelerate interest in solar–EV integration  
(Sarker et al., 2024). This chapter reviews four predictors: EV ownership, electric trading, charging variables,  
and battery storage.  
Theoretical Foundation: Technology–Organization–Environment (TOE) Framework  
The Technology–Organization–Environment (TOE) framework offers a comprehensive model for  
understanding the adoption of integrated household energy systems. TOE considers three contextual dimensions:  
(i) technological readiness (battery, charger, PV capacity), (ii) organizational capability (household resources,  
EV ownership), and (iii) environmental conditions (policies, tariffs, electric trading). Its suitability has been  
demonstrated in energy-transition and EV-related studies (Cho et al., 2025).  
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Ownership of EVs  
EV ownership influences household electricity demand and the feasibility of integrating residential solar PV.  
Globally, EV sales surpassed 10 million units in 2022 and continue rising (IEA, 2023). Malaysian EV adoption  
is slower due to cost and infrastructure barriers; however, increasing tax incentives are fueling uptake (Muzir et  
al., 2022). Studies show EV owners are more likely to invest in solar PV and battery storage to manage charging  
costs (Rotas et al., 2024; Albaba et al., 2025). EV ownership thus forms a key organizational determinant under  
TOE.  
Electric Trading  
Electric trading—including net-metering, feed-in tariffs, and bidirectional vehicle-to-grid (V2G) integration—  
enhances the financial performance of home PV-EV systems. Malaysia's NEM program allows households to  
export excess solar energy (SEDA, 2021), improving payback periods (Sarker et al., 2024). International studies  
indicate that V2G enables EV batteries to discharge power to the home or the grid, thereby enhancing energy  
flexibility (Ayoade & Longe, 2024). Electric trading fits within TOE’s environmental context.  
Charging Variables  
Charging variables include charger type (Level 1 or Level 2), charging timing, smart-charging capabilities, and  
user behavior. Smart charging—aligning EV charging with solar peak production—enhances the utilization of  
solar energy (Fachrizal et al., 2020). Level 2 chargers offer faster charging, allowing for greater daytime solar  
capture (Rotas et al., 2024). Charging behaviors significantly influence optimization, making this both a  
technological and organizational TOE factor.  
Battery Storage  
Battery storage is the most influential determinant of solar–EV system optimization. Batteries store surplus solar  
energy for nighttime EV charging, significantly reducing grid dependence (Barman et al., 2023). Technological  
advancements and declining battery prices promote adoption. Malaysian studies highlight the potential of  
second-life EV batteries to reduce storage costs (Sarker et al., 2024). Battery storage resides within TOE’s  
technological context and is confirmed in Chapter 4 as the strongest predictor.  
Conceptual Framework  
This study’s conceptual model integrates TOE with the following variables: 1. Ownership of EVs, 2. Electric  
Trading, 3. Charging Variables, 4. Battery Storage and Optimization of Solar PV System for Charging EVs at  
Home, as shown in Figure I below.  
Figure I: Conceptual Framework  
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Battery storage enhances solar utilization (Barman et al., 2023). Charging variables influence the matching of  
solar energy (Fachrizal et al., 2020). EV ownership determines charging demand (Albaba et al., 2025). Electric  
trading provides economic and operational flexibility (Sarker et al., 2024).  
This section reviewed global and Malaysian literature related to EV ownership, electric trading, charging  
variables, and battery storage. TOE provides a structured framework connecting technological, organizational,  
and environmental determinants of solar–EV optimization.  
METHODOLOGY  
This study employed a quantitative, cross-sectional survey design to examine the determinants influencing the  
optimization of solar photovoltaic (PV) systems for home electric-vehicle (EV) charging among Malaysian  
households. A quantitative approach was selected because it enables the systematic measurement of relationships  
between variables and allows statistical inference regarding the strength and direction of predictors (Creswell &  
Creswell, 2023). This design is widely used in research on renewable energy and technology adoption, where  
behavioral, technical, and environmental determinants are quantified to explain optimization patterns.  
The target population comprised Malaysian households who either owned a solar PV system, owned an EV, or  
intended to adopt solar-powered EV charging in the near future. Given the niche nature of this population, a non-  
probability, purposive-convenience sampling method was employed. This approach is appropriate when  
respondents possess specific characteristics relevant to the research context, such as knowledge of or interest in  
solar PV and EV technologies (Etikan et al., 2016). A total of 436 questionnaires were distributed through EV  
user groups, solar-energy communities, social media platforms, and personal networks. After removing  
incomplete or inconsistent responses, 384 valid responses were retained for further analysis. This sample size  
exceeded the minimum requirement recommended by Krejcie and Morgan (1970) for large populations at a 95%  
confidence level.  
Data were collected through a structured questionnaire consisting of three major sections. The first section  
captured demographic information, including gender, ethnicity, education level, income level, household type,  
and familiarity with solar and EV technologies. The second section measured the four independent variables—  
EV ownership, energy trading, charging variables, and battery storage—while the third section captured the  
dependent variable, namely the optimization of solar PV systems for EV charging. All measurement items were  
adapted from established literature on renewable-energy and EV integration (Albaba et al., 2025; Ayoade &  
Longe, 2024; Barman et al., 2023), and were rated on a five-point Likert scale, ranging from 1 (strongly disagree)  
to 5 (strongly agree), consistent with recommended practice for attitudinal measures (Joshi et al., 2015).  
Instrument validation was undertaken through both expert review and statistical checks. Content validity was  
ensured through evaluation by academic supervisors and renewable-energy specialists, who verified the  
alignment between items and constructs. Reliability was assessed using Cronbach’s alpha, and all constructs  
achieved coefficients above 0.80, indicating excellent internal consistency (Tavakol & Dennick, 2011). Item–  
total correlations also exceeded the minimum acceptable threshold suggested for behavioural research,  
confirming that each item contributed meaningfully to its respective construct.  
Data were analysed using SPSS version 29. Descriptive statistics were used to summarise demographic  
characteristics and provide an overview of the central tendencies of each construct. Bivariate relationships  
among variables were examined using Pearson’s correlation analysis. Subsequently, multiple regression analysis  
was conducted to identify the predictors of optimization of home solar PV systems for EV charging. The  
regression analysis included assessments of significance levels, standardized and unstandardized coefficients,  
and model fit indicators, consistent with guidelines for multivariate analysis (Hair et al., 2020). All regression  
assumptions, including linearity, normality, homoscedasticity, and absence of multicollinearity, were examined  
and found to be satisfactory. Ethical considerations were upheld by ensuring voluntary participation, obtaining  
informed consent, maintaining anonymity, and handling all collected data securely.  
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DATA ANALYSIS AND FINDINGS  
A total of 384 valid responses were analyzed to address the research objectives and examine the determinants  
influencing the optimization of solar photovoltaic (PV) systems for home electric vehicle (EV) charging. The  
analysis consisted of descriptive statistics, correlation tests, and multiple regression modelling, supported by a  
demographic profile of respondents.  
Demographic Profile of Respondents  
Table I summarises the demographic characteristics of the respondents. A total of 59% of participants were male  
and 41% were female. Malays constituted the majority (54.40%), followed by Chinese (27.30%), Indians  
(18.00%), and others (0.30%). Most respondents (60.68%) possessed a bachelor’s degree, while 16.15% held an  
SPM qualification, 12.76% held a diploma/STPM, and 9.89% had completed postgraduate studies. This  
demographic profile is consistent with groups typically associated with early adoption of solar technology and  
EV usage—namely, individuals with higher education levels and greater technological awareness.  
Table I. Summary Of Respondent Demographics (N = 384)  
Variable  
Gender  
Category  
Male  
Frequency (n) Percentage (%)  
225  
159  
209  
105  
69  
59.0  
Female  
Malay  
41.0  
54.40  
27.30  
18.00  
0.30  
Ethnicity  
Chinese  
Indian  
Others  
1
Bachelor’s Degree 233  
60.68  
16.15  
12.76  
9.89  
Education Level  
SPM  
62  
49  
38  
1
STPM/Diploma  
Postgraduate  
Others  
0.26  
Descriptive Statistics  
Descriptive statistics were computed for all constructs to assess the general perception of respondents regarding  
the integration of solar PV and EV charging. Table II presents the construct-level means and standard deviations.  
Respondents demonstrated moderate agreement regarding EV ownership and energy trading, as well as higher  
agreement on the importance of charging variables and battery storage. These results indicate that respondents  
perceive technological capabilities—specifically storage systems and charging infrastructure—as the most  
crucial components for optimizing solar-powered home EV charging.  
Table II. Descriptive Statistics Of Study Constructs  
Construct  
Mean  
Standard Deviation Interpretation  
Moderate agreement  
EV Ownership  
Moderate (≈3.5–3.8) ~0.60–0.70  
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Energy Trading  
Charging Variables High (≈3.9–4.2)  
Battery Storage Highest (≈4.0–4.3)  
Moderate (≈3.6–3.9) ~0.55–0.75  
Positive perception  
~0.50–0.70  
~0.55–0.65  
~0.60  
High importance  
Very high importance  
Strong optimization tendency  
Optimization (DV) High (≈3.9–4.1)  
Multiple Regression Analysis  
Multiple regression analysis was conducted to examine the influence of the four independent variables—EV  
ownership (IV1), energy trading (IV2), charging variables (IV3), and battery storage (IV4)—on the optimization  
of solar PV systems for home EV charging.  
As shown in Table III of the original analysis, the regression procedure was conducted in three stages (Test 1,  
Test 2, and Test 3). During Test 1 and Test 2, the results indicated that two variables, EV ownership (IV1) and  
energy trading (IV2), were not significant predictors. Their significance values were above the threshold of p >  
0.05, meaning they did not contribute meaningfully to predicting the optimization of home EV charging. Because  
these two variables consistently showed no significant effect across the first two tests, the researcher proceeded  
to Test 3 to reassess the model and confirm the significance of the variables.  
In Test 3, the analysis confirmed that charging variables (IV3) and battery storage (IV4) were the only significant  
predictors with p = 0.000, indicating strong statistical significance. EV ownership and energy trading remained  
insignificant, confirming that technological elements are more influential in optimizing solar EV charging  
performance than behavioral or market-related factors.  
Table III. Repeat Test Variables For The Coefficient Multiple Regression Analysis  
Variables  
Unstandardized Coefficients (B)  
Sig.  
Test 1  
-0.016  
0.075  
0.00  
Test 2  
-0.016  
0.075  
Test 3  
Test 1  
0.498  
0.140  
0.991  
0.000  
0.000  
Test 2  
0.492  
0.106  
Test 3  
1 Constant  
IV1  
-0.015  
0.510  
IV2  
IV3  
0.257  
0.678  
0.256  
0.678  
0.380  
0.370  
0.000  
0.000  
0.000  
0.000  
IV4  
Dependent Variable: Optimization of Solar PV System for Charging EVs at Home  
Based on the significant predictors in Test 3, the regression equation for predicting optimization of home EV  
charging using solar PV systems is expressed as:  
Y = -0.015 + 0.380(IV3) + 0.370(IV4)  
Where:  
(1)  
Y = Optimization of Charging Electric Vehicles at Home  
IV3 = Charging Variables  
IV4 = Battery Storage  
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This equation indicates that both charging variables and battery storage have positive influences on optimization.  
Battery storage exhibits the larger coefficient value, demonstrating its more substantial effect in maximizing  
solar energy utilization for EV charging. This finding aligns with current literature, which indicates that adequate  
energy storage significantly enhances solar-energy reliability and efficiency in residential EV charging  
applications.  
Overall, the findings demonstrate that the optimization of home EV charging using solar PV systems is driven  
primarily by technological determinants, particularly charging configurations and battery storage capacity.  
Although EV ownership and participation in energy trading schemes are relevant contextual factors, they do not  
directly predict optimization when technological variables are taken into account. These results reinforce the  
importance of technological readiness in solar-powered mobility systems and support the application of the  
Technology–Organization–Environment (TOE) framework in understanding household adoption patterns.  
DISCUSSION AND CONCLUSION  
Discussion  
The findings of this study provide meaningful insights into the optimization of solar photovoltaic (PV) systems  
for home electric vehicle (EV) charging in Malaysia. Among the four independent variables examined—EV  
ownership, energy trading, charging variables, and battery storage—only the technological factors (charging  
variables and battery storage) emerged as significant predictors. This result underscores the critical role of  
technological readiness in determining the efficiency of solar–EV integration.  
Charging variables were the strongest predictor, indicating that charger type, charging scheduling, and smart-  
charging capabilities greatly enhance the effectiveness of solar-powered EV charging. This aligns with previous  
studies highlighting the importance of synchronizing charging behavior with solar generation profiles to  
minimize grid dependency and maximize self-consumption efficiency. Battery storage also demonstrated a  
significant positive relationship with optimisation, reflecting its role in addressing solar intermittency and  
enabling households to use stored energy during peak EV-charging periods, especially in the evening when solar  
output declines.  
In contrast, EV ownership and participation in energy trading schemes were not significant predictors in the final  
regression model. These results suggest that while behavioural and environmental factors may influence interest  
or initial adoption, they do not directly shape optimisation outcomes when technological factors are considered.  
Within the Technology–Organization–Environment (TOE) framework, these findings reinforce the dominance  
of the technological dimension in determining the functional performance of residential solar–EV systems.  
Conclusion  
This study examined the determinants influencing the optimisation of solar photovoltaic (PV) systems for home  
electric-vehicle (EV) charging among Malaysian households. The results show that charging variables and  
battery storage are the most influential predictors, demonstrating that technological readiness—particularly the  
availability of smart-charging features and adequate energy storage capacity—plays a central role in enabling  
efficient solar EV integration. In contrast, EV ownership and energy trading were not statistically significant,  
suggesting that behavioural and environmental factors contribute less to optimisation outcomes when  
technological components are considered. The findings strengthen the technological dimension of the  
Technology–Organization–Environment (TOE) framework by providing empirical evidence that optimisation is  
driven primarily by system capabilities rather than user characteristics or market mechanisms. Practically, the  
study offers guidance for households, policymakers, and industry providers by highlighting the need to prioritize  
investments in home chargers, energy storage systems, and innovative charging solutions to maximize solar  
utilization and reduce grid dependency. Nevertheless, the study is limited by its use of purposive–convenience  
sampling, self-reported data, and a cross-sectional design, which may restrict generalisability across all  
Malaysian households. Future research should explore additional determinants such as tariff structures,  
government incentives, user charging behaviour, and advanced technologies, including vehicle-to-grid (V2G)  
systems. Overall, this study provides timely insights into residential renewable-mobility systems and supports  
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more informed decision-making for improving the performance and adoption of solar-powered EV charging in  
Malaysia.  
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