Integrated optimization of charging facility planning and en-route charging scheduling for heterogeneous electric bus systems

Joint optimization of mixed wireless charging facility placement and electric bus charging operations

This study aims to analyze charging strategy of battery electric bus (BEB) considering both economic and environmental impacts. Case study is conducted for one bus garage of NJ Transit with buses having long interstate routes. BEB power consumption data were collected to develop a regression model using temperature and speed as explanatory variables. The charging strategy necessary for transition to BEB fleets while maintaining the current service level was developed for four scenarios: depot charging with increased fleet size, opportunity charging with fast plug-in charger, opportunity charging with wireless power transfer, and opportunity charging with pantograph. Life cycle cost analysis (LCCA) was performed to calculate the initial investment and cumulative net present value for each scenario. Life cycle assessment (LCA) was performed based on cumulative energy demand (CED) and greenhouse gas emission (GHG). Results indicate that fast plug-in opportunity charging is the most cost-effective and environmentally sustainable solution, benefiting from low charger costs and smaller battery sizes due to mid-route charging. Increased fleet size would cause the highest environmental impact, primarily due to the larger number of buses and batteries required. While the methodology and general conclusions may inform similar evaluations across other NJ Transit garages, specific analysis for each location is necessary due to operational and scheduling differences.

ABSTRACT This study explores the institutional factors influencing electric bus adoption across 23 countries from 2010 to 2023. Utilizing a newly developed panel dataset, the research integrates and analyses data to identify key trends. Principal Component Analysis is applied to construct indices based on Scott’s institutional pillars, including Regulative, Technological Normative, Social Normative, Business Normative, and Cultural-Cognitive. Estimations are conducted using static econometric models, including fixed effects, random effects, and pooled Ordinary Least Squares, with robustness checks through specification tests and heterogeneity analysis, categorizing countries by median carbon emissions. Findings indicate that Technological, Social, and Business Normative factors significantly enhance electric bus production and sales, while Regulative factors negatively impact adoption, emphasizing the need for targeted electric bus policies. Cultural-Cognitive influences, though significant, present mixed effects, highlighting the need for further variable-specific investigation. This study offers valuable insights for policymakers and stakeholders, supporting the transition toward sustainable urban transportation.

The electrification of public transit has emerged as a pivotal pathway for deep urban decarbonization. However, existing carbon accounting methods predominantly rely on static grid emission factors and deterministic energy models, often overlooking spatiotemporal variability and uncertainty propagation. To address this limitation, this study establishes a dynamic, uncertainty-aware framework for the carbon accounting of electric bus systems. A hybrid deep learning architecture integrating Temporal Convolutional Networks (TCN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention mechanism is developed to capture multi-scale temporal dependencies in energy consumption. In parallel, a time-varying probabilistic grid emission model is formulated using period-specific distributions across diurnal intervals, and Monte Carlo simulation is employed to propagate uncertainty throughout the accounting chain. Using high-frequency operational telemetry from ten electric buses in Shenzhen, the proposed model achieved an [Formula: see text] of 0.9610 and an RMSE of 0.0523, outperforming ensemble learning methods, conventional deep learning baselines, and ablation variants. Leave-one-bus-out cross-validation further confirmed robust cross-vehicle generalizability, with a mean [Formula: see text] of [Formula: see text]. The results reveal pronounced heteroscedasticity in carbon emission profiles, with uncertainty expanding substantially during high-power transient events, while the time-varying emission factor model yields wider confidence intervals than static approaches. These findings demonstrate the importance of uncertainty-aware dynamic accounting and provide a robust data-driven basis for probabilistic carbon footprint estimation in urban public transport.

Electric buses are becoming a popular and environmentally-friendly option for public transportation. However, estimating the amount of energy these buses use when traveling on routes is a challenging problem due to practical limitations on when measurements can be taken. The network of bus stops is represented using an undirected graph, where each bus stop is a vertex. A Bayesian regression model is proposed for estimating the distributions of the unknown energy costs, which are treated as random variables. A Simulated Annealing algorithm is used to find a Bayesian D-optimal design for a follow-up experiment, given the posterior distribution of the energy costs. The proposed methods are applied to a real dataset on electric bus usage in Alexandria, VA.

The transition to electric transit bus fleets in cold-climate cities requires a comprehensive understanding of how charging strategies, extreme temperatures, and grid constraints influence energy demand and operating costs. This study presents a novel agent-based simulation and optimization framework that generates realistic operational scenarios for electric bus fleets by integrating vehicle dynamics, energy consumption, weather variability, and charging strategies. The framework identifies the minimum feasible fleet size and charger configuration required to meet operational constraints and determines cost-optimal charging schedules. A case study in Quebec City, Canada, evaluates two charging strategies (slow and fast) under mild and winter conditions. Results show that winter increases energy demand by up to 30%, requiring additional vehicles and chargers to sustain service reliability. While fast charging reduces upfront investment, its high contracted power costs make it more expensive over time. On the other hand, slow charging is more capital-intensive but yields lower operational costs and smoother grid integration. In this context, the developed framework supports climate-aware, economically robust planning for transit agency fleet electrification. • Integrates agent-based simulation with optimization for fleet planning. • Winter conditions increase energy demand by 30% and fleet size by 4%. • Fast charging creates steep 20 MW power peaks, increasing OPEX. • Slow charging lowers the 10-year total cost of ownership by 7%. • Cold climates require up to 35% more chargers to sustain reliability.

A missing value imputation method for electric bus charging load data based on time series decomposition and component-wise completion

Optimal battery electric bus system configuration: Energy management, seasonal variability, uncertainty, and renewable integration

An integrated fuzzy decision-making framework for the evaluation of electric buses in public transportation: A case study in Istanbul, Türkiye

From barriers to readiness: extending the TOE framework for electric bus adoption in Malaysian transport operations

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load.

This paper proposes a stochastic framework for the joint allocation of photovoltaic (PV) resources and electric bus parking lots (EBPLs) in radial distribution systems, aiming to minimize power losses, enhance voltage stability, and improve the voltage profile under uncertainties in PV generation and load. PV and EBPL siting and sizing are determined using an Improved Weighted Average Algorithm (IWAA) that incorporates a sine-cosine search to escape local minima and achieve a stronger exploration-exploitation balance. In the stochastic framework, PV output uncertainty is modeled via Monte Carlo simulation (MCS) using beta probability density functions (PDFs), while load uncertainty is represented with normal PDFs. The proposed methodology is evaluated across four cases-Case I: deterministic PV; Case II: deterministic PV + EBPL; Case III: stochastic PV; Case IV: stochastic PV + EBPL-on the IEEE 33- and 69-bus radial distribution systems. The outcomes show that the IWAA consistently outperforms the traditional WAA by yielding lower power losses, lower voltage deviations, and better voltage stability index in Cases I and II. Also, the results demonstrated that allocating the EBPL with PV (Case II) increases the net saving by 9.81% and 5.08% for the 33-bus and 69-bus systems, respectively, over Case I (without EBPL). Also, the stochastic model (Case III) outcomes showed that the net saving falls against the deterministic model (Case II), highlighting the effects of the uncertainties. Overall, these findings underscore that explicitly modeling uncertainty and co-locating EBPL with PV leads to more robust planning decisions and improved distribution-system performance under realistic operating conditions.

This study presents an Eco-Driving Level Evaluation Model for Electric Buses Entering and Leaving Stops, aiming to improve energy efficiency, passenger comfort, and operational performance in urban public transportation systems. Frequent stopping and starting at bus stops significantly affect the energy consumption and battery performance of electric buses. Driving behaviors such as harsh acceleration, sudden braking, improper speed control, and inefficient stop positioning can lead to excessive power usage, reduced battery life, and lower passenger comfort. Therefore, evaluating and optimizing eco-driving behavior during bus stop operations is essential for sustainable transportation management.The proposed model analyzes key driving parameters such as acceleration, deceleration, speed variation, stopping distance, dwell time, and energy consumption during the entering and leaving phases of bus stops. Using sensor data, GPS tracking, and machine learning techniques, the system assesses driver behavior and classifies eco-driving levels based on efficiency and safety performance. The model helps transport operators identify inefficient driving patterns and provide feedback for driver improvement. The results support the development of intelligent transportation systems by promoting energy-saving driving strategies, reducing operational costs, and enhancing the overall performance of electric bus services.

This study assesses the impact of regenerative braking on lithium-ion battery aging and operational efficiency of lithium-ion batteries in urban electric buses using a Rainflow-based cycle-counting framework. A previously developed simulation platform based on Energetic Macroscopic Representation (EMR) is employed to reproduce realistic daily driving cycles. Battery degradation is quantified by combining the Rainflow Counting Method with Miner’s Rule, enabling cumulative damage assessment across different depth of discharge (DoD) levels and regenerative braking intensities, kbr. Four representative cycling profiles—fixed 50%, 60%, and 70% DoD and a variable mixed-use scenario—were simulated under regenerative braking intensities ranging from 0% to 100%. Results indicate that regenerative braking extends average battery lifespan by approximately 0.9 years while increasing daily driving range by around 6 km. Profiles with lower DoD values, particularly when combined with moderate regenerative braking (kbr ≈ 0.3), achieved the most favourable balance between cycle induced degradation and energy recovery. Although higher DoD scenarios deliver greater mileage gains, they also accelerate capacity fade. The variable cycling profile demonstrated robust and consistent performance, highlighting the benefits of route and load variability. Additionally, lifetime mileage analysis demonstrates that intermediate DoD levels combined with regenerative braking maximize cumulative energy throughput while preserving service life. Overall, the proposed methodology offers a computationally efficient and practically applicable approach for battery life assessment under dynamic operating conditions, offering valuable insights for optimizing energy management strategies and electric bus fleet operations.

Echelon utilization of retired electric bus batteries: Economic and environmental potential

Policy initiatives and socio-economic impact of sustainable transportation: A case study of electric buses in India

The accelerating electrification of public transport systems has increased the need for objective and transparent decision-support tools in electric bus (e-bus) procurement. Although multi-criteria decision-making (MCDM) approaches are frequently employed to evaluate e-bus alternatives, limited attention has been paid to the consistency of rankings produced by different objective weighting techniques. This study addresses this gap by proposing an integrated evaluation framework that combines the CRITIC-MARCOS and Entropy-MARCOS methods to assess e-bus alternatives against technical and operational criteria. Six e-bus models are evaluated using nine performance indicators structured as benefit and cost criteria, reflecting the procurement context of a central public transport authority in a large metropolitan area. Criterion weights are independently calculated using the CRITIC and Entropy approaches and subsequently integrated into the MARCOS method to generate alternative rankings. To examine the robustness of the results, a sensitivity analysis based on the TOPSIS and Average Ranking Methods is conducted. The findings indicate that the proposed framework produces consistent and stable rankings across different weighting techniques. These results suggest that integrating multiple objective weighting methods within an MCDM framework can enhance transparency and reliability in high-investment public transport procurement decisions and support strategic planning for low-carbon urban mobility.

Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power (R2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations.

• Optimizing electric bus charging infrastructure and charging scheduling jointly. • Integrating shared micromobility and renewable energy with public transport. • Presenting a bi-level mixed-integer linear programming model. • Performing a large-scale case study in Gothenburg, Sweden. Public transport electrification contributes to the net-zero goal in the transport sector. However, high-power bus charging during peak hours places additional strain on the grid, while under-utilization of charging infrastructure limits its potential economic and social benefits. This study focuses on these challenges through integrated and shared optimization of battery electric buses (BEB) and shared micromobility systems (SMS) incorporating solar photovoltaic. We present a bi-level mixed-integer linear programming model (B-MILM) to jointly optimize BEB charging infrastructure, BEB charging schedules, solar PV installed capacity, and SMS charging schedule. The B-MILM is solved using a value-function-based exact approach. We derive a group of inequalities based on the problem characteristics to reduce solution time. A large-scale case study in Gothenburg, Sweden, demonstrates that solar photovoltaic and shared charging services yield annual cost savings 110% - 120% above investment costs for public transit agencies, even when the service fee revenue is excluded. Charging dispatching costs for e-scooter operators are reduced by up to 54%, and daily BEB charging grid loads decrease by 3% to 34% across seasons. The greenhouse emissions from electricity consumption of BEBs and e-scooters are reduced by 3%. The results offer new insights for sustainable charging and energy infrastructure planning and management for electric public transit.