Towards electric bus system: planning, operating and evaluating

The green transformation of public transportation is an indispensable way to achieve carbon neutrality. Governments and authorities are vigorously implementing electric bus procurement and charging infrastructure deployment programs. At this primary but urgent stage, how to reasonably plan the procurement of electric buses, how to arrange the operation of the heterogeneous fleet, and how to locate and scale the infrastructure are urgent issues to be solved. For a smooth transition to full electrification, this thesis aims to propose systematic guidance for the fleet and charging facilities, to ensure life-cycle efficiency and energy conservation from the planning to the operational phase.One of the most important issues in the operational phase is the charge scheduling for electric buses, a new issue that is not present in the conventional transit system. How to take into account the charging location and time duration in bus scheduling and not cause additional load peaks to the grid is the first issue being addressed. A charging schedule optimization model is constructed for opportunity charging with battery wear and charging costs as optimization objectives. Besides, the uncertainty in energy consumption poses new challenges to daily operations. This thesis further specifies the daily charging schedules with the consideration of energy consumption uncertainty while safeguarding the punctuality of bus services.In the context of e-mobility systems, battery sizing, charging station deployment, and bus scheduling emerge as crucial factors. Traditionally these elements have been approached and organized separately with battery sizing and charging facility deployment termed planning phase problems and bus scheduling belonging to operational phase issues. However, the integrated optimization of the three problems has advantages in terms of life-cycle costs and emissions. Therefore, a consolidated optimization model is proposed to collaboratively optimize the three problems and a life-cycle costs analysis framework is developed to examine the performance of the system from both economic and environmental aspects. To improve the attractiveness and utilization of electric public transportation resources, two new solutions have been proposed in terms of charging strategy (vehicle-to-vehicle charging) and operational efficiency (mixed-flow transport). Vehicle-to-vehicle charging allows energy to be continuously transmitted along the road, reducing reliance on the accessibility and deployment of charging facilities. Mixed flow transport mode balances the directional travel demands and facilities the parcel delivery while ensuring the punctuality and safety of passenger transport

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Transport accessibility and urban-rural connectivity are seen as critical aspects of rural economic development. In the transit network, passenger flow between urban-rural corridors demonstrates directional imbalances and low utilization of scarce resources. Freight transportation, on the other hand, lags due to poor geography, high operating costs, and scattered demand. This paper proposes a new mode of public transit that integrates passenger and freight transport, providing a carrier for logistics while compensating for the low utilization of passenger transport. In this mode, each timetabled round trip is divided into one dedicated passenger trip with high demand and one mixed-flow trip with on-demand requests. A space-time-state network is constructed considering the picking-up time window, loading/unloading service time, and electric bus energy replenishment. A mixed-integer linear programming model is developed to optimize the bus schedule that covers the travel demands and the charging requests with minimized travel costs. A Lagrangian relaxation framework with a dynamic programming algorithm and sub-gradient method is presented for problem-solving. The real-life rural-urban transport instance and a simulated network demonstrate the operation of the new mode and validate the efficiency of the proposed method. The innovative concept and the optimization framework are expected to serve as a reference for public administration to alleviate passenger and freight transportation bottlenecks in the urban-rural context

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Purpose: The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes. Design/methodology/approach: The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model. Findings: This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically. Originality/value: The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses

Consolidating Bus Charger Deployment and Fleet Management for Public Transit Electrification: A Life-Cycle Cost Analysis Framework

Despite rapid advances in urban transit electrification, the progress of systematic planning and management of the electric bus (EB) fleet is falling behind. In this research, the fundamental issues affecting the nascent EB system are first reviewed, including charging station deployment, battery sizing, bus scheduling, and life-cycle analysis. At present, EB systems are planned and operated in a sequential manner, with bus scheduling occurring after the bus fleet and infrastructure have been deployed, resulting in low resource utilization or waste. We propose a mixed-integer programming model to consolidate charging station deployment and bus fleet management with the lowest possible life-cycle costs (LCCs), consisting of ownership, operation, maintenance, and emissions expenses, thereby narrowing the gap between optimal planning and operations. A tailored branch-and-price approach is further introduced to reduce the computational effort required for finding optimal solutions. Analytical results of a real-world case show that, compared with the current bus operational strategies and charging station layout, the LCC of one bus line can be decreased significantly by 30.4%. The proposed research not only performs life-cycle analysis but also provides transport authorities and operators with reliable charger deployment and bus schedules for single- and multi-line services, both of which are critical requirements for decision support in future transit systems with high electrification penetration, helping to accelerate the transition to sustainable mobility

Limitations and suggestions of electric transit charge scheduling

A major factor hindering the popularization of electric buses (EBs) in the current automotive market is the high ownership cost of batteries and its significant upfront investment. For the daily maintenance of electric fleets, the amortized battery replacement cost is at least six times the charging cost. Thus, ensuring the healthy operation of the battery and prolonging the cycle life are some of the most concerned issues of the bus operators. In order to achieve the best operating mode, the operators are required to formulate an effective charging schedule with minimized battery wear. However, little quantitative formulation exists in prior literature to consider battery wear in bus charge scheduling. In this paper, a general formula is presented for battery wear cost consideration in charge scheduling based on the emerging literature. Then, the existing charge scheduling model is improved based on the proposed approach. A case study illustrates the significant difference in operating costs between charging plans developed with or without consideration of battery wear. The focus of this commentary is to present the crucial factors to improve the efficiency of EB operations and help make the charge scheduling models more realistic

Optimal charging plan for electric bus considering time-of-day electricity tariff

Purpose: The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill. Design/methodology/approach: Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus. Findings: The electricity costs of the bus route can be reduced by applying the optimal charging plans. Originality/value: This paper produces a viable option for transit agencies to reduce their operation costs

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​\ub0C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Abstract Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures