Drive Cycle Data Research Articles

An analytical approach to test a thermal management system for Li-ion battery under a real driving condition

A hybrid thermal management system (TMS) for high power lithium-ion battery modules of EVs with low energy consumption and high reliability was tested under a real state driving condition. An experimental investigation was performed to compare the hybrid TMS with an active air-cooling and a passive TMSs. We employed all three TMSs in standard weather conditions of 24 °C. For dynamic mode, a study of driving cycle in comparison with US, Europe, and Japan driving cycle data was conducted to perform a dynamic model based on a high traffic city to challenge our TMSs in a real driving state including high and standard discharge rate and a stop mode in which there was no air convection. The results showed that just in the hybrid TMS, the cell surface could reach a steady-state under 60 °C while the active TMS could keep temperature only for three cycles. Furthermore, our test proved that the proposed hybrid TMS maintains outstanding reliability and efficiency in the hot weather condition of 40 °C.

Battery Sizing for Electric Vehicles Based on Real Driving Patterns in Thailand

The rising population in suburban areas have led to an increasing demand for commuter buses. Coupled with a desire to reduce pollution from the daily routine of traveling and transportation, electric vehicles have become more interesting as an alternative placement for internal combustion engine vehicles. However, in comparison to those conventional vehicles, electric vehicles have an issue of limited driving range. One of the main challenges in designing electric vehicles (EVs) is to estimate the size and power of energy storage system, i.e., battery pack, for any specific application. Reliable information on energy consumption of vehicle of interest is therefore necessary for a successful EV implementation in terms of both performance and cost. However, energy consumption usually depends on several factors such as traffic conditions, driving cycle, velocities, road topology, etc. This paper presents an energy consumption analysis of electric vehicle in three different route types i.e., closed-area, inter-city, and local feeder operated by campus tram and shuttle bus. The driving data of NGV campus trams operating in a university located in suburban Bangkok and that of shuttle buses operating between local areas and en route to the city were collected and the corresponding representative driving cycles for each route were generated. The purpose of this study was to carry out a battery sizing based on the fulfilment of power requirements from the representative real driving pattern in Thailand. The real driving cycle data i.e., velocity and vehicle global position were collected through a GPS-based piece of equipment, VBOX. Three campus driving data types were gathered to achieve a suitable dimensioning of battery systems for electrified university public buses.

Temporal-Difference Learning-Based Stochastic Energy Management for Plug-in Hybrid Electric Buses

Plug-in hybrid electric buses (PHEBs), compared with traditional fuel-driven vehicles, can achieve higher fuel economy and lower pollution emissions. For a PHEB with a single-shaft parallel powertrain, a major challenge for researchers is to find approximate optimal energy management strategies that can run in real time. Motivated by this idea, this paper aims at minimizing PHEB fuel consumption with a temporal-difference (TD) learning method. First, historical driving cycle data from real-world bus routes are collected and processed and parameter variables of TD are introduced. Specially, this process is completed offline. Then, the configuration and main parameters of PHEB are presented, and a control-oriented dynamic system of the PHEB is constructed. Thereafter, the TD learning method based on historical data is introduced. Furthermore, the approximate optimal control strategy for energy management is proposed. Compared with the traditional optimal control strategy, the proposed method can realize real-time running without sacrificing the accuracy of optimization, because the learning method updates the estimates based on other learned estimates without calculating a final outcome. This method can learn directly from the data of running PHEBs without a simplified model of the PHEB, which can avoid the influence of model error. Finally, to verify this method, several different strategies are used for comparison. In addition, experimental results in real-world driving cycles demonstrate that the proposed method can improve the fuel economy obviously by up to 21% compared with a traditional charge-deleting, charge-sustaining scenario. Therefore, this novel method has great potential in realistic applications.

Design space exploration for waste heat recovery system in automotive application under driving cycle

Organic Rankine Cycle (ORC) systems have been recently considered for the promising application to Waste Heat Recovery (WHR) in automotive application. However, the design of ORC systems for highly transient operations typical of heavy-duty vehicles faces several challenges. This work extends the method of Design Space Exploration (DSE) to optimize the design of an ORC system for heavy-duty vehicle applications. The goal is to match the size of ORC system with the feature areas of driving cycle. Starting from drive cycle data, the feature areas are identified to represent the driving cycle. Then, a Weighted Least Square method is adopted to determine the initial design condition from feature areas, and based on the design condition, the initial ORC model is built for the further optimization. Particle Swarm Optimization (PSO) with parallel computation is adopted to optimize the performance of nonlinear ORC system with coupled constraints. The result shows that the optimal system can output 6.87% more power than the initial system and 20.08% more power than the inferior system over the feature areas of WHTC test. The analysis shown in this work also includes general design guidelines for optimizing the system architecture and optimally sizing the heat exchangers to improve the ORC thermal efficiency.

Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

City‐wide emissions modelling using fleet probe vehicles

Five UK cities will implement clean air zones to improve air quality (AQ), specifically in relation to oxides of nitrogen, and 253 UK local authorities have declared AQ management areas. Extended range electric vehicles can offer zero emission (ZE) operation but cities lack the ability to monitor and control when vehicles can switch from engine to battery. Project Autonomous and Connected vehicles for CleaneR Air (ACCRA) aims to develop and demonstrate a system allowing hybrid vehicle to become part of a city's urban traffic management control system; monitoring the vehicles’ location and operational state, and controlling the strategy to ensure ZE operation through geofenced zones with poor AQ. This study demonstrates a new methodology to model emissions using state-of-the-art instantaneous vehicle emissions modelling, geographic information system (GIS) and a new method for capturing vehicle behaviour using global positioning system. Probe vehicles were used for the pilot study in Leeds, UK, to capture drive-cycle data and the emissions are modelled. A GIS interface is used to match these emissions to the network. Finally, a high-resolution emissions map is generated to be used as input for AQ dispersion models and the ACCRA decision-making engine for the generation of geofencing AQ zones.

Modeling NOx Storage and Reduction for a Diesel Automotive Catalyst Based on Synthetic Gas Bench Experiments

To comply with stringent NOx emission regulations, automotive diesel engines require advanced aftertreatment catalytic systems, such as lean NOx traps (LNTs). Considering that test bench and chassis dyno experimental campaigns are costly and require a vast use of resources for the generation of data; therefore, reliable and computationally efficient simulation models are essential in order to identify the most promising technology mix to satisfy emission regulations. In the literature, a large number of simulation models for LNT kinetics can be found, realized for laboratory-scale samples and validated over synthetic gas bench (SGB) experimental tests, while full-size models validated over engine-dyno driving cycle data, crucial for industrial applications, are missing. In the current work, a simulation model of an LNT device is built to predict NOx storage and reduction, starting from SGB laboratory tests and finally validated over driving cycle data. The experiments including light-off, NOx storage and ...

Driving shaft lifetime enhancement by hybrid electrification

As a classical definition, the main aim of hybrid electric vehicle technology is to decrease the fuel consumption and emissions with the assistance of its power management algorithm. However, hybrid electric vehicles could also be optimized for fatigue minimization of the driving shaft to enhance its lifetime. To the best of our knowledge, there are no studies on hybrid electric vehicles regarding this concept. In this study, we model a conventional vehicle, convert it into hybrid electric vehicle in simulation environment, and optimize the power management algorithm by considering its driving shaft lifetime enhancement. The optimization is done by redesigning one of the previous equivalent cost minimization strategy studies, which includes a new state of charge sustaining approach. In this work, we reformulate the solution considering the assumptive torque–cycle life curve of the driving shaft instead of fuel consumption or emissions. Longitudinal vehicle model is prepared for simulations and the performance of the new strategy is compared with the conventional vehicle under the real driving cycle data. The results demonstrate a significant enhancement potential of 26% in driving shaft’s lifetime. Finally, we show the additional electric motor’s optimum torque tracking performance under a real driving cycle using the experimental testbed.

Characterisation and development of driving cycle for work route in Kuala Terengganu

Driving cycle is essential for researchers and also vehicle developers to study the performance of the vehicle mainly via simulations. However, the driving cycles are not the same for different countries or cities, although they may seem identical. In this paper, several driving cycle data were collected for different routes in Kuala Terengganu city at peak hours which were then split into several micro-trips. A genetic algorithm was used as a procedure for selecting the optimised micro-trip to develop a complete Kuala Terengganu driving cycle. Then, the proposed driving cycle was compared with other existing driving cycles such as Urban Dynamometer Driving Schedule, Highway Fuel Economy Test Cycle, Supplemental Federal Test Procedure and Environmental Protection Agency. The proposed complete Kuala Terengganu driving cycle was successfully developed with 10 micro-trips and a total time of 1600s. The results showed that the comparison of percentages error for characteristic parameters for the KT and UDDS driving cycle was the lowest at 161.4 percent compared to others. This indicated that KT driving cycle and UDDS driving cycle is similar to each other as the driving cycle for cities. As a conclusion, the KT driving cycle was successfully obtained using the GA method with all of the percentage error for all parameters of below 10 percent, except for the percentage of cruise time. The results for the comparison also proved that the KT driving cycle is similar to UDDS driving cycle where both are the driving cycle for cities.

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

Summary This paper presents a degradation testing of a lithium-ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium-ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium-ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3-month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge-discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

Application of Surrogate Modelling to the Optimisation of Kinetic Parameters in an Emissions Control Catalyst Model Using Vehicle Drive Cycle Data

This work describes the application of surrogate modelling to the optimisation of kinetic parameters in a kinetic model of a vehicle emissions control catalyst using engine or vehicle test data. Optimisation using conventional methods with a detailed kinetic model is slow as the model requires a relatively long time to simulate each drive cycle. The use of a surrogate model as a substitute for the detailed model can considerably accelerate the optimisation by drastically reducing the number of evaluations of the detailed model required. A surrogate model is simply a function that approximates the behaviour of the full model; accuracy is sacrificed for speed of evaluation. A few runs of the detailed model are required to train the initial surrogate. The parameters for these are determined using Latin hypercube sampling. After that, the surrogate model is used in place of the detailed model with a simplex optimisation method. Since evaluation of the surrogate is computationally inexpensive, the optimisation runs rapidly. The kinetic parameters obtained can then be evaluated with the full model and the surrogate model updated with the new information. Thus, the surrogate model is improved with each iteration. As an example of application, the extension of an existing kinetic model for a diesel oxidation catalyst to a model capable of predicting the effect of platinum group metal (PGM) loading (30 to 120 g ft.−3) using drive cycle data is presented. This optimisation methodology is useful for extending existing kinetic models to different agings, PGM loadings, catalyst formulation, etc.

Real-World Driving Cycle: Case Study of Baqubah, Iraq

This work presents a new real world driving cycle for Baqubah city, Iraq. The crucial factor for estimation of fuel consumption and emissions is the driving cycle. The suggested method is to generate microtrips, which calculated from real world driving cycle data and then choose the most frequent of them. The constructed driving cycle (1052 seconds) compared with some standards driving cycle including New York, federal test procedure, urban dynamometer driving schedule, united states, Japan and European driving cycle (NY, FTPCOL, UDDS, US06, JPN, and EUDC) light vehicle driving cycles. The parameters such as acceleration, deceleration, and idle percentage are presented to give an initial impression about this driving cycle. The results for these parameters for Baqubah driving cycle were 50.34%, 48.52%, and 25.61% respectively. Moreover, the average speed of Baqubah driving cycle was 21.63 km/h (13.45 mph). These results give strong encouragement considering hybrid electric vehicles for Baqubah city.

Performance Investigation of a Series-Parallel Petrol-Electric Vehicle

The energy savings potential of a 1325kg series-parallel petrol-electric vehicle was evaluated using simulated high way fuel economy test (HWFET) and urban dynamometer driving schedule (UDDS) drive cycle data. Analysis showed that, the average percentage reduction in fuel compared to the most efficient conventional car is approximately 60% combining both cycles. This paper also shows that, the regenerative braking system recovers at least 1% of the energy loss associated with the internal combustion engine in every 1.789km distance of the UDDS drive cycle and 3.756km in the highway drive cycle when compared to the conventional braking system.

On the Suitability of Electrochemical-Based Modeling for Lithium-Ion Batteries

Modeling of lithium-ion cells is a key task in the development of a battery management system to achieve battery pack safety and reliable operation. Electrochemical-based approaches enable modeling of internal electrochemical processes within the lithium-ion cell. Several reduced-order electrochemical-based modeling approaches amenable for online battery state estimation are reviewed in this paper. In particular, aging effects such as the solid-electrolyte interface layer growth is modeled. The single-particle-model (SPM) method is extended using the following novelties: 1) numerical solution of the diffusion equation in the solid phase; 2) sensitivity on the numerical solution accuracy considering the number of shell partitions; 3) a new parameterization method that identifies pertinent parameters; 4) state-of-charge and state-of-health estimation algorithms based on hybrid SPM (HSPM); and 5) validations of SPM- and HSPM-based estimation algorithms using drive cycle data.

Battery State-of-Power Peak Current Calculation and Verification Using an Asymmetric Parameter Equivalent Circuit Model

In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is presented for use with battery state estimation (BSE) algorithm development; particular focus is given to state-of-power (SOP) or peak power availability reporting. A practical optimization-based method is presented for model parameterization fitting. Two novel model-based SOP algorithms are proposed to improve voltage-limit-based power output accuracy in larger time intervals. The first approach considers first-order extrapolation of resistor values and open-circuit voltage (OCV) based on the instantaneous equivalent circuit model parameters of the cell. The second proposed approach, which is referred to as multistep model predictive iterative (MMPI) method, incorporates the cell model in a model predictive fashion. Finally, a SOP verification methodology is presented that incorporates drive cycle data to realistically excite the battery model. Simulation results compare the proposed SOP algorithms to conventional approaches, where it is shown that higher accuracy can be achieved for larger time horizons.

State‐of‐charge and state‐of‐health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries

Pragmatic approaches are proposed to enhance battery state estimation using Kalman filter (KF) and extended KF. Notable novelties introduced include: the use of state/parameter constraints, asymmetric equivalent circuit model behaviour, inclusion of nominal models, and current sensor measurement bias estimation and compensation. The so-called delta parameters are estimated to handle cell variations, aging, and online deviation of parameters. Strategic simplifications that enable the use of traditional KF algorithm are described. Unique filter structures are presented for state-of-charge and state-of-health estimation, the latter focuses on capacity and impedance estimation. The performance of the proposed approaches is demonstrated on experimental drive-cycle data designed for electric vehicle (EV) and hybrid EV applications.

A bi-level optimisation framework for electric vehicle fleet charging management

The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system.

A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System

One of the major challenges in a battery/ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This paper presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multiobjective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc–dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle data sets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NNs). The proposed online intelligent energy management controller is applied to a midsize electric vehicles (EV). A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy.

Method for Dimensioning Battery and Thermal Management Systems for Heavy-Duty Vehicle Applications Using Aged Battery Experimental Data and Advanced Modelling Techniques

When a battery degrades, capacity and power capability fade while losses in battery cells increase. Despite from fading capacity, an electric vehicle battery needs to deliver designed performance until battery end-of-life. To dimension sufficient margin for battery power capability and thermal management, comprehension of battery performance degradation is required. This paper proposes battery cell and module level laboratory testing combined with advanced modelling techniques to obtain design specifications from aged batteries. Extensive laboratory cell aging and characterization tests are performed to parametrise customised Simulink based dynamic electrical battery models with aged battery data at different degradation levels. Furthermore, a finite element thermal model implemented with COMSOL Multiphysics is used to model heat transfer in battery pack structure and thermal management system. This model is validated using experimental data from a prototype battery module. The Simulink model is used to check and validate electrical performance using measured drive cycle data and to provide data on battery losses as an input for the COMSOL model. The latter model is then used to check the performance of a thermal management system. This paper provides an example of this design approach by dimensioning a battery for an opportunity charged electric city bus. The dimensioning is made by using real drive cycle and energy consumption data from an electric bus operating in the city of Espoo, Finland. The chosen cells are then characterized and aged to near end-of-life. Simulations on electrical and thermal performance are then conducted using Simulink and COMSOL with parametrisations representing fresh and aged cells at different levels of degradation. These results are then compared with each other. This gives an indication on how much margin should be designed for power capability and thermal management and tells the performance based end-of-life criteria for the battery system. The results are discussed and recommendations made on battery dimensioning using specifications of aged batteries.

Simulated Real-World Energy Impacts of a Thermally Sensitive Powertrain Considering Viscous Losses and Enrichment

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.