Highway Fuel Economy Test Research Articles

Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Experimental study and performance evaluation of R744 thermal management systems with enhanced parallel cooling for electric vehicles

As worldwide environmental concerns grow, R744 (CO2) has become one of the preferred refrigerants in electric vehicle (EV) thermal management system due to its environmentally friendly characteristics. This study presents the development and evaluation of an Enhanced Parallel Cooling (EPC) system, a novel advancement designed to optimize the performance of R744 thermal management systems in EVs. A series of experiments are carried out to investigate the effects of key operating variables such as the coolant flow rate, and air velocity on the system efficiency. Meanwhile, Response Surface Methodology (RSM) is employed to determine the optimal control contour map under various environmental conditions. The maximum cooling capacity of the EPC system in supercharging mode is experimentally evaluated. Furthermore, a comparative analysis of driving range improvement under the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET) cycles is carried out. With the help of the Series Cooling Unit (SCU) in the EPC system, the maximum cooling capacity increases by 27.8 % to 9.89 kW, and the driving range increases by up to 14.6 km. The findings underscore the potential of the innovative R744 thermal management system architecture to improve operational efficiency and extend the driving range of EVs.

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

Research and evaluation of the arrangement of heat exchangers in electric vehicle thermal management systems for CO2 refrigerant

The application of transcritical CO2 cycle in the thermal management system of electric vehicles has become increasingly prevalent, but the existing thermal management system architecture has not been optimized to specifically enhance the performance of CO2 refrigerant. This paper analyzes the impact of various heat exchanger arrangements on CO2 thermal management system and evaluates system configurations based on a new performance index, cycle coefficient of performance. The effects of air temperature, ambient temperature, and heat load on system performance are conducted with a high precision model. The chiller is connected in parallel-series with indoor heat exchangers, resulting in a maximum coefficient of performance improvement of 27.89% at an indoor air inlet temperature of 10 °C, and a maximum coefficient of performance improvement of 21.69% at an ambient temperature of 5 °C, under simultaneous heating conditions. The radiator is placed on the windward of gas cooler, leading to a maximum coefficient of performance increase of 4.85% in heating mode, but a maximum coefficient of performance decrease of 9.84% in cooling mode. The evaluation of these configurations across different operating cycles reveals that the parallel-series connection is well-suited for the new European driving cycle, showing an 18.4% improvement in cycle coefficient of performance compared to the series-parallel connection. The windward arrangement of radiator proves effective for highway fuel economy test cycle in winter and the new European driving cycle in summer. This study provides effective instruction for configuration design and evaluation of transcritical CO2 vehicle thermal management systems.

A novel hybrid liquid-cooled battery thermal management system for electric vehicles in highway fuel-economy condition

A hybrid liquid cooling system that contains both direct and indirect liquid cooling methods is numerically investigated to enhance the thermal efficiency of a 21700-format lithium-ion battery pack during the discharge operation. One of the most significant challenges that liquid-based direct cooling systems face is the filling of the heat capacity of the coolant during the cooling process, hindering the effective dissipation of heat generated from batteries. This paper aims to introduce a battery thermal management system to address this issue. In the present study, the influences of immersion and flow cooling methods using HFE-7100 and water (as working fluids), inflow velocities of cooling channel and tubes, parallel and counter flow, battery cell space, and highway fuel-economy test on the temperature distribution of batteries were studied comprehensively. Results highlight that immersion cooling with water outperforms due to the lower thermal conductivity of HFE-7100. In addition, Flow cooling significantly reduces the battery pack's highest temperature and non-uniformity compared to immersion. According to the numerical results, using cooling tubes as an indirect cooling system integrated with the direct flow cooling method can remarkably improve the thermal efficiency of the battery pack. Moreover, results exhibited that counterflow between cooling tubes/channels can keep the highest temperature and temperature variance of Li-ion battery package below 32.15 °C and 3.04 °C, respectively. Varying battery cell spaces shows that a 14 mm space reduces the battery package's highest temperature by 1.54 °C compared to a 10 mm space. Finally, highway fuel-economy condition is applied to the simulations of this study, illustrating the effect of an innovative hybrid battery thermal management system for a 21700-type Li-ion battery pack.

Energy Management Strategy Based on Model Predictive Control‐Differential Evolution for Hybrid Energy Storage System in Electric Vehicles

This paper addresses challenges related to the short service life and low efficiency of hybrid energy storage systems. A semiactive hybrid energy storage system with an ultracapacitor and a direct current (DC) bus directly connected in parallel is constructed first, and then related models are established for the lithium‐ion battery, system loss, and DC bus. Based on the multiobjective evaluation function, a hybrid energy storage system Model Predictive Control‐Differential Evolution (MPC‐DE) energy management method is proposed. Experiments were conducted under China Light‐Duty Vehicle Test Cycle‐Passenger Car (CLTC‐P) and Highway Fuel Economy Test (HWFET) driving cycles. The experimental results show that compared with other methods, this strategy can effectively reduce the loss of lithium‐ion battery and ultracapacitor hybrid energy storage system and improve the system efficiency.

Driving Characteristics Analysis Method Based on Real-World Driving Data

All vehicles available for sale undergo certification tests mandated by law, and their certified fuel efficiency from these tests is indicated and disclosed. However, to address discrepancies between the certified fuel efficiency and actual driving conditions, more sophisticated certified modes, post-certification systems, and real-world driving tests have been introduced to complement the fuel efficiency certification system. The incorporation of a real-world driving mode, similar to certification modes, in vehicle research and development is of paramount importance from a directional perspective. To achieve this, various methods of configuring real-world driving modes have been proposed and applied in research. However, the validation of the correlation between fuel efficiency in real-world driving modes and certified driving modes is relatively limited. This study aimed to construct real-road driving cycles that are similar to the federal test procedure (FTP-75) and highway fuel economy test (HWFET) certification modes to determine the differences between the driving characteristics of the certification modes and real-road driving. The present study employed the commonly used relative positive acceleration analysis method along with a segmented short-trip technique for analysis. Additionally, variables such as fuel consumption and driving time were utilized to diversify our analytical methods. Test Routes A and B were configured for comparison with the fuel efficiency certification mode. An analysis of the relative positive acceleration (RPA) in both the test routes and certification mode confirmed that Test Route B, reflecting the actual driving patterns, closely follows the RPA distribution of the certification mode. Additionally, the fuel consumption for each short trip on each route was graphically represented, addressing aspects where determining the driving characteristics based solely on the RPA, such as the road grade, was difficult.

High-Accuracy Battery State of Charge Estimation Strategy Based on Deep Belief Network Cascaded With Extended Kalman Filter

Abstract Battery state of charge (SOC) estimation is one of the main functions of the battery management system in electric vehicles. If the actual SOC of the battery differs significantly from the estimated value, it can lead to improper battery usage, resulting in unexpected rapid voltage drops or increases, which can affect driving safety. Therefore, high-accuracy SOC estimation is of great importance for battery management and usage. Currently used SOC estimation methods suffer from issues such as strong dependence on model parameters, error propagation from measurements, and sensitivity to initial values. In this study, we propose a high-precision SOC estimation strategy based on deep belief network (DBN) feature extraction and extended Kalman filter (EKF) for smooth output. The proposed strategy has been rigorously tested under different temperature conditions using the dynamic stress test (DST) and urban dynamometer driving schedule (US06) driving cycles. The mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed strategy are controlled within 1.1% and 1.2%, respectively. This demonstrates the high-precision estimation achieved. To further validate the generality of this strategy, we also apply it to graphene batteries and conduct tests under US06 and highway fuel economy test (HWFET) driving cycles at temperatures of 25 °C and −10 °C. The test results show MAE of 0.47% and 2.01%, respectively.

Battery state-of-charge estimation using data-driven Gaussian process Kalman filters

This paper proposes methods of battery state-of-charge (SoC) estimation using Gaussian processes (GPs): GP-Unscented Kalman filter (GP-UKF) and GP-Particle filter (GP-PF). Based on Bayesian filtering frameworks, both GP-UKF and GP-PF use data-driven battery GP prediction and observation models trained via machine learning. In contrast to the classical GP-based UKF, our implementation of GP-UKF incorporates a straightforward technique of calculating sigma points of predicted voltage. In addition, the noise covariance matrices are automatically adjusted in the proposed GP-UKF using the GP prediction and observation models. In comparison with the existing GP-based PF, our implementation of GP-PF involves calculating the initial samples and weights of the previous SoC and estimating the SoC mean and variance from the samples and weights of the SoC posterior distribution. Furthermore, the likelihood function of the present voltage is automatically adjusted in the proposed GP-PF using the GP prediction and observation models. The SoC estimation performances of the proposed methods were evaluated via electric vehicle battery charging and discharging simulations according to the urban dynamometer driving schedule speed (UDDS) and highway fuel economy test (HWFET), in which the proposed GP-UKF achieved 60.13% and 35.17% increases in the estimation accuracy, and 88.53% and 79.92% decreases in the estimation uncertainty compared with a classical UKF, respectively. In addition, the proposed GP-PF achieved 47.72% and 58.97% increases in the estimation accuracy, and 99.27% and 97.16% decreases in the estimation uncertainty compared with a classical PF, respectively in the same UDDS and HWFET EV simulations.

Design of Two Fuel Cell Buses for Public Transport According to Two Different Operating Scenarios: Urban and Motorway

<div>The transport sector is one of the major parties responsible for carbon dioxide (CO<sub>2</sub>) and pollutants emissions in Europe. For this reason, one of the main commitments of the European Commission is its decarbonization by 2035/2040. To achieve this target, during the last decades, different propulsion technologies were developed such as hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs), and battery electric vehicles (BEV). The first two proposals can be considered as bridging technology between the internal combustion engine (ICE) and the BEV because they offer at the same time comparable performance as conventional powertrains and improved efficiency. However, both technologies are struggling with the tightening of pollutants and CO<sub>2</sub> limits. On the other hand, the BEV can offer zero emissions at the tailpipe, but it suffers from limited range capabilities and the lack of fast-charging infrastructures. Within this context, the fuel cell vehicle (FCV) appears as an interesting opportunity because it offers zero tailpipe emissions and equivalent refuelling time of the ICE. This article evaluates through mathematical simulations the performance of two fuel cell electric buses (FCEBs), which are supposed to work respectively in urban and highway driving conditions. The urban bus is equipped with a single fuel cell (FC) module of 85 kW-Net and an electric motor (EM) of 225 kW. The intercity bus is equipped with two FC modules with a total power of 170 kW-Net and two EMs of 225 kW each. A sensitivity to the battery capacity from 20 kWh to 40 kWh was performed for both FECBs. The power split between the FC module and the high-voltage battery was optimized with the Equivalent Consumption Minimization Strategy (ECMS). The two FCEBs were tested considering different portfolios of cycles: in the case of the urban bus in Braunschweig and the Standardized On-Road Test Cycles SORT1 and SORT2 were assumed as a reference, while cycles like the Highway Fuel Economy Test (HWFET), European Transient Cycle (ETC), and cruising at 100 km/h were assumed as reference for the intercity. Simulation results highlighted that the increase of battery capacity in the case of the urban bus from 20 kWh to 30 kWh reduces hydrogen (H<sub>2</sub>) consumption by 11% along the Braunschweig cycle. On the other hand, in the case of the intercity bus, the fuel consumption is less affected by the increase of capacity in the same range. In this case a reduction of 4.7% is estimated for the HWFET cycle, and it is less than 1% in the case of cruising conditions.</div>

A refrigerant-injection heat pump-based efficient integrated thermal management system for electric vehicles approaching the wide temperature range in China

The integrated thermal management system of electric vehicles is responsible for the temperature regulation of the cabin and electrical system. The wide temperature range of vehicles specifies the operating conditions required for different regions, which is −33 – 39 °C in China. Through experimental studies conducted on heat pump systems and thermal analyses of electrical systems, this study innovatively developed an efficient integrated thermal management system based on a refrigerant-injection heat pump. In addition, a simulation platform for the system was established using the advanced modeling environment for performing simulation of engineering systems (AMESim) software to evaluate the system performance. The results showed that the stability and efficiency of battery cooling based on the intermediate heat exchanger were better than those obtained with a dual evaporator setup. The proposed solution could reduce energy consumption by 30% at an ambient temperature of 35 ℃. The thermal management system with the heat recovery for the motor and electric control at high temperatures could reduce the energy consumption by 11.98% – 56.69% and meet the heating condition of −22.04 ℃. The integrated thermal management system based on a refrigerant injection heat pump widened the operating range during the highway fuel economy test (EPA-420-B-12-001). Aided by electric heating, the simulation system developed in this study could satisfy the load requirements for the wide temperature range in China.

Repercussion of effect of different drive cycles on evaluation of electrical consumption for electric four‐wheeler to achieve optimal performance of electric vehicle

AbstractElectric vehicle (EV) is at borne stage and facing lot of challenges at design and development stage. The performance of EV is solely depending on distance travelled by vehicle means range of vehicle and electrical consumed by the vehicle. These two performance parameters affects on sizing of EV component. The electric motor and electric battery are power and electrical source of the vehicle. For sizing, this component electrical consumption (EC) per kilometer (km) plays major role. The correct estimation of EC per km is very much necessary at the design stage for sizing of components. Drive cycles play major role for accurate estimation of EC per km. Many drive cycles (Indian drive cycle [IDC], modified Indian drive cycle [MIDC], urban drive cycle [UDC], worldwide harmonized light vehicles test procedure [WLTP], new European drive cycle [NEDC], and Artemis drive cycle) are currently used by many countries to predict the performance of vehicle. Various standard cycles are studies the effect of drive cycle on energy consumption of an electric four‐wheeler through analytical methods which results the optimal performance of vehicle. Current study investigates the analytical approach for accurate prediction of energy consumption per km to enhance the range of EV, which is major concern of EV buyers. Present work compares the range and electric consumption of electric four‐wheeler by using NEDC, highway fuel economy test cycle (HWFET), UDC, WLTP, and MIDC drive cycle. The range obtained from NEDC, HWFET, UDC, WLTP, and MIDC drive cycles are 114.20, 115.18, 78.16, 103.33, and 93.73 km, respectively. HWFET drive cycle provides higher than other drive cycle due to better road condition. Urban drive cycle provides low range of 64 km because of road condition.

Comparison of On-Road Highway Fuel Economy and All-Electric Range to Label Values: Are the Current Label Procedures Appropriate for Battery Electric Vehicles?

<div class="section abstract"><div class="htmlview paragraph">As consumers transition from internal combustion engine (ICE)-powered vehicles to battery electric vehicles (BEV), they will expect the same fuel economy label-to-on-road correlation. Current labeling procedures for BEVs allow a 0.7 or higher multiplier to be applied to the unadjusted fuel economy and range values. For ICE-powered vehicles, the adjustment factor decreases with increasing unadjusted fuel economy and can be lower than 0.7. To better inform consumers, starting in 2016, Car and Driver added an on-road highway fuel-economy test, conducted at 120 kph (75 mph), that augments the performance metrics that it's been measuring since the 1950s. For electric vehicles, testing includes an evaluation of the all-electric range.</div><div class="htmlview paragraph">The on-road test results were aligned with the certification information for each vehicle model including unadjusted and label fuel economy and range, road load force coefficients, and labeling options. Tractive energy and kinetic energy available for regenerative braking were computed from the certification information to evaluate the differences between the on-road testing and chassis-rolls testing conducted during the certification process.</div><div class="htmlview paragraph">Based on these results, the highway fuel economy label tends to be a very good predictor of the fuel economy observed during the on-road test at real highway speeds with vehicles powered by an ICE. However, most BEVs tested to date fall short of both their electric consumption and range label values. For BEVs, the difference between the label and on-road consumption and range is further exacerbated by other factors, such as extreme temperatures and suggestions by automakers to charge to less than 100 percent to extend battery life. Consequently, these results support the need to re-evaluate the labeling procedures for this emerging technology as it continues to become increasingly prominent in the marketplace.</div></div>

Energetic and Exergetic Performance Investigation of Different Topologies for Hybrid Fuel Cell Vehicles

The use of green energy has increased day by day. Environmentally friendly hybrid electric vehicles with low CO2 emissions have gained public attention. However, most battery electric vehicles are still having range problems, and emission values of hybrid electric vehicles are still not at the desired levels. Thus, fuel cell vehicles have gained some attention as good alternatives. The primary energy source for these vehicles is fuel cells, which are used in conjunction with batteries and supercapacitors to increase system performance. The combination of fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor systems are currently the most popular topologies in this technology. The performance of these topologies is related to the overall energy efficiency and exergy efficiency of the vehicle model. Energy and exergy are two basic terminologies used to determine system performance and quality. Exergy determines the thermodynamic losses that cannot be determined using energy formulations alone. Thereby, it is very important to use both terminologies together to examine the performance of topologies and to determine any system losses. For this purpose, in this study, fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor topologies were prepared and applied for Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test Cycle (HWFET), New European Driving Cycle (NEDC), and Federal Test Procedure (FTP) driving cycles. Comparisons of these topologies in fuel consumption, power performance, and energy and exergy efficiencies were performed for the driving cycles. Also, energy flow, during the driving cycle, has showed and interpreted for the fuel cell vehicle that is designed and analyzed.

Analysis of Engine and Powertrain Losses of a Passenger Type 4-Stroke Gasoline Vehicle in 4 Different Driving Cycles with GT-Suite Vehicle Simulation Program

Vehicle simulations have a very important place in the automotive R&D process. These simulations provide automotive companies with the benefits of saving money on model creation and the user changing many parameters throughout the vehicles as soon as possible. In this study, the engine and powertrain energy losses of a 4-stroke gasoline passenger car were investigated in FTP75 (Federal Test Procedure), HFET (Highway Fuel Economy Test), US06 and WLTP (Worldwide Harmonized Light Vehicles Test Cycles) cycles, in urban and extra-urban conditions. examined physically and by testing with the help of GT-Suite vehicle simulation program. Ac-cording to the analysis results, the lowest CO2 emission (127.58 g/km) and average fuel consumption (5.29 l/100km) were obtained in the HFET driving cycle. While the transmission and gear friction values were close to each other between the driving cy-cles, the lowest loss in terms of torque converter losses was obtained in the HFET driving cycle (0.2%).

Design of Stator Winding Turn Number of Tap-Change PMSM for EVs According to Driving Cycles

Driving cycles are commonly used to evaluate the fuel efficiency and mileage of internal combustion engine vehicles and electric vehicles (EVs). To improve the mileage of EVs, efficient propulsion motors and inverters must be employed, particularly at frequently used speed and torque points of driving cycles. In this study, the number of the stator winding turns of a tap-change permanent magnet synchronous motor (PMSM) used for EV propulsion was investigated to increase the motor and inverter energy efficiencies under an urban dynamometer driving schedule (UDDS), a highway fuel economy test (HWFET), and combined UDDS/HWFET driving cycles. The motor core loss due to the carrier harmonics, copper loss, and inverter loss were analyzed for various winding turn number combinations, and the most efficient combination was selected. Based on the analysis results, the tap-change motor winding turns that exhibited the least loss were rewound on a PMSM of a mass-produced EV. The demand energy of the proposed tap-change PMSM and that of a mass-produced PMSM in the UDDS, HWFET, and combined UDDS/HWFET driving cycles were compared through experimentation. The proposed PMSM exhibited energy improvements of 1.50%, 1.84%, and 1.65%, respectively.

A hybrid transient CFD-thermoelectric numerical model for automobile thermoelectric generator systems

Dynamic performance prediction of the automobile thermoelectric generator system is one of the research hotspots in the field of thermoelectric technology. In this work, a hybrid transient CFD-thermoelectric numerical model is proposed for the first time to predict the dynamic response characteristics of an automobile thermoelectric generator system. Taking the exhaust gas of a heavy truck under a highway fuel economy test driving cycle as the transient heat source, the transient numerical study on the automobile thermoelectric generator system is carried out. It is found that the dynamic output power of the automobile thermoelectric generator system changes more smoothly than exhaust temperature due to the effect of thermal inertia, while the conversion efficiency fluctuates greatly. The transient output power at t = 467 s and transient conversion efficiency at t = 635 s reach the highest values of 45.16 W and 39.68 %, respectively. Within the period of 765 s, the total power generation and average conversion efficiency of the automobile thermoelectric generator system are 26460 J and 3.29 %, respectively. Through the transient experimental validation, the average error of transient output voltage between experimental and model results is 6.43 %. This work fills the gap in the dynamic performance prediction of thermoelectric devices used for fluid waste heat recovery. The findings are helpful in better understanding the dynamic response characteristics of the automobile thermoelectric generator system.

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

Due to rapid rise in climate change, world is seeing a major technology shift from conventional vehicles to electric vehicles (EVs). Battery’s state of charge (SOC) is a critical parameter in EVs whose accuracy affects not only the battery management system but can also influence the driving range estimation for an EV. In this paper, a simulation-based hybrid technique is presented, that combines both adaptive extended Kalman filter (AEKF) and artificial neural network (ANN) for SOC estimation of lithium-ion battery. The proposed hybrid technique is validated using five different EV driving cycles, that is, LA92, US06, urban dynamometer driving schedule (UDDS), highway fuel economy test (HWFET) and a mixed driving cycle at four different temperature values, that is, 0°C, 10°C, 25°C and 40°C. To facilitate the potential EV users, a real-time SOC estimator is also implemented. The real-time SOC estimated through open circuit voltage method and Coulomb counting method is further used for reservation of a charging slot in a nearby charging station. Moreover, to monitor the charging status of a potential EV from a remote location, a cloud-based Internet of things (IoT) platform, that is, ThingSpeak, is used. This results into the reduction of waiting time for an EV user at a charging station. In addition to this, a comparative analysis is carried out between proposed hybrid method and existing methods. It is demonstrated that our hybrid technique achieves lower root mean square error and higher accuracy as compared to existing methods at different operating conditions.

Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress σ on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS).