Plug-in Hybrid Electric Vehicle Battery Research Articles

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

Energy management optimization in smart railway stations with the ability to charge plug-in hybrid electric vehicles

The increasing development of traffic in the world has led to an increase in the consumption of electrical energy in electric vehicles (EVs). One of the most widely used electric vehicles is the electric train. Thus, energy optimization becomes more crucial in electrical railways to reduce consumption and total electricity bill as well as environmental issues. Consequently, electrical railway energy management must be technically and economically efficient and effective. This paper proposes an energy efficiency optimization framework for intelligent railway stations that provide plug-in electric vehicle charging parking capacity use, renewable energy sources (REs), and regenerative braking energy (RBE). The structure of the proposed convex programming (CP) model is used for fast and efficient optimization of decision variables, Equipment size, and the cost function of the station for reducing the power purchased from the Grid. Furthermore, by presenting meta-heuristic algorithms, the state variables of the energy storage system (ESS) and Plug-in hybrid electric vehicles (PHEVs) battery have been optimized. The results obtained from the proposed method prove that the operation cost of the station decreases by 61.4 %.

Degradation Evaluation of Lithium-Ion Batteries in Plug-In Hybrid Electric Vehicles: An Empirical Calibration

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system.

A cost of ownership analysis of batteries in all-electric and plug-in hybrid vehicles

The ever-faster transformation of road vehicles from traditional fuel engines to electric motors, is leading to increasingly widespread research on and development of electric vehicles and related infrastructures. In this context, this article addresses the cost aspect of batteries from the owner’s perspective. Specifically, it proposes an analysis of the optimal usage cost of batteries in order to maximize the benefit-cost ratio and battery replacement intervals. In order to analyze battery degradation, various tests were utilized for both a full-battery electric vehicle (BEV) and a plug-in hybrid electric vehicle (PHEV). The results demonstrate greater wear of the PHEV battery when the vehicle is under charge-sustaining mode, that is, when using the combustion engine, while driving with frequent starts and stops. On the other hand, the degradation costs of the BEV battery are generally close to optimal in every scenario, in which the main parameter affecting battery wear is average daily mileage.

(Digital Presentation) Recycled Cathode Materials Enabled Superior Performance for Lithium-Ion Batteries

With the widely equipped Lithium-ion batteries (LIBs) in electronics and electric vehicles, proper handle spent LIBs has been the subject of increasing concern. Significantly raised concerns about resource constraints and environmental issues are brought by spent LIBs. Therefore, properly handling spent LIBs is urgent and necessary.[1] However, until now, getting manufacturers to recruit recycled materials has been a hard sell because recycled materials are deemed as inferior to commercial materials, which limits the development of recycling. Although previous publications stated that their recovered materials had a comparable performance as commercial materials, the results, based on coin cells and low electrode loading, cannot convince manufacturers to employ recycled materials in the new LIBs.[2] Here, we demonstrate that recycled cathode materials with optimized microstructure have the best industrial relevant testing results (up to 11Ah cells) so far and compare them with state-of-the-art commercial equivalent. Interestingly, the recycled materials not only pass all the aggressive industrial plug-in hybrid electric vehicle (PHEV) battery tests, but also outperform control counterparts in some tests. Specifically, 1 Ah cells with the recycled LiNi1/3Mn1/3Co1/3O2 have the best cycle life result reported for recycled materials and enable 4,200 cycles and 11,600 cycles at 80% and 70% capacity retention, which is 33% and 53% better than the state-of-the-art, commercial LiNi1/3Mn1/3Co1/3O2. Meanwhile, its rate performance is 88.6% better than commercial powders at 5C. Through detailed experimental and modeling analysis of pristine and cycled materials, we discover that the unique porous and larger inside void microstructure enables the superior rate and cycle performance and less phase transformation. Compared with the control sample, the surface area of the recycled LiNi1/3Mn1/3Co1/3O2 is 82.14% larger and the cumulative pore volume is 61.25% larger. Even some recycled particles have an outer diameter of the void space equal to 40% to 60% of the particle diameter. The unique microstructure can reduce 16% hoop stress during the discharge/charge process compared to control materials, and improve the lithium chemical diffusion coefficient, enabling the superior performance of cycle life and rate performance and less phase transformation. The results pave the way to re-introduce recycled materials into new batteries.[3] [1] M. Chen, X. Ma, B. Chen, R. Arsenault, P. Karlson, N. Simon, Y. Wang, Recycling End-of-Life Electric Vehicle Lithium-Ion Batteries, Joule 2019, 3, 2622.10.1016/j.joule.2019.09.014[2] X. Ma, L. Azhari, Y. Wang, Li-ion battery recycling challenges, Chem 2021.10.1016/j.chempr.2021.09.013[3] X. Ma, M. Chen, Z. Zheng, D. Bullen, J. Wang, C. Harrison, E. Gratz, Y. Lin, Z. Yang, Y. Zhang, F. Wang, D. Robertson, S.-B. Son, I. Bloom, J. Wen, M. Ge, X. Xiao, W.-K. Lee, M. Tang, Q. Wang, J. Fu, Y. Zhang, B. C. Sousa, R. Arsenault, P. Karlson, N. Simon, Y. Wang, Recycled cathode materials enabled superior performance for lithium-ion batteries, Joule 2021.10.1016/j.joule.2021.09.005

A Review on Aging-Aware System Simulation for Plug-In Hybrids

The lithium-ion battery is a vital powertrain component in plug-in hybrid electric vehicles (PHEVs). The fuel reduction potential and cost-effectiveness of these vehicles depend on the sizing of the powertrain components and their utilization, which is defined by the energy management system (EMS). The battery is affected by power and capacity reduction over the lifetime of the vehicle, which needs to be considered during the design process to ensure the performance goals throughout the vehicle’s lifetime. Current literature regarding battery aging usually contains experimental results, which are not transformed into a useful aging model for system simulations. Consequently, battery aging is often neglected, which is why this article intends to help researchers understand the degradation process of batteries in PHEVs and consider this in their simulation and dimensioning process. First, PHEV powertrain topologies and components are presented. Afterward, battery degradation mechanisms and recent findings are explained, followed by appropriate modeling approaches for different simulation targets. Finally, current aging-aware EMS literature is systematically reviewed, and the integration of the aging models is analyzed, so researchers in system simulation areas can improve their powertrain models.

Investigating the possibility of using second‐life batteries for grid applications

AbstractThis paper investigates the potential for use of batteries from retired plug‐in hybrid electric vehicles (PHEVs) in specific grid applications. In this study, project, a reference capacity test was performed to check the state of health, state of charge (SOC) and battery durability of a PHEV battery, and the pack was then tested under duty cycles for different grid applications to determine whether it would satisfy such applications in technical terms. The duty cycles considered are peak shaving, frequency regulation, photovoltaic (PV) smoothing and renewable energy firming. The data show that the battery can satisfactorily fulfil the requirements of peak shaving applications in terms of stability and battery retention capacity, and that the requirements of the frequency regulation service are also partially met. The untracked time for the battery signal was 4% (slightly exceeding the condition set for passing the tracking test, which is 2%). The SOC and temperature were within permitted limits. The battery, however, did not achieve good performance for PV smoothing or renewable energy (RE) firming. The untracked times were 14% and 11% for PV smoothing and RE firming, respectively (greatly exceeding the 2% condition for passing the tracking test). The SOC and temperature for the PV smoothing were within acceptable limits. The pack failed to complete the RE firming cycle as the SOC reached maximum safety limits after 6 h and 23 min, whereas the duty cycle is 10 h long.

Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations

Plug-in hybrid electric vehicles (PHEVs) are commonly operated with high-voltage (HV) components due to their higher power availability compared to 48 V-systems. On the contrary, HV-powertrain components are more expensive and require additional safety measures. Additionally, the HV system can only be repaired and maintained with special equipment and protective gear, which is not available in all workshops. PHEVs based on a 48 V-system level can offer a reasonable compromise between the greenhouse gas (GHG) emission-saving potential and cost-effectiveness in small- and medium-sized electrified vehicles. In our study, the lifecycle emissions of the proposed 48 V PHEV system were compared to a conventional vehicle, 48 V HEV, and HV PHEV for individual driving use cases. To ensure a holistic evaluation, the analysis was based on measured real-driving cycles including Global Position System (GPS) map-matched slope profiles for a parallel hybrid. Optimal PHEV battery capacities were derived for the individual driving use cases. The analysis was based on lifecycle emissions for 2020 and 2030 in Europe. The impact analysis revealed that 48 V PHEVs can significantly reduce GHG emissions compared to vehicles with no charging opportunity for all use cases. Furthermore, the findings were verified for two vehicle segments and two energy mix scenarios. The 48 V PHEVs can therefore complement existing powertrain portfolios and contribute to reaching future GHG emission targets.

Improvement of PHEV Equivalent Fuel Economy and Battery Life by Applying Traffic-Based SOC Management

Improvement of plug-in hybrid electric vehicle (PHEV) equivalent fuel consumption (EFC) and battery life are significant issues in the design of PHEVs. This research aims to develop an adaptive traffic-based controller to improve fuel economy and battery life. The optimal state of charge (SOC) trajectories in real-world and standard cycles are extracted using the modified dynamic programming (MDP) approach. The optimal data is clustered based on the driving features, under the light of fuzzy clustering. Then, a control map is formed based on the clustered data to estimate SOC trajectories as a reference in an adaptive control strategy. A battery life model is employed to investigate the effect of the proposed method on battery aging. This study reveals that traffic-based SOC management simultaneously reduces the operating and maintenance costs since it improves the equivalent fuel economy and battery life in various driving cycles. Moreover, the suggested energy management strategy is implemented on an embedded system by applying the model-based design (MBD) method. The comparison of hardware-in-the-loop (HIL) and model-in-the-loop (MIL) simulation shows that the suggested strategy is implementable and effective for real applications.

An Evaluation Framework for Second-Life EV/PHEV Battery Application in Power Systems

Energy storage is essential for balancing the generation and load in power systems. Building a battery energy storage system (BESS) with retired battery packs from electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) is one possible way to subsidize the price of EV/PHEV batteries, and at the same time mitigating forecast error introduced by load and renewable energy sources in power systems. This paper proposes a detailed framework to evaluate end-of-life (EOL) EV/PHEV batteries in BESS application. The framework consists of three parts. A generalized model for battery degradation is first introduced. It is followed by modeling the battery retirement process in its first life. Two vehicle types—EV and PHEV—as well as two retirement modes—nominal and realistic modes—are considered. Finally, the application of the second-life BESS in power systems is modeled in a detailed economic dispatch (ED) problem. This is how second-life BESS’s performance translates into cost savings on power generation. An optimization problem is formulated to maximize total cost savings in power generation over the battery’s second life. This is done by striking a balance between short-term benefit (daily cost savings) and long-term benefit (cost savings through service years). Numerical results validate the effectiveness of the proposed framework/models. They show that battery usage and retirement criterion in its first life directly affect the performance in its second life application. In our case study, EV battery packs possess larger EOL energy capacities and consequently generate more cost savings in the second life. However, the BESS built from retired PHEV batteries has higher cost savings per MWh. It is because, with the proposed degradation model, battery health is better preserved in PHEV applications. Compared to nominal retirement mode, realistic retirement mode results in extra cost savings due to the reduced first-life service years.

Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles

This paper proposes and validates a stochastic prognostic model for capacity loss and remaining useful life (RUL) in lithium-ion pouch cells with graphite anodes and NMC–LMO cathodes. The model was developed using data from an experimental campaign which studied the effect of C-rate, minimum SOC, temperature, and charge-depleting usage on aging in plug-in hybrid electric vehicle (PHEV) batteries. The proposed algorithm estimates capacity loss and RUL as a function of resistance and operating conditions including charge sustaining/depleting use and temperature, and its stochastic nature is able to capture the variability of the data. The battery resistance is estimated using a particle filter developed for an experimentally validated equivalent circuit battery model. The particle filter is designed to perform combined estimation of State of Charge and internal resistance, which is used as an input to the stochastic capacity loss model. Finally, the stochastic model predicts the capacity loss with a root mean square error (RMSE) of less than 1% and RUL with an RMSE of 1.6 kAh, and can be integrated into on-board battery management systems in PHEV to monitor the health of lithium-ion batteries.

Co-Simulation Methodology for PHEV Thermal System Development

<div class="section abstract"><div class="htmlview paragraph">Thermal development of automotive applications is a lot more complex than it used to be in the past. Specifically, for Plug-in Hybrid Electric Vehicles (PHEVs), all the sub-systems are so intertwined that it’s hard to analyze them as sub-systems only. A system level solution is needed for proper sizing of components. For early thermal development, a co-simulation method can ensure that we take into account the inter-dependency of all the thermal features in the car. As for example, a large PHEV battery may need to be passively cooled by refrigerant, which is also associated with the interior Heating, Ventilation, and Air Conditioning (HVAC) cooling system. For proper sizing of the condenser, chiller etc., one has to account for the battery cooling and cabin cooling as one system. There are also many thermal actuators on a PHEV, e.g. control valves, pulse-width-module (PWM) pumps, electric compressor, electric coolant heaters etc. Smart controls and calibration development early in the product development can impact sizing of front end cooling modules and other heat exchangers significantly. The design of hardware and software has to be simultaneous and synchronous in order to devise a properly optimized thermal system. This paper is going to explain the methodology for co-simulation for a PHEV and also illustrate some simulation results.</div></div>

Thermal analysis and electromagnetic characteristics of three single-sided flux pads for wireless power transfer

Abstract By employing Wireless Power Transfer (WPT), the complexities of wire utilization are eliminated in charging electric devices, especially Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs). WPT can be used in both stationary and dynamic modes to charge battery of EVs and PHEVs. There are few magnetic structures that are used as transmitter and receiver sides of WPT to improve the efficiency. A first of its kind, this paper conducted a temperature analysis for three substantial pads including Double-D Pad (DDP), Double-D Quadrature Pad (DDQP), and Bipolar Pad (BPP). For this, the electromagnetic finite element method and finite volume computational fluid dynamics were employed simultaneously. After the two-way solution of the electromagnetic and thermal energy equations, the temperature distribution in all the components of the primary and secondary pads was obtained in the aligned and misaligned positions. In doing so, the material characteristics were considered as temperature-dependent. Finally, with DDP as a transmitter; and DDP, DDQP, and BPP as receivers, prototyping was conducted to verify the analytical results, and to validate that the procedure employed in this study is capable of being applied to conventional and innovative types of WPT magnetic structures.

Cost of automotive lithium-ion batteries operating at high upper cutoff voltages

The potential for operating automotive battery packs at high upper cutoff voltages (UCV) has been explored using preliminary data on eight cathode materials. The pack level energy density, specific energy, and battery cost are calculated using the spreadsheet tool BatPaC. The tool used experimental data for some cathode materials such as the lithiated oxides of nickel manganese cobalt (NMC), nickel cobalt aluminum (NCA), layered lithium- and manganese-rich nickel manganese cobalt (LMRNMC). The half-cell data were obtained at UCVs between 4.2 and 4.7 V vs. Li/Li+. The experimental data showed LMRNMC with the highest lithiation capacity gain, increasing from 176 mAh·g−1 at 4.2 V to 260 mAh·g−1 at 4.7 V; this advantage is partly offset by its lower average voltage. Assuming optimized cell materials/design and an area-specific impedance of 12 Ω⋅cm2 for all the materials, the BatPaC results indicate that the specific energies or energy densities of the battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) battery packs with the LMRNMC and NMC cathodes can exceed 180 (BEV) and 160 (PHEV) Wh·kg−1 at UCV>4.6 V vs. Li/Li+. The costs of these battery packs are lowest at UCV = 4.7 V (vs. Li/Li+); estimated at 135–145 and 210–220 $·kWh−1 for BEV and PHEV packs, respectively.

Objective functions for plug-in hybrid electric vehicle battery range optimization and possible effects on the vehicle fleet

This study analyzes how, in a possible electrification of the car fleet through plug-in hybrid electric vehicles (PHEV), the choice of objective function, which potentially reflects different stakeholders’ interests, may influence the resulting optimal PHEV battery range, the PHEV share in the vehicle fleet, the fleet total cost of ownership (TCO) savings, and the fleet electric drive fraction under various economic conditions and policy options.The optimal battery range can differ considerably among objective functions, especially between the objectives of maximizing the number of PHEVs and maximizing driving on electricity. Increased viability of the PHEV, for instance, through lower battery costs, higher running cost savings, or PHEV-promoting subsidies, will strengthen this effect. Therefore, a high share of viable PHEVs in the vehicle fleet does not necessarily result in a high share of electric driving. When designing policies to promote PHEVs, both the short- and long-term policy objectives and their potential effects need to be considered explicitly.

Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

Mass roll-out of plug-in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi-zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real-time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm-based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.

Model predictive control of plug‐in hybrid electric vehicles for frequency regulation in a smart grid

Integration between energy storage systems and renewable energy sources (RESs) can effectively smooth natural fluctuations of the latter and ensure better frequency regulation. Optimal performance of the plug-in hybrid electric vehicle (PEHV) battery, having longer plug-in than driving time, makes it a good candidate for integration with RESs. Decentralised model predictive control (MPC) is proposed here for frequency regulation in a smart three-area interconnected power system comprising PHEVs. Two MPCs in each area are considered to manipulate the input signals of the governor and PHEV in order to tolerate frequency perturbations subject to load disturbances and RES fluctuations. Setting the parameters of the six MPC controllers is carried out simultaneously based on imperialist competitive algorithm (ICA) and bat-inspired algorithm (BIA). Time-domain based objective function is suggested to account for system non-linearities emanating from governor dead bands and turbine generation rate constraints. The proposed tuning procedures utilising ICA and BIA are completely accomplished off-line. Comparative simulation results are presented to confirm the effectiveness of the proposed design.

Lifetime Test Design for Second-Use Electric Vehicle Batteries in Residential Applications

This paper provides a comprehensive study on the lifetime test design of second-use electric vehicle plug-in hybrid electric vehicle ((PHEV)/hybrid electric vehicle(HEV)/battery electric vehicle(BEV)) batteries as the residential application. The work starts by modeling a realistic operation environment for the second-use batteries. As a residential application, the battery pack usage profile was simulated in detail with proposed steps and parameters. The paper then presents design procedures for transferring simulated battery pack usage profile into a lifetime test profile. Finally, a lifetime test case study is performed on a second-use PHEV (Ford C-max) battery pack. The extraction of the residential application usage profile, the configuration of the test platform, and the 150-h demonstration test results together explain the proposed test methodology.