Small Battery Research Articles

Benefits of battery hybridization in hydraulic turbines. Wear and tear evaluation in a Kaplan prototype

Kaplan turbines are nowadays used to provide Frequency Containment Reserve (FCR) to the grid due to their fast capacity to regulate their power maintaining high efficiency. However, this continuous power regulation increases the wear and tear of the regulation system considerably. To reduce the amount of movements in the regulation servomotors, and thus their wear and tear, a new technology is being investigated within the frame of the European project XFLEX Hydro. This new technology is based on hybridizing the hydro unit with a small size battery in parallel, this one being in charge of compensating the small frequency fluctuations in the grid by providing or absorbing power. In this paper, the benefits of the implementation of this new technology are evaluated. A Kaplan turbine prototype located in Vogelgrun, France, has been hybridized and different parameters have been monitored while the unit was working in hybrid mode and in normal standalone hydro mode. Wear and tear of the regulation system have been compared for both hybrid and standalone hydro modes. A reduction of about 25% in servomotors mileage and of 50% in fatigue damage have been obtained by hybridizing the unit.

Performance Assessment of PV-SH-BES System With Mode Transfer Capabilities

This article presents the performance evaluation of the solar photovoltaic small hydro battery based energy storage (BES) system having automatic mode switching capability. A BES allows the microgrid to operate in constant power mode depending upon the maximum power of a battery. Moreover, a block sparse proportionate least mean M-estimate algorithm is utilized for the system to operate in the variable power mode of the utility grid. A dc offset rejection loop is included to eliminate the offset from the load currents. A synchronization unit is employed that signals the static transfer switch to transfer between utility-interactive mode (UIM) and standalone mode (SM). The estimation of frequency is required for the system to synchronize with the utility grid, which is accomplished using a frequency adaptive filter. A comprehensive description of the system, in various operating modes, is given, i.e., SM, UIM, transition from SM to UIM, and transition from UIM to SM. Besides, the microgrid is validated with a hardware prototype and the synchronization is achieved by complying with the standards of voltage, phase angle difference, and frequency as mentioned in the IEEE 1547-2018 standard. The power quality index of the grid and hydro generator attributes in the utility mode and under load unbalance in the SM is provided, which complies with the IEEE 519 standard. Comparative performance is provided to depict the robustness of the control algorithm.

Impact of Optimal Sizing of Wind Turbine and Battery Energy Storage for a Grid-Connected Household With/Without an Electric Vehicle

This article determines the optimal capacities of small wind turbine (SWT) and battery energy storage (BES) for a grid-connected household (GCH) with or without an electric vehicle (EV) to minimize the overall cost of electricity (COE). Rule-based home energy management systems (HEMSs) are developed for two different configurations of the GCH: 1) with only SWT and 2) with SWT and BES. For each configuration, the HEMSs are developed for two cases: with and without an EV in the premises of the GCH. Uncertainties are also included in the arrival time, departure time, and initial state of charge (at arrival) of the EV. The abovementioned technique is then applied to a typical household in South Australia using the yearly load profile of the household and actual yearly wind speed data at an interval of one hour. To investigate the effects of stochastic nature of household load, EV, and wind power generation on various results, the optimization process is repeated using 10-year of actual wind speed data and probabilistic load and EV uncertainties. The results of several sensitivity analyses of various system parameters are presented. It has been found that the SWT can effectively decrease the COE of the household for both cases (with and without an EV). However, the current price of battery may not be in favor of further reducing the COE of the household.

Potential of Load Shifting in a Parking Garage with Electric Vehicle Chargers, Local Energy Production and Storage

The electrification of the transport sector is of crucial importance for a successful transition to a fossil-free society. However, the electricity grid constitutes a bottleneck. This article provides a case study based on a real-world parking garage with a smart grid infrastructure, called Dansmästaren. The analysis shows how renewable energy sources, energy storage technologies, and smart charging of electric vehicles can smooth out the load curve of the parking garage and relieve the electric grid during peak hours. Dansmästaren is located in Uppsala, Sweden, and equipped with 60 charging points for electric vehicles, a PV system, and a battery storage system. The study utilizes an energy flow model to show the potential of a realistically dimensioned smart energy system, that can benefit the parking facility in itself and the local distribution grid in a city, Uppsala, with grid capacity challenges. The results suggest that the parking garage demand on the local grid can be significantly lowered by smarter control of its relatively small battery energy storage. Moreover, further smart control strategies can decrease demand up to 60% during high load hours while still guaranteeing fully charged vehicles at departure in near future scenarios. The study also shows that peak shaving strategies can lower the maximum peaks by up to 79%. A better understanding of the potential of public infrastructures for electric vehicle charging helps to increase knowledge on how they can contribute to more sustainable cities and a fossil-free society.

Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks

Each sensor node in WSN is typically equipped with a limited capacity small battery. Energy-efficient communication is therefore considered a key component of network life extension. In addition, as the utilization of the sensor network increases, duplicate data and abnormal data is also collected to reduce the accuracy of the data in various environments. AI is used to recognize data anomaly values and increase packets' accuracy by removing out-of-range data. This can improve performance through optimal data transmission, resulting in increased network life, energy efficiency, and reliability. This paper proposes a protocol called MLQ-MAC that reflects the above. MLQ-MAC uses AI techniques to consider different types of data packets. The data collected by the sensor removes the measurement anomaly and duplicate data and stores it in a different transmission queue by priority. Efficient data transfer is possible by using an AI Discriminator for accurate classification before being stored on a transmission queue. The AI-Discriminator classifies a variety of factors, including the collection environment, characteristics of network applications, and so on. It also uses two new technologies: self-adaptation and scheduling for efficient transmission. In the protocol, the receiver adjusts the duty cycle according to to transmit urgency to improve network QoS. Finally, the simulation results show that the MLQ-MAC protocol reduces energy consumption at the receiver by up to 3.4% and per bit by up to 2.3% and improves packet delivery accuracy by up to 3%.

Intramuscular Microvascular Flow Sensing for Flap Monitoring in a Porcine Model of Arterial and Venous Occlusion.

Commercially available near infrared spectroscopy devices for continuous free flap tissue oxygenation (StO2) monitoring can only be used on flaps with a cutaneous component. Additionally, differences in skin quality and pigmentation may alter StO2 measurements. Here, we present a novel implantable heat convection probe that measures microvascular blood flow for peripheral monitoring of free flaps, and is not subject to the same issues that limit the clinical utility of near-infrared spectroscopy. The intratissue microvascular flow-sensing device includes a resistive heater, 4 thermistors, a small battery, and a Bluetooth chip, which allows connection to a smart device. Convection of applied heat is measured and mathematically transformed into a measurement of blood flow velocity. This was tested alongside Vioptix T.Ox in a porcine rectus abdominis myocutaneous flap model of arterial and venous occlusion. After flap elevation, the thermal device was deployed intramuscularly, and the cutaneous T.Ox device was applied. Acland clamps were alternately applied to the flap artery and veins to achieve 15 minutes periods of flap ischemia and congestion with a 15 minutes intervening recovery period. In total, five devices were tested in three flaps in three separate pigs over 16 vaso-occlusive events. Flow measurements were responsive to both ischemia and congestion, and returned to baseline during recovery periods. Flow measurements corresponded closely with measured StO2. Cross-correlation at zero lag showed agreement between these two sensing modalities. Two novel devices tested simultaneously on the same flap showed only minor variations in flow measurements. This novel probe is capable of detecting changes in tissue microcirculatory blood flow. This device performed well in a swine model of flap ischemia and congestion, and shows promise as a potentially useful clinical tool. Future studies will investigate performance in fasciocutaneous flaps and characterize longevity of the device over a period of several days.

A new deep eutectic solvent-agarose gel with hydroxylated fullerene as electrical “switch” system for drug release

A novel portable conductive system of hydroxylated fullerene (TEGs-C60) embedded on the deep eutectic solvent (DES, choline chloride/malic acid)-agarose gel was developed for on-demand curation with skin contact. It has been found with better elasticity, conductivity and thermal stability than agarose hydrogel. Especially, the electro-responsive drug releasing amount on the skin of mice and live rabbits through small batteries was proved to be 4–5 times of those without electrical stimulation, which reached the level of clinic daily dosage. Meanwhile, it also showed good biocompatibility and much higher antibacterial rate against S. aureus (78.67 ± 0.80 %) together with E. coli (92.08 ± 1.50 %) than those of commercial chitosan gel. The new electrical “switch” gel system provided an effective and useful development strategy for advanced drug delivery.

Open-source tool for battery module simulation and design

Electric and hybrid vehicles are two of the most promising solutions to meet the new emission requirements of the transportation sector. The energy storage system is their most critical component in terms of performance. Therefore, battery modeling plays an important role for designing and controlling battery modules. Up to now, the design of battery modules is conditioned by the use of expensive tools that involve long simulation times; in response to this, the present work introduces an open-source tool developed in Python to study the performance of battery modules and contribute to their design. To show the scope and use of the tool, a theoretical case study is presented. In particular, the effect of slight differences in cells behavior, due to manufacturing process or aging, is shown at the module level. A SOC difference of 2.5%, due to current imbalances, was obtained among the cells after discharge for a small battery module with new cells. This result points out the importance of accurate and fast module models to correctly predict the remaining travel range and the need for an online parameters identification procedure. In addition, the temperature distribution in the module along with the heat generated by the cells, also estimated by the tool, can be used for proper design and control of the battery cooling system.

Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computational complexity of deep learning models, most embedded HAR systems are based on simple and not-so-accurate classic machine learning algorithms. This work bridges the gap between on-device HAR and deep learning, proposing a set of efficient one-dimensional Convolutional Neural Networks (CNNs) that can be deployed on general purpose microcontrollers (MCUs). Our CNNs are obtained combining hyper-parameters optimization with sub-byte and mixed-precision quantization, to find good trade-offs between classification results and memory occupation. Moreover, we also leverage adaptive inference as an orthogonal optimization to tune the inference complexity at runtime based on the processed input, hence producing a more flexible HAR system. With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) we are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency, and energy consumption; (ii) thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3× in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, while reducing the memory occupation by more than 100×. The few methods that obtain better performance (both shallow and deep) are not compatible with MCU deployment; (iv) all our CNNs are compatible with real-time on-device HAR, achieving an inference latency that ranges between 9 μs and 16 ms. Their memory occupation varies in 0.05–23.17 kB, and their energy consumption in 0.05 and 61.59 μJ, allowing years of continuous operation on a small battery supply.

High-fidelity eye, head, body, and world tracking with a wearable device

We describe the design and performance of a high-fidelity wearable head-, body-, and eye-tracking system that offers significant improvement over previous such devices. This device’s sensors include a binocular eye tracker, an RGB-D scene camera, a high-frame-rate scene camera, and two visual odometry sensors, for a total of ten cameras, which we synchronize and record from with a data rate of over 700 MB/s. The sensors are operated by a mini-PC optimized for fast data collection, and powered by a small battery pack. The device records a subject’s eye, head, and body positions, simultaneously with RGB and depth data from the subject’s visual environment, measured with high spatial and temporal resolution. The headset weighs only 1.4 kg, and the backpack with batteries 3.9 kg. The device can be comfortably worn by the subject, allowing a high degree of mobility. Together, this system overcomes many limitations of previous such systems, allowing high-fidelity characterization of the dynamics of natural vision.

Technical note: Conservative storage of water vapour – practical in situ sampling of stable isotopes in tree stems

Abstract. Using water-stable isotopes to track plant water uptake or soil water processes has become an invaluable tool in ecohydrology and physiological ecology. Recent studies have shown that laser absorption spectroscopy can measure equilibrated water vapour well enough to support inference of liquid-stable isotope composition of plant or soil water, on-site and in real-time. However, current in situ systems require the presence of an instrument in the field. Here we tested, first in the lab and then in the field, a method for equilibrating, collecting, storing, and finally analysing water vapour for its isotopic composition that does not require an instrument in the field. We developed a vapour storage vial system (VSVS) that relies on in situ sampling into crimp neck vials with a double-coated cap using a pump and a flow metre powered through a small battery and measuring the samples in a laboratory. All components are inexpensive and commercially available. We tested the system's ability to store the isotopic composition of its contents by sampling a range of water vapour of known isotopic compositions (from −95 ‰ to +1700 ‰ for δ2H) and measuring the isotopic composition after different storage periods. Samples for the field trial were taken in a boreal forest in northern Sweden. The isotopic composition was maintained to within 0.6 ‰ to 4.4 ‰ for δ2H and 0.6 ‰ to 0.8 ‰ for δ18O for natural-abundance samples. Although 2H-enriched samples showed greater uncertainty, they were sufficient to quantify label amounts. We detected a small change in the isotopic composition of the sample after a long storage period, but it was correctable by linear regression models. We observed the same trend for the samples obtained in the field trial for δ18O but observed higher variation in δ2H than in the lab trial. Our method combines the best of two worlds, sampling many trees in situ while measuring at high precision in the laboratory. This provides the ecohydrology community with a tool that is not only cost efficient but also easy to use.

(Digital Presentation) Pre-Lithiation of Silicon Anodes By Thermal Evaporation of Lithium for Boosting Energy Density of Lithium Ion Cells

Lithium ion batteries (LIBs) do not only dominate the small format battery market for portable electronic devices, but have also been successfully implemented as the technology of choice for electric vehicles. However, for successful consumer acceptance and broad market penetration of electric vehicles, further improvements of LIBs in terms of energy density and cost along are required. The practically usable energy density of LIB cells is reduced by parasitic side reactions including electrolyte decomposition and formation of the “solid electrolyte interphase” (SEI) at the surface of the anode, as this process is related to the consumption of active lithium. Especially high-capacity silicon-based anodes suffer from ongoing lithium loss and rapid capacity fading.Pre-lithiation is considered as a highly appealing technique to compensate for active lithium losses and, therefore, to increase the practical energy density. A critical parameter for a pre-lithiation strategy is to establish a technique for achieving lithiation of the active storage material at the most uniform lateral and in-depth distributions possible. Despite extensive exploration of various pre-lithiation techniques, controlling the lithium amount precisely while keeping an even lithium distribution remains challenging.Here, we report the thermal evaporation of lithium metal as a novel pre-lithiation technique for silicon anodes that allows both, i.e., a precise control of the degree of pre-lithiation and a homogeneous deposition at the electrode surface. Silicon | Lithium metal interface and SEI characterization show that this approach enables the preserved Li to be consumed for the formation of a pre-formed SEI and creates moderate mechanical cracks by volume changes. The highly uniform Li on thin-film Si is beneficial for increasing capacity retention and significantly suppressing active lithium losses. The phase changes are evaluated and the terms dry-state and wet-state pre-lithiation (without/with electrolyte) are revisited. Finally, a series of electrochemical methods are also validated to allow a direct correlation of active lithium losses with the pre-formed SEI formation and improved electrochemical performance of silicon anodes. Figure 1

Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring.

The paper analyses the autonomy of a wireless body sensor that continuously measures the potential difference between two proximal electrodes on the skin, primarily used for measuring an electrocardiogram (ECG) when worn on the torso. The sensor is powered by a small rechargeable battery and is designed for extremely low power use. However, the autonomy of the sensor, regarding its power consumption, depends significantly on the measurement quality selection, which directly influences the amount of data transferred. Therefore, we perform an in-depth analysis of the power consumption sources, particularly those connected with the Bluetooth Low Energy (BLE) communication protocol, in order to model and then tune the autonomy of the wireless low-power body sensor for long-term ECG monitoring. Based on the findings, we propose two analytical models for power consumption: one for power consumption estimation in idle mode and the other one for power estimation in active mode. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power. The proposed models show a good fit to the measured power consumption at different ECG sensor sampling rates. This allows for power consumption analysis and sensor autonomy predictions for different sensor settings. Moreover, the results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate with suitable connection interval and by packing as much data as possible in a single BLE packet.

DC-biased dielectric measurements using an existing frequency-domain spectroscopy (FDS) instrument and series battery

We present a simple method for adding DC bias to frequency-domain spectroscopy (FDS) measurements by inserting a battery in series in the ‘high’ lead to the device under test. An instrument designed for FDS measurements with pure AC can be used in this way without modification, even for DC voltages above the instrument’s AC range. Issues and limitations of the method are discussed, along with some alternative methods. Experimental results from FDS measurements on well defined linear objects are compared with/without the DC bias, to check that the measurement is not disturbed by the DC source. The only detected difference was the expected effect of including the battery impedance in series with the specimen in the measurement. This effect was negligible for typical lab-specimen capacitances, around and below power frequency. The nature of battery impedance is further described, as multiple small batteries in series can strongly affect the results for large specimens and at high frequencies.

Neural Network-Based Routing Energy-Saving Algorithm for Wireless Sensor Networks.

With the evolvement, standards have changed, mobile Internet technology has also been upgraded, and it has also driven the development of smart objects mobile. With the continuous development of smart objects mobile, the bottleneck of small node size and low battery energy storage has not been solved in the end, which makes the research of wireless sensor network energy-saving technology become the focus, and the improvement of routing technology is an effective way to improve energy-saving technology. From the data transmission energy consumption of smart objects mobile, the routing algorithm of smart objects mobile is discussed and analyzed and the classical representative LEACH is the object of in-depth research. Routing algorithms can easily and reliably process network data and make the network work well and are widely used in highly secure military systems and smaller commercial networks. Aiming at these deficiencies, a corresponding improved algorithm is proposed, and it is tested through simulation and specific experiments to verify the correctness and the system's reliability. The SMPSO-BP algorithm converges when the number of iterations is about 600, which is earlier than the LEACH algorithm and the improved LEACH algorithm, so the SMPSO-BP algorithm is due to the other two algorithms. In the wireless sensor network routing energy consumption experiment, in addition, the SMPSO-BP algorithm uses less energy than the other two methods. Therefore, the energy-saving algorithm under the neural network data fusion mechanism is still feasible.

Research on refrigerant optimization and characteristic parameters based on thermoelectric refrigeration cooling garment

Cooling garments are gradually becoming a simple and energy-saving way to relieve the human body from thermal stress. In this article, we built a set of cooling garment prototypes based on thermoelectric cooling. A series of performance tests were carried out in a simulated high-temperature environment to select the appropriate refrigerant and on this basis to determine the best inlet temperature that can meet the refrigeration requirements of personnel. The experimental results show that water and 40% glycerin have own advantages as refrigerants. The energy utilization efficiency of the cooling garment is higher when water is used as the refrigerant and the average cooling effect is better when 40% glycerol is used as the refrigerant. The higher the energy utilization efficiency, the relatively small battery capacity required by the liquid cooling garment, so water is selected as the refrigerant in this article. The effective cooling power can reach 210 W when the ambient temperature is 35 °C, and the maximum coefficient of performance at this time is 3.3. Through the wear experiments, we determined that the optimal water inlet temperature when the ambient temperature is 35 °C is 22 °C, which reduces the average temperature in the microclimate area to about 28.5 °C, and the relative humidity in the microclimate area is reduced from 90% to 58%, which proves the effectiveness of wearing this cooling garment to improve the thermal comfort of the human body. In addition, it is found that the inlet temperature of the cooling garment has a critical value at 22–24 °C when the ambient temperature is 35 °C. The water inlet temperature near this critical value has a more significant change in the cooling effect.

Stability-Guided Strategies to Mitigate Dendritic Growth in Lithium-Metal Batteries

Dendritic growth is a leading cause of degradation and catastrophic failure of lithium-metal batteries. Deep understanding of this phenomenon would facilitate the design of strategies to reduce, or completely suppress, the instabilities characterizing electrodeposition on the lithium anode. We present a linear-stability analysis, which utilizes the Poisson-Nernst-Planck equations to describe Li-ion transport and, crucially, accounts for the lack of electroneutrality. This allows us to investigate the impact of electric-field gradients near the electrode surface on both ion diffusion and its anisotropy. Our analysis indicates that the use of anisotropic electrolytes (i.e., electrolytes with anisotropic diffusion coefficients of the Li ions) and the control of the local electric field can suppress dendritic growth of lithium metal. Specifically, changes in the local electric field can be used to enhance the longitudinal (perpendicular to the electrode) component of the cation diffusion coefficient tensor, which decreases the maximum growth rate of the dendrites. Electrolytes with electric field-dependent diffusion coefficients would reduce dendritic growth in small batteries, while anisotropic electrolytes (or separators with anisotropic pore structures or columnized membranes) are appropriate for batteries of any size.

A joint optimisation model for charger locating and electric bus charging scheduling considering opportunity fast charging and uncertainties

The continuous improvement of charging technologies makes fast charging applicable to overcoming the limited driving range of electric vehicles by providing en-route opportunity charging. Fast chargers can be deployed at bus stops to top up the battery for an electric bus during its dwelling time when passengers board and alight, which allows the usage of small size battery and avoids deadheading trips to depot or charging station for recharging. This paper examines the joint optimisation problem of locating fast chargers at selected bus stops and developing charging schedule to support electric bus operation considering time-varying electricity price and penalty cost if passengers’ extra waiting time, the delay time of starting a trip or the excessive travel time of completing a trip is caused by charging activities. To handle the uncertainties associated with the travel time and passenger demand, a robust model is proposed and formulated. The proposed methods are applied to multiple bus routes in Sydney in the numerical studies. The numerical results show that frequent charging activities occur at both intermediate and last stops during bus service trips, indicating that it is beneficial for buses to utilise passengers’ boarding and alighting time at intermediate stops and resting time at the last stop between two adjacent trips to top up battery. Sensitivity analysis on a series of system parameters, such as the acquisition cost of chargers, amortized battery price and charging power, is conducted. The numerical results verify the feasibility of deployment of fast chargers and corresponding top-up charging activities at both intermediate and last stops. Electric buses outperform the conventional diesel buses regarding energy consumption cost, especially considering the increasing diesel fuel price and its costly environmental impacts. Moreover, charging activities at intermediate stops will become more common when passenger demand (the volume of boarding and alighting passengers) increases at intermediate stops.

Numerical study of PCM thermal behavior of a novel PCM-heat pipe combined system for Li-ion battery thermal management

A three-dimensional numerical model is developed and validated against experimental data for a cylindrical lithium-ion battery module with a compact hybrid cooling system of phase change material (PCM) and heat pipes. This cooling system is particularly well-suited for small or medium-sized battery module or pack. The PCM process and its effect on battery thermal behavior are numerically investigated for cases based on different C-rate discharge processes (0.5C, 1C and 2C), different PCM properties (different mass fractions of expandable graphite) and different charge–discharge cycles of the battery module. The results indicate that the PCM liquid fraction distribution is not uniform during the discharge process, the PCM starts to melt from its outermost neighboring the batteries and its upper part gets more liquid due to natural advection of liquid PCM. The different melting rates within the PCM lead to slightly enlarged temperature difference between batteries. Increasing discharge C-rate further accentuates this temperature difference. A tested composite PCM of 12 wt% EG (expanded graphite) shows better heat dissipation performance than that of pure PCM, and consequently it is better suited for battery thermal management systems. The simulations for different charge–discharge cycles indicate that lowering C-rate and increasing recovery time can improve thermal performance of the battery module and thermal control is thus more effective.

An improved parallel regenerative braking system for small battery electric vehicle

PurposeThe purpose of this paper is to design an improved parallel regenerative braking system (IPRBS) for electric vehicles (EVs) that increases energy recovery with a constant brake pedal feel (BPF).Design/methodology/approachThe conventional hydro-mechanical braking system is redesigned by incorporating a reversing linear solenoid (RLS) and allowed to work in parallel with a regenerative brake. A braking algorithm is proposed, and correspondingly, a control system is designed for the IPRBS for its proper functioning, and a mathematical model is formulated considering vehicle drive during braking. The effectiveness of IPRBS is studied by analyzing two aspects of regenerative braking (BPF and regenerative efficiency) and the impact of regenerative braking contribution to range extension and energy consumption reduction under European Union Urban Driving Cycle (ECE).FindingsIPRBS is found to maintain a constant BPF in terms of deceleration rate vs pedal displacement during the entire braking period irrespective of speed change and deceleration rate. The regenerative ratio of IPRBS is found to be high compared with conventional parallel regenerative braking, but it is quite the same at high deceleration.Originality/valueA constant BPF is achieved by introducing an RLS between the input pushrod and booster input rod with appropriate controller design. Comparative analysis of energy regenerated under different regenerative conditions establishes the originality of IPRBS. An average contribution ratio to energy consumption reduction and driving range extension of IPRBS in ECE are obtained as 18.38 and 22.76, respectively.