Commercial Truck Research Articles

Comparison of Turbocharging and Pressure Wave Supercharging of a Natural Gas Engine for Light Commercial Trucks and Vans

To increase the efficiency of a natural gas engine, the use of a Miller camshaft was analysed. To avoid a decline in the low-end torque and also in the transient response, a pressure wave supercharger (Comprex™) was compared to the conventional single-stage turbocharger. The analyses for this conceptual comparison were performed experimentally, and the data were then used to run simulations of driving cycles for light commercial vehicles. A torque increase of 49% resulted at 1250 rpm when the Comprex™ was used in combination with a Miller camshaft. Despite the Miller camshaft, the Comprex™ transient response was still faster than the turbocharged engine. Using the same camshaft, the turbocharged engine took 2.5-times as long to reach the same torque. Water injection was used to increase the peak power output while respecting the temperature limitations. As the Comprex™ enables engine braking by design, we show that the use of friction brakes was reduced by two-thirds. Finally, a six-times faster catalyst warmup and an up to 90 °C higher exhaust gas temperature at the three-way catalytic converter added to the benefits of using the Comprex™ supercharger. The known drawbacks of the Comprex™ superchargers were solved due to a complete redesign of the machine, which is described in detail.

Health Cost Estimation of Traffic-Related Air Pollution and Assessing the Pollution Reduction Potential of Zero-Emission Vehicles in Toronto, Canada

Fossil fuel vehicles, emitting air toxics into the atmosphere, impose a heavy burden on the economy through additional health care expenses and ecological degradation. Air pollution is responsible for millions of deaths and chronic and acute health problems every year, such as asthma and chronic obstructive pulmonary disease. The fossil-fuel-based transportation system releases tons of toxic gases into the atmosphere putting human health at risk, especially in urban areas. This analysis aims to determine the economic burden of environmental and health impacts caused by Highway 401 traffic. Due to the high volume of vehicles driving on the Toronto Highway 401 corridor, there is an annual release of 3771 tonnes of carbon dioxide equivalent (CO2e). These emissions are mainly emitted onsite through the combustion of gasoline and diesel fuel. The integration of electric and hydrogen vehicles shows maximum reductions of 405–476 g CO2e per vehicle-kilometer. Besides these carbon dioxide emissions, there is also a large amount of hazardous air pollutants. To examine the impact of air pollution on human health, the mass and concentrations of criteria pollutants of PM2.5 and NOx emitted by passenger vehicles and commercial trucks on Highway 401 were determined using the MOVES2014b software. Then, an air dispersion model (AERMOD) was used to find the concentration of different pollutants at the receptor’s location. The increased risk of health issues was calculated using hazard ratios from literature. Finally, the health cost of air pollution from Highway 401 traffic was estimated to be CAD 416 million per year using the value of statistical life, which is significantly higher than the climate change costs of CAD 55 million per year due to air pollution.

Safety Performance Assessment of Connected Vehicles in Mitigating the Risk of Secondary Crashes: A Driving Simulator Study

Traffic crashes can be divided into primary and secondary crashes. Secondary crashes occur as a consequence of primary crashes within their spatiotemporal distances. Secondary crashes comprise nearly 20% of all crashes and 18% of fatal crashes, in which they can possibly have a higher crash severity than the primary crash. Interstate-80 in Wyoming is a major rural corridor with a high freight traffic volumes. The Federal Highway Administration selected Wyoming in which to deploy connected vehicle (CV) technology with a focus on commercial truck safety. Distress and rerouting applications were among the suite of CV pilot applications. Very few studies have investigated the safety performance of CVs in mitigating the risk of secondary crashes on heavy trucks, more specifically under adverse weather conditions. This study filled this gap by conducting a driving simulator experiment to assess the effectiveness of CV distress and rerouting applications in mitigating the effects of secondary crashes. A total of 23 truck drivers were recruited to this study. The analysis was conducted on the vehicle kinematics obtained from the driving simulator. A CV and a nonCV scenario were designed to compare the participants’ driving behavior under adverse weather conditions. The results showed that the tested CV applications succeeded in enhancing driving behaviors by reducing the operating speed as well as the speed variation, and all the participants avoided a secondary crash in the CV environment. In addition, the distress notification coupled with the road closure reduced the average operating speed by 26% from the provided speed limit.

Energy assessment of an electrically heated catalyst in a hybrid RCCI truck

Reactivity controlled compression ignition (RCCI) combustion showed great advantages in terms of engine-out NOx and soot emissions reduction. However, HC and CO emissions are larger than with conventional diesel combustion. Commercial oxidation catalysts can deal with the amount of HC and CO generated during the RCCI combustion only at warm conditions, thus limiting the implementation of this concept in conventional vehicles and in RCCI hybrid applications due to the large engine stop periods. This work aims to study the behaviour of an electrically heated catalyst used in a hybrid medium-duty truck operating with diesel-gasoline RCCI combustion. A parallel P2 full hybrid medium-duty truck is studied in transient conditions by means of numerical simulations validated with experimental data. The results are compared to those from the commercial diesel non-hybrid truck and the RCCI non-hybrid concept with a commercial oxidation catalyst. The results show that the fuel consumption increases about 2 % in combined cycles and 5 % in urban cases with respect to the non-hybrid RCCI case with a commercial oxidation catalyst. Finally, it was found that the RCCI hybrid concept with the electrically heated catalyst allows to achieve the EUVI targets for all the pollutant emissions with CO2 levels of 15 %.

Predicting unsafe driving risk among commercial truck drivers using machine learning: Lessons learned from the surveillance of 20 million driving miles

The emergence of sensor-based Internet of Things (IoT) monitoring technologies have paved the way for conducting large-scale naturalistic driving studies, where continuous kinematic driver-based data are generated, capturing crash/near-crash safety critical events (SCEs) and their precursors. However, it is unknown whether the SCEs risk can be predicted to inform driver decisions in the medium term (e.g., hours ahead) since the literature has focused on SCE predictions either for a given road segment or for automated breaking applications, i.e., immediately before the event. In this paper, we examine the SCE data generated from 20+ million miles-driven by 496 commercial truck drivers to address three main questions. First, whether SCEs can be predicted using disparate driving-related data sources. Second, if so, what the relative importance of the different predictors examined is. Third, whether the prediction models can be generalized to new drivers and future time periods. We show that SCEs can be predicted 30 min in advance, using machine learning techniques and dependent variables capturing the driver’s characteristics, weather conditions, and day/time categories, where an area under the curve (AUC) up to 76% can be achieved. Moreover, the predictive performance remains relatively stable when tested on new (i.e., not in the training set) drivers and a future two-month time period. Our results can inform dispatching and routing applications, and lead to the development of technological interventions to improve driver safety.

Automatic design system for generating routing layout of tubes, hoses, and cable harnesses in a commercial truck

Abstract Although many routing algorithms have been developed, it is difficult for designers in the automotive industry to adopt them because of the complicated preliminary steps that are required. This study presents a systematic framework for generating the routing layout of the tubes, hoses, and cable harnesses in a commercial truck. The routing layout design problem in a commercial truck is analysed and defined. For routing operations, a sequential graph-based routing algorithm is employed to rapidly provide a routing solution. Because a reference routing layout design does not exist in most engineering problems, a cell-based genetic algorithm combined with a modified maze algorithm is employed to generate a reference design. To consider the clamping condition of the routing components, a new fitness function in the genetic algorithm is implemented. The numerical study shows that the proposed routing algorithm provides a better reference routing layout design than the conventional algorithm. The proposed automatic design system was applied to the routing layout design problem of a commercial truck. It was demonstrated that the proposed framework satisfies all industrial practitioners’ functional requirements and provides a systematic method of solving the routing layout design problem, considering all its characteristics.

Preliminary design of a fuel cell/battery hybrid powertrain for a heavy-duty yard truck for port logistics

The maritime transport and the port-logistic industry are key drivers of economic growth, although, they represent major contributors to climate change. In particular, maritime port facilities are typically located near cities or residential areas, thus having a significant direct environmental impact, in terms of air and water quality, as well as noise. The majority of the pollutant emissions in ports comes from cargo ships, and from all the related ports activities carried out by road vehicles. Therefore, a progressive reduction of the use of fossil fuels as a primary energy source for these vehicles and the promotion of cleaner powertrain alternatives is in order.The present study deals with the design of a new propulsion system for a heavy-duty vehicle for port applications. Specifically, this work aims at laying the foundations for the development of a benchmark industrial cargo–handling hydrogen-fueled vehicle to be used in real port operations. To this purpose, an on-field measurement campaign has been conducted to analyze the duty cycle of a commercial Diesel-engine yard truck currently used for terminal ports operations. The vehicle dynamics has been numerically modeled and validated against the acquired data, and the energy and power requirements for a plug-in fuel cell/battery hybrid powertrain replacing the Diesel powertrain on the same vehicle have been evaluated. Finally, a preliminary design of the new powertrain and a rule-based energy management strategy have been proposed, and the electric energy and hydrogen consumptions required to achieve the target driving range for roll-on and roll-off operations have been estimated.The results are promising, showing that the hybrid electric vehicle is capable of achieving excellent energy performances, by means of an efficient use of the fuel cell. An overall amount of roughly 12 kg of hydrogen is estimated to be required to accomplish the most demanding port operation, and meet the target of 6 h of continuous operation. Also, the vehicle powertrain ensures an adequate all-electric range, which is between approximately 1 and 2 h depending on the specific port operation.Potentially, the hydrogen-fueled yard truck is expected to lead to several benefits, such as local zero emissions, powertrain noise elimination, reduction of the vehicle maintenance costs, improving of the energy management, and increasing of operational efficiency.

Theoretical and Experimental Vibration Analyses of the Steering Wheel of a Heavy Commercial Vehicle

In this study, the components of a particular heavy commercial truck that affect the steering wheel vibration are identified and the most common sources of this vibration are examined. Then, modal analyses and impact hammer tests are implemented to determine the root causes of the vibration. Finally, finite element modeling is applied, and all the results of the analyses are compared to present design improvement recommendations for the reduction in steering wheel vibration, which can be applicable in new projects.

Study of Metallic Housing of the Adder Gearbox to Reduce the Noise and to Improve the Design Solution

In the manufacture of commercial trucks, used in oil installations or the army, two identical engines are used on a single chassis, whose power is summed by a gearbox, a compact metal construction, which must meet multiple operating requirements. The paper studies the behavior of such an adding box, currently used in manufacturing, and an improved, welded solution that produces less noise and has a lower weight. The finite element method is used for modeling the gearbox in order to analyze stresses and strains and obtain a modal analysis of the system. The results obtained from the calculation are then verified by experimental measurements. The two versions are analyzed in parallel to highlight the advantages of the second version.

Decreased Virus-Neutralizing Antibodies Against Equine Herpesvirus type 1 In Nasal Secretions of Horses After 12-hour Transportation

This study evaluated the effects of 12-hour transportation on immune responses to equine herpesvirus type 1 (EHV-1) and type 4 (EHV-4). Possible replication of EHV-1 and EHV-4 was monitored by real-time PCR of nasal swabs and peripheral blood mononuclear cells (PBMCs), and changes in systemic and mucosal antibodies were investigated. Six healthy Thoroughbreds with transport experience were transported in commercial trucks, repeating the same three-hour route four times. Blood samples for cortisol measurement were taken before departure and every three hours. Nasal swabs, PBMCs, nasal wash and serum samples were collected before departure, at unloading, two and six days after arrival. Cortisol concentration increased significantly after three and six hours of transport (P < 0.05), confirming acute transport stress. However, no evidence of viral replication or lytic infection was observed, and serum virus neutralization (VN) titers for EHV-1 and EHV-4 were unchanged, except for one horse that showed a four-fold decrease in titer against EHV-1 after transportation. Urea and total IgA concentration in nasal washes increased significantly after transportation (P < 0.05), while total IgA/protein ratio was unchanged. A transient, ≥4-fold decrease in VN titers for EHV-1 in nasal wash concentrates was observed in four out of six horses after transportation (geometric mean titer declined from 202 to 57, P < 0.05), suggesting suppression of VN capacity in the nasal mucosa may contribute to susceptibility to EHV-1 after transportation. VN antibodies against EHV-4 in nasal secretion were not detected at any timepoint.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

Optimizing Energy Savings for a Fleet of Commercial Autonomous Trucks

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

Description of the Nature of Musculoskeletal Shoulder Injuries in a Cohort of Commercial Truck Drivers: A Retrospective Cross-Sectional Study

Commercial truck drivers (CTDs) are significantly affected by shoulder injuries; however, little is known about the unique mechanisms of injury (MOIs), specific injuries, or possible preventive measures among this group of workers. This study characterized the MOIs, musculoskeletal disorders (MSDs), and factors associated with MSDs of the shoulder among a group of CTDs. A retrospective medical record review was conducted of CTDs between 21 and 65 years of age who were seen for MSDs of the shoulder between 2007 and 2015. A total of 130 CTDs were included, who were aged 21 to 65 years. Commercial truck drivers were most often injured during a fall (35%) or while using chains, tarps, or straps (31%). The two most common MSDs were unspecified sprains/strains (58%) and rotator cuff tears (24%). Age was found to be associated with all MSDs (p = .001) and an increased risk of developing rotator cuff tears (p =.005). Seventy-four percent of CTDs who experienced a rotator cuff tear were 46 years of age or older. This study highlights the course of the injury in terms of diagnostics such as magnetic resonance imaging (MRI) and referral for surgery and describes the occupational activities associated with CTDs. These findings can inform employer injury prevention programs, patient and health care provider education, and future interventional research.

A Low Temperature Coolant Conditioning Cart for Testing DEF Thawing Systems

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Diesel Exhaust Fluid (DEF) systems are required to function in cold ambient temperatures below the freezing point of DEF. Manufacturers may demonstrate compliance by following an EPA guidance procedure described below [&lt;span class="xref"&gt;2&lt;/span&gt;], using whole vehicles at winter test sites at −18 deg C or lower. However, commercial trucks may have multiple variants with different DEF system layouts, so it is impractical to test every possible configuration. A climatic chassis dynamometer (CCD) can also be used for this test, but this is still expensive and time consuming, and does not address the problem of complexity. Instead, much time and expense can be saved by using simulation methods to identify worst case configurations, and to demonstrate with confidence that a limited number of tests will cover the whole possible range. This methodology can further be used to show that a range of vehicles can be represented with selected rig tests in a cold chamber. Such a methodology depends on the ability to generate repeatable test data in a cold room environment. Previously published work [&lt;span class="xref"&gt;1&lt;/span&gt;] has shown that it is difficult to achieve a repeatable test. In particular, it is challenging to provide coolant with repeatable and accurate temperature and flow profiles, between −40 deg C and +95 deg C. A coolant cart has been developed to provide a stream of conditioned coolant, starting at −40 deg C or higher, utilizing user-specified profiles of temperature and flow rate versus time. The desired profiles were designed to match test data logged from truck engines during warmup from a typical cold overnight temperature of −22 deg C. Surprisingly, no suitable test equipment was found to be commercially available, and the task of designing and building such equipment was found to be challenging. The need to function at −40 deg C restricted the choice of components. The requirement to follow user-defined profiles for both flow and temperature, within tight error bands and following sudden changes, also required unusual care in design. A dedicated coolant cart was therefore commissioned and built. This paper documents the design and operation of the cart. Major component descriptions are provided, with circuit and input/output detail, recharge time and operator interface and processing. The end result is a cart that provides for repeatable cold room testing of tank thaw performance to support a simulation-based verification method, thereby reducing the need for whole-vehicle testing by 75%.&lt;/div&gt;&lt;/div&gt;

Insights into motor carrier crashes: A preliminary investigation of FMCSA inspection violations

Many researchers have developed predictive models of crashes based on the safety scores of commercial truck companies, but these studies have been based on aggregated data at the truck company level—evaluating the total crashes and violations per company over a period of time. This level of aggregation obscures critical information. Here, a new approach to organizing non-aggregated data is taken, and logistic regression and random forest models are applied to non-aggregated FMCSA roadside inspection, violation, and crash data at the specific vehicle level. Resampling methods are used to improve model performance where there are relatively few events of interest—crashes. These results point not to specific “unsafe” drivers, but rather, patterns of unsafe behaviors or conditions that predict roadway crashes. Working toward reducing these behaviors systematically could save lives on US highways.

The association between crashes and safety-critical events: Synthesized evidence from crash reports and naturalistic driving data among commercial truck drivers

The past decade has witnessed continuous growth of naturalistic driving studies (NDSs). When analysing NDS data, safety-critical events (SCEs) are commonly used as surrogates for safety, since actual crashes are very rare. However, the association between SCEs and crashes is not consistent in previous studies and has not been verified among commercial truck drivers. Based on routinely collected kinematic data from 31,828 truck drivers in a large commercial trucking company, this paper examines the association between four types of SCEs (headway, hard brake, collision mitigation, and rolling stability) and crashes, as well as injuries and fatalities. Compared to existing studies on the subject, that are based on up to about 2 million miles driven, our study involves an estimated 2.3 billion miles driven. Bayesian negative binomial models were applied to examine the association between three outcomes (crashes, injuries, and fatalities) and the four SCEs. It was found that a unit increase in the number of any type of SCEs per 10,000 miles was associated with 8.4% (95% credible interval (CI): 8.0–8.8%) increase in crashes per mile and 8.7% (95% CI: 4.8–13.6%) increase in the number of injuries per mile. The increase was different in different types of SCEs: 3.3% (95% CI: 2.6–4%) for headways, 8.1% (95% CI: 7.5–8.7%) for hard brakes, 50.4% (95% CI: 41.4–60%) for rolling stability, and 22.2% (95% CI: 19.8–24.5%) for collision mitigation. The results are consistent when stratified by different business units and driver types. This study provides statistically strong and robust evidence that SCEs are positively associated with crashes and injuries among commercial truck drivers. NDS and kinematic data routinely collected by trucking companies provide a promising opportunity for future data analytic research.

The impact of construction of hill roads on the environment, assessed using the multi-criteria approach

ABSTRACT The environmental impact assessment (EIA) in regard to road widening of a 26 km stretch in Reasi District of Jammu and Kashmir (Union Territory) has been studied. Impact on the environment included temperature, deforestation, rainfall, landslides, blasting, surface and groundwater, air and soil pollution, habitat change, historical and socio-environmental factors. The study indicates that buses are the highest emitters of Hartridge Smoke Unit (HSU) followed by light commercial vehicles, heavy commercial trucks and cars, and three-wheelers in that order. The study also established that the make and age of the vehicle had negligible impact on the observed values of HSU and the light absorption coefficient (k). With the help of multi-criteria tools, the EIA has been quantified for four alternatives of a road stretch using fourteen criteria. The results show that the impact of the construction of two-lane highways on the environment is high, but that it can also be mitigated effectively.

Relationship between sleep problems and dangerous driving behaviors in Japanese short-haul commercial truck drivers: a cross-sectional survey using digital tachograph data

The aim of the study was to examine the relationship between sleep problems and objective driving behaviors of short-haul commercial truck drivers in Japan. One hundred and ten male short-haul commercial truck drivers’ sleep duration, sleep quality (Pittsburgh Sleep Quality Index), and frequency of sleepiness at the wheel were measured by survey in June 2019. In addition, their dangerous driving behaviors (i.e., overspeeding, sudden start and acceleration, sudden deceleration, and sudden braking) during June were calculated using digital tachograph data. Logistic regression analysis (LRA) was performed. The dependent variables were the presence/absence of dangerous driving behaviors, and sleep duration, sleep quality, and sleepiness were assigned as independent variables. Body mass index was adjusted as a confounding variable. LRA for sleep duration revealed that the “<5 h group” was associated with tendency of a higher adjusted odds ratio (2.87, 95% confidence interval = 0.87–9.45, p = 0.08) for sudden braking compared with the “6 h group.” LRA for sleep quality revealed that the “poor quality group” was associated with a higher adjusted OR (2.56, 95% CI = 1.09–6.01) for sudden braking compared with the “normal quality group.” LRA for the frequency of sleepiness revealed that the “frequently group” was associated with a higher adjusted OR (5.15, 95% CI = 1.10–24.13) for sudden braking compared with the “never group.” Nevertheless, sleep problems were nonsignificantly associated with other dangerous driving behaviors. Truck drivers with poor sleep quality and/or frequent sleepiness at the wheel displayed a 2.5–5.1 times greater risk of sudden braking than did drivers without these sleep problems.

Evidence of viral survival in representative volumes of feed and feed ingredients during long-distance commercial transport across the continental United States.

The hypothesis that feed ingredients could serve as vehicles for the transport and transmission of viral pathogens was first validated under laboratory conditions. To bridge the gap from the laboratory to the field, this current project tested whether three significant viruses of swine could survive in feed ingredients during long‐distance commercial transport across the continental US. One‐metric tonne totes of soybean meal (organic and conventional) and complete feed were spiked with a 10 ml mixture of PRRSV 174, PEDV and SVA and transported for 23 days in a commercial semi‐trailer truck, crossing 29 states, and 10,183 km. Samples were tested for the presence of viral RNA by PCR, and for viable virus in soy‐based samples by swine bioassay and in complete feed samples by natural feeding. Viable PRRSV, PEDV and SVA were detected in both soy products and viable PEDV and SVA in complete feed. These results provide the first evidence that viral pathogens of pigs can survive in representative volumes of feed and feed ingredients during long‐distance commercial transport across the continental United States.

Nitrogen and boron doped carbon layer coated multiwall carbon nanotubes as high performance anode materials for lithium ion batteries

Lithium ion batteries (LIBs) are at present widely used as energy storage and conversion device in our daily life. However, due to the limited power density, the application of LIBs is still restricted in some areas such as commercial vehicles or heavy-duty trucks. An effective strategy to solve this problem is to increase energy density through the development of battery materials. At the same time, a stable long cycling battery is a great demand of environmental protection and industry. Herein we present our new materials, nitrogen and boron doped carbon layer coated multiwall carbon nanotubes (NBC@MWCNTs), which can be used as anodes for LIBs. The electrochemical results demonstrate that the designed NBC@MWCNTs electrode possesses high stable capacity over an ultra-long cycling lifespan (5000 cycles) and superior rate capability even at very high current density (67.5 A g−1). Such impressive lithium storage properties could be ascribed to the synergistic coupling effect of the distinctive structural features, the reduced diffusion length of lithium ions, more active sites generated by doped atoms for lithium storage, as well as the enhancement of the electrode structural integrity. Taken together, these results indicate that the N, B-doped carbon@MWCNTs materials may have great potential for applications in next-generation high performance rechargeable batteries.