Cost-Effective Energy Management for Hybrid Electric Heavy-Duty Truck Including Battery Aging

2013 ◽  
Author(s):  
T. H. Pham ◽  
P. P. J. van den Bosch ◽  
J. T. B. A. Kessels ◽  
R. G. M. Huisman
Keyword(s):  
Energy Management ◽  
Combustion Engine ◽  
Life Time ◽  
Battery Life ◽  
Heavy Duty ◽  
Energy Management Strategy ◽  
Battery Power ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Power Capability

Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS’s operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

scholarly journals Fuzzy State Machine Energy Management Strategy for Hybrid Electric UAVs with PV/Fuel Cell/Battery Power System

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaohui Zhang ◽  
Li Liu ◽  
Yueling Dai
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Power Flow ◽  
Power Source ◽  
State Machine ◽  
Energy Management Strategy ◽  
Battery Power ◽  
Hybrid Electric ◽  
Fuzzy State

Energy management strategies are the key technology for hybrid electric UAVs. This paper proposes a fuzzy state machine (FSM) energy management strategy with an online potential to control the power flow for the hybrid electric UAV which includes the photovoltaic, fuel cell, and battery power sources. The FSM strategy couples the fuzzy logical strategy with a state machine strategy where the fuzzy logical strategy controls the power split between a fuel cell and a battery and the state machine deals with the power flow of photovoltaics and battery. To evaluate the FSM strategy, a simulation platform integrating the hybrid power system model and UAV model is developed with a Matlab/Simulink tool. An existed online thermostat control strategy for the same type of UAV is employed to compare with the proposed strategy based on the developed platform. The energy management process and the state of each power source are analyzed under a given mission scenario. The comparison of the two strategies about the power and energy contribution rates of each power source, the battery state of charge, and the hydrogen consumption is presented. The results indicate that the FSM strategy can satisfy the demand power effectively during the mission and performs better than the thermostat control strategy on power distribution and fuel consumption.

Predictive energy management for hybrid electric vehicles considering extension of the battery life

2017 ◽  
Vol 232 (4) ◽  
pp. 499-510 ◽  
Author(s):  
Xiaonian Wang ◽  
Siwei Ma ◽  
Jun Wang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Battery Life ◽  
Oil Consumption ◽  
Energy Management Strategy ◽  
Control Framework ◽  
Ageing Rate ◽  
Hybrid Electric

Hybrid electric vehicles offer potential for reducing the oil consumption and the generation of greenhouse gases, but they have a high battery price and short battery longevity. This paper presents an energy management strategy for power-split hybrid electric vehicles, which seeks not only to reduce the gasoline consumption but also to prolong the battery life. The instantaneous battery usage is penalized by its influence on the battery lifespan. This influence is defined by a new concept of battery ageing, i.e. the battery-fading index, which represents the ageing rate of the battery in various conditions. The multi-objective optimization problem is achieved by a model predictive control framework. The results indicate that the proposed energy management strategy effectively reduces battery fading with only a relatively small increase in the fuel consumption.

Energy Management Control of a Hybrid Electric Vehicle by Incorporating Powertrain Dynamics

2015 ◽  
Author(s):  
Mehran Bidarvatan ◽  
Mahdi Shahbakhti
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Management Strategies ◽  
Management Control ◽  
Energy Management Strategy ◽  
Ice Dynamics ◽  
Hybrid Electric

Energy management strategies in parallel Hybrid Electric Vehicles (HEVs) usually ignore effects of Internal Combustion Engine (ICE) dynamics and rely on static maps for required engine torque-fuel efficiency data. It is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper addresses this shortcoming by investigating effects of some major Spark Ignition (SI) engine dynamics and clutch dynamics on torque split management in a parallel HEV. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE and clutch dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by 8.7% for city and highway driving conditions in a combined common North American drive cycle. This fuel penalty is often overlooked in conventional HEV energy management strategies. A Model Predictive Control (MPC) of torque split is developed by incorporating effects of the studied influencing dynamics. Results show that the integrated energy management strategy can improve the total energy consumption of HEV by more than 6% for combined Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET)drive cycles.

scholarly journals Advanced Energy Management System For Hybrid Vehicle

2019 ◽  
Vol 8 (10) ◽  
pp. 3026-3030
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Battery Life ◽  
Energy Management System ◽  
Energy Management Strategy ◽  
Battery Power ◽  
Points Of Interest ◽  
Power Profile ◽  
Ultra Capacitor

The battery and ultra-capacitor hybrid storage system (HESS) is a system which can be adopted in the EV. By utilizing Ultra-capacitors, the batteries can be protected from high peak currents, which can be especially harming to the batteries. Consequently it is conceivable to expand the battery life. Battery and ultra-capacitor are connected through a bidirectional non-isolated multi input converter which has numerous points of interest. Fuzzy logic based energy management strategy is an efficient method to manage energy through HESS and furthermore regulating the SoC of ultra-capacitor while smoothing the battery power profile.

scholarly journals MPC Framework for the Energy Management of Hybrid Ships with an Energy Storage System

2021 ◽  
Vol 9 (9) ◽  
pp. 993
Author(s):  
Spyros Antonopoulos ◽  
Klaas Visser ◽  
Miltiadis Kalikatzarakis ◽  
Vasso Reppa
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Power Plants ◽  
Scale Effects ◽  
Storage System ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Specific Loading ◽  
Battery Capacity ◽  
The Impact

This paper proposes an advanced shipboard energy management strategy (EMS) based on model predictive control (MPC). This EMS aims to reduce mission-scale fuel consumption of ship hybrid power plants, taking into account constraints introduced by the shipboard battery system. Such constraints are present due to the boundaries on the battery capacity and state of charge (SoC) values, aiming to ensure safe seagoing operation and long-lasting battery life. The proposed EMS can be used earlier in the propulsion design process and requires no tuning of parameters for a specific operating profile. The novelties of the study reside in (i) studying the impact of mission-scale effects and integral constraints on optimal fuel consumption and controller robustness, (ii) benchmarking the performance of the proposed MPC framework. A case study carried out on a naval vessel demonstrates near-optimal and robust behaviour of the controller for several loading sequences. The application of the proposed MPC framework can lead to up to 3.5% consumption reduction due to utilisation of long term information, considering specific loading sequences and charge depleting (CD) battery operation.

Stirling system optimization for series hybrid electric vehicles

2021 ◽  
pp. 095440702110180
Author(s):  
Charbel R Ghanem ◽  
Elio N Gereige ◽  
Wissam S Bou Nader ◽  
Charbel J Mansour
Keyword(s):  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimization Technique ◽  
System Optimization ◽  
Energy Management Strategy ◽  
Fuel Savings ◽  
Auxiliary Power ◽  
Hybrid Electric ◽  
External Combustion

There have been many studies conducted to replace the conventional internal combustion engine (ICE) with a more efficient engine, due to increasing regulations over vehicles’ emissions. Throughout the years, several external combustion engines were considered as alternatives to these traditional ICEs for their intrinsic benefits, among which are Stirling machines. These were formerly utilized in conventional powertrains; however, they were not implemented in hybrid vehicles. The purpose of this study is to investigate the possibility of implementing a Stirling engine in a series hybrid electric vehicle (SHEV) to substitute the ICE. Exergy analysis was conducted on a mathematical model, which was developed based on a real simple Stirling, to pinpoint the room for improvements. Then, based on this analysis, other configurations were retrieved to reduce exergy losses. Consequently, a Stirling-SHEV was modeled, to be integrated as auxiliary power unit (APU). Hereafter, through an exergo-technological detailed selection, the best configuration was found to be the Regenerative Reheat two stages serial Stirling (RRe-n2-S), offering the best efficiency and power combination. Then, this configuration was compared with the Regenerative Stirling (R-S) and the ICE in terms of fuel consumption, in the developed SHEV on the WLTC. This was performed using an Energy Management Strategy (EMS) consisting of a bi-level optimization technique, combining the Non-dominated Sorting Genetic Algorithm (NSGA) with the Dynamic Programming (DP). This arrangement is used to diminish the fuel consumption, while considering the reduction of the APU’s ON/OFF switching times, avoiding technical issues. Results prioritized the RRe-n2-S presenting 12.1% fuel savings compared to the ICE and 14.1% savings compared to the R-S.

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