A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications
Abstract
:1. Introduction
2. Research Methodology
3. Battery and Supercapacitor in Hybrid Energy Storage System
4. DC/DC Converter Topologies for Energy Source
5. Energy Management Strategies for EV Applications
5.1. Rule-Based Energy Management Strategy
5.1.1. Deterministic Rule-Based Energy Management Strategy
5.1.2. Fuzzy-Rule-Based Energy Management Strategy
5.2. Optimization-Based Energy Management Strategy
5.2.1. Global Optimization Energy Management Strategy
- Firstly, there is the optimization of the parameters of a rule-based energy management control strategy. With this method, the energy management problem turns into a parameter optimization problem, and it is called a static optimization problem. Thus, a derivative-free static optimization method can be implemented, i.e., a genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA). In addition, a derivative-based static optimization method such as sequential quadratic programming (SQP) can also be applied.
- Secondly, the energy management problem of EV applications is considered as a dynamic, nonlinear, and constrained optimization problem. This is recognized as an optimal control problem. The optimization problem can be determined by dynamic optimization methods such as dynamic programming.
- Thirdly, the optimal control problem is approximately modeled as a mathematical problem. After that, the problem is solved by static optimization methods such as SQP.
Static Optimization Method
Dynamic Optimization Method
5.2.2. Real-Time Optimization Energy Management Strategy
Neural Network Control Strategy
Model Predictive Control Strategy
Reinforcement Learning Control Strategy
6. Discussion on Major Findings of Energy Management Strategies
7. Conclusions
- The findings declared the significance of the HESS for EVs in terms of performance, life cycle, and controllability via DC-DC power converters in the literature and provided scoring for a suitable configuration for applications.
- A review on the non-isolated half-bridge bi-directional DC-DC converter is proposed for smooth operation based on the limitations of low weight, low cost, and low loss with good dependability.
- An evaluation of the energy management strategies was provided where we found that rule-based real-time control is an acceptable solution for vehicle manufacturing. However, the performance of the real-time optimization strategy is acknowledged and has good prospects unless the economy of scale is established.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Configuration | Converter Size (Score 0–3) | DC Bus Voltage (Score 0–2) | Reliability (Score 0–1) | Total Score |
---|---|---|---|---|
(a) | None (0) | Follow battery (1) | High (1) | 2 |
(b) | 1 full size (1) | Constant (2) | Low (0) | 3 |
(c) | 1 medium size (2) | Follow battery (1) | High (1) | 4 |
(d) | 1 small size (3) | Follow battery (1) | High (1) | 5 |
(e) | 1 medium size (2) | Follow SC (0) | High (1) | 3 |
(f)–(h) | 1 full 1 medium size (1) | Constant (2) | Low (0) | 3 |
Paper | EMS | Control Structure | Main Contributions | Validation | Limitations | Year |
---|---|---|---|---|---|---|
[15,16,17,47,48] | Rule based; load follower | SC converter for current control within bandwidth |
| Real-world application |
| 2000–2010 |
[44,67,68] | SC converter for current control based on energy and power |
| Experimental validation |
| 2006–2009 | |
[29] | SC converter for voltage and current control |
| Experimental validation |
| 2011 | |
[14] | SC converter for power control based on vehicle dynamic |
| Simulation |
| 2015 | |
[69] | SC converter for current control based on vehicle dynamic |
| Simulation and lab-scale experiment |
| 2022 | |
[8] | Rule based; power split | SC converter for power control based on vehicle kinetic energy |
| Simulation |
| 2014 |
[26] | Rule based; adaptive power split | SC converter for current and voltage control |
| Real-time simulation |
| 2015 |
[50] | Rule based; power split using Kalman filter | Battery and SC converter for SOC control |
| Real-vehicle experiment |
| 2022 |
[51] | Rule based; conventional fuzzy logic | Battery and SC converter for power control |
| Simulation in ADVISOR 2002 |
| 2010 |
[53] | Battery and SC converter for energy and power control |
| Simulation and experiment |
| 2011 | |
[54] | Rule based; fuzzy sliding mode | Battery and SC converter for SOC control |
| Micro EV experiment |
| 2007 |
[55] | Rule based Harr-wavelet fuzzy logic | Battery and SC converter for power control |
| Tramway experiment |
| 2015 |
[57] | Offline optimization; sequential quadratic programming | Minimized battery’s SOC and velocity variation |
| Simulation |
| 2014 |
[33] | Offline optimization; non-dominated sorting genetic algorithm | Minimized fuel economy and cost |
| Simulation |
| 2009 |
[59] | Offline optimization; PSO-wavelet-transform | Estimation of battery and SC SOC and optimization of PID parameters |
| Simulation |
| 2014 |
[62] | Offline optimization; simulated annealing | Optimal battery power |
| Simulation in Autonomie |
| 2015 |
[63] | Offline optimization; dynamic programming | Optimal power distribution |
| Simulation |
| 2015 |
[15,70] | Real-time optimization; neural network | SC converter for current control |
| Real-vehicle experiment, real-time experiment |
| 2010, 2016 |
[34,71] | Real-time optimization; model predictive | Battery and SC converter for SOC control |
| Simulation |
| 2010,2020 |
[66] | Real-time optimization; deep-reinforcement learning | Fuel cell/battery/SC converter |
| Simulation |
| 2022 |
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Wangsupphaphol, A.; Phichaisawat, S.; Nik Idris, N.R.; Jusoh, A.; Muhamad, N.D.; Lengkayan, R. A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability 2023, 15, 11200. https://doi.org/10.3390/su151411200
Wangsupphaphol A, Phichaisawat S, Nik Idris NR, Jusoh A, Muhamad ND, Lengkayan R. A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications. Sustainability. 2023; 15(14):11200. https://doi.org/10.3390/su151411200
Chicago/Turabian StyleWangsupphaphol, Aree, Sotdhipong Phichaisawat, Nik Rumzi Nik Idris, Awang Jusoh, Nik Din Muhamad, and Raweewan Lengkayan. 2023. "A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications" Sustainability 15, no. 14: 11200. https://doi.org/10.3390/su151411200