In the rapidly evolving fields of electric vehicles and energy storage systems, the battery management system (BMS) plays a critical role in ensuring safety, optimizing performance, and extending battery lifespan. As a core control unit, the BMS must continuously monitor parameters such as voltage, current, and temperature across numerous battery cells. Efficient and reliable communication within the BMS is paramount, especially in large-scale configurations where hundreds of cells are managed. Traditional communication architectures, such as those based on Controller Area Network (CAN) buses or standard Serial Peripheral Interface (SPI) daisy-chains, often face significant challenges. While daisy-chaining offers cost-effective hardware simplification by reducing wiring complexity through device cascading, it introduces substantial protocol-layer inefficiencies. A primary issue is the high overhead from redundant data, particularly from cyclic redundancy check (CRC) codes and frame delimiters, which can degrade the effective data throughput. In many commercial BMS solutions, like those using automotive-grade AFE chips, redundancy can account for over 28% of the frame, reducing actual payload efficiency to below 75%. This inefficiency becomes exacerbated in long daisy-chains with many nodes, limiting scalability and real-time performance. Therefore, there is a pressing need to develop communication protocols that minimize redundancy while maintaining robust error detection. In this work, we propose and implement a novel low-redundancy communication link for battery management systems using a regenerative CRC scheme on an FPGA platform. Our approach dynamically optimizes frame structure and employs an incremental CRC update algorithm, significantly reducing frame length and enhancing communication efficiency. This article details the system design, optimization methodology, and verification results, demonstrating a practical solution for high-performance BMS applications.
The foundation of our low-redundancy communication link is an ISO SPI hybrid architecture, which combines the SPI protocol with differential signaling for daisy-chain connectivity. This architecture is well-suited for battery management systems due to its balance of simplicity and noise immunity. The overall system comprises several key components: a host device (typically an upper-level controller), a microcontroller unit (MCU) acting as the SPI master, a base chip that interfaces between the MCU and the daisy-chain network, and multiple stack chips (slave nodes) that directly monitor battery cells. Each stack chip features two differential ports (DC1 and DC2), each consisting of a receiver (RX) and transmitter (TX), enabling bidirectional data flow along the chain. The base chip converts SPI signals from the MCU to differential signals for transmission down the daisy-chain and vice versa, facilitating communication across potentially hundreds of nodes. This setup is common in BMS designs, but we enhance it with our optimized protocol to address redundancy issues.
To understand the redundancy problem, we first examine the traditional frame format used in daisy-chain communications for battery management systems. A typical frame includes a start frame (header), command frame (8 bits indicating read/write operations), address frame (16 bits for chip and register addressing), data frame (variable length depending on the number of stack chips), CRC frame (for error checking), and an end frame (footer). In conventional approaches, each data segment from a stack chip is accompanied by its own CRC code, leading to linear growth in frame length as more nodes are added. For a daisy-chain with N stack chips, each transmitting L bits of data, the total frame length in the traditional method can be expressed as:
$$ \text{Frame}_{\text{traditional}} = H + C + A + N \times (L + C_{\text{CRC}}) + F $$
where H is the header size, C is the command frame size, A is the address frame size, L is the data payload per chip, $C_{\text{CRC}}$ is the CRC code size (e.g., 16 bits), and F is the footer size. For instance, with a 16-bit data payload per chip and a 16-bit CRC, adding one more stack chip increases the frame by 32 bits. In a large BMS with 256 nodes, this results in frames exceeding 1000 bytes, causing latency and reduced bandwidth. The redundancy ratio R can be calculated as:
$$ R = \frac{N \times C_{\text{CRC}}}{\text{Frame}_{\text{traditional}}} \times 100\% $$
which often exceeds 25% in practical scenarios. This inefficiency stems from the independent CRC generation per data segment, which is necessary for error detection but costly in terms of overhead.
To mitigate this, we propose a regenerative CRC scheme that leverages incremental updates to compress the frame. Instead of appending a separate CRC for each data segment, our method generates a single, cumulative CRC that covers the entire data stream from all stack chips in the daisy-chain. This is achieved by having each stack chip compute a CRC not only on its own data but also on the combined data from itself and all downstream chips. As data propagates upstream, each node regenerates the CRC incrementally, effectively “folding” the error-checking into a compact form. The optimized frame format eliminates multiple CRC frames, retaining only one CRC frame at the end of the data stream. The new frame length is given by:
$$ \text{Frame}_{\text{optimized}} = H + C + A + N \times L + C_{\text{CRC}} + F $$
Comparing this to the traditional format, the reduction in length $\Delta$ is:
$$ \Delta = \text{Frame}_{\text{traditional}} – \text{Frame}_{\text{optimized}} = N \times C_{\text{CRC}} – C_{\text{CRC}} = (N – 1) \times C_{\text{CRC}} $$
For N=256 and $C_{\text{CRC}}$=16 bits (2 bytes), this yields a reduction of 510 bytes, significantly improving efficiency. The following table summarizes the frame structure comparison for different numbers of stack chips, assuming H=8 bits, C=8 bits, A=16 bits, L=16 bits, $C_{\text{CRC}}$=16 bits, and F=8 bits:
| Number of Stack Chips (N) | Traditional Frame Length (bits) | Optimized Frame Length (bits) | Reduction (bits) | Reduction Percentage |
|---|---|---|---|---|
| 1 | 72 | 56 | 16 | 22.22% |
| 4 | 168 | 104 | 64 | 38.10% |
| 16 | 552 | 296 | 256 | 46.38% |
| 256 | 8232 | 4136 | 4096 | 49.76% |
This table illustrates the progressive benefits of our approach, with nearly 50% redundancy reduction for large-scale battery management systems. The key innovation lies in the incremental CRC update mechanism, which maintains error detection coverage while minimizing overhead.
The core of our low-redundancy communication link is the design of the CRC generation and verification module, implemented on an FPGA. We adopt the CRC-16 standard with the polynomial $G(x) = x^{16} + x^{15} + x^2 + 1$, corresponding to the hexadecimal value 0x8005, which is widely used in industrial applications for its robust error detection capabilities. The polynomial can be represented in mathematical form as:
$$ G(x) = x^{16} + x^{15} + x^2 + 1 $$
In our regenerative scheme, each stack chip performs two CRC operations: one for incremental regeneration and another for verification of received data. Let $D_i$ denote the data from the i-th stack chip, and let $C_i$ be the cumulative CRC after processing data up to chip i. The incremental CRC update follows a recursive formula. For a given data input $d$ (8-bit or 16-bit segments), the CRC computation is defined by the polynomial division:
$$ C_{\text{new}} = (C_{\text{old}} \ll 8) \oplus \text{CRC\_TABLE}[d \oplus (C_{\text{old}} \gg 8)] $$
where $\oplus$ denotes bitwise XOR, $\ll$ and $\gg$ are shift operators, and CRC_TABLE is a precomputed lookup table for efficiency. In hardware, we implement a pipelined CRC accumulator module that processes data streams in real-time. This module, named crc_16_accumulate, takes input data and validity signals, computes the CRC iteratively, and stores the intermediate result in a register for the next cycle. The state update equation is:
$$ \text{crc\_reg}_{t+1} = f(\text{crc\_reg}_t, \text{data}_t) $$
where $f$ represents the CRC computation function based on the polynomial. The module operates at a clock frequency of 10 MHz, handling 8-bit data per cycle, which aligns with the SPI communication rate of 2 MHz used in our battery management system.
Building on this accumulator, we design a comprehensive CRC generation module (crc_gen) that integrates incremental regeneration and verification. This module inputs include the chip’s own data (16 bits), received data from the daisy-chain (8-bit segments), control signals, and a chip count indicating the node’s position in the chain. Internally, it uses two counters: rx_count1 to track the amount of data transmitted upstream for CRC regeneration, and rx_count2 to monitor received data for verification. The module outputs the regenerated CRC code, validity flags, and error indicators. The workflow for a stack chip at position k in an N-chip daisy-chain involves:
- Upon receiving data from downstream chips, incrementally update the cumulative CRC by combining own data $D_k$ with received data.
- When
rx_count1reaches a threshold (e.g., after processing all downstream data), output the final regenerated CRC $C_k$ to be sent upstream. - Simultaneously, compute a separate CRC on the received data alone for verification; freeze this value when
rx_count2indicates complete reception. - Upon receiving the downstream CRC frame, compare it with the frozen verification CRC; if mismatch, set an error flag and mark the data frame with a specific pattern (e.g., bits 1:0 set to 11) to locate the faulty node.
This dual-process ensures data integrity while minimizing frame length. The error detection capability is quantified by the probability of undetected errors, which for CRC-16 is approximately $2^{-16}$ for random errors, meeting the requirements of automotive safety integrity levels (ASIL) such as ASIL-B. The module’s logic can be summarized with the following equations for the CRC update and verification:
$$ C_k = \text{CRC}(D_k \parallel C_{k+1}) $$
$$ V_k = \text{CRC}(D_{k+1} \parallel D_{k+2} \parallel \dots \parallel D_N) $$
$$ \text{Error}_k = (V_k \neq \text{Received\_CRC}) $$
where $\parallel$ denotes concatenation, and $C_{k+1}$ is the CRC from the downstream chip. This incremental approach reduces hardware complexity and latency, making it suitable for real-time BMS applications.
To validate our design, we conducted extensive simulation and FPGA testing. The simulation environment used VCS and Verdi tools to model a daisy-chain with one base chip and three stack chips, representing a small-scale battery management system. The SPI clock was set to 2 MHz, and the system clock for each FPGA was 10 MHz. We simulated read operations where stack chips transmit voltage data (e.g., 0xA510, 0xA520, 0xA530) upstream. The simulation results confirmed correct functionality of the regenerative CRC module. For instance, for a stack chip S1 with own data 0xA510 and downstream data 0xA520 and 0xA530, the module successfully generated a cumulative CRC of 0x812E and verified the downstream CRC of 0x1A42. The waveforms showed proper counter increments and error handling, with no discrepancies in data flow.
For hardware verification, we built a physical test platform using five FPGA boards (Altera EP4CE10F17C8) and an STM32F103C8T6 microcontroller. One FPGA acted as the base chip, four as stack chips, and the MCU served as the host. Differential signals (DC_h/DC_l) connected the chips in a daisy-chain, and SPI communication at 2 MHz facilitated data exchange. Each FPGA had an independent 10 MHz clock source. We performed continuous read operations over 24 hours, transmitting voltage data from the stack chips (0xA510, 0xA520, 0xA530, 0xA540) and monitoring the communication link. The results, captured via SignalTap logic analyzer, demonstrated that the base chip correctly received and converted differential data to SPI format, and the stack chips accurately implemented the regenerative CRC scheme. For example, in stack chip S1, the incremental CRC output was 0x58EB, and the verification CRC was 0x838A, with no errors detected during normal operation. The frame length reduction was measured empirically: traditional frames would be 19 bytes (152 bits) for four chips, while our optimized frames were 13 bytes (104 bits), achieving a reduction of 31.58%, consistent with theoretical calculations. The table below summarizes the test results for different performance metrics:
| Metric | Value | Comments |
|---|---|---|
| Communication Rate | 2 Mbps (SPI) | Standard for BMS daisy-chains |
| System Clock Frequency | 10 MHz | Per FPGA node |
| Frame Length (4 chips) | 13 bytes | Optimized design |
| Frame Length Reduction | 31.58% | Compared to traditional 19 bytes |
| Error Detection Coverage | CRC-16 (0x8005) | Detects bursts up to 16 bits |
| Packet Loss Rate (24-h test) | 0% | Stable operation under normal conditions |
| Bit Error Rate (Normal) | < 10^{-4} | Within acceptable limits for BMS |
| Bit Error Rate (with 50 mV noise) | 8 × 10^{-3} | Meets ASIL-B requirements |
| Maximum Daisy-Chain Nodes | 256 | Supported by design scalability |
To assess robustness, we introduced white noise of 50 mV amplitude onto the differential links. The bit error rate increased to 8×10^{-3}, which remains compliant with automotive safety standards like ASIL-B, demonstrating the resilience of our communication link. This is crucial for battery management systems operating in electrically noisy environments, such as electric vehicles. The error rate under noise can be modeled using communication theory. For a differential signaling scheme with noise variance $\sigma^2$, the probability of bit error $P_b$ is approximately:
$$ P_b = Q\left(\frac{A}{\sigma \sqrt{2}}\right) $$
where $A$ is the signal amplitude and $Q$ is the Q-function. With our design, even at elevated error rates, the CRC-16 ensures that undetected errors remain extremely rare, with probability:
$$ P_{\text{undetected}} \approx P_b^{16} $$
which is negligible for practical purposes. This makes our low-redundancy link suitable for safety-critical BMS applications.
In conclusion, we have successfully designed and implemented a low-redundancy communication link for battery management systems using an FPGA-based regenerative CRC scheme. This work addresses the inherent inefficiencies in traditional daisy-chain communications, where redundant CRC overhead limits scalability and real-time performance. By dynamically optimizing frame structure and employing incremental CRC updates, we achieved a significant reduction in frame length—up to 31.58% for a 4-node chain and nearly 50% for large-scale systems—while maintaining robust error detection capabilities. The FPGA implementation, verified through simulation and hardware testing, demonstrates reliable operation under normal and noisy conditions, meeting automotive-grade requirements. Our solution enhances the efficiency and scalability of BMS architectures, enabling more responsive monitoring and control for electric vehicles and energy storage systems. Future work could explore integration with advanced BMS algorithms or extension to other communication protocols, further solidifying the role of low-redundancy links in next-generation battery management systems.
The implications of this research extend beyond battery management systems. The principles of regenerative CRC and frame optimization can be applied to other daisy-chained networks in industrial automation, IoT devices, and distributed sensor systems. For BMS specifically, the improved communication efficiency allows for higher data sampling rates, better state-of-charge estimation, and enhanced fault detection, contributing to safer and longer-lasting battery packs. As the demand for electric mobility and renewable energy storage grows, innovations like our low-redundancy link will be essential in building cost-effective and reliable battery management systems. We believe this work provides a solid foundation for further advancements in BMS technology, paving the way for smarter and more efficient energy management solutions.